The perennial fantasy

Two attacks, and one defense, of classical liberal ideas appeared over the weekend. "War and Pandemic Highlight Shortcomings of the Free-Market Consensus" announces Patricia Cohen on p.1 of the New York Times news section.  As if the Times had ever been part of such a "consensus." And Deirdre McCloskey reviews Simon Johnson and Daron Acemoglu's "Power and Progress," whose central argument is, per Deirdre, "The state, they argue, can do a better job than the market of selecting technologies and making investments to implement them." (I have not yet read the book. This is a review of the review only.) 

I'll give away the punchline. The case for free markets never was their perfection. The case for free markets always was centuries of experience with the failures of the only alternative, state control. Free markets are, as the saying goes, the worst system; except for all the others. 

In this sense the classic teaching of economics does  a disservice. We start with the theorem that free competitive markets can equal -- only equal -- the allocation of an omniscient benevolent planner. But then from week 2 on we study market imperfections -- externalities, increasing returns, asymmetric information -- under which markets are imperfect, and the hypothetical planner can do better. Regulate, it follows. Except econ 101 spends zero time on our extensive experience with just how well -- how badly -- actual planners and regulators do. That messy experience underlies our prosperity, and prospects for its continuance. 

Starting with Ms. Cohen at the Times, 

The economic conventions that policymakers had relied on since the Berlin Wall fell more than 30 years ago — the unfailing superiority of open markets, liberalized trade and maximum efficiency — look to be running off the rails.

During the Covid-19 pandemic, the ceaseless drive to integrate the global economy and reduce costs left health care workers without face masks and medical gloves, carmakers without semiconductors, sawmills without lumber and sneaker buyers without Nikes.

That there ever was a "consensus" in favor of "the unfailing superiority of open markets, liberalized trade and maximum efficiency" seems a mighty strange memory. But if the Times wants to think now that's what they thought then, I'm happy to rewrite a little history. 

Face masks? The face mask snafu in the pandemic is now, in the Times' rather hilarious memory, the prime example of how a free and unfettered market fails. It was  a result of "the ceaseless drive to integrate the global economy and reduce costs?" 

(Here, I have a second complaint -- the ceaseless drive to remove subjects from sentences. Who is doing this "ceaseless drive?" Where is the great conspiracy, the secret meeting of old white men "driving" the economy? Nowhere. That's the point of free markets.)

The free market has a plan, imperfect as it might be, for masks in a pandemic. Prices rise. People who really want and need masks -- doctors, nurses, police -- pay what it takes to get them. People who don't really need them -- nursery schools -- look at the price, think about the benefit, and say, "maybe not," or take other measures. People reuse masks. Producers, seeing high prices, work day and night to produce more masks. Others, knowing that every 10 years there is a spike in prices, pay the costs of storing masks to make great profits when the time comes. 

The actual story of masks in the pandemic is the exact opposite. Price controls, of course. Instantly, governments started prosecuting businesses for "price gouging" who dared to raise the price of toilet paper. Governments redistribute income; markets allocate resources efficiently. As usual, the desire to redistribute tiny amounts of income to those willing to stand in line to get toilet paper won out. An entrepreneur tried to start producing masks. The FDA shut him down. (I hope I recall that story right, send comments if not.) China wanted to ship us masks. Yes, China the new villain of globalization gone mad. But their masks were certified and labeled by EU rules, not US rules, so like baby formula they couldn't be imported and sold. 

More deeply, even I, devoted free-marketer; even at the late night beer sessions at the CATO institute, nobody puts mask distribution in a pandemic as the first job of free markets. There is supposed to be a public health function of government; infectuous diseases are something of an externality; safety protocols in government labs doing government funded research are not a free-market function. As we look at the covid catastrophe, do we not see failures of government all over the place, not failures of some hypothetical free market? California even had mobile hospitals after H1N1. Governor Brown shut them down to save money for his high speed train. We might as well blame free markets for the lines at the DMV.  

The idea that trade and shared economic interests would prevent military conflicts was trampled last year under the boots of Russian soldiers in Ukraine.

Does anyone think a prime function of free market economics is to stops wars, usually prosecuted by, eh, governments? The standard history of WWI is enough. We do allege that free markets, and free markets alone, make a country wealthy enough to fight and win wars, if the country has the will and desire to do so. The US and NATO military budget vs. Russia's, larger by a factor of 10 at least, seems to bear that out, along with the much greater quality of our weapons.  Heaven help us militarily once the protectionists lead us to state-directed penury. 

inflation, thought to be safely stored away with disco album collections, returned with a vengeance.

Did anyone every vaguely hint that inflation control is a function of free markets? Inflation comes from government monetary and fiscal policy.  

And increasing bouts of extreme weather that destroyed crops, forced migrations and halted power plants has illustrated that the market’s invisible hand was not protecting the planet.

Doe the Times even vaguely think of news as fact not narrative? There have been a lot of migrations. "Forced?" Many due to violence, poverty, ill government. None due to temperature. Halted power plants (more passive voice)? Yes, it was that pesky unfettered free market that shut down power plants... 

The favored economic road map helped produce fabulous wealth, lift hundreds of millions of people out of poverty and spur wondrous technological advances.

Well, a peek of sunlight, an actual correct fact! 

But there were stunning failures as well. Globalization hastened climate change and deepened inequalities. 

More fact free narrative spinning. How are "inequalities" plural? Globalization brought the sharpest decline in global inequality in the history of our species. Perhaps it "hastened climate change" in that if China had stayed desperately poor they wouldn't be building a new coal fired power plant a week. US emissions went down because of... choose 1: enlightened policy 2: fracking, a shift to natural gas made only possible by the curious US property rights system absent in Europe, and pretty much over the dead body of the entire energy regulatory apparatus. 

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Meanwhile over at WSJ, Deirdre is in classic form. (Again, I have not read the book, so this is Deirdre coverage.) The paragraph that caught my attention and demanded a blog post: 

We need [according to Acemoglu and Johnson] ... the legislation currently being pushed by left and right to try again the policies of antitrust, trade protection, minimum wage and, above all, subsidy for certain technologies. Messrs. Acemoglu and Johnson are especially eager to regulate digital technologies such as artificial intelligence. “Technology should be steered in a direction that best uses a workforce’s skills,” they write, “and education should . . . adapt to new skill requirements.” How the administrators of the Economic Development Administration at the Department of Commerce would know the new direction to steer, or the new skills required, remains a sacred mystery.

"Technology should be steered." There it is, the full glory of the regulatory passive voice. Steered by who? Deirdre answers the question with that gem of rhetoric, specificity.  "Administrators of the Economic Development Administration at the Department of Commerce" for example. 

The theme uniting the two essays: If there is one lesson of the last 20 years it is this: The catastrophic failure of our government institutions. From bungled wars, a snafu of financial regulation in 2008 just now repeated in FTX, SVB, and inflation the evident collapse of the FDA CDC and plain commonsense in the pandemic, the free market is bravely forestalling a collapse of government (and associated, i.e. universities) institutions. 

we need the state to use its powers “to induce the private sector to move away from excessive automation and surveillance, and toward more worker-friendly technologies.” Fear of surveillance is a major theme of the book; therefore “antitrust should be considered as a complementary tool to the more fundamental aim of redirecting technology away from automation, surveillance, data collection, and digital advertising.”

The question what institution has the technical competence to do this seems to be begging.  

“Government subsidies for developing more socially beneficial technologies,” the authors declare, “are one of the most powerful means of redirecting technology in a market economy.” 

Well, interpreting the sentence literally,  you have to give it to them. Government subsidies are powerful means of "redirecting technology." Usually to ratholes. 

Messrs. Acemoglu and Johnson warmly admire the U.S. Progressive Movement of the late 19th century as a model for their statism: experts taking child-citizens in hand.

Their chapters then skip briskly through history...seeking to show how at each turn new innovations tended to empower certain sections of society at the expense of others. The “power” that concerns them, in other words, is private power.

This is, in fact, the central question dividing free-marketers and others. Private power being subject to competition, we worry more about state power. The essence of state power is monopoly, and a monopoly of coercion, fundamentally violence.  

The heart of the book is that technological gains create winners and losers, and Acemoglu and Johnson want that directed by a nebulous bureaucracy. Which will somehow never be infected by, oh, Republicans, or turn in to the endless stagnation of most of the last millennium which actually did pursue policies that forbade technological improvement in order to sustain the incomes of incumbents. Deirdre, who coined the lovely phrase "trade tested betterment" takes it on. 

 During the past two centuries, the world has become radically better off, by fully 3,000% inflation adjusted. Even over the past two decades the lives of the poor have improved. The “great enrichment” after 1800 and its resulting superabundance has brought us out of misery. Even the poor workers who did not benefit in the short run have done so enormously in the long run. In 1960, 4 billion of the 5 billion people on the planet lived on $2 a day. Now it’s fallen to 1 billion out of 8, and the income average is $50 a day. The state didn’t do it, and forcing short-run egalitarianism or handing power to the Office of Economic Development can kill it, as it regularly has. Messrs. Acemoglu and Johnson see great imperfections in the overwhelmingly private sources of the enrichment. With such imperfections, who needs perfection?

Another way to see the problem is to remember the common sense, refined in Economics 101 and Biology 101, of entry at the smell of profit. ...The great fortunes they deprecate have the economic function of encouraging entry into the economy by other entrepreneurs who want to get rich. This competition cheapens goods and services, which then accrues to the poor as immense increases in real income.

Many fortunes, for instance, were made by the invention of the downtown department store. The profit attracted suburban competitors, and at the mall the department-store model began to fail. Jeff Bezos reinvented the mail-order catalog. He is imitated, and the fortunes are dissipated in enormous benefit to consumers called workers. 

.... It’s what happened and happens in a liberal economy.

The book uses a lot of history, surveyed by McCloskey. As before, it's criticized a bit as history lite. The history Deirdre covers has the usual imperfections of the free market. 

I wonder if the book has any history of success of this plan, of governments successfully guiding technological transformations to protect the rights and incomes of incumbents, without in the process killing technical change.  Governments habitually screw up basics like rent control. Figuring out what new technology will do is pretty much beyond the capacity of private investors and book-writing economists. The  idea that bureaucracy has the capacity to figure out not just what new technology will work, but to guide its social and distributional consequences seems... far beyond the historical record of bureaucratic accomplishment. But I am straying beyond my promise to review the review, not the book, before reading the latter. 

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I recognize the desire on both sides. Partisan politics needs "new" ideas and a "new" propaganda. In particular, the right is aching for something shiny and new that it can sell to voters, which it regards with the same sort of noblesse-oblige intellectual disdain as the left does. Mind the store, mend the institutions, freedom, rights, opportunity and make your own prosperity are, apparently, not sexy enough. So both sides need new initiatives, expanded governments, to excite the rabble. But we're not here to supply that demand, merely to meditate on actual cause-and-effect truth of what works. Beware the temptation. 

Update:  In retrospect, perhaps the issue is much simpler. The bulk of economic regulation serves exactly the purpose McCloskey basically alleges of Acemoglu and Johnson: Preserve rents of incumbents against the threats of technological improvements. From medieval guilds to trade protection to taxis vs. Ubers, that is really its main function. So we have an extensive bureaucracy that is very good at it, and extensive experience of just how well it works. Which is, very well, at protecting rents and stifling growth.  

The barn door

Kevin Warsh has a nice WSJ oped warning of financial problems to come.  The major point of this essay: "countercyclical capital buffers" are another bright regulatory idea of the 2010s that now has fallen flat. 

As in previous posts, a lot of banks have lost asset value equal or greater than their entire equity due to plain vanilla interest rate risk. The ones that haven't run are now staying afloat only because you and me keep our deposits there at ridiculously low interest rates. Commercial real estate may be next. Perhaps I'm over-influenced by the zombie-apocalypse goings on in San Francisco -- $755 million default on the Hilton and Parc 55, $558 million default on the whole Westfield mall after Nordstrom departed and on and on. How much of this debt is parked in regional banks? I would have assumed that the Fed's regulatory army could see something so obvious coming, but since they completely missed plain vanilla interest rate risk, and the fact that you don't have to stand in line any more to run on your bank, who knows?

So, banks are at risk; the Fed now knows it, and is reportedly worried that more interest rates to lower inflation will cause more problems. To some extent that's a feature not a bug -- the whole theory behind the Fed lowering inflation is that higher interest rates "cool economic activity," i.e. make banks hesitant to lend, people lose their jobs, and through the Phillips curve (?) inflation comes down. But the Fed wants a minor contraction, not full-on 2008. (That did bring inflation down though!) 

I don't agree with all of Kevin's essay, but I always cherry pick wisdom where I find it, and there is plenty. On what to do: 

Ms. Yellen and the other policy makers on the Financial Stability Oversight Council should take immediate action to mitigate these risks. They should promote the private recapitalization of small and midsize banks so they survive and thrive.

Yes! But. I'm a capital hawk -- my answer is always "more." But we shouldn't be here in the first place. 

Repeating a complaint I've been making for a while, everything since the great treasury market bailout of March 2020 reveals how utterly broken the premises and promises of post-2008 financial regulation are. One of the most popular ideas was "countercyclical capital buffers." A nice explainer from Kaitlyn Hoevelmann at the St. Louis Fed (picked because it came up first on a Google search), 

"A countercyclical capital buffer would raise banks’ capital requirements during economic expansions, with banks required to maintain a higher capital-to-asset ratio when the economy is performing well and loan volumes are growing rapidly.  " 

Well, that makes sense, doesn't it? Buy insurance on a clear day, not when the forest fire is half a mile upwind. 

More deeply, remember "capital" is not "reserves" or "liquid assets." "Capital" is one way banks have of getting money, by selling stock, rather than selling bonds or taking deposits. (There is lots of confusion on this point. If someone says "hold" capital that's a sign of confusion.) It has the unique advantage that equity holders can't run to get their money out at any time. In bad times, the stock price goes down and there's nothing they can do about it. But also obviously, it's a lot easier to sell bank stock for a high price in good times than it is just after it has been revealed that the bank has lost a huge amount of money, i.e. like now.  

Why don't banks naturally issue more equity in good times? Well, because buying insurance is expensive, and most of all there is no deposit insurance or too big to fail guarantee subsidizing stock. So banks always leverage as much as they can. Behavioralists will add that bankers get over enthusiastic and happy to take risks in good times. Why don't regulators demand more capital in good times, so banks are ready for the bad times ahead? That's the natural idea of "countercyclical capital buffers." And after 2008, all worthy opinion said regulators should do that. Only some cynical types like me opined that the regulators will be just as human, just as behavioral, just as procyclically risk averse, just as prey to political pressures in the future as they were in the past. 

And so it has turned out. Despite 15 years of writing about procyclical capital, of "managing the credit cycle," here we are again -- no great amounts of capital issued in the good times, and now we want banks to do it when they're already in trouble, and anyone buying bank stock will be providing money that first of all goes to bail out depositors and other debt holders. As the ship is sinking, go on amazon to buy lifeboats. Just as in 2008, regulators will be demanding capital in bad times, after the horse has left the barn. So, the answer has to be, more capital always! 

Kevin has more good points:  

Bank regulators have long looked askance at capital from asset managers and private equity firms, among others. But this is no time for luxury beliefs.

Capital is capital, even from disparaged sources. 

Policy makers should also green-light consolidation among small, midsize and even larger regional banks. I recognize concerns about market power. But the largest banks have already secured a privileged position with their “too big to fail” status. Hundreds of banks need larger, stronger franchises to compete against them, especially in an uncertain economy. Banks need prompt regulatory approval to be confident that proposed mergers will close. Better to allow bank mergers before weak institutions approach the clutches of the Federal Deposit Insurance Corp.’s resolution process. Voluntary mergers at market prices are preferable to rushed government auctions that involve large taxpayer losses and destruction of significant franchise value.

It is a bit funny to see the Administration against all mergers, and then when a bank fails, Chase gets to swallow up failing banks with government sweeteners. Big is bad is another luxury belief. 

Yes, banks are uncompetitive. Look at the interest on your deposits (mine, Chase, 0.01%) and you'll see it just as clearly as you can see lack of competition in a medical bill. But most of that competition comes from regulation, not evil behavior. As per Kevin: 

The past decade’s regulatory policies have undermined competition and weakened resiliency in the banking business. 

A final nice point: 

The Fed’s flawed inflation forecasts in the past couple of years are a lesson in risk management. Policy makers shouldn’t bet all their chips on hopes for low prices or anything else. Better to evaluate the likely costs if the forecast turns out to be wrong.

Maybe the lesson of the massive failure to forecast inflation is that inflation is just bloody hard to forecast. Rather than spend a lot of effort improving the forecast, spend effort recognizing the uncertainty of any forecast, and being ready to react to contingencies as they arise. (I'm repeating myself, but that's the blogger's prerogative.)  

Papers: Dew-Becker on Networks

I've been reading a lot of macro lately. In part, I'm just catching up from a few years of book writing. In part,  I want to understand inflation dynamics, the quest set forth in "expectations and the neutrality of interest rates," and an  obvious next step in the fiscal theory program. Perhaps blog readers might find interesting some summaries of recent papers, when there is a great idea that can be summarized without a huge amount of math. So, I start a series on cool papers I'm reading. 

Today: "Tail risk in production networks" by Ian Dew-Becker, a beautiful paper. A "production network" approach recognizes that each firm buys from others, and models this interconnection. It's a hot topic for lots of reasons, below.  I'm interested because prices cascading through production networks might induce a better model of inflation dynamics. 

(This post uses Mathjax equations. If you're seeing garbage like [\alpha = \beta] then come back to the source  here.) 

To Ian's paper: Each firm uses other firms' outputs as inputs. Now, hit the economy with a vector of productivity shocks. Some firms get more productive, some get less productive. The more productive ones will expand and lower prices, but that changes everyone's input prices too. Where does it all settle down? This is the fun question of network economics. 

Ian's central idea: The problem simplifies a lot for large shocks. Usually when problems are complicated we look at first or second order approximations, i.e. for small shocks, obtaining linear or quadratic ("simple") approximations. 

On the x axis, take a vector of productivity shocks for each firm, and scale it up or down. The x axis represents this overall scale. The y axis is GDP. The right hand graph is Ian's point: for large shocks, log GDP becomes linear in log productivity -- really simple. 

Why? Because for large enough shocks, all the networky stuff disappears. Each firm's output moves up or down depending only on one critical input. 

To see this, we have to dig deeper to complements vs. substitutes. Suppose the price of an input goes up 10%. The firm tries to use less of this input. If the best it can do is to cut use 5%, then the firm ends up paying 5% more overall for this input, the "expenditure share" of this input rises. That is the case of "complements." But if the firm can cut use of the input 15%, then it pays 5% less overall for the input, even though the price went up. That is the case of "substitutes." This is the key concept for the whole question: when an input's price goes up, does its share of overall expenditure go up (complements) or down (substitutes)? 

Suppose inputs are complements. Again, this vector of technology shocks hits the economy. As the size of the shock gets bigger, the expenditure of each firm, and thus the price it charges for its output, becomes more and more dominated by the one input whose price grows the most. In that sense, all the networkiness simplifies enormously. Each firm is only "connected" to one other firm. 

Turn the shock around. Each firm that was getting a productivity boost now gets a productivity reduction. Each price that was going up now goes down. Again, in the large shock limit, our firm's price becomes dominated by the price of its most expensive input. But it's a different input.  So, naturally, the economy's response to this technology shock is linear, but with a different slope in one direction vs. the other. 

Suppose instead that inputs are substitutes. Now, as prices change, the firm expands more and more its use of the cheapest input, and its costs and price become dominated by that input instead. Again, the network collapsed to one link.  

Ian: "negative productivity shocks propagate downstream through parts of the production process that are complementary (\(\sigma_i < 1\)), while positive productivity shocks propagate through parts that are substitutable (\(\sigma_i > 1\)). ...every sector’s behavior ends up driven by a single one of its inputs....there is a tail network, which depends on \(\theta\) and in which each sector has just a single upstream link."

Equations: Each firm's production function is (somewhat simplifying Ian's (1)) \[Y_i = Z_i L_i^{1-\alpha} \left( \sum_j A_{ij}^{1/\sigma} X_{ij}^{(\sigma-1)/\sigma} \right)^{\alpha \sigma/(\sigma-1)}.\]Here \(Y_i\) is output, \(Z_i\) is productivity, \(L_i\) is labor input, \(X_{ij}\) is how much good j firm i  uses as an input, and \(A_{ij}\) captures how important each input is in production. \(\sigma>1\) are substitutes, \(\sigma<1\) are complements. 

Firms are competitive, so price equals marginal cost, and each firm's price is \[ p_i = -z_i + \frac{\alpha}{1-\sigma}\log\left(\sum_j A_{ij}e^{(1-\sigma)p_j}\right).\; \; \; (1)\]Small letters are logs of big letters.  Each price depends on the prices of all the inputs, plus the firm's own productivity.  Log GDP, plotted in the above figure is \[gdp = -\beta'p\] where \(p\) is the vector of prices and \(\beta\) is a vector of how important each good is to the consumer. 

In the case \(\sigma=1\) (1)  reduces to a linear formula. We can easily solve for prices and then gdp as a function of the technology shocks: \[p_i = - z_i + \sum_j A_{ij} p_j\] and hence \[p=-(I-\alpha A)^{-1}z,\]where the letters represent vectors and matrices across \(i\) and \(j\). This expression shows some of the point of networks, that the pattern of prices and output reflects the whole network of production, not just individual firm productivity. But with \(\sigma \neq 1\) (1) is nonlinear without a known closed form solution. Hence approximations. 

You can see Ian's central point directly from (1). Take the \(\sigma<1\) case, complements. Parameterize the size of the technology shocks by a fixed vector \(\theta = [\theta_1, \ \theta_2, \ ...\theta_i,...]\) times a scalar \(t>0\), so that \(z_i=\theta_i \times t\). Then let \(t\) grow keeping the pattern of shocks \(\theta\) the same. Now, as the \(\{p_i\}\) get larger in absolute value, the term with the greatest \(p_i\) has the greatest value of \( e^{(1-\sigma)p_j} \). So, for large technology shocks \(z\), only that largest term matters, the log and e cancel, and \[p_i \approx -z_i + \alpha \max_{j} p_j.\] This is linear, so we can also write prices as a pattern \(\phi\) times the scale \(t\), in the large-t limit \(p_i = \phi_i t\),  and  \[\phi_i =  -\theta_i + \alpha \max_{j} \phi_j.\;\;\; (2)\] With substitutes, \(\sigma<1\), the firm's costs, and so its price, will be driven by the smallest (most negative) upstream price, in the same way. \[\phi_i \approx -\theta_i + \alpha \min_{j} \phi_j.\] 

To express gdp scaling with \(t\), write \(gdp=\lambda t\), or when you want to emphasize the dependence on the vector of technology shocks, \(\lambda(\theta)\). Then we find gdp by \(\lambda =-\beta'\phi\). 

In this big price limit, the \(A_{ij}\) contribute a constant term, which also washes out. Thus the actual "network" coefficients stop mattering at all so long as they are not zero -- the max and min are taken over all non-zero inputs. Ian: 

...the limits for prices, do not depend on the exact values of any \(\sigma_i\) or \(A_{i,j}.\) All that matters is whether the elasticities are above or below 1 and whether the production weights are greater than zero. In the example in Figure 2, changing the exact values of the production parameters (away from \(\sigma_i = 1\) or \(A_{i,j} = 0\)) changes...the levels of the asymptotes, and it can change the curvature of GDP with respect to productivity, but the slopes of the asymptotes are unaffected.

...when thinking about the supply-chain risks associated with large shocks, what is important is not how large a given supplier is on average, but rather how many sectors it supplies...

For a full solution, look at the (more interesting) case of complements, and suppose every firm uses a little bit of every other firm's output, so all the \(A_{ij}>0\). The largest input  price in (2) is the same for each firm \(i\), and you can quickly see then that the biggest price will be the smallest technology shock. Now we can solve the model for prices and GDP as a function of technology shocks: \[\phi_i \approx -\theta_i - \frac{\alpha}{1-\alpha} \theta_{\min},\] \[\lambda \approx  \beta'\theta + \frac{\alpha}{1-\alpha}\theta_{\min}.\] We have solved the large-shock approximation for prices and GDP as a function of technology shocks. (This is Ian's example 1.) 

The graph is concave when inputs are complements, and convex when they are substitutes. Let's do complements. We do the graph to the left of the kink by changing the sign of \(\theta\).  If the identity of \(\theta_{\min}\) did not change, \(\lambda(-\theta)=-\lambda(\theta)\) and the graph would be linear; it would go down on the left of the kink by the same amount it goes up on the right of the kink. But now a different \(j\) has the largest price and the worst technology shock. Since this must be a worse technology shock than the one driving the previous case, GDP is lower and the graph is concave.  \[-\lambda(-\theta) = \beta'\theta + \frac{\alpha}{1-\alpha}\theta_{\max} \ge\beta'\theta + \frac{\alpha}{1-\alpha}\theta_{\min} = \lambda(\theta).\] Therefore  \(\lambda(-\theta)\le-\lambda(\theta),\) the left side falls by more than the right side rises. 

Does all of this matter? Well, surely more for questions when there might be a big shock, such as the big shocks we saw in a pandemic, or big shocks we might see in a war. One of the big questions that network theory asks is, how much does GDP change if there is a technology shock in a particular industry? The \(\sigma=1\) case in which expenditure shares are constant gives a standard and fairly reassuring result: the effect on GDP of a shock in industry i is given by the ratio of i's output to total GDP. ("Hulten's theorem.") Industries that are small relative to GDP don't affect GDP that much if they get into trouble. 

You can intuit that constant expenditure shares are important for this result. If an industry has a negative technology shock, raises its prices, and others can't reduce use of its inputs, then its share of expenditure will rise, and it will all of a sudden be important to GDP. Continuing our example, if one firm has a negative technology shock, then it is the minimum technology, and [(d gdp/dz_i = \beta_i + \frac{\alpha}{1-\alpha}.\] For small firms (industries) the latter term is likely to be the most important.  All the A and \(\sigma\) have disappeared, and basically the whole economy is driven by this one unlucky industry and labor.   

Ian: 

...what determines tail risk is not whether there is granularity on average, but whether there can ever be granularity – whether a single sector can become pivotal if shocks are large enough.

For example, take electricity and restaurants. In normal times, those sectors are of similar size, which in a linear approximation would imply that they have similar effects on GDP. But one lesson of Covid was that shutting down restaurants is not catastrophic for GDP, [Consumer spending on food services and accommodations fell by 40 percent, or $403 billion between 2019Q4 and 2020Q2. Spending at movie theaters fell by 99 percent.] whereas one might expect that a significant reduction in available electricity would have strongly negative effects – and that those effects would be convex in the size of the decline in available power. Electricity is systemically important not because it is important in good times, but because it would be important in bad times. 

Ben Moll turned out to be right and Germany was able to substitute away from Russian Gas a lot more than people had thought, but even that proves the rule: if it is hard to substitute away from even a small input, then large shocks to that input imply larger expenditure shares and larger impacts on the economy than its small output in normal times would suggest.

There is an enormous amount more in the paper and voluminous appendices, but this is enough for a blog review. 

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Now, a few limitations, or really thoughts on where we go next. (No more in this paper, please, Ian!) Ian does a nice illustrative computation of the sensitivity to large shocks:

Ian assumes \(\sigma>1\), so the main ingredients are how many downstream firms use your products and a bit their labor shares. No surprise, trucks, and energy have big tail impacts. But so do lawyers and insurance. Can we really not do without lawyers? Here I hope the next step looks hard at substitutes vs. complements.

That raises a bunch of issues. Substitutes vs. complements surely depends on time horizon and size of shocks. It might be easy to use a little less water or electricity initially, but then really hard to reduce more than, say, 80%. It's usually easier to substitute in the long run than the short run. 

The analysis in this literature is "static," meaning it describes the economy when everything has settled down.  The responses -- you charge more, I use less, I charge more, you use less of my output, etc. -- all happen instantly, or equivalently the model studies a long run where this has all settled down. But then we talk about responses to shocks, as in the pandemic.  Surely there is a dynamic response here, not just including capital accumulation (which Ian studies). Indeed, my hope was to see prices spreading out through a production network over time, but this structure would have all price adjustments instantly. Mixing production networks with sticky prices is an obvious idea, which some of the papers below are working on. 

In the theory and data handling, you see a big discontinuity. If a firm uses any inputs at all from another firm,  if \(A_{ij}>0\), that input can take over and drive everything. If it uses no inputs at all, then there is no network link and the upstream firm can't have any effect. There is a big discontinuity at \(A_{ij}=0.\) We would prefer a theory that does not jump from zero to everything when the firm buys one stick of chewing gum. Ian had to drop small but nonzero elements of the input-output matrix to produces sensible results. Perhaps we should regard very small inputs as always substitutes? 

How important is the network stuff anyway? We tend to use industry categorizations, because we have an industry input-output table. But how much of the US industry input-output is simply vertical: Loggers sell trees to mills who sell wood to lumberyards who sell lumber to Home Depot who sells it to contractors who put up your house? Energy and tools feed each stage, but don't use a whole lot of wood to make those. I haven't looked at an input-output matrix recently, but just how "vertical" is it? 

****

The literature on networks in macro is vast. One approach is to pick a recent paper like Ian's and work back through the references. I started to summarize, but gave up in the deluge. Have fun. 

One way to think of a branch of economics is not just "what tools does it use?" but "what questions is it asking?  Long and Plosser "Real Business Cycles," a classic, went after idea that the central defining feature of business cycles (since Burns and Mitchell) is comovement. States and industries all go up and down together to a remarkable degree. That pointed to "aggregate demand" as a key driving force. One would think that "technology shocks" whatever they are would be local or industry specific. Long and Plosser showed that an input output structure led idiosyncratic shocks to produce business cycle common movement in output. Brilliant. 

Macro went in another way, emphasizing time series -- the idea that recessions are defined, say, by two quarters of aggregate GDP decline, or by the greater decline of investment and durable goods than consumption -- and in the aggregate models of Kydland and Prescott, and the stochastic growth model as pioneered by King, Plosser and Rebelo, driven by a single economy-wide technology shock.  Part of this shift is simply technical: Long and Plosser used analytical tools, and were thereby stuck in a model without capital, plus they did not inaugurate matching to data. Kydland and Prescott brought numerical model solution and calibration to macro, which is what macro has done ever since.  Maybe it's time to add capital, solve numerically, and calibrate Long and Plosser (with up to date frictions and consumer heterogeneity too, maybe). 

Xavier Gabaix (2011)  had a different Big Question in mind: Why are business cycles so large? Individual firms and industries have large shocks, but \(\sigma/\sqrt{N}\) ought to dampen those at the aggregate level. Again, this was a classic argument for aggregate "demand" as opposed to "supply."  Gabaix notices that the US has a fat-tailed firm distribution with a few large firms, and those firms have large shocks. He amplifies his argument via the Hulten mechanism, a bit of networkyiness, since the impact of a firm on the economy is sales / GDP,  not value added / GDP. 

The enormous literature since then  has gone after a variety of questions. Dew-Becker's paper is about the effect of big shocks, and obviously not that useful for small shocks. Remember which question you're after.

My quest for a new Phillips curve in production networks is better represented by Elisa Rubbo's "Networks, Phillips curves and Monetary Policy," and Jennifer La'o and  Alireza Tahbaz-Salehi's “Optimal Monetary Policy in Production Networks,” If I can boil those down for the blog, you'll hear about it eventually.  

The "what's the question" question is doubly important for this branch of macro that explicitly models heterogeneous agents and heterogenous firms. Why are we doing this? One can always represent the aggregates with a social welfare function and an aggregate production function. You might be interested in how aggregates affect individuals, but that doesn't change your model of aggregates. Or, you might be interested in seeing what the aggregate production or utility function looks like -- is it consistent with what we know about individual firms and people? Does the size of the aggregate production function shock make sense? But still, you end up with just a better (hopefully) aggregate production and utility function. Or, you might want models that break the aggregation theorems in a significant way; models for which distributions matter for aggregate dynamics, theoretically and (harder) empirically. But don't forget you need a reason to build disaggregated models. 

Expression (1) is not easy to get to. I started reading Ian's paper in my usual way:  to learn a literature start with the latest paper and work backward. Alas, this literature has evolved to the point that authors plop results down that "everybody knows" and will take you a day or so of head-scratching to reproduce. I complained to Ian, and he said he had the same problem when he was getting in to the literature! Yes, journals now demand such overstuffed papers that it's hard to do, but it would be awfully nice for everyone to start including ground up algebra for major results in one of the endless internet appendices.  I eventually found Jonathan Dingel's notes on Dixit Stiglitz tricks, which were helpful. 

Update:

Chase Abram's University of Chicago Math Camp notes here  are also a fantastic resource. See Appendix B starting p. 94 for  production network math. The rest of the notes are also really good. The first part goes a little deeper into more abstract material than is really necessary for the second part and applied work, but it is a wonderful and concise review of that material as well. 

 

Cost Benefit Comments

The Biden Administration is proposing major changes to cost-benefit analysis used in all regulations. The preamble here, and the full text here. It is open for public comments until June 20. 

Economists don't often comment on proposed regulations. We should do so more often. Agencies take such comments seriously. And they can have an afterlife. I have seen comments cited in litigation and by judicial decisions. Even if you doubt the Biden Administration's desire to hear you on cost-benefit analysis, a comment is a marker that the inevitable eventual Supreme Court case might well consider. Comments tend only to come from interested parties and lawyers. Regular economists really should comment more often. I don't do it enough either. 

You can see existing comments:  Search for Circular A-4 updates to get to https://www.regulations.gov/docket/OMB-2022-0014, then select “browse all comments.” (Thanks to a good friend who sent this tip.) 

Take a look at comments from an MIT team led by Deborah Lucas here and by Josh Rauh. These are great models of comments. You don't have to review everything.   Make one good point. 

Cost benefit analysis is useful even if imprecise. Lots of bright ideas in Washington (and Sacramento!) would struggle to document any net benefits at all. Yes, these exercises can lie, cheat, and steal, but having to come up with a quantitative lie can lay bare just how hare-brained many regulations are. 

Both Josh and the MIT response focus on the draft proposal's use of ultra-low discount rates, ranging from historic TIPS yields to arguments for zero or negative "social" discount rates. Josh emphasizes a beautiful compromise: always show the annual stream of costs and benefits. Then it's easy enough to apply different discount rates. No Black Boxes. 

Discount rates seem like a technical issue. But they matter a lot for climate policies, or for policies with substantial cost but putatively permanent benefits, because of the long horizons. For example, climate change is alleged to create costs of 5% of GDP in 100 years. So, let's assume a 0% discount rate -- treat the future just like the present. How much is it worth spending this year to eliminate additional climate change in 2100? Spend means real spending, real reductions in everyone's standard of living, not just funny money billions on twitter. 

If you answered "5% of GDP" (roughly $3,500 per person) that's wrong, for two crucial reasons. First, the economy grows over time. At a modest 2% real growth, US GDP will be 7.4 times as large in 100 years as it is today, or 640% greater. (e^2=7.4). Thus, 5% of GDP in 100 years, discounted at 0%,  is 7.4 x 5% or 37% of today's GDP, or $17,500 per person today. Second, the gain is forever -- 5% of 2123 GDP, but 5% of 2124 GDP, and so on. Discounted at a zero rate, 5% of 2123 forever after that is worth... an infinite amount today. But GDP keeps growing after 2123. If you discount at anything less than the growth rate of GDP -- 2% in my example -- 5% of (growing) GDP forever is worth an infinite amount!  So what if $250 billion subsidizing huge battery long range electric cars made by union labor in the USA from hypothetical US made lithium mines might, all in, save a thimbleful of carbon per car (is it even positive?), if the benefits are infinite, go for it. 

If you discount by a low, but somewhat more reasonable number like 7%, then a dollar in 100 years is worth 0.09 cents today (100 x e^-7). Now you know where to put your thumb on the climate scales! 

You might be wondering, if our great grandchildren are going to be so fantastically better off than we are, let them deal with it. Or you may be wondering that maybe there are other things we could do with money today that might speed up this magical growth process and do 5% better. For an infinite amount of money, is there nothing we can do to raise the growth rate from 2% to 2.05%? 

The latter opportunity cost question is, I think, a good way to think of discount rates. The average real return on stocks is something like 5%, at least. The average pre-tax marginal product of capital is higher; pick you number but it's in the range of 10% not 1%. The right "discount rate" is the rate of return on alternative uses of money. Josh and the MIT team are exactly right to point out that using the rate of return on risk free government bonds is a completely mistaken way to discount the very risky costs of climate damage -- that 5% is a very poorly known number -- and the even riskier benefits of the government's shifting climate policy passions. But I think phrasing the experiment in terms of opportunity costs rather than proper discounting of risky streams makes it more salient, despite the decades I have spent (and an entire book!) on the latter approach. Businesses can take $1 today and turn it in to, on average, $1.07 next year. Why take away that money for a project that yields $1.00 or $1.01 next year? 

The former question has a deeper consequence. Why should we suffer to help people, even our grandchildren, who will be on average 7.4 times better off than we are? How much would you ask your great grandparents to sacrifice to make you 5% better off than you are today? 

Here the low discount rate clashes interestingly with another part of the proposal: equity and transfers. 

From the preamble p. 12: 

A standard assumption in economics, informed by empirical evidence (as discussed below), is that an additional $100 given to a low-income individual increases the welfare of that individual more than an additional $100 given to a wealthy individual. Traditional benefit-cost analysis, which applies unitary weights to measures of willingness to pay, does not usually take into account how distributional effects may affect aggregate welfare because of differences in individuals’ marginal utility of income. Related to the topic of distributional analysis is the question of whether agencies should be permitted or encouraged to develop estimates of net benefits using weights that take account of these differences.26 The proposed revisions to Circular A-4 suggest that agencies may wish to consider weights for each income group affected by a regulation that equal the median income of the group divided by median U.S. income, raised to the power of the elasticity of marginal utility times negative one.

Now wait a darn-tootin' minute. The "standard" doctrine in economics is that you cannot make intra-personal utility comparisons. Utility is ordinal, not cardinal. Here cardinal-utility utilitarianism with equal Pareto-weights is about to be carved into federal stone. (To decide social benefit of taking from A and giving to B, you construct a social welfare function \(u(c_A) + \lambda u(c_B)\). This needs you to use the same \(u()\) for A and B, and agree on a Pareto-weight \(\lambda\) implicitly one here.) 

Imagine a simple regulation: take a dollar from Joe ($100,000 income) and give it to Kathy ($50,000 income). By this standard such a straightforward transfer passes a cost-benefit test.  

But this does not get applied over time. Taking a dollar from you and me, and at a discount rate of 0% giving it to our great grandchildren who will be 7.4 times better off should set off massive inequity alarm bells. Nope. 

Indeed, you can deduce a discount rate from the inequality goal. Pure undiscounted intergenerational equity requires a discount rate proportional to the economic growth rate. 

(With power utility, an intervention that costs A $1 to give B $\(e^{rt}\) just passes a cost-benefit test if \[c_A^{-\gamma} = e^{rt} (c_B)^{-\gamma}.\]  If B is \(e^{gt}\) times as well off as A, \(c_B=e^{gt} \times c_A\) then we need  \(r=\gamma g\). \( \gamma\) is usually a number a bit bigger than one.  The preamble's discussion of \(\gamma\) values is pretty good, settling on a number between one and two. However, they haven't really heard of the finance literature: 

Evidence on risk aversion can be used to estimate the elasticity of marginal utility. In a constant-elasticity utility specification, the coefficient of relative risk aversion is the elasticity of marginal utility. There are numerous different estimates of the coefficient of relative risk aversion (CRRA), using data from a variety of different markets, including labor supply markets,29 the stock market,30 and insurance markets.31 Relevant estimates vary widely, though assumed values of the CRRA between 1 and 2 are common.32

30 Robert S. Pindyck, “Risk Aversion and Determinants of Stock Market Behavior,” The Review of Economics and Statistics 70, no. 2 (1988): 183-90 uses stock market data and estimates the CRRA to be “in the range of 3 to 4"

Since then, of course, the whole equity premium literature sprang up with coefficients 10 to 50. Shh. That would justify insane levels of equity. 

The draft also encourages all sorts of unquantifiable non-economic "benefits," but I'll leave that for another day. 

Read and comment. 

BTW, despite my negative tone and picking on these elements, much of the draft is quite good. Here is a particularly nice piece, from p. 26 of the full text 

j. A Note Regarding Certain Types of Economic Regulation

In light of both economic theory and actual experience, it is particularly difficult to demonstrate positive net benefits for any of the following types of regulations:

 price controls in well-functioning competitive markets;

 production or sales quotas in well-functioning competitive markets;

 mandatory uniform quality standards for goods or services, if the potential problem can be adequately dealt with through voluntary standards or by disclosing information of the hazard to buyers or users; or

 controls on entry into employment or production, except (a) where needed to protect health and safety (e.g., Federal Aviation Administration tests for commercial pilots) or (b) to manage the use of common property resources (e.g., fisheries, airwaves, Federal lands, and offshore areas).

Well, FAA tests and rules for commercial pilots is not actually quite so obvious and really needs a cost benefit test. "Commercial pilot" does not mean "airline pilot," it means can you do anything in an airplane and get money for it. But leave that for another day, these principles if applied could clean out a lot of mischief. Well, I guess many on the progressive left or nascent national-conservative right would deny there is such a thing as a "well-functioning competitive market." 

Update: 

I should have added: It's insane to make a once and for all cost benefit analysis, especially for projects with 100 year horizons. All regs should be re evaluated every 5 to 10 years, and use experience to update costs and benefits. 

 

   

Hoover Monetary Policy Conference Videos

The videos from the Hoover Monetary Policy Conference are now online here.  See my previous post for a summary of the conference. 

The big picture is now clearer to me. Phil Jefferson rightly asked, what do you mean off track? Monetary policy is doing fine. Interest rates are, in his view, where they should be. He argued the case well. 

But now I have an answer: The Fed has had three significant institutional failures: 1) Its inflation target is 2%, yet inflation exploded to 8%. The Fed did not forecast it, and did not see it even as it was happening. (Nor did many other forecasters, pointing to deeper conceptual problems.) 2) In the SVB and subsequent mess, the Fed's regulatory apparatus did not see or do anything about plain vanilla interest-rate risk combined with uninsured deposits. 3) I add a third, that nobody else seems to complain about: In 2020 starting with treasury markets, moving on to money market funds, state and local financing,  and then an astonishing "whatever it takes" that corporate bond prices shall not fall, the Fed already revealed that the Dodd-Frank machinery was broken. (Will commercial real estate be next?) 

Yet there is very little appetite for self-examination or even external examination. How did a good institution, filled with good, honest, smart and devoted public servants fail so badly? That's not "off-track" that's a derailment. 

Well, two sessions at the conference begin to ask those questions, and the others aimed at the same issues. Hopefully they will prod the Fed to do so as well, or at least to be interested in other's answers to those questions. 

(My minor contributions: on why the Taylor rule is important here, where I think I did a pretty good job; and comments on why inflation forecasts went so wrong at  1:00:16 here.)

Bradley Prize speech, video, and thanks

The videos and speeches of the Bradley prize winners are up. My video here (Grumpy in a tux!), also the speech which I reproduce below. All the videos and speeches here (Betsy DeVos and Nina Shea) My previous interview with Rick Graeber, head of the Bradley foundation. 

Bradley also made a nice introduction video with photos from my childhood and early career. (A link here to the introduction video and speech together.) And to avoid us spending all our talks on thanking people, they had us write out a separate thanks. That seems not to be up yet, but I include mine below. I am very thankful, humbled to be included in such august company, and not so boorish that I would not have spent my whole talk without mentioning that, absent the separate opportunity to say so. 

Bradley prize remarks (i.e. condense three decades of policy writing into 10 minutes): 

Creeping stagnation ought to be recognized as the central economic issue of our time. Economic growth since 2000 has fallen almost by half compared with the last half of the 20th Century. The average American’s income is already a quarter less than under the previous trend. If this trend continues, lost growth in fifty years will total three times today’s economy. No economic issue — inflation, recession, trade, climate, income diversity — comes close to such numbers.

Growth is not just more stuff, it’s vastly better goods and services; it’s health, environment, education, and culture; it’s defense, social programs, and repaying government debt.

Why are we stagnating? In my view, the answer is simple: America has the people, the ideas, and the investment capital to grow. We just can’t get the permits. We are a great Gulliver, tied down by miles of Lilliputian red tape.  

How much more can the US grow? Looking around the world, we see that even slightly better institutions produce large improvements in living standards. US taxes and regulations are only a bit less onerous than those in Canada and the UK, but US per capita income is 40% greater. Bigger improvements have enormous effects. US per capita income is 350% greater than Mexico’s and 950% greater than India’s. Unless you think the US is already perfect, there is a lot we can do. 

How can we improve the US economy? I offer four examples.

I don’t need to tell you how dysfunctional health care and insurance are. Just look at your latest absurd bill. 

There is no reason that health care cannot be provided in the same way as lawyering, accounting, architecture, construction, airplane travel, car repair, or any complex personal service. Let a brutally competitive market offer us better service at lower prices. There is no reason that health insurance cannot function at least as well as life, car, property, or other insurance. It’s easy to address standard objections, such as preexisting conditions, asymmetric information, and so on.

How did we get in this mess? There are two original sins. First, in order to get around wage controls during WWII, the government allowed a tax deduction for employer-based group plans, but not for portable insurance. Thus preexisting conditions were born: if you lose your job, you lose health insurance. Patch after patch then led to the current mess. 

Second, the government wants to provide health care to poor people, but without visibly taxing and spending a lot. So, the government forces hospitals to treat poor people below cost, and recoup the money by overcharging everyone else. But an overcharge cannot stand competition, so the government protects hospitals and insurers from competition. You’ll know health care is competitive when, rather than hide prices, hospitals spam us with offers as airlines and cell phone companies do.  

There is no reason why everyone’s health care and insurance must be so screwed up to help the poor. A bit of taxing and spending instead — budgeted, appropriated, visible — would not stymie competition and innovation. 

Example 2: Banking offers plenty of room for improvement. In 1933, the US suffered a great bank run. Our government responded with deposit insurance. Guaranteeing deposits stops runs, but it’s like sending your brother-in-law to Las Vegas with your credit card, what we economists call an “incentive for risk taking.” The government piled on regulations to try to stop banks from taking risks. The banks got around the regulations, new crises erupted, new guarantees and regulations followed. This spring, the regulatory juggernaut failed to detect simple interest rate risk, and Silicon Valley Bank had a run, followed by others. The Fed and FDIC bailed out depositors and promised more rules. 

This system is fundamentally broken. The answer: Deposits should flow to accounts backed by reserves at the Fed, or short-term treasuries. Banks should get money for risky loans by issuing stock or long term debt that can’t run. We can end private-sector financial crises forever, with next to no regulation. 

There is a lesson in these stories. If we want to improve regulations, we can’t just bemoan them. We must understand how they emerged. 

As in health and banking, a regulatory mess often emerges from a continual patchwork, in which each step is a roughly sensible repair of the previous regulation’s dysfunction. The little old lady swallowed a fly, a spider to catch the fly, and so on. Now horse is on the menu. Only a start-from-scratch reform will work.

Much regulation protects politically influential businesses, workers, and other constituencies from the disruptions of growth. Responsive democracies give people what they want, good and hard. And in return, regulation extorts political support from those beneficiaries. We have to fix the regulatory structure, to give growth a seat at the table.  

Economists are somewhat at fault too. They are taught to look at every problem, diagnose “market failure,” and advocate new rules to be implemented by an omniscient, benevolent planner. But we do not live in a free market. When you see a problem, look first for the regulation that caused it.

Example 3: Taxes are a mess, with high marginal rates that discourage work, investment and production; disappointing revenue; and massive, wasteful complexity. How can the government raise revenue while doing the least damage to the economy? A uniform consumption tax is the clear answer. Tax money when people spend it. When earnings are saved, invested, plowed into businesses that produce goods and services and employ people, leave them alone.

Example 4: Bad incentives are again the unsung central problem of our social programs.  Roughly speaking, from zero to about sixty thousand dollars of income, if you earn an extra dollar, you lose a dollar of benefits. Fix the incentives, and more people will get ahead in life. We will also better help the truly needy, and the budget.

Some more general points unite these stories:

Focus on incentives. Politics and punditry are consumed with taking from A to give to B. Incentives are far more important for economic growth, and we can say something objective about them. 

Find the question. Politics and punditry usually advance answers without stating the question, or shop around for questions to justify the same old answers. Most people who disagree with the consumption tax really have different goals than funding the government with minimum economic damage. Well, what do you want the tax system to do? State the question, let’s find the best answer to the question, and we can make a lot of progress.

Look at the whole system. Tax disincentives come from the total difference between the value your additional work creates and what you can consume as a result. Between these lie payroll, income, excise, property, estate, sales, and corporate taxes, and more, at the federal, state, and local level. Greg Mankiw figured his all-in marginal tax rate at 90%, and even he left out sales, property, and a few more taxes. Social-program disincentives come from the combined phaseout of food stamps, housing subsidies, medicaid or Obamacare subsidies, disability payments, tax credits, and so on, down to low-income parking passes. And look at taxes and social programs together. A flat tax that finances checks to worthy people is very progressive government, if you want that. Looking at an individual tax or program for its disincentives or progressivity is silly. 

The list goes on. Horrible public education, labor laws, licensing laws, zoning, building and planning restrictions, immigration restrictions, regulatory barriers, endless lawsuits, prevailing-wage and domestic-content rules, are all sand in the productivity gears. Oh, and I haven’t even gotten to money and inflation yet! 

And that just fixes our current economy. Long-term growth comes from new ideas. Many economists say we have run out of ideas; growth is ending; slice the pie. I look out the window and I see factory-built mini nuclear power plants that the Nuclear Regulatory Commission is strangling; I see a historic breakthrough in artificial intelligence, facing an outcry for the government to stop it. I see advances in biology that portend much better health and longevity, but good luck getting FDA approval or increasingly politicized research funding.

Many conservatives disparage this “incentive economics” as outdated and boring. That attitude is utterly wrong. Incentives, and the freedom, rights, and rule of law that preserve incentives, remain the key to tremendous and widespread prosperity. And it is hard work to understand and fix the incentives behind today’s problems.

Yes, supply is less glamorous than stimulus. “Fix regulations” is a tougher slogan than “free money for voters.” Efficiency requires detailed reform in every agency and market, the Marie-Kondo approach to our civic life. But it’s possible. And we don’t need to reform all the dinosaurs. As we have seen with telephones, airlines, and taxis, we just need to allow new competitors, to allow the buds of freedom to grow.

Many people ask, “How can we get leaders to listen?” That’s the wrong question. Believe in democracy, not bending the emperor’s ear. Take action. My fellow prizewinners have grabbed the levers of influence that belong to citizens of our free society, and done hard work of reforming its institutions. And ideas matter. The Hoover Institution motto is “ideas defining a free society.” The Bradley Foundation tonight celebrates good ideas, and is devoted to spreading them. When voters, media, the chattering classes, and institutions of civil society understand, advance and apply these ideas, politicians will swiftly follow.   

Notes:

Growth: Real GDP 1950:I was $2186 billion, and per capita $14500; in 2000:I, $12935 and per capita $45983; in 2022:IV, $20182 and per capita 60376. From these numbers, average log real GDP growth 1950-2000 was 3.56% From 2000-2002, 1.96%. In per capita terms,  2.31% and  1.20%. (2.31-1.20)x22 = 24.4. 

Cross-country comparison: Calculations based on purchasing-power-adjusted GDP per capita: US $69,287, Canada $52,790, UK $50,890, Mexico $19,587, India $7,242. Source: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD The PPP adjustment tries to take account that some things are cheaper in other countries. Converting at the exchange rate produces even larger differences. US $70.248, Canada $51,987, UK $46,510, Mexico $10,065, India $2,256. Source: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

Mankiw: http://www.nytimes.com/2010/10/10/business/economy/10view.html

Thanks

I have been fortunate to benefit from the effort, time, wisdom and affection of so many people, and many institutions that supported their efforts.

Of course it starts with my parents, Eric and Lydia Cochrane. They expected children to think and speak at the family dinner table. They exposed me to different cultures, on the south side of Chicago and in Italy, sometimes beyond my desires. They set an example by how they lived: They steadfastly followed their intellectual pursuits with extreme honesty. They treated people with a radical egalitarianism. And then left me alone to pursue my own passions. 

I was lucky to learn from some extraordinary and dedicated teachers, at the Ancona Montessori School, the U of C Lab school, Italian public schools, and Kenwood high school. There, in an inner city public school, Arlene Gordon (Math), Judith Stein (English) Walter Sherrill (Chemistry) and especially Joel Hofslund (Physics) gave me absolutely first rate experience. Thanks also to Ed Shands’ patient coaching of our swim team. 

I moved on to MIT to study physics. This was more impersonal, and a difficult time for me, but as it turned out a superb education in the kind of mathematical modeling essential to economics. 

I went on to study economics at the University of California at Berkeley. Faculty took PhD teaching seriously, not just of their own research, and I soaked it up. I thank especially my advisers, Roger Craine, Tom Rothermberg, and George Akerlof.  Many of their lessons are vivid today, but like my parents they provided only gentle guidance and feedback on my own imperfect quests. 

I was supremely luck to land a job at the University of Chicago. I learned a tremendous amount in the wide open collegial atmosphere at Chicago, thanks in large part to Lars Hansen and Gene Fama, but also colleagues too numerous to mention in this short space.  Generations of MBA and PhD students also pushed me hard to understand economics and became lifelong friends and colleagues. 

At just the right moment Hoover came calling, allowing me the time and institutional support to blossom as a public intellectual and commenter as well as an academic. A special thanks to John Raisin for that. 

No man is an island. The world of ideas is a conversation. Everything I know has been shaped by teachers, friends, colleagues, collaborators, students, journal editors, referees, and others who took the time and effort to help me think about things. 

Many small interactions have had a crucial effect on my life. A coffee conversation at a conference with John Campbell resulted in our best known academic paper. A lunch conversation with Luigi Zingales produced my first public writing during the financial crisis. As a result, Amity Shlaes invited me to a conference. Howard Dickman, then at the Wall Street Journal, liked my presentation and asked, “Why don’t you write opeds for us?” I answered, “Why don’t you stop rejecting them?” My oped career was born. And so forth. I thank these and many more, and lady luck who put us together. 

Of course my greatest thanks go to my wonderful wife, Elizabeth Fama. We met the night I returned to Chicago. It was love at first sight. We were engaged on the second date. She has been my best friend and constant companion ever since, though marriage to a passionate researcher, busy teacher and lover of time consuming sports cannot have been easy. Together we raised four amazing children, Sally, Eric, Jean, and Lake, who fill my heart with love, and now that they are grown a bit of nostalgia. 

Bob Lucas and his papers

My first post described a few anecdotes about what a warm person Bob Lucas was, and such a great colleague. Here I describe a little bit of his intellectual influence, in a form that is I hope accessible to average people.

The “rational expectations” revolution that brought down Keynesianism in the 1970s was really much larger than that. It was really the “general equilibrium” revolution. 

Macroeconomics until 1970 was sharply different from regular microeconomics. Economics is all about “models,” complete toy economies that we construct via equations and in computer programs. You can’t keep track of everything in even the most beautiful prose. Microeconomic models, and “general equilibrium” as that term was used at the time, wrote down how people behave — how they decide what to buy, how hard to work, whether to save, etc.. Then it similarly described how companies behave and how government behaves. Set this in motion and see where it all settles down; what prices and quantities result. 

But for macroeconomic issues, this approach was sterile. I took a lot of general equilibrium classes as a PhD student — Berkeley, home of Gerard Debreu was strong in the field. But it was devoted to proving the existence of equilibrium with more and more general assumptions, and never got around to calculating that equilibrium and what it might say about recessions and government policies. 

Macroeconomics, exemplified by the ISLM tradition,  inhabited a different planet. One wrote down equations for quantities rather than people, for example that “consumption” depended on “income,” and investment on interest rates. Most importantly, macroeconomics treated each year as a completely separate economy. Today’s consumption depended on today’s income, having nothing to do with whether people expected the future to look better or worse. Economists recognized this weakness, and a vast and now thankfully forgotten literature tried fruitlessly to find “micro foundations” for Keynesian economics. But building foundations under an existing castle doesn’t work. The foundations want a different castle. 

Bob’s “islands” paper is famous, yes, for a complete model of how unexpected money might move output in the short run and not just raise inflation. But you can do that with a half a page of simple math, and Bob’s paper is hard to read. It’s deeper contribution, and the reason for that difficulty, is that Bob wrote out a complete “general equilibrium” model. People, companies and government each follow described rules of behavior. Those rules are derived as being the optimal thing for people and companies to do given their environment. And they are forward-looking. People think about how to make their whole lives as pleasant as possible, companies to maximize the present value of profits. Prices adjust so supply = demand. Bob said, by example, that we should do macroeconomics by writing down general equilibrium models. 

General equilibrium had also been abandoned by the presumption that it only studies perfect economies. Macroeconomics is really about studying how things go wrong, how “frictions” in the economy, such as the “sticky” wages underlying Keynesian thinking, can produce undesirable and unnecessary recessions. But here too, Bob requires us to write down the frictions explicitly. In his model, people don’t see the aggregate price level right away, and do the best they can with local information. 

That is the real influence of the paper  and Bob’s real influence in the profession. (Current macroeconomic modeling reflects the fact that the Fed sets interest rates, and does not control the money supply.) You can see this influence in Tom Sargent’s textbooks. The first textbook has an extensive treatment of Keynesian economics. It’s about the most comprehensible treatment there is — but it is no insult to Tom to say that in that book you can see how Keynesian economics really doesn’t hang together. Tom describes how, the minute he learned from Bob how to to general equilibrium, everything changed instantly. Rational expectations was, like any other advance, a group effort. But what made Bob the leader was that he showed the rest how to do general equilibrium. 

This is the heart of my characterization that Bob is the most important macroeconomist of the 20th century. Yes, Keynes and Friedman had more policy impact, and Friedman’s advocacy of free markets in microeconomic affairs is the most consequential piece of 20th century economics. But within macroeconomics, there is before Lucas and after Lucas.  Everyone today does economics the Lucas way. Even the most new-Keynesian article follows the Lucas rules of how to do economics. 

Once you see models founded on complete descriptions of people, businesses, government, and frictions, you can see the gaping holes in standard ISLM models. This is some of his stinging critique, such as “after Keynesian macroeconomics.” Sure, if people’s income goes up they are likely to consume more, as the Keynesians posited. But interest rates, wages, and expectations of the future also affect consumption, which Keynesians leave out. “Cross equations restrictions” and “budget constraints” are missing. 

Now, the substantive prediction that monetary policy can only move the real economy via unexpected money supply growth did not bear out, and both subsequent real business cycles and new-Keynesianism brought persistent responses. But the how we do macroeconomics part is the enduring contribution. 

The paper still had enduring practical lessons. Lucas, together with Friedman and Phelps brought down the Phillips curve. This curve, relating inflation to unemployment, had been (and sadly, remains) at the center of macroeconomics. It is a statistical correlation, but like many correlations people got enthused with it and started reading it as stable relationship, and indeed a causal one. Raise inflation and you can have less unemployment. Raise unemployment in order to lower inflation. The Fed still thinks about it in that causal way. But Lucas, Friedman, and Phelps bring a basic theory to it, and thereby realize it is just a correlation, which will vanish if you push on it. Rich guys wear Rolexes. That doesn’t mean that giving everyone a Rolex will have a huge “multiplier” effect and make us all rich. 

This is the essence of the “Lucas critique” which is a second big contribution that lay readers can easily comprehend. If you push on correlations they will vanish. Macroeconomics was dedicated to the idea that policy makers can fool people. Monetary policy might try to boost output in a recession with a surprise bit of money growth. That will wok once or twice. But like the boy who cried wolf, people will catch on, come to expect higher money growth in recessions and the trick won’t work anymore. 

Bob showed here that all the “behavioral” relations of Keynesian models will fall apart if you exploit them for policy, or push on them, though they may well hold as robust correlations in the data. The “consumption function” is the next great example. Keynesians noticed that when income rises people consume more, so write a consumption function relating consumption to income. But, following Friedman’s great work   on consumption, we know that correlation isn’t always true in the data. The relation between consumption and income is different across countries (about one for one) than it is over time (less than one for one). And we understand that with Friedman’s theory: People, trying to do their best over their whole lives don’t follow mechanical rules. If they know income will fall in the future, they consume a lot less today, no matter what today’s current income. Lucas showed that people who behave this sensible way will follow a Keynesian consumption function, given the properties of income overt the business cycle. You will see a Keynesian consumption function. Econometric estimates and tests will verify a Keynesian consumption function. Yet if you use the model to change policies, the consumption function will evaporate. 

This paper is devastating. Large scale Keynesian models had already been constructed, and used for forecasting and policy simulation. It’s natural. The model says, given a set of policies (money supply, interest rates, taxes, spending) and other shocks, here is where the economy goes. Well, then, try different policies and find ones that lead to better outcomes. Bob shows the models are totally useless for that effort. If the policy changes, the model will change. Bob also showed that this was happening in real time. Supposedly stable parameters drifted around. (This one is also very simple mathematically. You can see the point instantly. Bob always uses the minimum math necessary. If other papers are harder, that’s by necessity not bravado.) 

This devastation is sad in a way. Economics moved to analyzing policies in much simpler, more theoretically grounded, but less realistic models. Washington policy analysis sort of gave up. The big models lumber on, the Fred’s FRBUS for example, but nobody takes the policy predictions that seriously. And they don’t even forecast very well. For example, in the 2008 stimulus, the CEA was reduced to assuming a back of the envelope 1.5 multiplier, this 40 years after the first large scale policy models were constructed. Bob always praised the effort of the last generation of Keynesians to write explicit quantitative models, to fit them to data, and to make numerical predictions of various policies. He hoped to improve that effort. It didn’t work out that way, but not by intention. 

This affair explains a lot of why economists flocked to the general equilibrium camp. Behavioral relationships, like what fraction of an extra dollar of income you consume, are not stable over time or as policy changes. But one hopes that preferences, — how impatient you are, how much you are willing to save more to get a better rate of return — and technology — how much a firm can produce with given capital and labor — do not change when policy changes. So, write models for policy evaluation at the level of preferences and technology, with people and companies at the base, not from behavioral relationships that are just correlations. 

Another deep change: Once you start thinking about macroeconomics as intertemporal economics — the economics that results from people who make decisions about how to consume over time, businesses make decisions about how to produce this year and next — and once you see that their expectations of what will happen next year, and what policies will be in place next year are crucial, you have to think of policy in terms of rules, and regimes, not isolated decisions. 

The Fed often asks economists for advice, “should we raise the funds rate?” Post Lucas macroeconomists answer that this isn’t a well posed question. It’s like saying “should we cry wolf?” The right question is, should we start to follow a rule, a regime, should we create an institution, that regularly and reliably raises interest rates in a situation like the current one? Decisions do not live in isolation. They create expectations and reputations. Needless to say, this fundamental reality has not soaked in to policy institutions. And that answer (which I have tried at Fed advisory meetings) leads to glazed eyes. John Taylor’s rule has been making progress for 30 years trying to bridge that conceptual gap, with some success.  

This was, and remains, extraordinarily contentious. 50 years later, Alan Blinder’s book, supposedly about policy, is really one long snark about how terrible Lucas and his followers are, and how we should go back to the Keynesian models of the 1960s. 

Some of that contention comes back to basic philosophy.  The program applies standard microeconomics: derive people’s behaviors as the best thing they can do given their circumstances. If people pick the best combination of apples and bananas when they shop, then also describe consumption today vs. tomorrow as the best they can do given interest rates. But a lot of economics doesn’t like this “rational actor” assumption. It’s not written in stone, but it has been extraordinarily successful. And it imposes a lot of discipline. There are a thousand arbitrary ways to be irrational.  Somehow though, a large set of economists are happy to write down that people pick fruit baskets optimally, but don’t apply the same rationality to decisions over time, or in how they think about the future. 

But “rational expectations” is really just a humility condition. It says, don’t write models in which the predictions of the model are different from the expectations in the model. If you do, if your model is right, people will read the model and catch on, and the model won’t work anymore. Don’t assume you economist (or Fed chair) are so much less behavioral than the people in your model. Don’t base policy on an attempt to fool the little peasants over and over again. It does not say that people are big super rational calculating machines. It just says that they eventually catch on. 

Some of the contentiousness is also understandable by career concerns. Many people had said “we should do macro seriously like general equilibrium.” But it isn’t easy to do. Bob had to teach himself, and get the rest of us to learn, a range of new mathematical  and modeling tools to be able to write down interesting general equilibrium models. A 1970 Keynesian can live just knowing how to solve simple systems of linear equations, and run regressions.  To follow Bob and the rational expectations crowd, you had to learn linear time-series statistics, dynamic programming, and general equilibrium math. Bob once described how tough the year was that it took him to learn functional analysis and dynamic programming. The models themselves consisted of a mathematically hard set of constructions. The older generation either needed to completely retool, fade away, or fight the revolution. 

Some good summary words: Bob’s economics uses"rational expectations,” or at least forward-looking and model-consistent expectations. Economics becomes “intertemporal," not “static” (one year at a time). Economics is “stochastic” as well as “dynamic,” we can treat uncertainty over time, not just economies in which everyone knows the future perfectly. It applies “general equilibrium" to macroeconomics. 

And I’ve just gotten to the beginning of the 1970s. 

When I got to Chicago in the 1980s, there was a feeling of “well, you just missed the party.” But it wasn’t true. The 1980s as well were a golden age. The early rational expectations work was done, and the following real business cycles were the rage in macro. But Bob’s dynamic programming, general equilibrium tool kit was on a rampage all over dynamic economics. The money workshop was one creative use of dynamic programs and interetempboral tools after another one, ranging from taxes to Thai villages (Townsend). 

I’ll mention two. Bob’s consumption model is at the foundation of modern asset pricing. Bob parachuted in, made the seminal contribution, and then left finance for other pursuits. The issue at the time was how to generalize the capital asset pricing model. Economists understood that some stocks pay higher returns than others, and that they must do so to compensate for risk. The understood that the risk is, in general terms, that the stock falls in some sense of bad times. But how to measure “bad times?” The CAPM uses the market, other models use somewhat nebulous other portfolios. Bob showed us that at least in the purest theory, that stocks must pay higher average returns if they fall when consumption falls. (Breeden also constructed a consumption model in parallel, but without this “endowment economy” aspect of Bob’s) This is the purest most general theory, and all the others are (useful) specializations. My asset pricing book follows. 

The genius here was to turn it all around. Finance had sensibly built up from portfolio theory, like supply and demand: Given returns, what stocks do you buy, and how much to you save vs. consume? Then, markets have to clear find the stock prices, and thus returns, given which people will buy exactly the amount that’s for sale and consume what is produced. That’s hard. (Technically, finding the vector of prices that clears markets is hard. Yes, N equations in N unknowns, but they’re nonlinear and N is big.)  

Bob instead imagined that consumption is fixed at each moment in time, like a desert island  in which so many coconuts fall each day and you can't store them or plant them. Then, you can just read prices from people’s preferences. This gives the same answer as if the consumption you assume is fixed had derived from a complex production economy. You don’t have to solve for prices that equate supply and demand. Brilliantly, though prices cause consumption to individual people, consumption causes prices in aggregate. This is part of Bob’s contribution to the hard business of actually computing quantitative models in the stochastic dynamic general equilibrium tradition. 

Bob, with Nancy Stokey also took the new tools to the theory of taxation. (Bob Barro also was a founder of this effort in the late 1980s.) You can see the opportunity: we just learned how to handle dynamic (overt time, expectations of tomorrow matter to what you do today) stochastic (but there is uncertainty about what will happen tomorrow) economics (people make explicit optimizing decisions) for macro. How about taking that same approach to taxes? The field of dynamic public finance is born. Bob and Nancy, like Barro, show that it’s a good idea for governments to borrow and then repay, so as to spread the pain of taxes evenly over time. But not always. When a big crisis comes, it is useful to execute a “state contingent default.” The big tension of Lucas-Stokey (and now, all) dynamic public finance: You don’t want any capital taxes for the incentive effects. If you tax capital, people invest less, and you just get less capital. But once people have invested, a capital tax grabs revenue for the government with no economic distortion. Well, that is, if you can persuade them you’ll never do it again. (Do you see expectations, reputations, rules, regimes, wolves in how we think of policy?) Lucas and Stoney say, do it only very rarely to balance the disincentive of a bad reputation with the need to raise revenue in once a century calamities. 

Bob went on, of course, to be one of the founders of modern growth theory. I always felt he deserved a second Nobel for this work. He’s absolutely right. Once you look at growth, it’s hard to think about anything else. The average Indian lives on $2,000 per year. The average American, $60,000. That was $15,000 in 1950. Nothing else comes close. I only work on money and inflation because that’s where I think I have answers. For us mortals, good research proceeds where you think you have an answer, not necessarily from working on Big Questions. 

Bob brilliantly put together basic facts and theory to arrive at the current breakthrough. Once you get out of the way, growth does not come from more capital, or even more efficiency. It comes from more and better ideas. I remember being awed by his first work for cutting through the morass and assembling the facts that only look salient in retrospect. A key one: Interest rates in poor countries are not much higher than they are in rich countries. Poor countries have lots of workers, but little capital. Why isn’t the return on scarce capital enormous, with interest rates in the hundreds of percent, to attract more capital to poor countries? Well, you sort of know the answer, that capital is not productive in those countries.  Productivity is low, meaning those countries don't make use of better ideas on how to organize production.  

Ideas too are produced by economics, but, as Paul Romer crystallized, they are fundamentally different from other goods. If I produce an idea, you can use it without hurting my use of it. Yes, you might drive down the monopoly profits I gain from my intellectual property. But if you use my Pizza recipe, that’s not like using my car. I can still make Pizza, where if you use my car I can’t go anywhere. Thus, the usual free market presumption that we will produce enough ideas is false. (Don’t jump too quickly to advocate government subsides for ideas. You have to find the right ideas, and governments aren’t necessarily good at subsidizing that search.) And the presumption that intellectual property should be preserved forever is also false. Once produced it is socially optimal for everyone to use it. 

I won’t go on. It’s enough to say that Bob was as central to the creation of idea-based growth theory, which dominates today, as he was to general equilibrium macro, which also dominates today.

Bob is an underrated empiricist. Bob's work on the size distribution of firms (great tweet summary by Luis Garicano) similarly starts from basic facts of the size distribution of firms and the lack of relationship between size and growth rates. It's interesting how we can go on for years with detailed econometric estimates of models that don't get basic facts right. I loved Bob's paper on money demand for the Carnegie Rochester conference series. An immense literature had tried to estimate money demand functions with dynamics, and was pretty confusing. It made a basic mistake, by looking at first differences rather than levels and thereby isolating the noise and drowning out the signal. Bob made a few plots, basically rediscovered cointegration all on his own, and made sense of it all. And don't forget the classic international comparison of inflation-output relations. Countries with volatile inflation have less Phillips curve tradeoff, just as his islands model featuring confusion between relative prices and the price level predicts. 

One last note to young scholars. There is a tendency today to value people by the number of papers they produce, and how quickly they rise through the ranks. Read Bob’s CV. He wrote about one paper a year, starting quite late in life. But, as Aesop said, they were lions. In his Nobel prize speech, Bob also passed on that he and his Nobel-winning generation at Chicago always felt they were in some backwater, where the high prestige stuff was going on at Harvard and MIT. You never know when it might be a golden age. And the AER rejected his islands paper (as well as Akerlof's lemons). If you know it's good, revise and try again. 

I will miss his brilliant papers as much as his generous personality. 

Update: See Ivan Werning's excellent "Lucas Miracles" for an appreciation by a real theorist.