I recently attended a Hoover Institution monetary policy conference entitled.”How to get back on track”. But exactly what does it mean to get back on track? That question got me thinking about why I struggle so much when people ask me whether I agree with current Fed monetary policy.  I find the question difficult to answer because there are two distinct senses in which monetary policy can be off track: 1. The current stance of policy can be too easy or too tight.  I.e., the fed funds target might be too low or too high relative to the natural rate. 2. The Fed might have the wrong policy regime.  They might be doing growth rate targeting whereas they ought to be doing level targeting.  That makes policy errors more likely. I find that the average person sees the first question as being more important, whereas for me being “on track” is mostly about the second question.  Thus some of my readers might assume that right now I in some sense “agree with Fed policy”, even though I actually disagree with the policy.  Yes, I don’t see much evidence that the current stance of monetary policy is too easy or too tight, but if the economy ends up in the ditch next year then I’ll probably blame the Fed.  Would that be unfair Monday morning quarterbacking?  I’ll use another sports analogy to try to illustrate my point. Suppose a technical foul were called on the Lakers, and coach Steve Kerr of the Warriors chose Kevin Looney to shoot the technical free throw.  I would severely criticize this decision, as Looney only shoots 60% whereas he could have used Steph Curry (who shoots 90% on free throws.) Now suppose someone asks me to predict the path of Looney’s free throw.  I’ll say that I forecast it to go right through the basket.  Yes, he’s fairly inaccurate, but I have no idea whether he’ll miss left or right or short or long.  Think of a probability distribution with “fat tails”, where the center of the distribution is right on the basket.  He’s not very accurate, but I am not aware of any systematic bias.   Even though I predict Looney’s shot will go toward the basket, I’d still criticize coach Kerr’s decision to use Looney if the shot missed.  Similarly, while the current stance of monetary policy seems OK, the Fed’s “let bygones be bygones” policy regime produces a much less stable monetary policy than would a level targeting approach. People will often point out to me that the financial markets did not predict a big inflation problem in mid-2021.  In that case, is it fair to criticize the Fed for what happened later?  Isn’t that just Monday morning quarterbacking?  I’d say criticism is fair, because they should have had a regime in place where they promised to get back to the NGDP trend line after an overshoot.  That promise would have made the initial overshoot much smaller. Just as I would predict Kevin Looney’s shot to go toward the basket despite his poor skill at shooting, I will usually (not always) predict the future path of NGDP to be roughly where the Fed wants it to be.  And I suspect that the markets have the same view. We don’t have an NGDP futures market, but the markets we do have seem to be implicitly predicting a slowdown in NGDP growth, but no severe recession.  Thus interest rates are expected to fall later this year, but remain above 4%.  A fall in interest rates would only occur if NGDP growth slows, and yet if there were a severe recession then interest rates would fall to well below 4%.  So far, so good. At the conference, St Louis Fed President James Bullard suggested that a soft landing is still very much in play.  He pointed out that unlike in the early 1980s, inflation expectations are close to 2%.  It’s much easier to bring down inflation if the higher rates have not yet become embedded in the public’s expectations.  The Fed still has more credibility than in the early 1980s. I mostly agree with Bullard, but I am a tad less optimistic due to my worry about the policy regime.  Yes, markets seem to be forecasting a fairly good outcome.  But that’s just the midpoint of the distribution—there’s still a worryingly wide range of possible outcomes. We need to switch from a Kevin Looney Fed to a Steph Curry Fed.  We need to shift from flexible inflation targeting to NGDP level targeting, so that when NGDP begins to drift off course there be an immediate move in market interest rates that will nudge the Fed in the right direction. PS.  It is the 30th anniversary of the Taylor Rule, and it was nice to see the conference honor John Taylor for his role in making monetary policy more precise during the late 1900s.  Recessions became less frequent after 1982, which is about the time the Fed began using a more Taylor Rule-type approach to policy.  I see level targeting as the next step. PPS.  Don’t take this post as a criticism of Looney, who is an excellent rebounder. PPPS.  I’m tempted to say that level targeting would make monetary policy almost “Stephertless”, but I won’t. (0 COMMENTS)

It is well known to economists that a rational decision-maker will not include sunk costs in his decisions. Since sunk costs are unrecoverable by definition, they are have nothing to do with decisions made now for the future. Only future costs are recoverable: you simply have not to incur them. Some people do not seem to understand that. In a report on Apple’s imminent and risky launch of “mixed reality” googles, the Wall Street Journal tells us (“Apple Is Breaking Its Own Rules With a New Headset,” May 12, 2023—my underlines): Executives and tech analysts say Apple isn’t waiting longer because it would take too much time to make its ideal version, competitors are already in the market and the company has already devoted a lot of capital and resources into developing the headset. What the company has already spent in development costs should not weigh in whether it launches the product now or later or never. The past development money is sunk and not revoverable whatever decision is made now. Given the direction of the arrow of time, one makes decisions to change the future, not to change the past (except if you work for the Ministry of Truth, but even then you can only change the past as perceived starting from today). Suppose that you have invested $500 dollars in some project that is not producing and unlikely to produce any return, but that investing a supplementary $10 in the project will very likely bring a net profit of $5. The $10 will thus bring a return of 50%, notwithstanding that the accounting return on the total investment will be less than 1% ($5 / $510). Of course, you should invest the $10, except if you are an extreme risk-averter or you know another investment that will bring a return of more than 50% with near certainty. But, when you make the decision now, only the 50% return on $10 will guide your decision, not the return of less than 1%. Indeed, if the return on the $10 were forcasted to be 2% (20¢), a rational decision-maker would probably decline to invest as there are likely better returns available on the market or elsewhere within the company. Cut your loss or, as the saying goes, don’t throw good money after bad money, because losing money, or not making as much as you could, does not reduce costs already sunk. The rule apply to other types of costs and returns too. If you have spent one year creating a Frankenstein monster because (say) you needed a hunting buddy, and you discover that your creature is now likely to kill you instead, it would be bad thinking to factor in the solution “all the time I spent bringing him to life!” That time is gone forever and you won’t get it back. Regrets don’t change the past. Good decisions aim at the future, even if only the immediate future (such as not to be killed by your Frankenstein creature). When Apple releases its product, the company will obviously think that it will profitable even if, as the WSJ reports makes clear, fixes will have to be found and further development to be financed. But the project’s sunk costs at any point in time don’t influence the company’s decision at that moment to continue or not pouring money into it. If any new investment in the project is ever estimated to have no prospect of future satisfactory return, investment will stop whether “a lot” or not of sunk costs have gone into it. Why would the WSJ reporters write the sentence quoted above? I can think of four possibilities. (1) The “executives and tech analysts” consulted by the reporters are a representative sample of all executive and tech analysts, which implies that no executives or tech analysts understand sunk costs. This is very unlikely, for an executive or perhaps even a tech analyst who does not understand that would not stay, or have stayed, long on competitive markets. (2) There are some executives or tech analysts who do understand sunk costs, but the reporters missed them or ignored their opinions. (3) The reporters themselves or their editor don’t clearly understand sunk costs. (4) It is just sloppy writing. I don’t know which one or which one, or which combination, of hypotheses #2, #3, or #4 is true, but whichever it is exposes a failure in providing the information that most of the WSJ readers pay for. It is not because most of the other medias are economically illiterate the WSJ is justified to follow them. In my opinion, this newspaper is one of the very top sources of reliable information in the world—which is why I read it regularly and thus find more occasions to criticize it (while I don’t often read Breitbart, the Chronicle of Higher Education, or the Backwoodsman). But I hope these occasions would be less frequent. (0 COMMENTS)

If you read or watch nothing else about Covid-19 today or this weekend, make sure you watch Margery Smelkinson’s 4-minute testimony. It’s here. (0 COMMENTS)

Back in April, my often co-author and friend Charley Hooper was asked to testify on Zoom in front of a citizens’ commission in Canada that was investigating various responses to Covid-19. Here is the link to the Frontier Centre’s post on his testimony. You can then click on the link there to watch his testimony (or click on this link.) Some highlights follow. 4:00: The mismatch. 5:50: The controversy over ivermectin. 8:20: When various drugs were available to deal with Covid-19. 9:00: History of ivermectin. 10:40: Drugs used for disease A often work with disease B. 11:30: Ivermectin kills lots of viruses. 11:58: Does it work? 12:10: Merck and FDA attack ivermectin. 14:00: FDA claimed that ivermectin is not an anti-viral. 15:00: FDA’s motives: EUA, off-label usage 17:40: Merck’s motives 18:50: Notice the host’s question. 20:30: The TOGETHER trial. 24:00: The other studies, many of which found benefits. 26:00: Host’s question about early treatment. 27:25: A basic principle with treating viruses is to treat early. 28:00: Other drugs not as good and some have serious side effects, such as acute kidney failure and harm to DNA. 29:50: Statistical significance. 31:00: Results versus narrative. “Only” 91.2% sure or 93% sure. 34:00: How ridiculous this would be. 35:30: Conclusion: In pandemic try drugs off the shelf. 39:30: How could this be done better? Get rid of FDA censorship: allow drug companies to make money (a little slim on details here) after patent has expired. 45:30: Repeal efficacy requirement. (0 COMMENTS)

Scott Alexander recently argued that building more housing in a given city causes housing prices to go up in that city. He acknowledged that previous studies had found the opposite relationship, but suggested that he was more impressed by the strong positive correlation between population and prices when there are big changes: Matt Yglesias tries to debunk the claim that building more houses raises local house prices. He presents several studies showing that, at least on the marginal street-by-street level, this isn’t true. I’m nervous disagreeing with him, and his studies seem good. But I find looking for tiny effects on the margin less convincing than looking for gigantic effects at the tails. When you do that, he has to be wrong, right? I also believe that looking at the tails (big changes) is often more revealing than looking at lots of small changes.  But only if you’ve got causality right.  And in this case, Alexander hasn’t necessarily done so. Here’s an analogy.  Suppose you wanted to compare the mainstream view of monetary policy (raising interest rates is deflationary) with the NeoFisherian view (raising interest rates is inflationary.)  So you focused your attention on cases where there were truly massive increases in interest rates–say to 20%, 30% or 50%.  In virtually all of those cases, inflation will be very high when nominal interest rates are very high.  That seems to support the NeoFisherian position.  (I wonder how Alexander feels about this debate.) But this sort of correlation doesn’t address the issue of causation, and thus most economists reject the notion that a high interest rate policy is inflationary, despite the clear correlation.  They see this as an example of “reasoning from a price change”. In my research on market reactions to policy news during the 1930s, I argued that big changes were especially revealing.  But in those sorts of “event studies” the direction of causation is clear—policy news leads to immediate changes in asset prices.   Big changes don’t help if the house price model you are criticizing is also consistent with the stylized fact that bigger cities tend to be more expensive.  That stylized fact would be true even if building more houses reduced house prices at the margin. (0 COMMENTS)

Michael Spence, who shared the 2001 Nobel Prize in economics, is an emeritus professor of economics at Stanford University’s Graduate School of Business. He recently wrote an op/ed titled “In defense of industrial policy.” It is gated at Project Syndicate but appears ungated in the Jordan Times. His case is, at best, flabby. It lacks any empirical evidence and takes as given, without argument or evidence, the idea that many government interventions have benefits that exceed costs. A few highlights: The objective of industrial policies is to alter market outcomes in ways that align them better with a country’s broader economic and social objectives. Free-market purists may bristle, but in the real world many relatively uncontroversial and even widely supported, government interventions shape market outcomes. The first 10 words are correct. But what does he mean by “a country’s broader economic and social objectives?” Does he mean the economic objectives of a president and a Congress? I think so. But why should we give weight to their preferences? Is it the old “we voted for them” argument? But what about the fact that even the majority of registered voters, let alone the majority of U.S. adults, didn’t vote for them? Moreover, given everything we know about the incentives of politicians and about who rises to the top, aren’t their objectives likely to be fairly narrow rather than broad? He says that “Free-market purists may bristle.” That’s probably true. But wouldn’t even many clear-thinking people who aren’t free-market purists bristle also at the ease with which Spence would have government dictate to the rest of us? By focusing on free-market purists, though, a group that he must know is relatively tiny, he manages to avoid actually making an argument. He’s saying, in effect, we can dismiss them because they’re purists. He then writes: For example, public-sector investment in infrastructure, education and the economy’s science and technology base is considered an essential complement to private investment, mitigating risks, increasing returns and bolstering overall economic performance. Other widely accepted interventions that alter market outcomes include antitrust or competition policy, measures to overcome information gaps and asymmetries and regulation to address negative externalities, protect user data and guarantee the safety of everything from airplanes to food. Let’s consider this paragraph bit by bit. Note Spence’s use of the passive voice in his first sentence. Considered by whom? He doesn’t tell us. Probably considered by Spence and certainly by many others. But on what basis do they reach these conclusions? Blank out. Now consider his second sentence. It’s true that the interventions he mentions are widely accepted. But should they be? He seems to take as given that because they’re widely accepted, they’re good ideas. And do we get safer airplanes because government regulates? Maybe. But I can tell you how we get more dangerous airplanes in general aviation than we would likely get with minimal or no regulation. The FAA makes it so costly for a small airplane producer to get permission to sell innovative products, that many possible products don’t exist. So airplane owners hang on to older airplanes for 40 or 50 years. The odds are, given that safety is a normal good and that real income and wealth have increased a lot in the last 50 years, that without costly regulation, many new safer airplanes would exist and send at least a few of the older less-safe airplanes to the garbage dump. But Spence doesn’t consider this possibility. Spence then writes: But these are responses to known market failures. Industrial policies, at least the most divisive ones, go a step further, reshaping the supply side of the economy in pursuit of objectives other than efficiency in the allocation of resources. His first sentence is simply an assertion. He gives no basis for it. His second sentence, though, is correct. Aware that he must deal with the issue of the government picking winners and losers, he writes: The second component, however, has proved divisive. Critics point out that selective public investment in any industry’s productive capacity amounts to picking winners and losers. In their view, governments are not well-equipped to take on this task, not least because vested interests can capture the decision-making process. Though this argument in favor of relying on market outcomes should not be dismissed out of hand, it should be met with some scepticism, not least because it is often rooted in an almost religious commitment to unfettered competition. In fact, industrial policy can be essential to a country’s long-term economic survival, as in the case of defence, particularly in times of war. The first paragraph of the two above is promising. Spence shows that he’s aware of the special interests that could distort the process and lead to bad results. But the second paragraph undercuts that. The argument, he says, “is often rooted in an almost religious commitment to unfettered competition.” For some people who make that argument, that’s probably true. But whether it’s rooted in such a commitment isn’t relevant. The questions is, “Is the argument correct?” But by bringing in the idea that some of the people who disagree with him might be “religious” about their disagreement, he cleverly avoids actually dealing with the argument. Spence then writes: The real question is not whether industrial policy is worth pursuing, but how to do it well. Government capacity is decisive: to act effectively as an investor and major buyer of products and services, the government needs people with talent and experience, receiving compensation to match, and well-designed institutions. Moreover, goals should be precise, limited and clear, and guardrails must be erected to protect against private-sector capture. Industrial policy is not corporate welfare. Isn’t that only one of the real questions? Isn’t another question whether industrial policy is worth pursuing? Is this question irrelevant? Spence seems to think so. Spence asserts that the Defense Advanced Research Projects Agency (DARPA) and the government support of the COVID-19 vaccine are examples of successful industrial policies. That might be true, but he doesn’t give evidence to support his claim. Spence admits that there are industrial policy failures but points out that venture capitalists fail too. He writes: No one expects every investment made by a venture-capital fund to be a home run. Governments should be afforded the same leeway. A decent track record is good enough to make industrial policy pay off for taxpayers. His first sentence is absolutely correct. But the difference between venture capitalists and governments is that venture capitalists are betting their own money while governments are betting our money. That’s a big difference. The very essence of economics is its focus on incentives. The incentives for venture capitalists and government officials are quite different. If the venture capitalists succeed, they might make a lot of money; if they fail, they lose their own money. If government officials succeed, they might get a nice promotion and $10,000 or $20,000 more annually; if they fail, they might not even get demoted. I would have thought that the difference in incentives would be one of the first concerns that would arise in the mind of an accomplished economist. But maybe Spence would say that that’s because I’m religious.   Postscript: Here is the biography of Michael Spence that I wrote in The Concise Encyclopedia of Economics. (0 COMMENTS)

In my previous post in this series, I described how Randall Holcombe separates our instrumental preferences (the outcomes we prefer) from our expressive preferences (what we prefer to express). But there is another, more important preference classification he outlines. Holcombe suggests a major factor in how our preferences are formed is through the interaction of what he calls anchor preferences and derivative preferences. What are these different kinds of preferences? Let’s start with anchor preferences: Anchor preferences are those that define people’s political identities. They define how people see themselves, and how they want others to see them. Derivative preferences are, as the name might suggest, preferences that are derived from one’s anchor preferences. As Holcombe puts it: People may identify as members of a political party, a political movement, an ideology, an issue, an individual candidate, or a religion. Their political preferences anchor on this identity. Most policy preferences are derivative preferences, derived from the preferences associated with the person’s anchor. People’s political identity forms an anchor, and most of their policy preferences are derived from that anchor. Anchor preferences can be broadly defined. People might anchor on a particular issue of principle – single issue voters are a classic example of how this can work: Consider the contentious issue of abortion. Some people may hold strong views that women have the right to determine whether to continue a pregnancy. As the slogan goes, “My body, my choice.” Others may hold the strong view that abortion is murder. They will anchor on candidates and parties that reflect their strong views. Having anchored onto the political party most aligned with their anchor preference, people will tend to adopt the rest of the platform of that party as derivative preferences: American voters who favor a woman’s right to make the choice are likely to favor the Democratic party, and policy preferences on other issues like gun control, the tax structure, government involvement in health care, and redistribution programs are likely to be derivative of those of their anchors. Those who oppose abortion, likewise, are likely to have derivative preferences that follow the Republican party. It is not a coincidence that people who tend to be pro-choice on the abortion issue also tend to favor stronger gun control. Having chosen an anchor, most policy preferences are derivative. However, people may not anchor on particular issues, but might anchor onto their political identity as a member of a party. They think of themselves as being Republicans, or Democrats, and anchor to those parties, deriving their political preferences from those anchors: Individuals who anchor as Democrats will tend to support more government gun control, more government involvement in health care, and a woman’s right to have an abortion. People do not start with those preferences and then decide, “I am a Democrat.” Rather, they start with their political identity as Democrats and conclude, “I am a Democrat, so I favor gun control, more government involvement in health care, and a woman’s right to have an abortion.” These preferences are derivative preferences, derived from the policy positions advocated by the individual’s anchor. When people anchor to a political party, one consequence is that the official party platform can reverse its position on what was supposed to be an issue of major importance, and citizens who anchor on their party identity will simply alter their derivative preferences to follow along with the party: The Republican party, at least since Ronald Reagan’s presidency, supported free trade, but after President Trump won on a protectionist platform aimed at China, Mexico, and other countries, most Republicans did not push back and argue that Trump’s protectionist policies were out of step with the party’s values. Rather, they supported Trump’s trade policies. These are voters whose beliefs about free trade were simply a derivative preference, derived from their anchor preference of identification with the Republican party. When the Republican party advocated free trade, so did they. And when the Republican party turned away from free trade, so did they. In the same way, after Trump’s rise to prominence in the Republican party, support for free trade among Democrats shot up dramatically, to significantly higher levels than Republican support for free trade during the presidency of George W. Bush. Putting it mildly, it is highly unlikely that this rapid rise in support for free trade among Democrats was caused by millions of members of the party suddenly reading a basic economics textbook and simultaneously realizing the case for free trade is very strong, nor can the sudden loss of support for free trade among Republicans be realistically explained by the reverse process. The far more likely explanation is that voters, by the tens of millions, will simply alter their positions on issues to fit whatever the partisan politics of the moment dictates. This is just one of many examples where major political parties in the United States can alter their positions on issues of great importance, even swapping positions with the opposing party, yet the people supporting or opposing those parties remain largely unchanged. Holcombe reviews a wide range of literature that helps explain why most policy preferences are derivative for most people. Among the relevant factors is the endowment effect – people value their political identities simply by having them and will be reluctant to change them. There is also the bandwagon effect – when it seems like most members of your identity group, peer group, or social circle are going in a particular direction, most people go along, particularly when there is nothing instrumental to gain by dissenting. The desire to reduce cognitive dissonance is also at play. Holcombe uses the metaphor of grocery shopping to outline some of the differences between market preferences and political preferences: Shoppers who shop at a supermarket take their carts from isle to isle, placing goods in their carts that they want to purchase. Every item in the cart is chosen by the shopper because the shopper wants the item, and the items the store stocks that the shopper does not want does not go into the shopper’s cart. Shoppers get exactly the bundle of goods they want. However, the contents of a political shopping cart are formed in a very different way: If shopping were done in supermarkets as it is done in elections, competing candidates would fill shopping carts with items they wanted to offer the voters, and voters would then be offered the choice of a cart filled by one candidate or another. Rather than shoppers personally deciding what would go into their carts, candidates would decide, and shoppers would be offered only the choice of carts filled by one of the candidates. To extend the analogy, supporting a party or candidate means expressing a preference for everything in that candidate’s cart. If shopping were done this way, it’s all but certain that everyone’s cart will lack many desired items and contain other items they’d never buy if it were up to them. But since the contents of the cart isn’t up to them, voters simply go along with whatever the bundle contains: The voters are offered one total bundle of public policies or another and cannot customize their political shopping carts the way they can their market shopping carts. To minimize cognitive dissonance, citizens can adjust their preferences to conform with the contents of their anchors’ carts. There is no reason not to do so, because the cart they actually get will be the same regardless of the preference they express. So far, I’ve focused on Holcombe’s analysis of how preferences are influenced and formed among voters. But a key component in Holcombe’s book is how policy preferences are formed by the elite. In what way do the preferences of the elite differ from voters, and more importantly, what are the differences in the incentive structures faced in preference formation between elites and voters? That will be the subject of the next post. (0 COMMENTS)

In a recent post, Scott Alexander doubled down on his argument that building more housing generally results in higher housing prices. In his previous post, he pointed to the fact that housing is generally more expensive in bigger cities. I provided a counterexample, citing Houston and Austin. I also argued that big cities are expensive for reasons unrelated to their large quantity of housing, such as the advantages provided by agglomeration. Exogenous supply increases, such as making it easier to build new housing, generally reduce housing prices. Here’s how Alexander responded to one of my arguments: I started the post with a graph of about 50 cities, showing a positive correlation between density and price. I’m having trouble seeing how Sumner’s point isn’t just “if you remove 48 of those cities and cherry-pick two, the relationship is negative”. I think Alexander misunderstood this argument, so let me go back and make the point more carefully.  It will be helpful to make two separate arguments: 1. You’d expect big cities to be more expensive even if building housing reduces housing prices. 2. Austin and Houston are not the only counterexamples; there are many other such anomalies. Let’s start with a simple model where there are zero restrictions on housing construction in all cities.  Assume that America has 100 cities, each with industries of varying productivity.  In that model, the largest cities will be next to the areas of greatest productivity.  (Those areas of productivity were once linked to natural factors such as ports and mineral resources, but are increasingly linked to industries with network effects like finance, energy, entertainment and tech.)  Again, assume there are no barriers to building, that is, construction of housing is so unconstrained that every single city makes modern Houston seem like a NIMBY stronghold.  Housing prices in this hypothetical economy will be higher in the larger cities, ceteris paribus, even though building additional housing might well depress prices.  That’s because in a larger city, people will pay more for convenience (i.e. a good location).  That would be true even if those bigger cities were not more productive.  For instance, people would pay extra to reduce commuting costs, or to be close to amenities.  But the bigger cities are (by assumption) also more productive, which provides another reason for housing prices to be higher in bigger cities—higher wages.  This thought experiment suggests that the empirical relationship Alexander relies upon to make his argument would apply even if his argument were not true. One response is that these industries with network effects only exist in places like New York because there are lots of people, and that you can’t have lots of people without lots of housing.  That’s true, but has no bearing on the question of how additional housing affects prices at the margin.  And from the previous thought experiment, it’s clear that looking at simple correlations doesn’t resolve that problem. Alexander might reasonably respond that my model is overly simplistic.  Building restrictions differ from one city to another.  In that case, you might expect some exceptions to the iron law that bigger cities are more expensive than smaller cities.  And since (he assumes) I found only one such anomaly, the correlation among the remaining cities is too strong to explain by (exogenous) factors other than city size.   Actually, I cited Austin and Houston merely as one example, and I picked this example because they are located in the same state.  In fact, there are many, many examples of larger cities that are cheaper than smaller cities.  And in virtually every single case the explanation is that the smaller but more expensive cities have more restrictions on building.  In the list below, the metro areas on the left are larger and cheaper than the metro areas on the right: Chicago > > > San Francisco Bay Area Dallas/Forth Worth > > >  Washington DC Houston > > >  Boston or Seattle San Antonio > > > Portland Phoenix > > >  San Diego Colorado Springs > > > Boulder In each case, the larger metro area is cheaper than the smaller one.  And in each case there are stricter limits on building in the more expensive city. That doesn’t prove Alexander is wrong.  It’s possible that the reason for more expensive housing in the NIMBY cities has nothing to do with restrictions on building.  But I doubt it.  I suspect that if Houston had adopted severe restrictions on building in the 1980s, then people would now attribute the resulting high housing prices as being due to all the oil money sloshing through its economy.  But Houston decided to open its doors to anyone who wished to move there.  As a result, even though Houston is the global energy capital and is full of well paid petroleum engineers, and even though energy is one of the world’s largest industries, Houston is still a cheap place to live.  Instead, it got much bigger. BTW, Houston also has well paid aerospace engineers, well paid business executives and well paid doctors, etc.  There’s more than enough money in Houston to drive up housing prices if they had restricted building. It may appear that Alexander is “Reasoning from a quantity change.”  After all, it makes no sense to discuss the impact of a change in quantity on price, without knowing why quantity changed.  If Oakland gets more housing because it deregulates, prices will probably fall.  If Oakland gets more housing because it becomes trendier, then prices will probably rise.  Alexander seems to understand this problem, and thus at least implicitly seems to believe that every single factor that might boost Oakland’s housing supply would have the effect of boosting demand by more than supply.  That is, he seems to believe that NIMBY policies make cities cheaper.  That’s theoretically possible, but seems unlikely in most cases. To recap my argument: 1. The correlation Alexander cites proves nothing–you’d expect bigger cities to be more expensive even if (at the margin) building more housing did not raise prices. 2. Alexander is correct that if his model were wrong then you’d expect some exceptions to the generally positive relationship between density and price, due to differential restrictions on building.  But plenty of such exceptions do exist, and almost always in exactly the places predicted by YIMBY proponents of more building.  Elsewhere in his post he dismisses intercity comparisons between trendy coastal cities and heartland cities.  Nonetheless, the examples I provide show that I didn’t just cherry pick one exception with my Austin/Houston comparison, there are many such anomalies.  And one can find such anomalies even within coastal areas.  The Bay Area is more expensive than LA, despite being smaller.  It has more restrictive zoning.  The Boston metro area is more expensive than metro DC, despite being smaller.  Boston has more restrictive zoning.   And you can’t say that LA and DC are not desirable markets. Just to be clear, our dispute has absolutely no policy implications.  For instance, I said: If building more housing raises its price, then the argument for more construction is even stronger. And Alexander responded: I agree with all this. This is important, because his previous post had seemed to indicate that he thought it was a “problem” that new construction led to higher housing prices.  He previously said:  This is a coordination problem: if every city upzones together, they can all get lower house prices, but each city can minimize its own prices by refusing to cooperate and hoping everyone else does the hard work. What “hard work”? In the new post he makes it very clear that this is not a “problem”.  He supports the YIMBY position. Thus we both support making it easier to build housing in Oakland, although he thinks this would raise prices and I think it would lower them.  If I’m wrong, that is, if more housing boosted house prices in Oakland, then we both agree that this would be an especially good result.  And I concede that I might be wrong. PS.  Slightly off topic, it’s worth recalling why new houses should be “unaffordable” for average Americans.  Think of a steady-state society with only 100 families, living in 100 houses of varying quality.  Assume that each house lasts 100 years.  Each year, the worst house is torn down, and a new house is built.  In order for the quality of the housing stock to rise by 1%/year, the new house must be twice as good as the average existing house.  Each year, the families all shift over one house, moving gradually to better and better properties.  The richest family lives in the newly built house, which is (by assumption) unaffordable to the other 99 families. In a more realistic model with population growth, not every new house is unaffordable to the bottom 99%.  But even with population growth, new construction in an economy with rising living standards will tend to be much nicer than the existing stock of housing, which means that new construction will generally be “unaffordable” to the average family.  If new housing is affordable to the average family, then society will not progress. PPS.  Alexander describes my previous post as follows: 6. Comments By Famous People Who Potentially Have Good Opinions Scott Sumner is an economist and blogger I don’t see myself as a famous person, but I share his view that I have the potential to hold good opinions. As far as the question of whether I do actually hold good opinions, I’ll let readers decide.  (0 COMMENTS)

  Lind’s misleading language about wages. To follow what I’m about to say, it’s best to start with Michael Lind’s recent article titled “Libertarians’ Big Lie on Wages,” Compact, May 9, 2023, and then read Don Boudreaux’s critique of it. Don’s critique is quite good. I noticed a few things, though, that he didn’t comment on. It’s clear from context that Lind is discussing the minimum wage and so that’s the issue Don addresses. But what I noticed is that Lind subtly biases the case against libertarians by claiming that they are against higher wages for workers. Lind starts with: Are higher wages bad for low-wage workers? One of the most common arguments against higher wages and better benefits for low-income workers is the assertion that, while higher wages might make some workers better off, by making the goods and services that workers provide more expensive, those higher wages will make other low-wage workers worse off as consumers. Do you see the term “minimum wage” in there? Neither do I. As I said above, it’s clear that Lind is discussing the minimum wage but you wouldn’t know it from the opening paragraph. Who would be against higher wages and better benefits for workers when they result from free markets? Indeed, that is the history of the last approximately 200 years. Workers’ wages have risen, with dips during recessions and depressions. And free-market economists, many of whom could reasonably be called libertarians, have been among the most vociferous cheerleaders for these higher wages. As we have pointed out agains and again, the average worker in the United States is much better off than he/she was 40 years ago, was much better off 40 years ago than 40 years before that, etc. But what Lind does is make the careless reader think that we are a bunch of misanthropes who want workers not to be better off. And notice this: This kind of brazen promulgation of an obvious falsehood is typical of libertarian propaganda, which is a mishmash of dubious assertions and outright lies: Higher wages hurt those whom they are meant to help; free trade always and everywhere benefits both sides; mass immigration has no effect on wages; high CEO salaries mysteriously don’t lead to price increases, but slightly higher wages for low-wage workers always do; and so on. (italics added) The reason I italicized the clause above is that the part about high CEO salaries is like the issue of higher wages but is not like the issue of higher minimum wages. Free-market economists don’t generally have a problem with high CEO salaries that come about in a free market just as we don’t have a problem with, and even strongly favor, higher wages for low-wage workers that come about in a free market. Were Lind to make his point straightforwardly so as not to mislead the reader, the italicized part would have to be “high CEO salaries dictated by law don’t lead to price increases, but slightly higher minimum wages for low-wage workers always do.” But in that case, he wouldn’t find many free-market economists holding that view. Most of us would say that a government that made CEO pay higher than the market pay would lead to price increases, however small. But then Lind’s rhetorical point would lose its power. Lind generously throws around the word “lies” to refer to thoughts of people who disagree with him. I won’t reciprocate. But I will say that Michael Lind is very confused and that if you don’t notice his rhetorical switch, you will be too.     (0 COMMENTS)

In total, JCT [Joint Committee on Taxation] estimates the value of the value of fossil fuel tax expenditures at approximately $11.8 billion for the five-year period of 2022-2026. The tax expenditures denoted as de minimis and the ones not quantified are not included in this estimate. This is from “Statement of Ted Gayer,” before the U.S. Senate Committee on the Budget hearing on “Who Pays the Price: the Real Cost of Fossil Fuels,” May 3, 2023. Gayer is the president of the Niskanen Center. The whole thing, which is only 7 pages long, is worth reading. Here’s what I find striking: how low the numbers are. They average $2.4 billion annually. This is strikingly from so many claims we often hear that fossil fuels are subsidized by hundreds of billions annually. The number Gayer comes up with is just 1.2 percent of $200 billion. Moreover, nowhere in his testimony does he mention gasoline taxes. In 2020, state and local governments collected $53 billion in revenue from taxes on motor fuels. Of course, it is true that most of these revenues go to road building and maintenance. The pic above is from his testimony, and covers one of the 4 types of “tax expenditures” that Gayer discusses. (0 COMMENTS)