I’m now about halfway done with the storyboards for my new non-fiction graphic novel, Build, Baby, Build: The Science and Ethics of Housing.  This time around, I’ll be published by the Cato Institute, a think tank I’ve been working with since the summer of 1991.  If all goes well, this will be the first volume in an entire Cato library of books modelled after my Open Borders – works that combine high scholarly standards with compelling sequential art to explore underrated policy ideas.  While fan favorite Zach Weinersmith was not available to illustrate my new book, I have found another stellar artist for the job, Ady Branzei (aka “Sebastian Soric“).  Ady and I are both fans of classic Disney, so expect a lot of Disney homage. Will I be content merely popularizing other people’s research?  My official position is my non-fiction graphic novels are serious scholarship.  How so?  Because thoughtfully synthesizing interdisciplinary research is original research!  The papers aren’t going to synthesize themselves, after all.  My project is not to summarize a list of articles, but to collect research worthy of broader attention, then fuse it together to create a novel policy perspective.  Along the way, I offer many new arguments and insights.  And while arguments and insights are usually too swiftly communicated to publish as research papers, that’s because academia sadly rests on the Labor Theory of Value. I keep tweaking the Table of Contents for Build, Baby, Build, but here’s the current plan: Chapter 1: The Home that Wasn’t There. Here I explain why the supply-and-demand story for rising housing prices, though true, is deeply misleading.  Why?  Because regulation is strangling housing supply, especially in desirable locations.  In a free market, housing would be very affordable throughout the country because building up and in is easy.  We have the technology; what we lack is permission to use it. Chapter 2: The Manufacture of Scarcity.  Now I go over the empirical work that measures the effect of housing regulation on housing prices.  Standard estimates of the effect are massive.  It is very plausible that U.S. housing would be 50% cheaper under laissez-faire. Chapter 3: The Panacea Policy.  This part starts by exploring estimates of the effects of housing deregulation on GDP.  But then it explains how deregulation would help cure a long list of social ills.  Housing deregulation will reduce inequality, increase social mobility, enrich and uplift working-class males, curtail “deaths of despair,” raise birth rates, fight crime, restore the American Dream, and give an ideologically divided nation something constructive to do together.  Hence, the “panacea policy.” Chapter 4: The Tower of Terror.  Needless to say, housing deregulation is extremely unpopular.  This is where I explore all the standard externalities arguments: congestion, pollution, noise, aesthetics, and so on, with four main rebuttals.  First, these negative externalities are greatly overestimated.  Second, since gratis is not great, the prudent remedy is not restricting construction, but using tolls, fees, and taxes to address specific drawbacks of development.  Third, building has enormous and almost totally neglected positive externalities.  That’s why people currently pay a fortune to live in New York: the net value of all the good and all the bad of living in this metropolis is very good indeed.  Fourth, even when an isolated housing regulation is helpful, it puts us on a slippery slope to disaster.  Which is no hyperbole, because the disaster is here already.  A beautiful confirmation of Rizzo and Whitman’s work on slippery slopes, by the way. Chapter 5: Dr. Yes.  This is the housing analogue of the Open Borders chapter, “All Roads Lead to Open Borders.”  Utilitarians, egalitarians, libertarians, Kantians, Christians, and virtually everyone else should, on their own terms, support housing deregulation.  This shouldn’t be a liberal or conservative issue.  It should be an issue where liberals and conservatives hold hands and say “kumbaya” together.  I’ll also probably discuss keyhole solutions here. Chapter 6: Getting to YIMBY. How do we get from the world of draconian regulation, high prices, and cramped quarters to the world of freedom, low prices, and spacious living?  Tough, but every policy journey starts with a non-fiction graphic novel, right? Time to get back to work.  I leave you with one of my favorite draft pages. (0 COMMENTS)

In the City Journal, I have a review of Mariana Mazzucato’s new book – alas, not as brilliant as John Kay’s one. I am afraid my opinion of this new work is not necessarily better than the one I had of her previous ones. In this new book, Mazzucato uses the moonshot as an example of what “mission oriented directionality” can produce, when applied to the whole of the economy. There is not much new in Mazzucato’s new book compared to her previous ones. What always astonishes me about her (and similarly, other industrial policy advocate types’) views is how highly complex processes are sketched into very simple drawings. The processes by which things are produced and marketed are seen as the reflection of easily identifiable and relatively simple decisions by those on top. There is a little bit of that in the contemporary discussion over Covid-19 vaccines (at least in Europe). People talk about the making and producing of such vaccines as something which should be “expected”, somehow from Big Pharma, like Santa delivers presents on Christmas. That policy decisions other than pouring money at it can affect the production process is uncontemplated. It is easy to talk of “missions” and “directionality” but it is often quite controversial which mission should be undertaken – and with what kind of resources. Plus, an economy is not about one single need, picked by foresighted decision makers, in periods other than war time. The needs of people are many; not all of them can be met in the same way, let alone at the behest of some legislator who knows which ones should be privileged at the expense of others. I think a commentator had it right: “The moonshot approach, a concentrated effort to accomplish one large, specific, scientific/engineering project, with no real concern for cost, cannot be applied to an entire national economy.” (0 COMMENTS)

Megan McArdle, Washington Post columnist and previous EconTalk guest, joined host Russ Roberts in this episode to discuss the “dazzling hindsight” with which we view recent crises. Why can’t we seem to get it right in crises like the COVID-19 pandemic and the recent Texas energy crisis? And why don’t we seem to learn from our mistakes? “When an unlikely thing hasn’t happened for a while, we think it’s never going to happen again. And, when an unlikely thing just happened, we think we need to take a lot of preparation against it,” says McArdle. As a journalist, McArdle has had a unique opportunity to revisit her own predictions (and nasty reviews!), an opportunity she’s taken advantage of. In this conversation, she shares some of what she’s learned, and now we’d like to hear your thoughts. Answer our questions below in the comments, or use them to start an all new conversation offline. We love to hear from you, and we’ll always be here for the conversation.     1- Roberts and McArdle discuss the recent winter storm in Texas that left millions without power. How much insurance should we have for events like this? (You might want to think about insuring for prevention versus adaptability.) McArdle also argues that Americans have zero political taste for these sorts of expenditures, so how can this distaste be accommodated?   2- The conversation turns to our experience of the COVID pandemic. To what extent is the pandemic a black swan event? Why does McArdle draw a distinction between stockpiling supplies and stockpiling capacities? To what extent do you think we’ll be better prepared for another such event in the future, and why?   3- What were some of the missteps and lost opportunities of the Trump administration in response to COVID, according to McArdle? What would she have done differently to leverage “a huge benefit of soft power.” How well do you think her suggestions would have worked, and why?   4- How have the costs of the COVID pandemic been felt differentially by income- for example,  the costs of school closures. Roberts asks, “who plans, who takes the risk, who builds the insurance, the capacity for adaptation?” How would you answer this question? Can such planning mitigate these income effects ?   5- McArdle is critical of the politicization of public health. What does she mean when she says we would we have been better prepared for this pandemic in 1930? Is she right??? Explain.   (0 COMMENTS)

One groups wants to preserve the traditional way of living, with an extended family dwelling in little single-family homes. Another group wants to embrace progress, erecting soaring futuristic skyscrapers: We’ve seen this dynamic play out over and over again, all over the world.  What might surprise you is that in this case the progressive group that wants to build massive skyscrapers is a Native American tribe, while the people who wish to live according to the old ways are the European and Asian residents of Vancouver, Canada. This reminds me of Connecticut, another state full of complacent westerners that wish to preserve things just the way they’ve always been.  A few decades ago, a group of Native Americans saw a huge unmet need for gambling services, and erected some truly enormous facilities in the countryside of eastern Connecticut. In Vancouver, the residents of the affected neighborhood are fighting the new development, but according to The Economist they will almost certainly lose.  You can’t fight progress when someone else has sovereignty over the area in question: It’s easier to elect a pope than to approve a small apartment building in the city of Vancouver,” says Ginger Gosnell-Myers, of Nisga’a and Kwakwak’awakw heritage, and formerly the city’s first-ever indigenous-relations manager. Such is the power of local NIMBYS that it is difficult to build new homes, and legions of young people are doomed to live with their parents for years, if not decades. But on some land the normal rules do not apply. No one can tell the Squamish First Nation, an indigenous group, what to build on their territory. One patch of its reserve is in Kitsilano, a ritzy part of Vancouver. Despite being close to the city centre, it is full of single-family homes and duplexes. Residents fiercely resist the construction of tall buildings. But they cannot stop the Squamish from erecting 59-storey skyscrapers. This year could see the ground broken for Senakw—12 towers containing 6,000 flats, mostly for renting. There is a serious point to all of this.  Regulatory competition can be good.  It might sound “efficient” to have a provincial, national or even supra-national organization set all the rules.  But if they make the wrong call then people have no option to do things a different way.  If you have many competing jurisdictions, then any attempt by one area to stand in the way of progress will simply push people toward nearby areas where enterprises are willing to respond to their needs: In 2019 the city vowed to put up 20,000 new rental units. Senakw would meet roughly a quarter of that target, points out Ms Gosnell-Myers. “The Squamish Nation is more responsive to average Vancouverites than Vancouver city hall.” (0 COMMENTS)

The Nobel Factor,1 a 2016 book by Avner Offer and Gabriel Söderberg, looked to be right up my alley. Offer, an emeritus professor of economic history at the University of Oxford, and Söderberg, a researcher in economic history at Uppsala University in Sweden, do a broad overview of most of the winners and briefly lay out their contributions. They also criticize many of the Nobel Prize winners, sometimes with ad hominem arguments. They have two major themes. Their first is that the Nobel Prize was initiated in the late 1960s as a way to raise the public’s respect for economics as a science. The second is that economics fails at being empirical and the pro-free-market views of many of the winners reflect an ideological commitment more than a scientific understanding. They succeed at the first and fail at the second. Even though I think they fail at the second, and I’ll say why shortly, the book is chock full of interesting facts. Here’s one nugget: In 1968, economist Milton Friedman, a major player in the Mont Pelerin Society [MPS], nominated philosopher John Rawls for membership in MPS. Rawls became a member and withdrew three years later. This is from David R. Henderson, “The Nobel Factor: What Does the Prize Reward?” Econlib.org, April 5, 2021. It’s my review of The Nobel Factor: The Prize in Economics, Social Democracy, and the Market Turn, by Avner Offer and Gabriel Söderberg. My review is mainly critical, but I end on a high note: Being a glass-half-full person, I’ll end on two positive notes that show that maybe the authors are not so closed as they sometimes seem to economic freedom. They note that Robert Fogel, co-winner of the 1993 price, wrote that slavery “was economically as efficient as free farming.” They then spot the error, writing, “What he meant to say was that it was as profitable, which is not the same thing. This finding was hardly consistent with the economic conception of efficiency, which stressed free choice, or with its focus on individual welfare, such individuals presumably including slaves.” Bravo! The second glimmer of hope is in their recognition of the difference between voluntary and coercive funding. In discussing how the funding for the economics Nobel differs from the funding of the other five, the authors write, “Alfred Nobel’s motivation was sublime, and the money came out of his will; the chain of causes for the economics prize was something of a farce, and was paid for by Swedish taxpayers.” I couldn’t have said it better. Read the whole thing. (0 COMMENTS)

with Peter van Doren   U.S. house prices surged in 2020, rising 11.2 percent for major metropolitan areas according to the Case–Shiller Home Price Index and 12 percent according to the Federal Housing Finance Agency. This has raised concern that a new housing bubble has formed and is threatening a repeat of the “Great Recession” of early this century. If “housing bubble” means that home prices rise in a relatively short time and then fall back to long-term trend, then a bubble likely has formed over the past year. But this bubble, by itself, would be different—and less ominous—than what happened early this century. That said, there is something troubling going on in the U.S. housing market and government should take steps to address it. The sharp increase in U.S. house prices over the years 1998–2006 was driven by a rise in demand (or, speaking carefully, an outward shift in the demand curve) for homes in major coastal metropolitan areas and some other places, even as home construction reached high levels. Those areas were booming economically, attracting workers who bought or rented dwellings in the cities and their suburbs and exurbs. At the time, financial markets (and their regulators) considered instruments tied to mortgages to be (to borrow a Victorian idiom) “safe as houses,” encouraging them to finance all this homebuying. Small-time investors got in on the buying spree by purchasing second (and additional) houses, either to “flip” or rent out in the hot market. When gasoline prices spiked in 2005–2008, long commutes from the suburbs and exurbs became costlier, bringing financial hardship to households and subsequent mortgage defaults. Mortgage-related investments soured, setting off a financial crisis. The crisis spread to the broader economy, which was already struggling with a business downcycle from the suddenly cooled housing market and its effects on related industries and household wealth, yielding the Great Recession. This time around, there’s at least one important difference: the spike in home prices over the past year is to a large extent supply-driven. Because of the coronavirus pandemic, homeowners have become less inclined to put their houses on the market, and government-mandated forbearance has further reduced the availability of homes. As the Wall Street Journal reported in January, the number of houses for sale in November 2020 was 22 percent less than the year before. This decreased supply has disrupted the normal “churn” in housing markets as households grow in size and seek bigger homes and then shrink and seek smaller ones. (We note there also is evidence of recent increased demand for housing in suburbs and exurbs as newly telecommuting workers flee population-dense areas because of the pandemic. We’ll see if this outmigration persists long-term and if it affects overall house prices or just increases suburban and exurban prices relative to urban ones.) The supply constraint should ease as more Americans are vaccinated against the coronavirus and the nation approaches herd immunity. It’s likely that, once the pandemic fades, pent-up churn and delayed foreclosures will result in a flood of homes hitting the market, accompanied by a decline in house prices. The bubble of the last year should deflate without much hardship. That said, things are not well in the U.S. housing market. The surge in prices over the last year is just the tip of a longer rise in house prices dating back to the end of the housing bust in 2012. Indeed, the increase can be traced all the way back to 1998, suggesting the 1998–2006 price increase wasn’t a bubble, but rather the 2006–2012 bust was the short-lived departure from trend. Higher home prices hurt homebuyers (especially young professionals and new families), both because of higher mortgages (though low interest rates in recent years have kept monthly payments lower than they would have been otherwise) and by reducing the supply of homes that would better fit families’ particular needs. This needs to change. The decline in long-term interest rates is one contributor to the higher house prices, as well as the prices of other assets. Another contributor—one that government can address—is its many anti-housing policies currently in place, from restrictive zoning and “smart growth” requirements, to tariffs on building materials that have sent lumber, metal, and home appliance prices soaring, to immigration restrictions that have reduced construction labor. Fortunately, government can undo those anti-housing policies without increasing public spending, taking on risk, or imposing more burdensome regulations. This would not only produce more housing units, but also increase the supply of existing housing for sale and put downward pressure on overall home prices. Such reform shouldn’t cause a painful sudden collapse in house prices. The time involved in home construction—not to mention the interminable permitting and inspection process—would moderate the flow of new housing. Hopefully, there would be a gentle, long-term decline that would return house prices to their pre-1998 trend. But even if house prices would just plateau or slow their rise, that would give many American households a much-needed break.   Peter Van Doren and Thomas A. Firey are senior fellows at the Cato Institute and, respectively, editor and managing editor of Cato’s policy journal Regulation.   (0 COMMENTS)

Now that we correctly understand Hsieh-Moretti’s results, let’s put them in context. 1. Immigration researchers have focused heavily on the economic effects of full deregulation of immigration.  Hsieh-Moretti (henceforth HM), in contrast, focus on the economic effects of moderate housing deregulation.   Their chief hypothetical is not, “What would happen if there were zero housing regulation?” but “What would happen if the Bay Area and NYC only had as much housing regulation as the rest of the U.S.?” 2. Immigration researchers find truly enormous economic benefits of full deregulation; roughly speaking, open borders would double Gross World Product.  HM’s results aren’t quite as dramatic, but in absolute terms they  still boggle the mind.  Their conservative estimate is that moderate housing deregulation would increase US GDP by 14%.  Their corresponding optimistic estimate is +36%. 3. In both cases, we’re talking trillions of dollars of annual gain, implying an astronomical present value. 4. How can the gains be so big?  Because (a) the regulations have a large effect per person, and (b) affect large numbers of people.  Big times big equals enormous. 5. What’s the mechanism that yields these gains?  The answer in both cases is the same: Moving workers to places with higher productivity.  Deregulating immigration lets workers in low-productivity countries move to high-productivity countries.  Deregulating housing encourages workers in low-productivity regions to move to high-productivity regions. 6. In both cases, focusing solely on the direct victims of regulations is misleading.  The direct victims of immigration restriction are would-be migrants deterred by the First World’s immigration restrictions.  But the whole world loses the benefit of the extra stuff they would have created if they moved.  Similarly, the direct victims of housing regulation are would-be internal migrants deterred by rich regions’ housing restrictions.  But the whole country (indeed, the whole world) loses the benefit of the extra stuff they would have created if they moved. 7. How can such enormous gains be so overlooked?  For immigration, I’m convinced the main answer is anti-foreign bias, but that’s barely relevant for housing deregulation. 8. So what’s the right story?  I’m still weighing a few competing explanations. (a) Housing regulation increased very gradually from the 1960s on, and its direct victims tend to be young.  So the obvious victims barely know what they’re missing – and therefore rarely raise their voices in protest to alert the rest of society. (b) The main victims of housing regulation are not people who pay high prices for real estate, but people who stay in low-productivity regions because the cost of housing in high-productivity regions is too high.  Since the latter victims are barely visible, it’s hard to feel much pity for them.  Indeed, since the losers rarely see the houses and jobs they could have had, they don’t even feel much self-pity. (c) The main victims of deregulation, in contrast, are ultra-visible and ultra-relatable.  New construction leads to lower real estate prices and at least temporary inconvenience for long-term residents.  Remember Up? (d) Pessimistic bias leads people to obsess over the downsides of deregulation, while ignoring enormous upsides – even for existing owners. (e) Given populist resentment of markets and business, real estate developers inspire severe antipathy.  They’re ideal instantiations of the hated “fatcat” archetype. (f) Housing regulation is really boring for most people. 9. If the whole U.S. housing market were as regulated as the Bay Area, the benefits of liberalizing immigration would be modest.  What’s the point of telling people “You’re free to come work here” if they can barely afford to rent a shack?  Fortunately, housing regulation varies widely by city and state.  So even though most migrants can’t afford to move to the most productive regions of the U.S., they can totally afford to migrate to the rest of the country.  And the less-productive regions of the U.S. are still vastly more productive than almost anywhere in the Third World. 10. I’ve long urged libertarians to put immigration deregulation at the top of the pro-liberty agenda.  Now I’m going to urge them to make housing deregulation their #2 priority.  And to be the change I want to see in the world, I am now writing a second graphic novel on this topic.  Working title: Build, Baby, Build: The Science and Ethics of Housing.  Stay tuned for updates!   (1 COMMENTS)

Some brief but straightforward algebra. The part of my post yesterday that dealt with deadweight loss from taxes was a little brief. Even if you go to the original article in Defining Ideas, you won’t find the actual computation. So here it is. DWL is is proportional to t^2, where t is the tax rate. (t^2 means t-squared) Original tax rate for our high-income Californian is 54.1%. New tax rate for our high-income Californian is 56.7%. This 2.6-percentage-point increase is a 4.8% increase. (2.6/54.1 = 0.048, which is 4.8%.) So why doesn’t the DWL increase by just 4.8%? Because of the square relationship I referred to in the article and above. Let original DWL be DWL1. Because of the proportionality property, we can lump all the other components of DWL into C. C is the same whether we are dealing with the original tax rate or the new higher tax rate. So DWL1 = C*t1^2, where t1 is the original tax rate. DWL2 = C*t2^2, where t2 is the new tax rate. To get the percent increase in DWL, first divide DWL2 by DWL1. DWL2/DWL1 = C*t2^2/C*t1^2 = t2^2/t1^2. t1 = 0.541; t2 = 0.567. So DWL2/DWL1 = 0.567^2/0.541^2 = 0.321/0.293 = 1.096. Therefore DWL increases by 9.6%.  QED.   (0 COMMENTS)

A Book Review of The Nobel Factor: The Prize in Economics, Social Democracy, and the Market Turn, by Avner Offer and Gabriel Söderberg.1 Since 1996, I’ve had a deal with the editors of the Wall Street Journal. I get up early on the West Coast the day the Nobel Prize in Economic Science is announced, decide within an hour whether I know enough to write an article on the winner(s), and, if I do know enough, get the article to the editors later that morning. For 19 of the last 25 years, I’ve been able to come through. So The Nobel Factor,1 a 2016 book by Avner Offer and Gabriel Söderberg, looked to be right up my alley. Offer, an emeritus professor of economic history at the University of Oxford, and Söderberg, a researcher in economic history at Uppsala University in Sweden, do a broad overview of most of the winners and briefly lay out their contributions. They also criticize many of the Nobel Prize winners, sometimes with ad hominem arguments. They have two major themes. Their first is that the Nobel Prize was initiated in the late 1960s as a way to raise the public’s respect for economics as a science. The second is that economics fails at being empirical and the pro-free-market views of many of the winners reflect an ideological commitment more than a scientific understanding. They succeed at the first and fail at the second. Even though I think they fail at the second, and I’ll say why shortly, the book is chock full of interesting facts. Here’s one nugget: In 1968, economist Milton Friedman, a major player in the Mont Pelerin Society [MPS], nominated philosopher John Rawls for membership in MPS. Rawls became a member and withdrew three years later. One person whose work and thoughts the authors highlight is Swedish economist Assar Lindbeck, who died earlier this year at age 90. I had always thought of Lindbeck as a socialist but the authors lay out a much more nuanced story. Early in his life, Lindbeck was a Social Democrat but the authors argue that even though in his twenties he was close to the party elite, he was “already inclined towards heresy.” Of what did his heresy consist? Lindbeck parted ways with some of the more interventionist views of the Social Democrat Party. For instance, he was very outspoken against rent control. Disappointingly, the authors don’t quote Lindbeck’s famous broadside against rent control: “In many cases rent control appears to be the most efficient technique presently known to destroy a city—except for bombing.” They argue instead that Lindbeck opposed rent control for the narrowest of motives. They write that to Lindbeck, “Rent control was bad, because he himself had to queue for an apartment.” His experience, they write, was “atypical.” Their claim would surprise the dozens of economists who have studied rent control over decades and understand that when a government keeps a price from rising in the face of either inflation or increases in demand, the result is a shortage and, yes, queues. The two authors’ view is that rent control is “a social intervention intended to mitigate monopoly pricing.” Again, that would surprise most economists who have carefully studied rent control and observe a fairly competitive market in rental housing. The reason the authors highlight Lindbeck is that he was pivotal in persuading the Nobel Foundation to go along with the idea of a Nobel Prize in economics and was also a major player for a quarter of a century, from 1969 to 1994, in deciding who got the prize. What happened was that Per Asbrink, the governor of the Riksbank (Sweden’s equivalent of the U.S. Federal Reserve) from 1955 to 1973, wanted to raise the respect given to economics. He proposed to his aide Assar Lindbeck the idea of a Nobel Prize in economics. Lindbeck agreed that it made sense and, interestingly, consulted Swedish economist Gunnar Myrdal about the idea. Myrdal agreed. Possibly not coincidentally, Myrdal was co-winner of the prize in 1974 along with his ideological rival Friedrich Hayek. The authors put a heavy interpretation on those facts. They argue that, given Asbrink’s and Lindbeck’s criticisms of the size of Sweden’s welfare state, the initiation of the prize “was a belated incident in one of the central plots of modern history, the distributional struggle between the owners of wealth and the rest of society.” They make a decent case that the prize came about due to a struggle between those who were more in favor of free markets and a limited welfare state and those who wanted more intervention in the market and an expanded welfare state. It’s a much bigger step, though, to argue, as they do, that being in favor of free markets and reining in the welfare state amounts to supporting “the owners of wealth” and ignoring the rest of society. Basic economics casts grave doubt on that interpretation. Reducing heavy taxes on capital increases the incentive to create more capital. More capital makes labor more productive, increasing average wages. We saw this in a big way after the 2017 Trump cut of the corporate income tax rate from 35 percent to 21 percent. Median incomes and real wages grew substantially from 2017 to 2019, and especially from 2018 to 2019.2 The authors, quite rightly, criticize economic models in which the actors have perfect information. They note that Friedrich Hayek criticized such models also, both in his contributions to the socialist calculation debate and in arguably his most famous article, “The Use of Knowledge in Society.” They write, “Hayek assumed (more realistically) that every individual in the economy could know only a small part of the whole, conveyed to him by prices. Markets co-ordinated this multitude of individual choices and gave rise to the ‘spontaneous order’ of the liberal society.” Then they add, “This order was assumed, not proven with any rigour.” It’s true that Hayek didn’t prove it: the idea of economics as a set of theorems was foreign to him. But he certainly didn’t just assume. In his “Use of Knowledge” article, Hayek gave his famous example of how participants in the market for tin, whether as buyers or sellers, didn’t need to know whether the price of tin rose because of an increase in demand or a decrease in supply in order to know how to adjust to the higher price of tin. That’s not proof, but it’s way more than assumption. It accords with what we see every day in real-world markets if we pay attention. “The authors use this absence of proof to cast doubt on the efficiency of a free market but their preferred alternative, Social Democracy, ‘is pragmatically successful, analytically coherent, economically efficient, ethically attractive, and theoretically modest.'” The authors use this absence of proof to cast doubt on the efficiency of a free market but their preferred alternative, Social Democracy, “is pragmatically successful, analytically coherent, economically efficient, ethically attractive, and theoretically modest.” Also, they write, “Social Democracy was willing to pull health, education, welfare, and housing out of the market, to de-commodify the provision of well-being.” And how has that worked? They give little evidence. Moreover, surely an important component of well-being is food, without which we are unable to have any kind of being. In most advanced societies, a large part of food provision, though regulated, is carried on in a relatively free market. Given their endorsement of Social Democracy, would the authors favor “de-commodifying” food? And if not, why not? One of the more interesting Nobel Prize winners they discuss is British economist James Mirrlees, an adviser to the Labour Party, who was co-winner of the 1996 award for his theory of optimal taxation. His famous two results were that because of the damaging effect of income taxes on incentives, the marginal tax rate on the top earner should be zero and most tax rates should be between 20 and 30 percent. As I noted in my October 1996 Wall Street Journal article, “When Economics Rises Above Politics,”3 Mirrlees was stunned by his own result. “I must confess,” he wrote, “that I had expected the rigorous analysis of income taxation in the utilitarian manner to provide arguments for high tax rates. It has not done so.” Indeed. How do the authors handle Mirrlees’s finding, given how at odds it was with their own preferences on tax rates? Here’s how: “Optimal taxation demonstrates how liberal formalists could end up endorsing conservative norms. They put together hybrid theories which combined the bad faith of asymmetric information with the good faith implicitly assumed in equilibrium analysis.” A few pages earlier they had argued that bad faith “is just as likely in private transactions as in public ones.” They don’t explain how bad faith even applies to taxation. Are they saying that people will cheat on their taxes? I don’t think they are, but if they are saying that, wouldn’t that imply, all else held constant, that marginal tax rates should be lower rather than higher because then the incentive to cheat would be less? They quote French economist Thomas Piketty’s statement that economists advocating that rich people pay zero tax “have an unfortunate tendency to defend their private interest while implausibly claiming to champion the general interest.” That’s classic Piketty misstatement because it’s hard to find an economist who advocates that the rich should pay zero tax; at most Mirrlees advocated that the most productive person should pay a marginal tax rate of zero, not a zero tax. To their credit, the authors defend Mirrlees from the Piketty ad hominem, writing, “There is no reason to assume that Mirrlees had any ulterior motives.” The authors don’t extend that same generosity to 1976 Nobel winner

Book Review of Predict and Surveil: Data, Discretion, and the Future of Policing, by Sarah Brayne.1 Sarah Brayne has done some excellent sociological research by spending several years embedded in the LAPD [Los Angeles Police Department]-one of the most technologically advanced police departments in the country. By doing so, she has given us a chance to glance behind the curtain of big data in policing just as it is in its ascendency. While she issues some warnings and even possible solutions to the inevitable overreach for which such a shift allows, she also admits that there are some real advantages to be gained in terms of crime prevention (although it’s too early to properly assess the overall trade-offs between privacy and crime prevention). “Will big data lower crime and reign in police abuse? Or will it exacerbate structural inequalities that already typify our criminal justice system?” The most striking thing about this excellent and timely book is that it leaves one in a state of healthy ambivalence: Will big data lower crime and reign in police abuse? Or will it exacerbate structural inequalities that already typify our criminal justice system? Predict and Surveil1 will be of great interest to anyone thinking deeply about privacy, policing, and the way either of these interact with the law in this technologically revolutionary moment. The Way We Live Now We all have a general sense that we’re leaving digital trails wherever we go, and there’s been much consternation about the use of such data in the marketplace. Brayne argues that we are dealing not just with a giant leap forward in terms of the sheer quantity of data that’s being collected on us, but also in terms of the aggregation of data from multiple sources, which allows individuals to be closely monitored. While police are often legislatively restricted in terms of what data they can collect on citizens, they can now work around most limitations since there are no limits on what data they can buy from private sources. Private companies, like Palantir, that help police aggregate their own data have encouraged this ‘function creep’ in which police become consumers of private data collections as well as of government intelligence originally meant for other purposes. Brayne notes that Palantir itself was “originally designed for counter-insurgency efforts in Iraq and Afghanistan,” and I couldn’t help but think of Coyne and Hall’s Tyranny Comes Home, a close documentation of the migration of military efforts to the control of the domestic population (7). Brayne is quick to point out benefits, such as in her opening example of a body dump case that was solved in just two days with the help of a license plate reader and the gang-tracking system CalGang. Predictive algorithms that send officers to particular areas (called “red boxes”) to patrol may have a real deterrent- or at least, displacement-effect. Further, data can do as much to exonerate as to convict us, since it can demonstrate that we were nowhere near a crime scene, for instance. Big data also has the potential to finally provide a watcher for the watchmen as well, as police activity can be more closely monitored and analyzed too. However, before the reader is tempted to romanticize the efficiency, precision, and potential social advantages of big data policing, Brayne suggests a few ways it can go very wrong. Power Imbalances First, while one would hope that data-driven policing would reduce bias and disproportionate enforcement by removing the ‘human element,’ it can also serve to reinforce and perpetuate these things. The best example she gives is the LASER program and the attendant Chronic Offenders Registry, although we find out at the end of the book that this particular program has been shut down due to public outcry. Police prioritize targets with a point system -five points if someone is in a gang, five for prior convictions, and so on. This allows the more likely offenders to float to the top of consideration when searches return a large number of results. So far, so good. But one point is also added any time someone has contact with the police, even if officers are just having friendly conversations, or ticketing someone for jaywalking or failing to use their turn signal. There’s a strong incentive to fill out field interview cards (FI’s) in order to build data by getting people into the system (64-67). She recounts a few shocking, but quite real, scenarios, as when parolees are visited by police three or four times in one day, or “ghetto-birds” (police helicopters) fly over a neighborhood 80-90 times a week. It’s not hard to imagine how a person from a rough neighborhood could start racking up points without any criminal activity at all. Since high-crime neighborhoods are far more likely to be under surveillance, people are more likely to be caught for trivial things, even though only a small percentage of the neighbors are involved in the sorts of dangerous activities that we’re really interested in addressing. The higher one appears on the points list, the more likely one is to be surveilled, leading to more interactions with the police, more points, and so on. In contrast to this snowballing of police interaction and surveillance for marginalized groups, those with more political clout can avoid surveillance. Gun owners have successfully avoided a federal gun registry, and police themselves actively resist their own surveillance on the job through their police unions. Brayne tells of a ride-along in which she expressed surprise that the officer had to call in his location. When she informed him that she had assumed the cars all had GPS trackers, he responded that they do, but that the police unions threw a fit and so they’ve never been turned on. Brayne does not argue that there should be a federal gun registry or that officers should be tracked everywhere they go. She simply points out that those least able to legally and politically resist being constantly surveilled are also those most likely to be under surveillance. Data and the Law A central take-away of the last portion of the book involves the inability of traditional privacy law to deal with the way data works now. Here, Brayne makes her boldest claim: “[l]egal constructs like the third party doctrine are set up in a way that is basically inapplicable to modern life” (131). The third party doctrine holds that government officials can access any information that I voluntarily give out to others without violating my Fourth Amendment rights. But in today’s environment, it’s nigh impossible to communicate with other people at all without “exposing data to a third party,” so that “the third party exception has become the exception that swallows the rule.” Computer-aggregated data is just not the same animal as the data an officer can collect by reading a letter I wrote to a friend. Furthermore, she highlights the programmatic nature of police surveillance: “ongoing, cumulative, and sometimes suspicionless data collection and use” (129). In response to these new challenges, she considers various academic views, including the suggestion that administrative law, rather than criminal law, may be a more appropriate way to govern this type of surveillance. She cites Orin Kerr’s work on the Fourth Amendment as an example of the hope that regular calibrations through case law will still be sufficient, even in our digital age. She also worries that the exponential increase in plea bargaining and the correspondent loss of trials means that many relevant cases to this issue will never see the inside of a courtroom. What’s worse, so much of the big data story happens before any real evidence is collected, by a kind of case-building process that will remain unseen even if the case itself does come to trial. Police have even learned how to create ‘parallel constructions’, a process of lying about the way that the case actually progressed in order to obscure that a surveillance strategy had anything to do with the course the case took. Assessing the Trade-Offs Although police departments talk big, there is no overwhelming evidence that algorithms are serving us better than humans have when it comes to good policing. We certainly see some correlation between big data platform implementations and reductions in crime, but those reductions are often a continuation of a trend that had already begun before such resources were available. Remember that the LAPD is a first adopter, so Brayne is giving us a window into a world that is only just beginning in many cities. For more on these topics, see the EconTalk episodes Franklin Zimring on When Police Kill and Cathy O’Neil on Weapons of Math Destruction. Brayne has high hopes for positive uses of data in policing, such as the direction of non-punitive interventions (as in mental health cases) and to clear current cases and solve cold cases. But she’s right to warn us that we do not yet know whether the dangers of big data are outweighed by the possible gains, and we won’t know that till we have more independent research. Her most chilling warning, though, is the legal one. Is the world of big data so different from the old world that the usual adjustments of the common law will be unable to maintain the reality of privacy? And for those of us who want to avoid the expansion of administrative law to address our new reality, what’s the alternative? Footnotes [1] Sarah Brayne. Predict and Surveil: Data, Discretion, and the Future of Policing. Oxford University Press, 2020. * Rachel Ferguson is a Professor of Managerial Philosophy, co-chair of the Lindenwood Honors College, and Director of the Liberty and Ethics Center in the Hammond Institute. Her research interests include Hume’s classical liberalism, the philosophy of economics, and Aristotelian virtue theory. As an Amazon Associate, Econlib earns from qualifying purchases. (0 COMMENTS)