Matt’s response, I suspect, would be that if we deem the earnings of lower income workers to be too small to afford the basic amenities to which everyone ought to have access, then the thing to do is to write them checks, and the right level at which to do that is the federal level. I’d say that’s basically right.

If people are bound and determined to have local governments provide wage or housing subsidies, however, then it’s worth noting that the local government will be wanting to raise revenue as effectively as possible in order to make that happen. And a good way to begin is by raising revenue, as much as possible, through increases in the size of the tax base rather than through hikes in tax rates. And a good way to do that, of course, is by allowing lots of residents who’d like to live in the city to live in the city, by permitting the development of sufficient housing capacity to hold them. Even if you doubt that more housing supply will make housing cheap enough for lower income households, you still ought to appreciate that more taxpayers means more revenues and more money for pet social programs.

More residents will also mean more infrastructure needs, of course. A city that begins by addressing congestion through market pricing of scarce road, rail, and parking resources will find that it ends up earning a lot of money in the process. A city that is more willing to use the market to drive housing and infrastructure investment will find that it has more revenue available to allocate to progressive ends.

The rub, of course, is that cities ideologically disposed toward a more market-oriented approach to housing and infrastructure will also be less likely to favor additional progressive spending — and vice-versa. But perhaps, with enough argument and conversation, that can change.

ED LUCE had in piece in yesterday’s Financial Times on America’s labour market, which attracted quite a lot of attention. Here’s one interesting snippet:

Finally, a growing share of whatever jobs the economy is still managing to create is in the least productive areas. Of the five occupations forecast by the Bureau of Labor Statistics to be the fastest growing between now and 2018, none requires a degree. These are registered nurses, “home health aides”, customer service representatives, food preparation workers and “personal home care aides”.

Manufacturing is nowhere in the top 20, and such jobs cannot replace the pay and conditions once typical of that sector. “The food preparation industry cannot sustain a middle class,” says Dan DiMicco, chief executive of Nucor, one of America’s two remaining big steel companies, whose company motto is “a nation that builds and makes things”.

Matt Yglesias notes that the emphasis on the importance of “manufacturing” is a bit foolish:

To understand this problem, you need to start with the fact that if I build a factory where people take fresh peas and put them in cans that’s a “manufacturing” facility full of manufacturing jobs and people who “make things.” But if I build a facility where people take fresh peas, mix them with some basil and a touch of mint, plus olive oil, parmigiano reggiano, and pine nuts then purée them to serve you a delicious pea pesto that’s a lowly service sector employment cite that couldn’t possibly generate good jobs. Similarly if I make pasta then dry it and stick it in boxes, I’m manufacturing. If I make fresh pasta and serve it to you on a plate with my pea pesto that’s services. The difference between manufacturing and services is not an ontological void between making things and not making things. It’s really a gap between putting things in boxes and not putting them in boxes. Like if you build a bookshelf and ship to a store and I buy it, that’s manufacturing. If I hire you to come to my house and install custom built-in shelves, that’s services.

I’m happy to see both Mr Yglesias and Kevin Drum note today that while the distinction between manufacturing and services is often meaningless, the distinction between tradability and non-tradability of products is most certainly not.

Tradable goods and services can, by definition, be consumed well away from the point of production. The international price of tradable products is therefore constrained within a fairly small range; you can try to sell a product for much more than its foreign equivalent, but don’t expect anyone to buy it. What this suggests is that real income differences across countries are largely attributable to differences in productivities within the tradable sector. This finding is associated with what economists call the Balassa-Samuelson effect, after economists Béla Balassa and Paul Samuelson.

In order to earn a higher wage than a worker in another country producing goods that trade at a more or less equal price, an employee must be more productive. The higher wage in the tradable sector will lead to a rising wage for workers in non-tradable sectors—that is, those producing non-transportable products like haircuts for local economies—as local firms must pay a competitive wage to attract employees. An overall higher level of income in an economy, in other words, is only possible thanks to higher productivity in the production of tradables.

The trouble, as Mr Luce rightly points out, is not necessarily that America is losing jobs in manufacturing. It’s that it is failing to create jobs in the tradable sector. Almost all net new job creation in America over the past 20 years has occurred in non-tradables: things like health care, for instance, or education. This is potentially a very serious issue. If America isn’t creating new jobs in the tradable sector, it is presumably because creation of such jobs is economically problematic: expected returns from worker outputs are less than the expected cost of hiring the worker. Put differently, it would seem that American productivity growth has not kept up with American labour costs across the economy as a whole.

Now, this isn’t necessarily the explanation for sustained high unemployment in America. Normally, we’d expect American wages to fall, either through nominal wage declines or a weakening of the exchange rate, until American labour costs are back in line with productivity and the market for labour clears. To explain unemployment, we probably need to look at a breakdown in that adjustment process. If the problem one is aiming to diagnose is one of prolonged stagnation in earnings, however, then this is an important dynamic to examine.

Second:

LET’S talk a little more about production of tradables and American stagnation (how’s that for an attention-grabbing lede?). Recently, I mentioned that productivity in the tradable sector is important, because it essentially governs the real incomes an economy can pay. And I noted that over the past two decades, virtually all of America’s net job creation has occurred in the non-tradable sector, which seems problematic. It’s probably useful to dig into this a bit more.

Mobility within the American economy is quite high. The rate of migration has declined in recent decades, but it is still quite common for households to live in multiple cities around the country over the course of a career. Because there are high levels of mobility within America, economists assume that real wages adjust across the economy so that the marginal resident of a city is indifferent between staying or moving out. Imagine a marginal resident of New York, for instance, who earns a high wage but also pays a lot in rent. If his wage drops or his rent rises (or if rents or wages change elsewhere) so that his purchasing power is reduced relative to what he might earn in, say, Dallas, then we assume he’ll probably move to Dallas. And if there’s a big gap, then we’d assume that a migratory flow between the two cities would occur until the marginal resident is once again indifferent. This isn’t a smooth, frictionless process in the real world, but it’s probably not a bad approximation for how things work.

Now, in order for the marginal resident to be indifferent between high-wage San Jose and low-wage Phoenix, the cost of living must be very different in the two places. As it happens, lots of things on the coasts are more expensive than they are across the Sunbelt, but the most striking and important gap is in housing costs. The median value of an owner-occupied home in San Jose is about 3 times that in Phoenix and almost 5 times that in Houston. The reason for this is fairly straightforward. Wages are much higher in the former metro than in the latter two. Population in San Jose should therefore grow until costs there rise to eliminate the gap in real living standards. This could occur through a rise in congestion costs or through population growth substantial enough to bid down nominal wages. As it happens, rich coastal cities tend to tightly restrict growth in housing supply, which quickly translates high demand into high home prices. Housing costs act as a lid of sorts, adjusting so that existing home supply is occupied, with the marginal resident indifferent between staying and going. Again, in practice, this isn’t a clean process. Last decade, home prices rocketed up along the coasts, and a stream of households flowed from the coasts to cheaper Sunbelt cities, all part of the mechanics of leveling out big real wage gaps.

While the marginal resident of Phoenix and San Jose is assumed to be indifferent between the two cities, however, there is still a real productivity gap. Housing costs across the Sunbelt have to be low to attract workers because wages are low, and wages are low because the productivity of the tradable sector in these cities is relatively low. That alone, however, shouldn’t impact the country’s ability to create jobs in the tradable sector. Productivity is lower in many Sunbelt cities, but so are wages.

Why, then, do we see very little net job creation in tradables, and lots in non-tradables? One possibility is that there are transfers across the economy which bid up wages in the non-tradable sector of growing cities. Consider this map.

(You can see the results of a similarly motivated analysis here.) What we see is that federal government spending results in large and persistent net transfers from some states to others. Moreover, very productive states like Massachusetts, New York, Washington, and California subsidise low productivity Sunbelt locales like Arizona and Florida. It’s not too hard to imagine how this might work. Sunbelt states are attractive to lots of different people, but retirees are well represented among migrants to the south. Retirees receive a lot of federal money through Social Security, Medicare, and other programmes. This produces net transfers from productive states which help bid up wages in non-tradable sectors—like health services, which is among the nation’s fastest growing employment categories—above the level that productivity in the tradable sector would normally permit. At that wage level, it’s difficult for firms in the tradable sector of these fast growing cities to profitably employ people; wage rates are above that justified by productivity.

That dynamic alone may go a long way toward explaining America’s labour market difficulties, but we can take the analysis a few steps further. There are a number of factors that make productive metropolitan areas an attractive location for firms, but economists increasingly emphasise the role of knowledge spillovers. A number of pieces of data point toward the growing importance of these spillovers. The relationship between large, skilled cities and high levels of productivity appears to be tightening. Research consistently finds an important spatial dimension to measures of idea dispersion. Wage figures also make the point; talented workers are enjoying bigger wage increases within large, skilled cities.

Changing technology seems to be driving these changes. New information and communication technologies are increasing the returns to ideas by expanding the markets over which they can be applied. Skilled cities, which help develop and disperse ideas, are therefore becoming more important. It is these ideas that are responsible for much of the value creation in the economy. And if we go back and look at the paper by Michael Spence and Sandile Hlatshwayo on net job creation, we see that while non-tradable sectors have been responsible for nearly all of the economy’s employment growth since 1990, the tradable sector has generated the bulk of the economy’s increase in value added. We can draw a line between idea creation, value added, and productive metropolitan areas, but this line does not continue on to employment growth.

Now if we focus in on the labour force in a place like Silicon Valley, we see that worker productivity is not uniformly dependent on these spillovers. Some individuals enjoy a slight bump in productivity when they locate to Silicon Valley. Others, however, will find that their productivity level—and their compensation—is hugely dependent on the existence of these spillovers. It seems probable that these relative dependencies on spillovers are related to skill levels and job types. An accountant with a specialisation in the tax difficulties confronted by online businesses is likely to benefit from locating in Silicon Valley, but might be nearly as productive in Phoenix. A computer systems engineer developing new business models based on the cloud may find his productivity and earning power significantly reduced if he is relocated to a metropolitan area with far fewer firms and workers operating at the technological frontier.

As costs rise in Silicon Valley, which type will move away first? It seems clear that the workers most involved in—and most dependent on—these productivity enhancing spillovers will be those for whom the real wage trade-off with cheaper cities is least attractive. Both kinds of workers face a similar drop in costs when they move, but the spillover-dependent worker faces a disproportionate decline in his nominal income.

What this means is that while population is flowing from high productivity to low productivity cities, this is not generating a proportional transfer in productivity. Migrating workers—even those who continue to work in a tradable sector—are those that were least involved in the process of idea creation in their old city, and they therefore contribute little to the development of a new spillover cluster in their new city. There is a relationship between population growth and productivity growth, but it is skill dependent, and the skills upon which it depends are very underrepresented in the migratory flows from the coasts to the Sunbelt. Migration to the Sunbelt is therefore failing to raise productivity in tradable sectors to a level sufficient to justify new hiring at prevailing wage rates.

The picture that emerges is one in which employment growth in high productivity, tradable industries is constrained at the rate of housing supply growth in skilled cities. And that rate is slow; for much of the past decade, Houston approved about ten times more new housing each year than San Jose. Value creation in high productivity cities continues, but a lot of that value is siphoned off through taxes and transfered to residents of low productivity cities, who use it to buy non-tradable services. That dynamic would seem to be the main mechanism through which America has been generating net job growth over the past two decades.

At least in this story, which might well be mistaken. Hopefully other analyses will shed more light on the picture.

And third:

I JUST wanted to add one additional, brief thought on yesterday’s post on migration, productivity, and job growth. It was pointed out to me on Twitter that federal government transfers are not the only mechanism through which value generated in productive, tradable-oriented cities is redistributed to less productive, non-tradable-oriented cities. Profits that accrue in one location but which are spent elsewhere might have the same effect. Or consider this example:

As technological progress raises the productivity of skilled, coastal cities it raises the demand to live in those cities and, because housing supply is limited, the price of housing in those cities. Much of the economic value of working in those places is capitalised into local home prices. With increasing frequency, older workers are leaving the workforce, cashing out of their expensive homes in productive places, and moving to the Sunbelt. Having relocated from, say, San Jose to Phoenix, the retiree can afford a grand home at a fraction of the price, and the rest of the gain from relocating becomes a stream of income used to fund consumption, including an expanding array of health services. That, in turn, bids up the wages within the non-tradable sector, making it difficult to produce tradable goods in a cost-effective way. And once again, national employment in tradables expands no faster than the pace of housing supply growth in highly productive cities. Value added can continue to rise in those cities, however, but much of it is redirected to employment growth in lower productivity non-tradable sectors. And so we get a couple of decades of stagnation in tradable employment and median wages.

How can we implement a “race with machines” strategy? the solution is organizational innovation: co-inventing new organizational structures, processes, and business models that leverage ever-advancing technology and human skills. Joseph Schumpeter, the economist, described this as a process of “creative destruction” and gave the entrepreneurs the central role in the development and propagation of the necessary innovations. Entrepreneurs reap rich rewards because what they do, when they do it well, is both incredibly valuable and far too rare…

Because the process of innovation often relies heavily on the combining and recombining of previous innovations, the broader and deeper the pool of accessible ideas and individuals, the more opportunities there are for innovation…

We are in no danger of running out of new combinations to try. Even if technology froze today, we have more possible ways of configuring the different applications, machines, tasks, and distribution channels to create new processes and products than we could ever exhaust…

Most of the combinations may be no better than what we already have, but some surely will be, and a few will be “home runs” that are vast improvements. The trick is finding the ones that make a positive difference. Parallel experimentation by millions of entrepreneurs is the best and fastest way to do that. As Thomas Edison once said when trying to find the right combination of materials for a working lightbulb: “I have not failed. I’ve just found 10,000 ways that won’t work.”

I kept waiting for some mention of the geographic component of innovation, but it never came, even as the authors name-checked innovative tech company after innovative tech company located in Silicon Valley. I kept waiting for the famous Alfred Marshall quote:

When an industry has thus chosen a locality for itself, it is likely to stay there long: so great are the advantages which people following the same skilled trade get from near neighbourhood to one another. The mysteries of the trade become no mysteries; but are as it were in the air, and children learn many of them unconsciously. Good work is rightly appreciated, inventions and improvements in machinery, in processes and the general organization of the business have their merits promptly discussed: if one man starts a new idea, it is taken up by others and combined with suggestions of their own; and thus it becomes the source of further new ideas. And presently subsidiary trades grow up in the neighbourhood, supplying it with implements and materials, organizing its traffic, and in many ways conducing to the economy of its material.

I think that the authors have basically gotten the state of innovation right: we are approaching a critical point at which impressive progress in information technology becomes explosive progress. And I think that the authors are right that the extent to which we are able to take advantage of these technological developments will hinge on how successful America’s tinkerers are at experimenting with new business models and turning them into new businesses. But I also think that there is a critical geographic component to that process of experimentation and entrepreneurship and, as I wrote in my book, I think we are systematically constraining the operation of that component.

High housing costs constitute a substantial regulatory tax burden on residence in many high productivity areas. These are the places where the tinkerers are having their ongoing innovative conversation. But if the tinkerers are driven away, the conversation loses depth and breadth, and we lose many of the combinations that might go on to be the next big company — the next big employer. That, to me, is a very worrying idea.

We study the location and productivity of more than 1,000 research and development (R&D) labs located in the Northeast corridor of the U.S. Using a variety of spatial econometric techniques, we find that these labs are substantially more concentrated in space than the underlying distribution of manufacturing activity. Ripley’s K-function tests over a variety of spatial scales reveal that the strongest evidence of concentration occurs at two discrete distances: one at about one-quarter of a mile and another at about 40 miles. These findings are consistent with empirical research that suggests that some spillovers depreciate very rapidly with distance, while others operate at the spatial scale of labor markets. We also find that R&D labs in some industries (e.g., chemicals, including drugs) are substantially more spatially concentrated than are R&D labs as a whole.

Tests using local K-functions reveal several concentrations of R&D labs (Boston, New York-Northern New Jersey, Philadelphia-Wilmington, and Washington, DC) that appear to represent research clusters. We verify this conjecture using significance-maximizing techniques (e.g., SATSCAN) that also address econometric issues related to “multiple testing” and spatial autocorrelation.

We develop a new procedure for identifying clusters – the multiscale core-cluster approach — to identify labs that appear to be clustered at a variety of spatial scales. We document that while locations in these clusters are often related to basic infrastructure, such as access to major roads, there is significant variation in the composition of labs across these clusters. Finally, we show that R&D labs located in clusters defined by this approach are, all else equal, substantially more productive in terms of the patents or citation-weighted patents they receive.

First, Renn asks about the distinction between employment density and residential density. At a very local level, this is obviously important, as he points out. The economy of the employment-dense downtown Washington neighborhood is very different from that of the population-dense Adams Morgan neighborhood. These distinctions are interesting and important, but not that critical for the argument I’m making. Most of the studies I cite focus on employment density at fairly large scales — counties or metropolitan areas, for instance. At those scales employment and population densities closely track each other, as one would expect, and the population numbers are easier to get at higher frequencies for more places and at smaller scales (which is useful in computing weighted-average densities).

Next, Renn writes:

Another is that the book heavily focuses on the supply side of the equation (i.e., the ability to build) as opposed to the demand side. But arguably demand had a bigger role to play in driving up housing costs in places like NYC…

No amount of new supply would have been able to keep up with the significant deregulation of finance that occurred in the last two decades (repealing Glass-Steagall, raising the ceilings on the share of national deposits held by a single institution, etc) and Helicopter Ben’s printing presses.

Also, assume we were able to moderate the price of housing in these places somewhat. What would that do for us? Well, NYC and SF would still remain among the most expensive places in America to live. This means that they are going to continue to draw principally those who are able to tap into the particular wealth generating functions of those regions, along with those who particularly value the amenity set of those cities or who are following network path migration. However, the industry groups that are now predominant in those cities are ones that employ almost exclusively high end labor. So what this would mean in my view is that instead of say the top 1% being able to live and work in these places, we extend that down to the top 2% or 3%.

I would disagree that I focus on the supply side. A critical part of my argument is that technology has increased the productive role of big, dense cities, leading to a significant increase in demand to live in such places. Without rising demand, tight supply has no effect on prices. I focus on supply as the solution, because the only way to reduce prices on the demand side is to make the cities less attractive, which seems like an unpleasant option to me.

Deregulation increased the return to some portions of the finance trade, but it seems to me that the impact of technology has been more significant (Bernanke has essentially nothing to do with it; note that these trends are now several decades old). New technologies allow top financiers to control vast sums of money in investments around the world with the greatest of ease. Hedge funds aren’t a new innovation; they’ve been around half a century. But hedge-fund activity exploded in recent decades thanks mostly to the direct impact of technology (and the indirect impact of technology on top incomes, which created more demand for high-end money management).

There is absolutely a level of supply growth that could have dealt with soaring demand and prevented skyrocketing housing costs. There’s no technical infeasibility to such growth. The obstacles are political and cultural. During the industrial revolution, America’s large cities grew by 500% or more. There is no tolerance for growth at anywhere near that level now. That doesn’t mean it’s impossible, just outside our comfort zone.

At any rate, marginal improvements are the best we can hope for. I’m not suggesting that freer building in big cities will eliminate inequality at a stroke. I’m merely suggesting that a more liberal approach to building is one important way to help maintain the dynamism and broad-based opportunity of the American economy.

Two other points. First, Renn echoes some other writers in wondering how much of the higher productivity in cities is due to compositional effects, that is, from exclusion of low-ability workers. The answer, as I acknowledge in the book, is some. But there is a significant effect over and above this. Controlling for individual characteristics, there is a benefit to being in a smart city. That is, if you take individuals with similar skill levels and put them in different cities, the one in the city with more skilled workers will be more productive. Gated cities don’t thrive because of exclusion; they actively make the workers there more productive.

Finally, Renn makes a good point when he writes:

So to really boost employment, we’d need more than just cheaper land making labor more available at a reasonable cost. That might provide some boost. But we would also need business model innovation.

I agree! And one of the points I aimed to make in the book is that during periods of dramatic technological change cities become more important, because they are laboratories of experimentation. Early last century, within America’s large industrial cities, there was a constant conversation taking place across competitors. Firms worked constantly to improve new technologies, but also to find ways to build successful businesses around them.

Just recently, a Google employee named Steve Yegge wrote a memo to his coworkers which was accidentally posted publicly. It’s a fascinating look at the process of innovation in Silicon Valley. Not simply product innovation; Yegge specifically discusses the way that cultural differences at Google rivals give them an edge in exploiting the business potential of the platform. Yegge previously worked at Amazon, and he writes in a fashion which suggests that his audience will also be intimately familiar with the strategies and goings on at companies like Facebook and Apple. It is as if these ideas are in the air, to use Alfred Marshall’s phrase. It is in places like Silicon Valley that people are grappling with how best to deploy the wondrous new technological tools at our disposal, and how best to build businesses around them.

This is why dense, productive, innovative cities are so important. It’s why it’s important to make sure that workers can afford to live in such places. The jobs these innovators create will be good ones. And they will create more of them if the local labor market isn’t incredibly tight thanks to high housing costs.

Renn suggests that my solutions may merely mean that we squeeze a few more horsepower out of the American economic engine. I’m happy to agree with that. That, after all, is how all of humanity’s modern progress has occurred. All of the stunning technological innovations of the industrial revolution didn’t lead to rocketing economic growth; since the early 19th century, potential growth has held remarkably steady at around 2% a year. It’s just that across decades, the effect of pushing growth up just that little bit has an enormous impact. If America had growth half a percentage point faster each year for the past two decades, we’d all be much, much richer.

And here’s an interview on the book at Reason.

Avent cites research showing that denser American cities have higher labor productivity. For example, a 1996 study uses 1988 data to show a correlation between the urban densities and the wages and salaries earned in the various states such doubling job density raises incomes by 6 percent.

Six percent isn’t very much, and in order to reach even this conclusion, the authors of the study left out states that had lots of mining industries, which depend more on mineral density than population density. If they hadn’t left those states out, Alaska would have been the most-productive state. Even if some industries do benefit from density, not all do.

Avent cites several other studies, but does not mention this one, which compares density with technological innovation, a field that–unlike mining–strongly depends on the personal contacts that supposedly are gained from density. The study did find a correlation between density and innovation, but it also found an optimal density and an optimal city size. In other words, at some point, more density is no longer better. In particular, the optimal urban area size is about 750,000 people and the optimal density is about 2,000 jobs per square mile–roughly Baltimore or Philadelphia. Baltimore and Philadelphia are far from the nation’s densest urban areas, and in fact Houston is right between them.

O’Toole seems to accept the relationship; indeed, he directs us to a paper in which the authors conclude:

These findings confirm the widely held view that the nation’s densest locations play an important role in creating the flow of ideas that generate innovation and growth.

His principal criticism seems to be that in the paper in question, the authors estimate an optimal density which is not, in his view, that dense. That’s not much of a complaint, in my view. I’ve been careful not to specify an optimal density; in fact, I suspect that such a density is likely to vary significantly across industries, institutions, and with transportation and communication technologies. The empirical evidence supporting a positive relationship between density and productivity is strong. There is comparatively little work on optimal densities. At any rate, the policy implications don’t seem to tilt in O’Toole’s supposed ideological direction. I can’t imagine why a libertarian would want to go around telling willing city residents that they’re living at too high a density. And if productivity falls above certain densities, the market should quickly adjust; we’d see little wage growth in cities near the threshold, and consequently little growth in housing costs. Of course that’s not what we observe.

O’Toole goes on:

Another problem with Avent’s first claim is that most of the studies he cites compare the relationship between density and productivity at a single point in time. But the whole point of The Death of Distance is that this relationship is getting weaker all the time. This study, for example, found that the correlation between density and productivity declined from 41 percent in 1940 to 7 percent in 1980. It seems likely that, by the time any city can do anything about its density, the benefit of being denser will be negligible.

Now, this is really remarkable. I argue — I think quite clearly — that the relationship between density and productivity appears to be growing stronger, precisely because of technological developments. I cite research indicating that since the 1980s R&D spillovers appear to have become more important, that in recent decades the return to talent has increased in dense cities, and that in recent decades density explains a surprisingly large share of real wage growth. I point to Ed Glaeser’s work, which indicates that the “Death of Distance” phenomenon that gutted urban industry appears to have strengthened knowledge agglomerations and increased the return to ideas. Cato’s chief urban scholar seems to have no knowledge of these strands of the literature, nor does he appear to have spent all that much time with my book.

O’Toole then argues that I’m incorrect to blame NIMBYs for high housing costs. Instead, local zoning rules are to blame. Again, he seems strangely ignorant of the arguments I actually made. I write that residents of dense cities oppose new development and use a variety of mechanisms — tight zoning rules, historical preservation, and political pressure among them — to achieve their ends. I’m not sure how O’Toole thinks most city rules get on the books; presumably, local interests have had something to do with it.

Finally, there’s this:

Perhaps the greatest flaw is in the third step of Avent’s reasoning. “I don’t wish to tell anyone where to live, and I certainly don’t want the government having its way in the matter,” says Avent. But if the NIMBYs and their urban-growth boundaries and large-lot zoning disappeared, these regions would all become less dense, not more dense. Housing costs would become more reasonable, and the areas might actually become more productive, but not because of density.

The trouble is that, although Avent doesn’t support coercive government policies, many other density advocates do. And Avent specifically cites transit-oriented developments as a way of increasing density without realizing the huge amount of coercion that is required for many of those developments, including prescriptive zoning, tax subsidies, and artificial restraints on low-density development so that some people will have little choice but to live in the higher densities.

Frankly, I have no idea what kind of economic model he’s using here or what evidence he has in mind. I’m not sure what would happen if zoning rules were relaxed across the board. Home price data suggest that there would be a lot of new construction in dense, high-cost cities. Given the high cost of land, it seems likely that densities would rise. Based on the research I’ve examined, and that which O’Toole himself cites, it seems likely that this growth would result in more people living in dense areas and earning higher wages, thanks to rising labor productivity. Now, maybe O’Toole is arguing that cities would like to grow outward but are prevented in doing so by NIMBY rules, such that relaxed zoning would lead to sharply reduced densities. Again, there is scant evidence for this assertion, but I’m happy to admit that it’s a possibility. Either way, I can’t see why O’Toole wouldn’t be in favor of making it easier to build new housing — of whatever sort the market wants — in cities, especially those where home prices and productivity levels are high.

I discuss several different ways to make it easier for cities to satisfy demand for housing, and transit-oriented development is one of them. It seems clear that government intervention would be required to support such a policy, and O’Toole seems very unhappy about this. Whether he’s just as unhappy about the taxes, infrastructure spending, and zoning rules that typically accompany suburban growth isn’t clear. But in general, I’m interested in changes that increase, on net, a landowner’s ability to build as he wishes. It seems clear to me that TOD is a useful way to get local governments to accept upzoning, and landowners seem more than happy to build as much as the government will let them. Libertarians might not approve, but it seems to be a more liberal approach than the status quo.

I do appreciate O’Toole’s engagement with the book, such as it is. I sincerely hope that libertarians will consider the arguments I make and warm to the theme.