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Economists don’t like seeing economic inefficiency, and there’s a lot of it out there to bother us. But some of the very worst we see is in cities; there are many incredible inefficiencies in city land use and in supporting utilities. Which of course makes economists wonder: how could we do better?
Here is one idea that should seem obvious to most economists, but even so I can’t find much discussion of it. So let me try to think it through. What if we auctioned off cities, whole?
Specifically, imagine that we sell all the land and immobile property in an urban region, including all the municipal property, plus all the rights to make urban governance choices. We sell this to a single buyer, who might of course be a consortium. The winning bid would have to be higher than the prior sum of all regional property values, plus a gain of say 50%. The money would be paid to all the prior property owners in proportion to prior property values. (“Prior” should be well before the auction was announced.)
The winning buyer would control all property and governance in this region for a specific time period, say twenty years, after which they’d have to divide the region into at least a thousand property units and auction all them off again individually. Urban governance would revert back to its previous system, except that there’d be a single up-or-down vote on one proposal for a new governance regime offered by this buyer, using previous rules about who can vote in such things.
The idea here is of course to “internalize the externalities”, at least for a while. This single buyer would encompass most of the varying conflicting interests that usually cause existing inefficiencies. And they’d have the power to resolve these conflicts decisively.
OK, now let’s ask: what could go wrong? Well first maybe no bidder could actually collect enough money to make a big enough bid. Or maybe the city inefficiencies aren’t big enough to produce the 50% added value requirement. Or twenty years isn’t long enough to fix the deep problems. Or maybe the plan leaks out too early and pushes up “prior” property values. In these cases, there’d be no change, so not much would be lost.
Another thing that could go wrong would be that larger units of government, like states or nations, might try to tax or regulate this single buyer so much as to take away most of their gains from this process. In expectation of this outcome, no one would bid enough for the city. And again there’d be no change, so little would be lost. So we should try to set this up to avoid such taxation/regulation, but knowing that the downside isn’t terrible if we fail.
Finally, the new city owner might price-discriminate against residents who are especially attached to the city, and so are especially unwilling to leave. Like an old couple whose children all live nearby. Or a big firm with an expensive plant located there. If the new owner cranks up their rent high, these folks might lose on net, even if they are paid a 50% bonus on property values. Of course one might try to set rules to limit price-discrimination, though that might create the over-regulate scenario above. Also, if selling off cities whole became a regular thing, then people may learn to not get too attached to any one city.
I don’t see any of these problems as overwhelming, so I’d endorse trying to do this. But I don’t actually expect many places to try it, because I think most voters whose support would be needed would see their status as threatened. They’d be offended by the very idea of a single powerful actor having strong control over their lives, even if that actor had to pay dearly for the right, and even if they end up better off as a result. So I’d guess it is pride that most goeth before our city falls.
As I’ve mentioned before, people tend to love cities even as they hate firms, mainly because firms tend for-profit, while cities tend democratic. People now mostly accept for-profit firms because the non-profit ones don’t offer attractive jobs or products. Similarly, I’d predict that if there were many for-profit cities most people would be okay with them, as they’d be reluctant to move to worse-run non-profit cities. Also, if almost all firms were non-profit, people might be reluctant to rely on for-profit firms due to their bad public image. Multiple equilibria are possible here, and we may not be in the best one.
Added 9p: Many commentaries seem to fear private city owners evicting undesirable people from the city, in contrast to democratically controlled cities which they see as fountains of altruism toward such people. But see here, here, here, or consider that democracies regularly vote to exclude immigrants who would in fact benefit them materially.
At the state and local level, government is indeed engaged in redistribution — but it’s redistribution from the poor and the middle class to the wealthy. (more)
A new Journal of Regional Science paper (ungated here) has a fascinating thesis: what makes US cities big and growing lately is not computers, education, creativity, or socializing. Instead it is task connectivity.
Authors Kok and Ter Weel have data on 140K workers in the 168 biggest US cities. Each worker has one of 326 jobs, and each job has weights for 41 different kinds of tasks (listed in table 2). From this they create a measure of what fraction of time workers of each city spend on each task.
They then look at correlations between tasks of these city times. Two tasks that are highly correlated across cities, so that when a city does one task more it usually also does the other task more, are said to be “connected.” It is presumably useful to co-locate connected tasks. If, for a focal task, one adds up all the correlations between that focal task and all the other tasks, one gets a “task connectivity” for that focal task. “Info input” and “work output” type tasks are less connected, and have declined over time, while “mental process” and “interact with others” type tasks are more connected and have increased.
Averaging the connectivity of tasks done in a city, one gets the task connectivity of that city. Kok and Ter Weel find:
Cities with a relatively highly connected task structure seem to be larger, less specialized, and more skilled than cities with lower levels task connectivity. These cities also seem to employ workers for which social skills are relatively more important.
The correlation with city size is pretty strong:
Looking at employment growth of cities from 1990 to 2009, Kok and Ter Weel find that cities with less task connectivity grew less. Other bad signs for city growth are being big, having high rent, being specialized (like Hollywood and silicon valley), being in the Midwest and not in the West, and being cold in July. After controlling for these features, however, these other features were not growth signs: worker education, computer use, use of social skills, doing routine tasks, and local workers well matched to local jobs.
This paints a plausible picture, but one quite different than we usually see. If you want to be a big growing city, forget all that stuff you usually hear about recruiting educated “creative” workers, getting into computers and automation, promoting social interactions, or specializing in a particular industry. Instead have a nice climate, try to attract industries and jobs that do connected tasks, and get your rents down by increasing your building supply.
This also implies that which cities will win is pretty predictable. If the real estate market hasn’t yet recognized this, then do the calc, and invest in the good cities, and drop the bad ones.
Added noon: A similar result is found at the national level. HT Michael Hendrix.
In a recent Ipsos/Reuters poll, which questioned 11,383 people in 24 countries, about half believed that they would be at a disadvantage in earning promotions because of the lack of face-to-face contact. Previous research suggests part-time telecommuters do not communicate less frequently with managers. … After four years of experience, the average male telecommuter will earn about 6.9% less than a non-telecommuter. (more)
Telecommuting requires the use of various types of media to communicate, such as the telephone and email. Emails have a time lag that does not allow for immediate feedback; telephone conversations make it harder to decipher the emotions of the person or team on the phone; and both of these forms of communication do not allow one to see the other person. Typical organization communication patterns are thus altered in telecommuting. For instance, teams using computer-mediated communication with computer conferencing take longer to make group decisions than face-to-face groups. (more)
Decades ago many futurists predicted that many workers would soon telecommute, and empty out cities. Their argument seemed persuasive: workers who work mainly on computers, or who don’t have to move much physical product, seem able to achieve enough coordination to do their jobs via phone, email, and infrequent in-person meetings. And huge cost savings could come from avoiding central city offices, homes near them, and commuting between the two. (For example, five firms might share the same offices, with each firm using them one day per week.)
But it hasn’t remotely happened that way. And the big question is: why?
Some say telecommuters would shirk and not work as much, but it is hard to see that would remain much of a problem with a constant video feed watching them. Bryan Caplan favors a signaling explain, that we show up in person to show our commitment to the firm. But a firm should prefer employees who show devotion via more total work, instead of wasting hours on the road. Yes inefficient signaling equilibria can exist, but firms have many ways to push for this alternate equilibrium.
The standard proximate cause, described in the quote above, is that workers and their bosses get a lot of detailed emotional info via frequent in-person meetings. Such detailed emotional info can help to build stronger feelings of mutual trust and affiliation. But the key question is, why are firms willing to pay so much for that? How does it help firm productivity enough to pay for its huge costs?
My guess: frequent detailed emotional info helps political coalitions, even if not firms. Being able to read detailed loyalty signals is central to maintaining political coalitions. The strongest coalitions take over firms and push policies that help them resist their rivals. If a firm part adopted local policies that weakened the abilities of locals to play politics, that part would be taken over by coalitions from other parts of the firm, who would then push for policies that help them. A lack of telecommuting is only one of a long list of examples of inefficient firm policies than can be reasonably be attributed to coalition politics.
Some people hope that very high resolution telepresence could finally give enough detailed emotional info to make telecommuting workable. And that might indeed give enough info to build strong mutual trust and loyalty. But it is hard to make very high resolution telepresence feel natural, and we still far from having enough bandwidth to cheaply send that much info.
Furthermore, by the time we do we may also have powerful robust ways to fake that info. That is, we might have software that takes outgoing video and audio feeds and edits them to remove signs of disloyalty, to make people seem more trusting and trustworthy than they actually are. And if we all know this is possible, we won’t trust what we see in telepresence.
So, for telepresence to actually foster enough loyalty and trust to make telecommuting viable, not only does it need to feel comfortable and natural and give very high bandwidth info, but the process would need to be controlled by some trusted party, who ensures that people aren’t faking their appearances in ways that make it hard to read real feelings. Setting up a system like that would be much more challenging that just distributing something like Skype software.
Of course eventually humans might have chips under their skin to manipulate their sight and sound in real physical meetings. And then they might want ways to assure others aren’t using those. But that is probably much further off. (And of course ems might always “fake” their physical appearance.)
Again, I have hopes, but only weak hopes, for telepresence allowing for mass human telecommuting.
Added 3July: Perhaps I could have been clearer. The individual telecommuter could clearly be at a political disadvantage by not being part of informal gossip and political conversation. He would have fewer useful allies, and they would thus prefer that he or she not telecommute.
It is well-known that while electricity led to big gains in factory productivity, few gains were realized until factories were reorganized to take full advantage of the new possibilities which electric motors allowed. Similarly, computers didn’t create big productivity gains in offices until work flow and tasks were reorganized to take full advantage.
Auto autos, i.e., self-driving cars, seem similar: while there could be modest immediate gains from reducing accident rates and lost productive time commuting, the biggest gains should come from reorganizing our cities to match them. Self-driving cars could drive fast close together to increase road throughput, and be shared to eliminate the need for parking. This should allow for larger higher-density cities. For example, four times bigger cities could plausibly be twenty-five percent more productive.
But to achieve most of these gain, we must make new buildings with matching heights and locations. And this requires that self-driving cars make their appearance before we stop making so many new buildings. Let me explain.
Since buildings tend to last for many decades, one of the main reasons that cities have been adding many new buildings is that they have had more people who need buildings in which to live and work. But world population growth is slowing down, and may peak around 2055. It should peak earlier in rich nations, and later in poor nations.
Cities with stable or declining population build a lot fewer buildings; it would take them a lot longer to change city organization to take advantage of self-driving cars. So the main hope for rapidly achieving big gains would be in rapidly growing cities. What we need is for self-driving cars to become available and cheap enough in cities that are still growing fast enough, and which have legal and political support for driving such cars fast close together, so they can achieve high throughput. That is, people need to be sufficiently rewarded for using cars in ways that allow more road throughput. And then economic activity needs to move from old cities to the new more efficient cities.
This actually seems like a pretty challenging goal. China and India are making lots of buildings today, but those buildings are not well-matched to self-driving cars. Self-driving cars aren’t about to explode there, and by the time they are cheap the building boom may be over. Google announced its self-driving car program almost four years ago, and that hasn’t exactly sparked a tidal wave of change. Furthermore, even if self-driving cars arrive soon enough, city-region politics may well not be up to the task of coordinating to encourage such cars to drive fast close together. And national borders, regulation, etc. may not let larger economies be flexible enough to move much activity to the new cities who manage to support auto autos well.
Alas, overall it is hard to be very optimistic here. I have hopes, but only weak hopes.
Urban economics studies the spatial distribution of activity. In most urban econ models, the reason that cities aren’t taller is that, per square meter of useable space, taller buildings cost more to physically make. (Supporting quotes below.) According to this usual theory, buildings only get taller when something else compensates for these costs, like a scarce ocean view, or higher status or land prices.
Knowing this, and wondering how tall future cities might get, I went looking for data on just how fast building cost rises with height. And I was surprised to learn: within most of the usual range, taller buildings cost less per square meter to build. For example, for office buildings across 26 US cities, 11-20 stories tend to be cheaper than 5-10 stories, which are cheaper than 2-4 stories (quote below). I also found data on two sets of Chinese residential buildings. Here is cost to build per square meter (on Y axis) vs. height in meters (on X axis) for 24 buildings 3 to 39 stories tall, built in Hong Kong in the early 1990s:
Here are 36 buildings 2 to 37 stories tall, built in Shanghai between 2000 and 2007:
The Shanghai buildings don’t get more expensive till after about 20 stories, while Hong Kong buildings are still cheap at 40 stories.
Now I have no doubt that some elements of cost, like structural mass, rise with height, and that there is some height where such costs dominate. But since there are scale economies in making bigger buildings, it isn’t obvious theoretically where rising structure costs overwhelm scale economies.
Perhaps the above figures are misleading somehow. But we know that taking land prices, higher status, and better views into account would push for even taller buildings. And a big part of higher costs for heights that are rarely used could just be from less local experience with such heights. So why aren’t most buildings at least 20 stories tall?
Perhaps tall buildings have only been cheaper recently. But the Hong Kong data is from twenty years ago, and most buildings made in the last years are not at least 20 stories tall. In fact, in Manhattan new residential buildings have actually gotten shorter. Perhaps capital markets fail to concentrate enough capital in builders’ hands to enable big buildings. But this seems hard to believe.
Perhaps trying to build high makes you a magnet for litigation, envy, and corrupt regulators. Your ambition suggests that you have deeper pockets to tax, and other tall buildings nearby that would lose status and local market share have many ways to veto you. Maybe since most tall buildings are prevented local builders have less experience with them, and thus have higher costs to make them. And many few local builders are up to the task, so they have market power to demand higher prices.
Maybe local governments usually can’t coordinate well to build supporting infrastructure, like roads, schools, power, sewers, etc., to match taller buildings. So they veto them instead. Or maybe local non-property-owning voters believe that more tall buildings will hurt them personally. (The big city nearest me actually has a law against buildings over 40 meters tall.)
Note that most of these explanations are variations on the same theme: local governments fail to coordinate to enable tall buildings. Which is in fact my favored explanation. City density, and hence city size, is mainly limited by the abilities of the conflicting elements that influence local governments to coordinate to enable taller buildings.
Remember those futurist images of dense tall cities scraping the skies? The engineers have done their job to make it possible. It is politics that isn’t yet up to the task.
Those promised quotes: Continue reading "Why Aren’t Cities Taller?" »
Back in March I wrote:
Somewhere around 2035 or so … the (free) energy used per [computer] gate operation will fall to the level thermodynamics says is required to [logically] erase a bit of information. After this point, the energy cost per computation can only fall by switching to “reversible” computing designs, that only rarely [logically] erase bits. … Computer gates … today … in effect irreversibly erase many bits per gate operation. To erase fewer bits instead, gates must be run “adiabatically,” i.e., slow enough so key parameters can change smoothly. In this case, the rate of bit erasure per operation is proportional to speed; run a gate twice as slow, and it erases only half as many bits per operation. Once reversible computing is the norm, gains in making more smaller faster gates will have to be split, some going to let gates run more slowly, and the rest going to more operations. (more)
The future of computing, after about 2035, is adiabatic reservable hardware. When such hardware runs at a cost-minimizing speed, half of the total budget is spent on computer hardware, and the other half is spent on energy and cooling for that hardware. Thus after 2035 or so, about as much will be spent on computer hardware and a physical space to place it as will be spent on hardware and space for systems to generate and transport energy into the computers, and to absorb and transport heat away from those computers. So if you seek a career for a futuristic world dominated by computers, note that a career making or maintaining energy or cooling systems may be just as promising as a career making or maintaining computing hardware.
We can imagine lots of futuristic ways to cheaply and compactly make and transport energy. These include thorium reactors and superconducting power cables. It is harder to imagine futuristic ways to absorb and transport heat. So we are likely to stay stuck with existing approaches to cooling. And the best of these, at least on large scales, is to just push cool fluids past the hardware. And the main expense in this approach is for the pipes to transport those fluids, and the space to hold those pipes.
Thus in future cities crammed with computer hardware, roughly half of the volume is likely to be taken up by pipes that move cooling fluids in and out. And the tech for such pipes will probably be more stable than tech for energy or computers. So if you want a stable career managing something that will stay very valuable for a long time, consider plumbing.
Will this focus on cooling limit city sizes? After all, the surface area of a city, where cooling fluids can go in and out, goes as the square of city scale , while the volume to be cooled goes as the cube of city scale. The ratio of volume to surface area is thus linear in city scale. So does our ability to cool cities fall inversely with city scale?
Actually, no. We have good fractal pipe designs to efficiently import fluids like air or water from outside a city to near every point in that city, and to then export hot fluids from near every point to outside the city. These fractal designs require cost overheads that are only logarithmic in the total size of the city. That is, when you double the city size, such overheads increase by only a constant amount, instead of doubling.
For example, there is a fractal design for piping both smoothly flowing and turbulent cooling fluids where, holding constant the fluid temperature and pressure as well as the cooling required per unit volume, the fraction of city volume devoted to cooling pipes goes as the logarithm of the city’s volume. That is, every time the total city volume doubles, the same additional fraction of that volume must be devoted to a new kind of pipe to handle the larger scale. The pressure drop across such pipes also goes as the logarithm of city volume.
The economic value produced in a city is often modeled as a low power (greater than one) of the economic activity enclosed in that city. Since mathematically, for a large enough volume a power of volume will grow faster than the logarithm of volume, the greater value produced in larger cities can easily pay for their larger costs of cooling. Cooling does not seem to limit feasible city size. At least when there are big reservoirs of cool fluids like air or water around.
I don’t know if the future is still plastics. But I do know that a big chuck of it will be pipes.
Added 10Nov 4p: Proof of “When such hardware runs …” : V = value, C = cost, N = # processors, s = speed run them at, p,q = prices. V = N*s, C = p*N + q*N*s2. So C/V = p/s + q*s. Pick s to min C/V gives p = q*s2, so two parts of cost C are equal. Also, C/s = 2*sqrt(p*q).
Added 6Nov2014: According to 2012 data, pipes turn out to be the most “complex” product, i.e. the product that most indicates that a nation is able to produce many difficult things.
Futurists sometimes get excited about new ways to encourage cooperation in Prisoner’s Dilmena like games. For example, future folks might interact via quantum games, future AIs might show each other their source code, or future clans of em copies might super-cooperate with one another. Folks who know just enough economics to be dangerous sometimes say that this “changes everything”, i.e., that future economies will be completely different as a result. In fact, however, not only do we already have lots decent ways to encourage cooperation, such as talking and reputation, we also consistently forgo such ways to better encourage flexibility and specialization.
As I reviewed in my last post, we have strong reasons and abilities to cooperate within family clans, especially when such clans heavily intermarry and live and work closely together over many generations. And our farming era ancestors took big advantage of this. To function and thrive, however, our industry era economy had to suppress such clans, to allow more flexibility and specialization. Industry needs people to frequently change where they live, what kinds of jobs they do, and who they work with, and to play fair within industry-era reimagined firms, cities, and nations. Strong family clans instead encouraged stability and nepotism, and discouraged people from moving to cities and new jobs, and from cooperating fairly with and showing sufficient loyalty to other families within shared firms, cities, and nations.
Our industry era institutions consistently forgo the extra cooperation advantages of strong family clans, to gain more flexibility and specialization. This is now a huge net win. Our descendants are likely to similarly forgo advantages from new ways to cooperate, if those similarly reduce future flexibility and specialization. For example, future societies of brain emulations are likely to be wary of strongly self-cooperating clans of copies of the same original human. While such copy clans have even stronger reasons to cooperate with each other than family clans, copy clans might cause future organizations to suffer even more than do family-based firms, cities, and nations today from clan-based nepotism, and from low quality and inflexible matches of skills to jobs. Ems firms and cities are thus likely to be especially watchful for clan nepotism, and to avoid relying too heavily on any one clan.
Yes game theory captures important truths about human behavior, including about costs we pay from failing to fully cooperate. But prisoner’s dilemma style failures to cooperate in simple games comprises only a tiny fraction of all the important things that can and do go wrong in a modern economy. And we already have many decent ways to encourage cooperation. I thus conclude that future economies are unlikely to be heavily redesigned to take advantage of new possible ways to encourage prisoner’s dilemma style cooperation.
In the last few weeks I’ve come across many sources emphasizing the same big theme that I hadn’t sufficiently appreciated: our industrial world was enabled and has become rich in large part because we’ve reduced the power and importance of extended families. This post ends with a long list of quotes, but I’ll summarize here.
In most farmer-era cultures extended families, or clans, were the main unit of social organization, for production, marriage, politics, war, law, and insurance. People trusted their clans, but not outsiders, and felt little obligation to treat outsiders fairly. Our industrial economy, in contrast, relies on our trusting and playing fair in new kinds of organizations: firms, cities, and nations, and on our changing our activities and locations to support them.
The first places where clans were weak, like northern Europe, had bigger stronger firms, cities, and nations, and are richer today. Today people with stronger family cultures are happier and healthier, all else equal, but are less willing to move or intermarry, and are nepotistical in firms and politics. Family firms do well worldwide, but by having a single family dominate, and by being smaller, younger, and less innovative.
Thus it seems that strong families tend to be good for people individually, but bad for the world as a whole. Family clans tend to bring personal benefits, but social harms, such as less sorting, specialization, agglomeration, innovation, trust, fairness, and rule of law.
All those promised quotes: Continue reading "Beware Extended Family" »
There have been three major eras of human history: foraging, farming, and industry. During each era our economy has grown at a roughly steady exponential rate, and I’ve written before about some intriguing patterns in these growth eras: eras encompassed a similar number of doublings (~7-10), transitions between eras were much shorter than prior doubling times, and such transitions encompassed a similar number of growth rate doublings (~6-8). I’ve also noted that transition-induced inequality seems to have fallen over time.
I just noticed another intriguing pattern, this time in community sizes. Today in industrial societies roughly half of the population lives in metropolitan areas with between one hundred thousand and ten million people, with a mid size of about a million. While good data seems hard to find, during the farming era most people seem to have lived in communities (usually centered around a village) of between roughly three hundred and three thousand people, with a mid size of about a thousand. Foragers typically lived in mobile bands of size roughly twenty to fifty, with a best size of about thirty.
So community sizes went roughly from thirty to a thousand to a million. The pattern here is that each new era had a typical community size that was roughly the square of the size during the previous era. That is, a city is roughly a village of villages, and a village is roughly a band of bands. We could extend this patter further if we liked, saying that an extended family group has about four to eight members, with a mid size of six, so that a band is a family of families. (We might even go further and say that a family is a couple of couples, where a couple has two or so members.)
If previous growth patterns were to continue, I’ve written before that a new growth era might appear sometime in roughly the next century, and over a few years the economy would transition to a new growth rate of doubling every week to month. If this newly-noticed community size pattern were to continue, the new era would have communities of size roughly a trillion, perhaps ranging from ten billion to a hundred trillion.
Admittedly, after a year or two of this new era, things might change again, to yet another era. And the growth and community size trends couldn’t both continue to that next era, since a community size of a trillion trillion would require much more than twenty doublings of growth. So these trends clearly have to break down at some point.
I’ve been exploring a particular scenario for this new era: it might be enabled and dominated by brain emulations, or “ems.” Interestingly, I had already estimated an em community size of roughly a trillion based on other considerations. Ems could take up much less physical space than do humans, and since ems could visit each other in virtual reality without moving physically, em community sizes would be less limited by travel congestion costs.
So what should one call a city of cities of a trillion souls? A “world”?