Author Archives: legallysociable

Do private schools keep wealthy families in American big cities?

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In response to last week’s argument that bad people send their kids to private school, Megan McArdle suggests urban private schools have kept wealthy families in big cities.

However, I think that Benedikt isn’t thinking through what would actually happen if everyone felt a moral obligation to send their kids to public schools. What would actually happen is that Allison Benedikt wouldn’t live in Brooklyn, because New York, like most of the rest of the U.S.’s cities, would have lost all of its affluent families in the 1970s — the ones who stayed largely because private school, and a handful of magnet schools financed by the taxes of people who sent their kids to private school, allowed them to maintain residence without sending their kids into middle- and high-schools that had often become war zones. Anyone with any choices left that system, one way or another. But because New York had a robust system of private and parochial schools, they didn’t necessarily need to leave the city to leave the violence behind…

Now, Benedikt could lecture you until the cows came home about your moral obligation to public schooling, but you still wouldn’t leave your kids in a school where the teachers were being set on fire (and neither, I imagine, would Benedikt). If you couldn’t send your kids to private school, you’d just move. That, in fact, is what happened to most urban school systems; any resident who had any means at all picked up and moved outside the city’s borders, beyond the legal limits of busing so that there could be no question of bused students importing these problems to their kids’ schools…

Benedikt’s dictum makes sense only if parents can’t move. If they can — and bid up the value of real estate in good school districts — then making parents send their kids to the local schools probably doesn’t mean that all the parents in mixed-income neighborhoods will put their children, and their effort, into the local school. It probably means that they’ll leave the mixed-income neighborhood, taking their tax dollars with them.

This is nominally public schooling, but in fact, as I once remarked, parents who think that they are supporting public schooling by moving to a pricey district with good schools are actually supporting private schooling. They’re just confused because the tuition payment comes bundled with hardwood floors and granite countertops.

Cities need and/or desire to have wealthy residents because they provide tax dollars. Perhaps this is the deal cities make with such residents: we need you so we will provide you with the opportunity to spend your money how you wish regarding the education of your kids. So, cities and politicians try to support public schools but also allow space for private schools, setting up a two-tier system where wealthier families can buy into the second track.

Another thought: McArdle’s argument makes the assumption that schooling is the primary factor that pushes families out of the city into the suburbs. Schools are a huge factor but not necessarily the only one. Her argument also highlights an interesting feature of middle/upper-class American society: do all you can for the children.

It would be interesting to look for data to test McArdle’s argument but it seems like you would need a city or a few cities where private schools weren’t available in order to make a comparison.

h/t Instapundit

Greener driving doesn’t just involve greener cars; could also make a smarter, greener road

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In addition to greener cars, improvements to the infrastructure of roads would help make the whole system greener:

In Toronto, a university team has rolled out a software system that enables traffic lights to learn how cars and trucks flow under them—and then adjust their patterns of reds and greens to move that traffic more smoothly. The software, which uses artificial intelligence techniques, is installed at 59 intersections in downtown Toronto. The team’s computer modeling says this system of “smart self-learning traffic lights” reduces travel times by 25 percent and lowers carbon-dioxide emissions by 30 percent, according to a report issued this spring by the University of Toronto’s Baher Abdulhai, who is one of the system’s designers.

A slick piece of traffic-light software doesn’t get the juices flowing as much as, say, a battery-powered car that can rocket from zero to 60 in fewer than four seconds and never needs to fill up at a gas station. (That car would be the Tesla Roadster.) But such ho-hum advances may matter more. The United States has approximately 100,000 plug-in electric vehicles on the road, according to Plug In America, an electric-vehicle advocacy group. Though that’s a big jump from a few years ago, it still constitutes just 0.04 percent of the roughly 250 million cars of all types on American roads. And given that not quite 16 million new cars are sold in the United States annually, turning over today’s auto fleet will take many years. That means techniques that make the existing mass of cars move around more efficiently could have a much bigger near-term effect than radically environmentally friendlier ways to spin a car’s wheels…

The automotive analog of the smart grid is what some have dubbed the smart road. Companies from Google to major auto makers are testing cars that either are fully driverless or use technology to minimize a driver’s role in controlling the vehicle. One ostensible benefit of Big Brother sitting at the wheel is that he’d probably operate the car in a way that gets better gas mileage than you would. In Europe, a consortium of institutes and companies that includes Volvo is developing what it calls “road trains.” The concept, funded by the European Commission, is part NASCAR and part George Jetson…

Other, less technologically radical smart-road trappings have begun rolling out on a bit larger scale. More and more cities around the world have car-sharing programs, which use wireless technology to enable someone who has signed up to find an available car using a computer or smartphone and unlock it using a program’s membership card. Typically a user pays per-minute or per-hour for the car. When she’s done with it, she parks it near her destination, either in one of the car-sharing program’s designated spots or in a regular on-street parking space. The details vary according to the program. Because at least some members do away with owning a car, each shared car reduces the number of total cars on the road.

Fewer drivers tooling around city streets in their cars in search of parking spaces could have a sizable effect on the roads. An analysis of several studies conducted over many decades suggests that a whopping 30 percent of traffic in large cities is caused by drivers looking for parking spots, according to a 2006 report  by Donald Shoup, a UCLA urban-planning professor, who with his students conducted his own deep dive into traffic in Los Angeles’ Westwood Village. More traffic, of course, means more fuel consumed and more greenhouse gas emitted.

Perhaps all of these approaches would be best. It would be interesting to compare the costs and the beneficial impact of all of these options: having greener cars likely passes a lot of the costs to new car buyers but the other options dealing with the infrastructure could spread the costs across taxpayers and new apps or information (like Waze) could be put in the hands of drivers.

Additionally, these options bypass appear to bypass one sticking point for many Americans: feeling like they have to give up their car or that the government is trying to make driving more difficult. By making driving easier and letting them feel more in control (with some cost of course), they then don’t feel like their “right to drive” is being impinged upon. At the same time, this article doesn’t weigh all of these options versus increased mass transit.

Would an artist want to be known for showing art in a “McMansion” space?

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In linking to an article about modern artists using larger spaces to show off large pieces of art, one commentator suggests artists are exhibiting their works in McMansions:

Art gallery “McMansions”?: That’s what’s happening in NYC and other cities, where gallery owners are building warehouse-sized spaces to showcase GREAT BIG modern art pieces. Perhaps this is a good use for obsolete industrial spaces.

Here is the problem: what artist would want to be connected to McMansions? While art that critiques McMansions may be okay (and there have been a number of examples in recent years – just search this blog), trying to positively link artists to McMansions is not likely to be welcomed.

Plus, these large art spaces are far bigger than McMansions:

White Cube caused a stir nearly two years ago when it opened a 58,000 square-foot gallery in south London. That’s bigger than a football field. In January, Swiss gallery Hauser & Wirth converted a former roller rink and nightclub in New York’s Chelsea neighborhood into a 24,700 square-foot gallery—complete with an artist-designed bar serving free coffee on weekends. “We don’t need to sell coffee,” said director Marc Payot.

Austrian dealer Thaddaeus Ropac opened the world’s second-largest gallery last October when he transformed a group of eight factory buildings on Paris’s outskirts into a 50,000 square-foot art complex. The $10 million space has allowed him to carve up areas for performance art and outfit several apartments for visiting artists like Anselm Kiefer. But recently, Mr. Ropac realized that his artists didn’t want to use the complex’s studio for fear of attracting onlookers, so he’s rented even more space a few blocks away. “I don’t want my artists to feel like they’re in a zoo,” he said

These spaces are not exactly mass-produced or garish in the same way as McMansions.

First American inpatient hospital Internet addiction facility to open

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Internet addiction is a growing topic of discussion and the first hospital inpatient facility to address it is set to open soon in Pennsylvania:

The voluntary, 10-day program is set to open on Sept. 9 at the Behavioral Health Services at Bradford Regional Medical Center. The program was organized by experts in the field and cognitive specialists with backgrounds in treating more familiar addictions like drug and alcohol abuse.

“[Internet addiction] is a problem in this country that can be more pervasive than alcoholism,” said Dr. Kimberly Young, the psychologist who founded the non-profit program. “The Internet is free, legal and fat free.”…

Young and other experts are quick to caution that mere dependence on modern technology does not make someone an Internet addict. The 20-year-old who divides his time between his girlfriend and “World of Warcraft” likely does not require intensive treatment. The program is designed for those whose lives are spiraling out of control because of their obsession with the Internet. These individuals have been stripped from their ability to function in daily life and have tried in the past to stop but cannot…

Last May, the American Psychiatric Association released its Diagnostic and Statistical Manual of Mental Disorders 5, or DSM-5, for the first time listed “Gaming Disorder” in Section III of the manual, which means it requires further research before being formally identified as a disorder.

This bears watching. This will likely be a real problem for a small subset of the population and yet critics of the Internet could continue to use it to criticize all Internet use. How exactly this is constructed as a social problem (or not) will strongly influence how this is perceived in the United States.

It would be interesting to know why exactly the first hospital facility is being set up in central Pennsylvania. Why not elsewhere?

Efficiency the reason we have the telephone button layout we have today

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Bell Labs made a number of important discoveries decades ago including making the choice of how telephone buttons should be laid out:

This layout is so standardized that we barely think about it. But it was, in the 1950s, the result of a good deal of strategizing and testing on the part of people at Bell Labs. Numberphile has dug up an amazing paper — published in the July 1960 issue of “The Bell System Technical Journal” — that details the various alternative designs the Bell engineers considered. Among them: “the staircase” (II-B in the image above), “the ten-pin” (III-B, reminiscent of bowling-pin configurations), “the rainbow” (II-C), and various other versions that mimicked the circular logic of the existing dialing technology: the rotary.

Everything was on the table for the layout of the ten buttons; the researchers’ only objective was to find the configuration that would be as user-friendly, and efficient, as possible. So they ran tests. They experimented. They sought input. They briefly considered a layout that mimicked a cross.

And in the end, though, Numberphile’s Sarah Wiseman notes, it became a run-off between the traditional calculator layout and the telephone layout we know today. And the victory was a matter of efficiency. “They did compare the telephone layout and the calculator layout,” she says, “and they found the calculator layout was slower.”

It is interesting that they searched for what was most efficient. This is not surprising; telephones are pieces of technology and the user is likely to want to dial the number as quickly as possible so they can get on with the phone call. But, efficiency isn’t necessarily everything. Imagine Steve Jobs and Apple, an organization known for their designs, made this initial choice: would they have chosen something more elegant or would they have selected efficiency as well? It is a small thing yet it hints at George Ritzer’s McDonaldization thesis where efficient and rational approaches tend to win out in our world.

A side note: Bell Labs should be better known in the United States for their role in developing new technologies.

Transforming Rosemont from a small suburb to a entertainment and commercial center

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The suburb of Rosemont, Illinois has changed quite a bit in recent decades with a strong push from local leaders:

“Now Rosemont pretty much has everything people need,” Stephens said. “There is no need to go to downtown Chicago.”

That’s essentially been the philosophy of Rosemont since its incorporation in 1956. The village covers only 2.5 square miles. But it’s blessed with being at the center of a transportation hub. It’s in the shadows of O’Hare International Airport. It stands at the convergence of I-90 and I-294. And it has a stop on the CTA’s Blue Line el.

Donald Stephens’ ambition was to convince travelers to O’Hare that they didn’t need to go to Chicago. So he built hotels and restaurants, the Donald A. Stephens Convention Center, Rosemont Horizon (now Allstate Arena), Rosemont Theatre, Rosemont Stadium for softball, Muvico 18, a movie multiplex, and MB Financial Park, a de-facto town square filled with restaurants and entertainment venues, including a bowling alley and ice skating rink…

Beyond a great location, Rosemont made the decision early on that it wanted to attract commercial development, said Steve Hovany, president of Strategy Planning Association, a Schaumburg-based real estate consulting firm.

This sounds like a classic case of the political economy model for urban growth. One key family, now spanning two separate mayors, made decisions alongside business and local leaders to pursue economic growth. They made use of an existing advantage in the community, being located near transportation options, and attracted new opportunities. The only piece missing from the article is some explanation from the leaders themselves why they did all of this. Just to put Rosemont on the map? Or, to make money for leaders as well as the community who then benefits quite a bit from property and sales taxes (items many suburbs wish they had).

Using algorithms to analyze the literary canon

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A new book describes efforts to use algorithms to discover what is in and out of the literary canon:

There’s no single term that captures the range of new, large-scale work currently underway in the literary academy, and that’s probably as it should be. More than a decade ago, the Stanford scholar of world literature Franco Moretti dubbed his quantitative approach to capturing the features and trends of global literary production “distant reading,” a practice that paid particular attention to counting books themselves and owed much to bibliographic and book historical methods. In earlier decades, so-called “humanities computing” joined practitioners of stylometry and authorship attribution, who attempted to quantify the low-level differences between individual texts and writers. More recently, the catchall term “digital humanities” has been used to describe everything from online publishing and new media theory to statistical genre discrimination. In each of these cases, however, the shared recognition — like the impulse behind the earlier turn to cultural theory, albeit with a distinctly quantitative emphasis — has been that there are big gains to be had from looking at literature first as an interlinked, expressive system rather than as something that individual books do well, badly, or typically. At the same time, the gains themselves have as yet been thin on the ground, as much suggestions of future progress as transformative results in their own right. Skeptics could be forgiven for wondering how long the data-driven revolution can remain just around the corner.

Into this uncertain scene comes an important new volume by Matthew Jockers, offering yet another headword (“macroanalysis,” by analogy to macroeconomics) and a range of quantitative studies of 19th-century fiction. Jockers is one of the senior figures in the field, a scholar who has been developing novel ways of digesting large bodies of text for nearly two decades. Despite Jockers’s stature, Macroanalysis is his first book, one that aims to summarize and unify much of his previous research. As such, it covers a lot of ground with varying degrees of technical sophistication. There are chapters devoted to methods as simple as counting the annual number of books published by Irish-American authors and as complex as computational network analysis of literary influence. Aware of this range, Jockers is at pains to draw his material together under the dual headings of literary history and critical method, which is to say that the book aims both to advance a specific argument about the contours of 19th-century literature and to provide a brief in favor of the computational methods that it uses to support such an argument. For some readers, the second half of that pairing — a detailed look into what can be done today with new techniques — will be enough. For others, the book’s success will likely depend on how far they’re persuaded that the literary argument is an important one that can’t be had in the absence of computation…

More practically interesting and ambitious are Jockers’s studies of themes and influence in a larger set of novels from the same period (3,346 of them, to be exact, or about five to 10 percent of those published during the 19th century). These are the only chapters of the book that focus on what we usually understand by the intellectual content of the texts in question, seeking to identify and trace the literary use of meaningful clusters of subject-oriented terms across the corpus. The computational method involved is one known as topic modeling, a statistical approach to identifying such clusters (the topics) in the absence of outside input or training data. What’s exciting about topic modeling is that it can be run quickly over huge swaths of text about which we initially know very little. So instead of developing a hunch about the thematic importance of urban poverty or domestic space or Native Americans in 19th-century fiction and then looking for words that might be associated with those themes — that is, instead of searching Google Books more or less at random on the basis of limited and biased close reading — topic models tell us what groups of words tend to co-occur in statistically improbable ways. These computationally derived word lists are for the most part surprisingly coherent and highly interpretable. Specifically in Jockers’s case, they’re both predictable enough to inspire confidence in the method (there are topics “about” poverty, domesticity, Native Americans, Ireland, sea faring, servants, farming, etc.) and unexpected enough to be worth examining in detail…

The notoriously difficult problem of literary influence finally unites many of the methods in Macroanalysis. The book’s last substantive chapter presents an approach to finding the most central texts among the 3,346 included in the study. To assess the relative influence of any book, Jockers first combines the frequency measures of the roughly 100 most common words used previously for stylistic analysis with the more than 450 topic frequencies used to assess thematic interest. This process generates a broad measure of each book’s position in a very high-dimensional space, allowing him to calculate the “distance” between every pair of books in the corpus. Pairs that are separated by smaller distances are more similar to each other, assuming we’re okay with a definition of similarity that says two books are alike when they use high-frequency words at the same rates and when they consist of equivalent proportions of topic-modeled terms. The most influential books are then the ones — roughly speaking and skipping some mathematical details — that show the shortest average distance to the other texts in the collection. It’s a nifty approach that produces a fascinatingly opaque result: Tristram Shandy, Laurence Sterne’s famously odd 18th-century bildungsroman, is judged to be the most influential member of the collection, followed by George Gissing’s unremarkable The Whirlpool (1897) and Benjamin Disraeli’s decidedly minor romance Venetia (1837). If you can make sense of this result, you’re ahead of Jockers himself, who more or less throws up his hands and ends both the chapter and the analytical portion of the book a paragraph later. It might help if we knew what else of Gissing’s or Disraeli’s was included in the corpus, but that information is provided in neither Macroanalysis nor its online addenda.

Sounds interesting. I wonder if there isn’t a great spot for mixed method analysis: Jockers’ analysis provides the big picture but you also need more intimate and deep knowledge of the smaller groups of texts or individual texts to interpret what the results mean. So, if the data suggests three books are the most influential, you would have to know these books and their context to make sense of what the data says. Additionally, you still want to utilize theories and hypotheses to guide the analysis rather than simply looking for patterns.

This reminds me of the work sociologist Wendy Griswold has done in analyzing whether American novels shared common traits (she argues copyright law was quite influential) or how a reading culture might emerge in a developing nation. Her approach is somewhere between the interpretation of texts and the algorithms described above, relying on more traditional methods in sociology like analyzing samples and conducting interviews.

Author argues the singular American suburban dream is splintering into multiple dreams

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In the new book The End of the Suburbs, Leigh Gallagher argues the suburban dream is changing:

That gets to what you say at the very end: the American dream won’t be singular anymore. There will be different dreams.

And they will be dreams. They won’t be houses. They won’t be buildings. Somewhere along the way the American Dream morphed from being a dream, an opportunity, to being a house. That’s no longer the case for a lot of people…

The future you outline are these “urban burbs”-style developments where people don’t have to drive more than a mile or two and they can reach other urban burbs by transit. How close are we to that on a broad scale?

We’re far away from being these network of nodes where everybody is hooked up to everyone else by public transit and we all read three hours more a day. We’re far from that. But the important thing is, people are recognizing that we can’t just keep doing what we’ve been doing. It’s not satisfying people. And it’s no longer meeting the market demand. Home-builders only react when they think the market wants something. And they’re starting to react.

One could argue that even at the peak of mass suburbanization, sometime between the late 1940s and mid 1960s, there have always been some different visions of suburbia. The common image is similar to what happened in the Levittowns: mostly white city dwellers fleeing the city and seeking out more private spaces in the suburbs. But, even then there were pockets of different kinds of suburbs, whether they were more industrial suburbs, suburbs with mostly African-American residents (see Places of Their Own by Andrew Wiese), and working-class suburbs (see My Blue Heaven by Becky Nicolaides).

Thus, this may an issue of the dominant trends in building and development (more urban suburban places) but it is also about the dominant image or narrative of the suburbs, particularly that of critics, falling apart. If suburbs become more dense on the whole, does it make them more palatable to everyone? How dense do they need to be before they are viewed as something very different?

Krugman: prediction problems in economics due to the “sociology of economics”

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Looking at the predictive abilities of macroeconomics, Paul Krugman suggests there is an issue with the “sociology of economics”:

So, let’s grant that economics as practiced doesn’t look like a science. But that’s not because the subject is inherently unsuited to the scientific method. Sure, it’s highly imperfect — it’s a complex area, and our understanding is in its early stages. And sure, the economy itself changes over time, so that what was true 75 years ago may not be true today — although what really impresses you if you study macro, in particular, is the continuity, so that Bagehot and Wicksell and Irving Fisher and, of course, Keynes remain quite relevant today.

No, the problem lies not in the inherent unsuitability of economics for scientific thinking as in the sociology of the economics profession — a profession that somehow, at least in macro, has ceased rewarding research that produces successful predictions and rewards research that fits preconceptions and uses hard math instead.

Why has the sociology of economics gone so wrong? I’m not completely sure — and I’ll reserve my random thoughts for another occasion.

This is an occasional discussion in social sciences like economics or sociology: how much are they really like a science in the sense of making testable predictions (not about the natural world but for social behavior) versus whether they are more interpretive. I’m not surprised Krugman takes this stance but it is interesting that he says the issue is within the discipline itself for rewarding the wrong things. If this is the case, what could be done to reward successful predictions? At this point, Krugman is suggesting a problem without offering much of a solution. As a number of people, like Nassim Taleb and Nate Silver, have noted in recent years, making predictions is quite difficult, requires a more humble approach, and requires particular methodological and statistical approaches.

How related are home sales and car sales?

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Americans like big houses as well as cars. But, are sales of homes related to sales of cars?

Driving to work the other day I heard a radio analyst assert that the recent increase in home sales is responsible for the increase in automobile sales (McMansions come with at least two car garages you know!) The short piece didn’t offer much in terms of quantitative information and this made me wonder what data was used to support such a claim. The analyst could have looked at SEC (Securities and Exchange Commission) filings, the equities and derivatives market, or perhaps research from industry associations such as the National Association of Realtors; the latter would prompt me to consider confirmation bias.

If only considering home sales, Federal Reserve Board economist Andrew Paciorek recently published an engaging paper describing the effects of household formation on housing demand. Paciorek asserts that in the past 30 years the aging population has moved into smaller homes, which is intuitive from the practicality it offers seniors. Paciorek also postulates that the poor labor market has depressed the headship rate, which is defined as the percent of people who are heads of household via U.S. Census population projections.

According to the S&P/Case Shiller Home Price Index report, the average U.S. home is now worth approximately 10 percent more than it was a year ago, marking the largest annual improvement since the market turned south in 2006. What of the automobile market though? American popular culture paints home and car ownership as inseparable in the “American Dream”. The most recent J.D. Power report projects August sales to increase 12 percent compared to last year, the highest monthly sales volume since 2006.

It would be easy to paint a picture of recovery for these industries based on sales revenues, although there is no indication of a casual relationship between the two. These reports are meant for the average consumer only in a sense to stir up positive sentiment, which in turn spurs more discretionary spending. It is more plausible that these reports are meant for the real stakeholders: shareholders and potential investors. We can surmise that in a world of algorithmic high frequency trading and complex derivatives based on yet other derivatives, that the common equities market does not always correlate to the real-world P&L performance. I recall a former boss’s retort of traditional value investing: ‘The market can stay irrational longer than you can stay solvent’.

The conclusion here is that this is a “common sense explanation” without much merit in data. And, I wonder if this is a classic case of the casual observer making a spurious association: both car sales and home sales go back in a better economy.

This is also interesting because of the number of times in the last decade or so when journalists and commentators have linked the building of McMansions to consuming other large objects, particularly SUVs. The idea behind these comparisons is that Americans in general have learned to consumer more bigger items. However, I’ve never seen any data that the same people who purchase McMansions are necessarily the same people purchasing SUVs, super-sized fast food, bulk items at big box stores, and other large items that fit into a category of excessive consumption.