Cultural Caviar

Moneyball for Real Estate

May 13, 2015

Multiple Pages
Moneyball for Real Estate

Where’s the best place to move for your children’s sake?

For several years now, a Harvard Big Data project has been crunching confidential IRS numbers to discover the enduring secrets of why some parts of the country nurture more prosperous young people than other places. Is it public transportation? Is it integration?

Or have the Harvard analysts failed to understand their own numbers due to ideological and academic prejudices?

As I pointed out last week, the analysis of baseball statistics has made historic progress over the last 40 years because of the influx of amateur kibitzers. In contrast, in more important fields such as medicine and social policy, our society hasn’t gotten as far for reasons such as an excessive respect for professional titles and the fear of discovering some truth that is politically off-limits.

This week I’ll offer an amateur Moneyball analysis of the giant database created by The Equality of Opportunity Project’s staff at Harvard under celebrated economics professor Raj Chetty, a domestic policy adviser to Mrs. Clinton. I’ll demonstrate some tricks I’ve learned over the decades for how to do reality checks on academic analyses.

Chetty’s goal is to find out where in America you should relocate to so your children could grow up to enjoy the American Dream. Then, the rest of the country should copy whatever policies Dreamville, USA is getting right.

This is a great topic for a Big Data exploration. Young couples talk about where to move incessantly in private, and many will spend huge amounts of money for more desirable locations (which are summarized and euphemized with the shorthand phrase “good schools”).

Chetty explained to an interviewer:

“We’re trying to understand the determinants of intergenerational mobility. A simple way to think about it is ‘your odds of moving up in the income distribution,’ [which is] kind of the core ideal of the American dream. We want to investigate what factors seem to increase kids’ chances of moving up in the income distribution and what we can do to promote the outcomes of disadvantaged youth.”

Chetty talked the IRS into letting his research team look at several tens of millions of people’s tax returns from 1996-2000. Then they looked at ten million of their original sample’s dependents, teenagers born in 1980-82, and tracked down their 1040s from 2011-2012 when the second generation was around age 30. (This data was, hopefully, anonymized.)

On the other hand, while Chetty has been making intermittent progress since 2013 at figuring out how to think about his vast pile of numbers, he clearly doesn’t have much of a knack for understanding his adopted country’s complicated social landscape. Imagine if you had moved with your family at age nine to India. How well even today would you understand its baffling social geography?

“In other words, one of Chetty’s big lessons is that if you are a blue collar worker, you should move to a county that will be booming a decade and a half from now for reasons you can’t possibly anticipate.”

Back in 2013 and 2014, Chetty made a big splash with his now retracted findings that teens living in the Southeast in the late 1990s, especially the sad denizens of Charlotte, North Carolina, had the least equality of opportunity. There was much gnashing of teeth in the media about the long, vicious legacy of Jim Crow.

But, it was pointed out to Chetty, it seems backwards to look at where kids lived in 1996-2000 when the bigger impact in terms of their income as adults in 2011-12 is likely where they are living in 2011-12. So, as of May 2015, Chetty has compromised and unveiled a new methodology for ranking 2478 counties by where young people moved to while they were still dependents of their parents. From this, Chetty has made up a list of Good Counties and Bad Counties.

Chetty’s new theory that it only benefits children economically in the long term to be moved from a Bad County to a Good County while still early in life echoes Londoner Samuel Johnson’s observation to Scottish immigrant James Boswell: 

“Much may be made of a Scotchman, if he be caught young.”

Some of Chetty’s latest findings are discombobulating to The Narrative: for example, immigrant-rich big liberal cities, such as Manhattan, turned out to be bad for Americans to move to.

A close reading of the new 2015 paper by Chetty and Nathaniel Hendren, “The Impacts of Neighborhoods on Intergenerational Mobility: Childhood Exposure Effects and County-Level Estimates,” reveals much that is plausible. For example, the effect of local culture, such as gangs, can be different on boys and girls. Chetty and Hendren write:

This suggests that there are pockets of places across the U.S., like Baltimore MD, Pima AZ [Tucson], Wayne County (Detroit) MI, Fresno CA, Hillsborough FL [Tampa], and New Haven CT, which seem to produce especially poor outcomes for boys.

New Haven County is a fine place to live if you have daughters and you are a Tiger Mother professor at Yale Law School, but it’s a terrible place to move to if you have poor black sons. Chetty has no data on what percentage of boys who were moved to Baltimore, Detroit, or New Haven weren’t earning much in 2011-12 because they were in jail, but it’s obviously a considerable risk.

In contrast, Tucson, Fresno, and Tampa were all home construction boomtowns that got wiped out by the bursting of the Housing Bubble in 2008, a memorable cataclysm whose effects on his data Chetty doesn’t seem to have pondered.

Conversely, girls whose parents moved them when they were teens in the 1990s to now booming and low crime Manhattan are likely to pay a penalty in terms of lower family income in 2011-2012 because they are less likely to be married than if they had been moved to Salt Lake City.

Fertility is actually a promising avenue for Chetty to pursue in the future. As we’ll see below, his income calculations are stricken with problems, but he appears to have the data to estimate the answers to questions such as: where should you move if you want your child to present you with a legitimate grandchild by the time you are, say, 70? That is the kind of thing you aren’t supposed to discuss in public these days, but I’d be surprised if Mr. and Mrs. Chetty don’t worry about it.

Unfortunately, Chetty’s attempts to get a grip on income inequality are still inadequate due to shortcomings in his methodology and analysis.

To help you see what some of the problems with Chetty’s work is, let’s walk through the top and bottom of his new rankings of 2,478 counties. When thinking about Big Data, I’ve long found it extremely useful to look at the highest and lowest examples in detail to see what kind of patterns leap out. It’s extremely easy these days to look up facts about outliers, so more people should do it. This doesn’t seem to be a common practice among academic data analysts, however, who evidently fear contamination by bias and stereotypes. But instead they wind up suffering from ignorance, which is worse.

From Chetty’s website you can download his rankings of upward income mobility for all the counties in America. One of his tables focuses on families that averaged in the lower half in 1996-2000 and the other in the upper half. The median family in the bottom half was, of course, at the 25th percentile in income in the 1990s, while, due to regression toward the mean, their typical offspring is by 2011-2012 at the 41st percentile for his or her age group.

The teenage dependents of families in the top half (a.k.a., 75th percentile) in the later 1990s have typically regressed as 30somethings by 2011-2012 to the 56th percentile. But in a few places, such as Fairfax County, Virginia, the affluent have done a striking job of staying affluent across the generations.

In Chetty’s ranking of income impact on below average families, the single worst county for kids’ future income is Shannon, South Dakota. Raising your kids in Shannon County in the late 1990s, would likely drive down their income by 2011-12 by 35% relative to the average county in America.

What’s so bad about Shannon County? Well, a quick glance at Wikipedia shows that since 2014, it’s been called Oglala Lakota County. This American Indian county is entirely within the notoriously tragic Pine Ridge Indian Reservation, font of all sorts of bad news since the Wounded Knee Massacre in 1890. It was the home of American Indian radicalism in the 1970s and is notorious today for its horrific alcoholism.

Hence, Chetty’s system passes this first reality check well: If you’d asked me to name the Worst Place in America, Pine Ridge likely would have been among the first half dozen guesses I would have come up with.

On the other hand, the example of Pine Ridge calls into question a key assumption in Chetty’s new methodology: that people moving between counties comprise a representative, random sample. But who in the world would move their children to the Pine Ridge Indian Reservation, where 103 young people between ages 12 and 24 attempted suicide this winter? The Sioux who move away from Pine Ridge are likely the more determined and sober, while the ones who slink home, children in tow, are probably those defeated by life in the outside world.

In total, six of Chetty’s Bottom 25 worst counties are majority aboriginal (five Indian reservations plus Nome, Alaska).

Another nine of Chetty’s Bottom 25 are majority black (and the remaining ten are all above the national mean in percentage black).

The sheer blackness of Chetty’s Worst Places Lists is so obvious that Chetty has to admit it:

One of the salient findings in Chetty et al. (2014) is that areas with a higher fraction of African Americans have much lower observed rates of upward mobility.

But this result has been a recurrent embarrassment to him. He really doesn’t want to bring up the Occam’s Razor explanation: that while every family regresses over the generations toward the mean, blacks regress toward a lower mean income than do whites.

Race is such a powerful factor influencing how much money the next generation earns that Chetty takes some pains to obscure this fact. Maryland sociologist Philip N. Cohen pointed out how Chetty’s 2014 paper tries to euphemize the role of blackness behind related factors like de facto “segregation:”

Instead, they drop percent Black for racial segregation. I have no idea why, especially considering … [I]n these normalized correlations, fraction Black has a stronger relationship to mobility than racial segregation or economic segregation! In fact, it’s just about the strongest relationship on the whole long table (except for single mothers, with which it is of course highly correlated).

A widespread assumption, although not one that Chetty brings up, is that white political oppression is what’s keeping the black man down economically. For example, Chetty’s tenth worst county is Coahoma County in Mississippi’s cotton-growing Delta. The county seat is Clarksdale, home of bluesman Robert Johnson’s legendary crossroads where Highway 49 intersects Bob Dylan’s Highway 61. A couple of years ago, much of the national press became obsessed with implying that the murder of “Mississippi’s first openly gay mayoral candidate” was the work of Clarksdale’s KKK-ridden White Male Power Structure cops, even though the black killer had already confessed to the black sheriff in a town with a black mayor.

So, black political power doesn’t seem to build much of a basis for prosperity for the next generation.

An extremely curious aspect of Chetty’s list is that many of the supposedly Worst Places in America to Raise Children had fast-growing populations between 2000 and 2010, often due to a mix of black, Hispanic, and white newcomers pouring in. For example, the third most economically depressing county according to Chetty, behind only two Indian reservations, was Forsyth County, North Carolina, whose county seat is Winston-Salem. Yet, Forsyth’s population has grown 19% since 2000, much of that black and Latino newcomers.

If Forsyth County, and the Carolinas in general according to Chetty’s results, are such hellholes, why did so many families move there? What does Chetty know that all these families didn’t know?

Well, he knows what happened in 2008. Chetty’s hindsight is 20-20.

Let’s look at the whitest county among his Worst 25, Horry County, South Carolina, which is on the Atlantic just south of the North Carolina state line. We modern Americans can think more objectively about relatively white places like this Golf Capital of the World. Horry is home to the immense Myrtle Beach resort that features at least 91 golf courses on its Grand Strand. Despite Chetty’s assertion of its awfulness, Horry has grown 52 percent in population since 2000.

So, what was it about Myrtle Beach that made it bad for the next generation? Does golf undermine the moral fiber of youth?

Nah. The example of Myrtle Beach’s ups and downs just makes clear a recurrent problem with Chetty’s methodology: even though he’s claiming to find long-term verities about how best to organize communities, his findings are extremely susceptible to temporary local booms and busts. Chetty’s 1996-2000 baseline era represented a historic golf resort construction boom in Horry County, with unemployment dropping as low as 2.5 percent in 1998-99. In contrast, Chetty’s 2011-2012 measurement era was part of the collapse of golf course construction, with unemployment never dropping below 10 percent in Horry County from 2009-2013.

Not surprisingly, Myrtle Beach’s endless growth and low cost of living brought in large numbers of people in the 1990s for construction and tourism jobs. They did well in 1996-2000, but their kids wound up getting the fuzzy end of the lollipop in 2011-2012. But, lately, rich Chinese looking to buy American golf courses to launder their ill-gotten gains have discovered Myrtle Beach, so the future looks brighter than the recent past.

To sum up, blue collar Americans were doing well building golf condos in Myrtle Beach in the 1990s. But then golf went out of fashion, so the construction boom collapsed. But now Myrtle Beach is coming back because Chinese Communist Party white-collar criminals are really getting into golf.

In other words, one of Chetty’s big lessons is that if you are a blue collar worker, you should move to a county that will be booming a decade and a half from now for reasons you can’t possibly anticipate.

To illustrate that, take a look at Chetty’s 25 Best Counties for the lower half of the income scale. The top county, the positive doppelganger to Oglala Lakota County, is Sioux County, Iowa, where kids grew up to make 35 percent more than the national average would predict. Ironically, while Oglala Lakota County is almost all Sioux, Sioux County is 97 percent white.

Sioux County is an impressive place, sort of the Palo Alto of the recent Midwestern farm boom that drove land prices up steadily from 2004 through 2013 (before dropping in 2014). It has fantastic soil for growing corn, and climate change has been making the local weather more beneficent over the last three decades. In late 2011, some farmland in Sioux County sold for a Midwestern record of $20,000 per acre, which would be $3.2 million for a typical 160-acre farm. Sioux County also has some cultural advantages, such as ethnic homogeneity (in 1980 it was found to be the most Dutch place in America). It’s also quite socially conservative, with 80 percent of the population belonging to churches.

While growers in California demand more illegal immigrants, Sioux County voters prefer to use productivity-expanding technology to do the work themselves. Republican Congressman Steve King, an outspoken immigration restrictionist, earned 83 percent of Sioux County’s votes in 2012.

Chetty’s 25 Best Counties for the working class look an awful lot like Sioux County. There are five more in Iowa, six in Nebraska, four in North Dakota (with its energy boom), three in South Dakota, two in Utah, one in Kansas, one in Colorado, one in Montana, and one in Wyoming.

Despite their prosperity due to recent booms in farm produce and energy, these rural counties aren’t attracting many new residents. While halcyon Sioux County, with its biotechnology industry, grew seven percent in population from 2000 to 2010, Chetty’s second best, Cedar County, Nebraska, has lost population in every Census after 1930. Productivity goes up constantly, so the most successful farmers buy out their rivals. It’s a healthy economic system, but not one that can absorb this country’s ever-growing population caused by immigration.

One lesson that Chetty is reluctant to draw from his data is that large-scale immigration appears to be bad for the American working class. His Top 25 counties are closer to the Canadian than the Mexican border.

A repeated theme that runs through Chetty’s latest paper is that white bread places like Minneapolis and Salt Lake City have more beneficial cultures than vibrant, diverse ones such as Detroit, Baltimore, and New Orleans:

For females in below-median income families, New Orleans has the lowest causal effect on family income; every additional year spent in New Orleans lowers their incomes by -0.285 (s.e. 0.098) percentiles, a reduction of 0.932%. In contrast, we find that Salt Lake City, Utah has the highest causal effect on the family incomes of females. Every year spent growing up in Salt Lake City increases a female child’s income from a below-median income family by 0.234 percentiles, or roughly 0.767%.

Who knew?

Similarly:

Put differently, these suggest that 20 years of exposure to Bucks County, PA as opposed to Baltimore, MD for males in below-median income families would increase their income by 44.7%.

Bucks County is a traditional bucolic escape for Manhattan literati. It’s where Oscar Hammerstein II taught his foster son Stephen Sondheim how to compose Broadway musicals. Freddie Gray would not have felt at home in Bucks County.

The highest-ranking large county on the list is DuPage County, Illinois, a set of leafy Chicago suburbs west of O’Hare Airport. DuPage is a convenient place to live and work if you are a corporate frequent flier. DuPage has everything you’d expect in an upscale county: I almost bought a house there a few times and played golf there many times.

But a close inspection of Chetty’s tables suggests DuPage just stumbled into the top ranking due to fortuitous economic cycles rather than underlying permanent cultural or policy differences. Telling evidence is that DuPage’s own Western suburb, Kane County, which is to DuPage County as DuPage is to Chicago’s Cook County, did miserably, ranking 2239th out of 2478 counties.

Why?

DuPage is a mature suburb with a vast number of stable corporate jobs, but little population growth (up only one percent from 2000 to 2010) due to zoning and high home prices. In contrast McHenry County was a fast growing exurb (up 28 percent in population from 2000 to 2010). But, like most exurbs in 2008, McHenry got hammered by the mortgage mess and the rise in gasoline prices, drying up construction and realtor income. McHenry’s unemployment rate had dropped as low as 3 percent in 2008, but was running close to 10 percent in 2011-2012 when Chetty measured incomes.

Most of the other high-performing large counties are upscale suburban ones like Snohomish, Washington (Seattle suburbs); Bergen, New Jersey (across the George Washington Bridge from Manhattan); Bucks, Pennsylvania (a famously artsy exurb of New York City); Contra Costa, California (San Francisco Bay area); and Fairfax, Virginia (Washington D.C.).

On Chetty’s list of the best counties for the Upper Half of the population, Fairfax is, by far, the highest ranked large county. Fairfax has always been prosperous, but lately it’s started to look like The Capitol in The Hunger Games.

Why?

Because of 9/11. The War on Terror poured hundreds of billions upon the Beltway Bandits, many of whom live in Fairfax County to conveniently serve the CIA and Pentagon.

In other words, when choosing where to move your family when looking for blue-collar work, you should attempt to accurately forecast bizarre world-shaking historical events that will direct colossal economic windfalls upon some places while squeezing others dry. Try to anticipate what supervillains lurking in caves are plotting.

The example of Fairfax points out that Chetty’s lists are driven by post-hoc reasoning. Back in 1996, both Myrtle Beach and Fairfax County looked liked they’d have pretty good futures, but the South Carolina golf resort was unexpectedly cratered by Baby Boomers aging out of their Golf Phase, while the CIA headquarters’ home county was lavishly rewarded for failing to anticipate what Osama bin Laden was up to.

Post-hoc reasoning isn’t useless. For instance, Pine Ridge will probably continue to remain a messed-up place until somebody discovers natural gas on it or somebody else figures out a biochemical solution for Native Americans’ propensity toward alcoholism.

The worst places for the upper half of the income range are, by Chetty’s calculations, a mixture of Indian reservations, black counties, exurban counties that got slammed by 2008 before they could mature (e.g., Orange, North Carolina, home of the U. of North Carolina), and, most interestingly, huge multicultural immigrant-heavy cities like D.C., New York, and the more multicultural counties of the Southern California megalopolis.

Some of this is just an artifact of Chetty’s repeated refusal to adjust for cost of living differences. Many Angelenos, for instance, moved inland to San Bernardino and Riverside Counties, accepting a lower salary to get a lower mortgage, while natives of the Inland Empire in turn moved to even cheaper places such as Texas.

But, just eyeballing his rankings suggests that mass immigration isn’t doing Americans much good overall.

In summary, Chetty’s data still suffers from crippling problems with:

- Regression toward the mean (especially among races)
- Temporary booms and busts
- Cost of living differences.

Yet, these should not be impossible challenges for him to overcome in future iterations.

SIGN UP
Daily updates with TM’s latest


Comments



The opinions of our commenters do not necessarily represent the opinions of Taki's Magazine or its contributors.