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We Need to Make it Easier for US Workers to Get to Where the Good Jobs Are
The jobless rate is falling faster in Decatur, Illinois — an aging industrial city south of Chicago — than almost anywhere else in America. More than three percentage points in the past year. But look closer, as Wall Street Journal reporters Mark Peters and Ben Leubsdorf do, “and this city of 75,000 resembles many communities across the industrial Midwest, where the unemployment rate is falling fast in part because workers are disappearing: moving away, retiring or no longer looking for a job.”
The relocation issue is particularly interesting. The piece tells the story of a laid-off Caterpillar worker, Denny Ryder, who left Decatur last year for Winston-Salem, North Carolina. He found work at a Caterpillar contractor:
While Mr. Ryder was confident he could find a job in Decatur, he didn’t feel it would match the wages and benefits at Caterpillar, where he worked for 19 years. “I probably could have lost a lot of money and found a job in Decatur,” said Mr. Ryder, who has taken to life in North Carolina, from enjoying the hills to swimming in the ocean for the first time.
Bad for Decatur, perhaps, but good for this individual worker. The ability to relocate and find work is a positive for any economy, and there should be more of it in this one. AEI’s Michael Strain recommends that US unemployment programs should include a relocation subsidy:
A program like this already exists under the Trade Adjustment Assistance program. Certain workers who have secured employment in a new city can receive a relocation allowance of up to 90 percent of the “reasonable and necessary expenses” of moving, plus an additional lump-sum payment of up to $1,250. The unemployment-insurance system could create a similar program for the long-term unemployed, possibly financed by letting them take an advance on their UI benefits.
Actually, it would also be pretty awesome if it was easier for the employed to move to cities where their skills could be put to better use. In a new study, “Why Do Cities Matter? Local Growth and Aggregate Growth,” Chang-Tai Hsieh and Enrico Moretti find that while New York, San Francisco, and San Jose were three of America’s most productive cities from 1964 through 2009, “growth in these three cities had limited benefits for the U.S. as a whole. The reason is that the main effect of the fast productivity growth in New York, San Francisco, and San Jose was an increase in local housing prices and local wages, not in employment.” From the study’s summary:
Despite some of the strongest rates of local growth, New York, San Francisco and San Jose were only responsible for a small fraction of U.S. growth in this period. By contrast, almost half of aggregate US growth was driven by growth of cities in the South. We then provide a normative analysis of potential growth. We show that the dispersion of the conditional average nominal wage across US cities doubled, indicating that worker productivity is increasingly different across cities. We calculate that this increased wage dispersion lowered aggregate U.S. GDP by 13.5%. Most of the loss was likely caused by increased constraints to housing supply in high productivity cities like New York, San Francisco and San Jose. Lowering regulatory constraints in these cities to the level of the median city would expand their work force and increase U.S. GDP by 9.5%. We conclude that the aggregate gains in output and welfare from spatial reallocation of labor are likely to be substantial in the U.S., and that a major impediment to a more efficient spatial allocation of labor are housing supply constraints. These constraints limit the number of US workers who have access to the most productive of American cities. In general equilibrium, this lowers income and welfare of all US workers.
More Americans should be living and working in and near high productivity cities. More from the study:
Our results thus suggest that local land use regulations that restrict housing supply in dynamic labor markets have important externalities on the rest of the country. Incumbent homeowners in high wage cities have a private incentive to restrict housing supply. By doing so, these voters de facto limit the number of US workers who have access to the most productive of American cities. For example, Silicon Valley—the area between San Francisco and San Jose—has some of the most productive labor in the globe.
But, as Glaeser (2014) puts it, “by global urban standards, the area is remarkably low density” due to land use restrictions. In a region with some of the most expensive real estate in the world, surface parking lots, 1-story buildings and underutilized pieces of land are still remarkably common due to land use restrictions. While the region’s natural amenities—its hills, beaches and parks—are part of the attractiveness of the area, there is enough underutilized land within its urban core that housing units could be greatly expanded without any reduction in natural amenities. Our findings indicate that in general equilibrium, this would raise income and welfare of all US workers.
In principle, one possible way to minimize the negative externality created by housing supply constraints in high TFP cities would be for the federal government to constraint U.S. municipalities’ ability to set land use regulations. Currently, municipalities set land use regulations in almost complete autonomy since the effect of such regulations have long been thought as only local. But if such policies have meaningful nationwide effects, then the adoption of federal standard intended to limit negative externalities may be in the aggregate interest.
An alternative is the development of public transportation that links local labor markets characterized by high productivity and high nominal wages to local labor markets characterized by low nominal wages. For example, a possible benefit of the high speed train currently under construction in California is to connect low-wage cities in California’s Central Valley — Sacramento, Stockton, Modesto, Fresno — to high productivity jobs in the San Francisco Bay Area. This could allow the labor supply to the San Francisco economy to increase overnight without changing San Francisco housing supply constraints.
An extreme example is the London metropolitan area. A vast network of trains and buses allows residents of many cities in Southern England – including far away cities like Reading, Brighton and Bristol– to commute to high TFP employers located in downtown London. Another example is the Tokyo metropolitan area. While London and Tokyo wages are significantly above the UK and Japan averages, they would arguably be even higher in the absence of these rich transportation networks. Our argument suggests that UK and Japan GDP are significantly larger due to the transportation network.
Even a better bus system would be a good start. Also, new research suggests where kids live can have a pretty big impact on their income mobility. From “The Impacts of Neighborhoods on Intergenerational Mobility: Childhood Exposure Effects and County-Level Estimates” by Raj Chetty and Nathaniel Hendren:
The high housing prices that families often must pay to achieve better outcomes for their children may partially explain the persistence of poverty in large American cities. One approach to addressing this problem is to provide subsidized housing vouchers that enable families to move to better (e.g., lower-poverty) neighborhoods. In a companion paper (Chetty, Hendren, and Katz 2015), we show that the Moving to Opportunity experiment – which randomly assigned families subsidized housing vouchers to move to low poverty areas – significantly improved long-term outcomes for children who moved at young ages, providing direct support for such policies.
Of course, given limits to the scalability of policies that seek to move families, one must also find methods of improving neighborhood environments in areas that currently generate low levels of mobility. Our study does not directly identify which policies are most successful in achieving this goal, but our findings provide support for policies that reduce segregation and concentrated poverty in cities (e.g., affordable housing subsidies or changes in zoning laws) as well as efforts to improve public schools. The broader lesson of our analysis is that social mobility should be tackled at a local level by improving childhood environments. Much remains to be learned about the best ways to make such improvements. We hope the county-level data constructed here will ultimately offer new solutions to increase opportunities for disadvantaged youth throughout the United States
Anyway, here are a few other blog posts on housing policy, including the work of Moretti:
— That Financial Times study on US income inequality and housing totally misses the point
– Some smart thoughts on housing, jobs, and economic growth
— How government housing policy worsens inequality and harms economic growth
— Venture capitalist Sam Altman of Y Combinator on how to boost US innovation and growth
Published in Economics
I have been saying this for years to anyone who would listen.
Fantastic post and great thinking.
So I am reading along interested in the concepts when I get to….FEDERAL ZONING LAWS AND HIGH SPEED TRAINS…….
Say it ain’t so James!
The feds could also stop favoring home ownership over renting. It is easier to move if one is renting.
As someone who used to telework full time, and now teleworks frequently, I’d encourage the attitude that going into an office is something that you should only do if you have to. It unclogs rush hour, cuts down on office overhead, etc.
Yes, not every profession is suited for it … but many of them are. To the degree that we can exploit the benefits, it eases the infrastructure burden on everyone else.
It also means that if a job opens up halfway across the country, you don’t have to disrupt everyone’s lives to apply for the job.
Hi Ross C
Re “So I am reading along interested in the concepts when I get to….FEDERAL ZONING LAWS AND HIGH SPEED TRAINS…….”
“Say it ain’t so James!”
Same feelings here. Very interesting topic that curled up and died under the weight of government.
A coupl’a unfortunate things:
James is the only one on Ricochet who routinely posts on things economic.
It seems James is a program/policy addict. The answer to everything is yet another government program or policy. Even here, he identifies local zoning laws as limiting growth. But then his argument for ‘deregulation’ isn’t actually de-regulation so much as it is Re-regulatiion at the Federal level.
I have never seen James reply to any comment. Of course, I haven’t been here all that long, but still…what I’ve seen isn’t encouraging.
I suppose that my first thought of “perhaps the low density of San Francisco-San Jose might be a good thing considering earthquake risks?” is negated by Tokyo, huh?
Mr. Pethokoukis is a very, very infrequent commenter. Smart guy, but I don’t like that he doesn’t mix it up.
I have wondered about the whole “high productivity” thing. I work in “high tech” in a very small city (<50,000) in Montana. Could I make more money in Silicon Valley or another large city? Yes. Would I be any more productive? No. I would also loose all of that increase in wages to high cost of living and commuting an hour (or more) everyday instead of 5 minutes. However, if you calculate productivity the way economists do, I would be more productive because I would make more money.
Recently in my town a couple of high tech businesses (one software, the other photonic hardware) were bought by large national public corporations. The interesting thing is that instead of pulling up shop and moving the business to “high productivity” areas they are expanding here.
Turns out that Montanans are smart, have an excellent work ethic, and accept low wages (49th out of 50).
Workers didn’t seem to have much of a problem moving off of the farm to work in manufacturing post WWII. Why any need for subsidization?
I believe what he is saying is that local zoning laws can harm the national economy. And to the extent that local laws are causing harm, the federal government should be able to restrict the ability of local governments to take harmful action. That is different than claiming that the feds should be doing local zoning.
Restricting local developers by requiring large amounts of open space, rather than letting the market decide how much open space is available, resulted in distortions in the housing market that helped create the housing bust.
So. Took a peek at the Hsiegh Moretti paper James quoted from at length
They built a mathematical model of the US economy and calculated how much certain cities contributed to US growth since the 1960’s
Now this is something we have actual data on. So we can compare the model’s estimates to the empirical facts.
NY / San Fran / Silicon Valley
Actual data = 19.3%
Model = 6.1%
37 Rustbelt Cities
Actual data = -28.5%
Model = 6.1%
Yes. Thats right. The model estimates that NY /SF /Sil V contributed exactly as much to US GROWTH as Detroit, Dekatur etc. Even though the actual numbers, you know, like the real data, show that that these Rust Belt cities reduced growth by over 25%.
That is to say, they built a model of Galileo’s famous experiment where he dropped two balls of differing mass from the top of the Leaning Tower …. And in the model one of the balls fell UP!
Any reasonable person, reaching this point, must conclude that the model is just plain wrong. But economists don’t do that. If the mathematics is pretty enough and the conclusion is one you like …. Data schmata …. Who cares about messy old data? They just go on extrapolating policy decisions from what the model ‘reveals.’ Even though the only thing the model actually reveals is that it is wrong.
Ages ago as a grad student, one of our professors would assign pre-publication economics journal papers as reading assignments. He’d begin the next class asking :
“So … Is this something important, deep and insightful? Or is this just another algebra/calculus mistake or erroneous assumption?”
In my opinion, the Hsiegh Moretti paper is the later rather than the former.
Heres a thought. Maybe we could put in place policies that allow our economy to grow and businesses to thrive so there are jobs all over the country instead of just in the high density population areas mainly centered around and driven by concentrated government spending either directly or through
croniescontractors.Maybe, just maybe, people do not want and should not be required to abandon their families, their friends, their roots, their churches, their support systems just to put food on the table.
We need to grow the economy and stop telling our people how to do more with less that is becoming the accepted status quo. I lived under this BS with Carter so I have seen this play before. If we could just get some bloody adults in DC to do the right thing we can all go back to making a living again.
Which is the entire point of the exercise. The model is designed to explain how the impact of increasing productivity might have different results in different areas. The model is attempting to show what would have been the share of GDP growth if labor were allowed to flow to where it was the most productive, had something closer to free market housing prices existed in NY/SF/Silicon Valley.
Hsiegh and Moretti are not trying to compare their model results to actual output growth, they are comparing actual output growth to a potential output. This has to be modeled–and it is fair to talk about how good the model is–because we can’t visit the alternative universe where everything is identical to this one, except restrictive zoning didn’t occur in some areas.
Hi Muleskinner
The value of any mathematical model is that it accurately ( more or less ) captures the essence of what is going on. How do we tell this? If it was physics or chemistry or biology we could do experiments to test whether or not the results of those experiments are similar to what the model shows. If they are, then the model appears credible. And if they aren’t then the model needs to be reevaluated.
In economics, we can’t really do experiments. The best we can do is ‘back check’ these models. That is see whether or not they can ‘predict’ the past. So we can take these models for a test drive through the historical data and see whether or not a model accurately (more or less) describes what has occurred in the past.
For me, the first step is directionality. Did the things that model says should go up actually go up? Did the things the model says should go down actually go down?
Then I’d look at relative values. Did the things the model says should go up a lot actually go up more than other things? Did the things the model says should go down a lot actually go diwn more than other things? Did things that the model says shouldn’t change much actually not change much?
If these things are more or less true then then a model has credibility to me. If not, then it “has some ‘splainin to do.” Why does the model differ from the data? Have conditions changed? Is the data suspect? I’m perfectly willing to hear that out. But if the model’s estimates don’t square with the actual data, and there isn’t some reasonable explanation for that discrepancy, then I’m forced to conclude that the model is wrong. And if the model is wrong, then I’m not interested in anything else it purports to predict. The only question of interest is ‘where and how did it go astray?’
Synchronicity – this morning, I read Kevin Williamson take down Paul Krugmann on NRO, talking about this general topic. It was such a good piece, I can’t help but mention it here.
Example. Suppose I approach you and say :
“I’m an economist and an investment professional and my models of the markets indicate that X, Y and Z will yield significantly above-market results over the next 12 months.”
How do you feel if it turns out that, in August of 2008, my model was indicating that shares in Lehman Brothers and investments in mortgage backed CDOs would yield significant above-market results over the next 12 months?
Would your opinion change if back in August 2008 my model was indicating you should load the boat with US Treasuries?
Just because you can rationalize a utilitarian outcome from a policy prescription is not enough to go forward with it by a mile. Subsidiarity (i.e. the push down of government to the lowest possible level) should by a bedrock conservative value.
California animal rights activists get the state to pass a law setting the minimum size of chicken cages. The local government is willing to restrict the freedom of California farmers to decide what is best for their animals and businesses; however, the local government is not willing to let local farmers take the full economic impact of their laws. So they ban the sale of eggs that do not conform to the local standard. Because California imports a sizable number of eggs and it is costly to separate California-approved eggs from standard eggs, stores force farmers in Iowa to conform to California standards or will lose their market for eggs. The price of all eggs sold in the US goes up by 5% to 8%. It isn’t much (this one thing), so why should I care? After all it is a local (state) decision.