The Insane Wait Times to Get an Appointment for a US Visitor Visa

The wait times to get an appointment to get a visa to visit the US are absurdly long. To get an appointment for a Visitor Visa in New Delhi, for example, takes 291 days. In Mexico City the wait time is 581 days. In Nairobi, Kenya it takes 664 days!  Moreover, “It should be noted that the “Wait Times for a Nonimmigrant Visa to be Processed” information by country does not include time required for administrative processing.”

In the past, I’ve criticized India for making it cumbersome to get a tourist visa–greatly lowering much needed tourism revenues in India–but India has been moving in the right direction. The US in contrast is just an embarassment.

Hat tip: Todd Moss on twitter.

When should rhetoric be racially salient?

Utilizing a correlational design (N = 498), we found that those who perceived COVID-19 racial disparities to be greater reported reduced fear of COVID-19, which predicted reduced support for COVID-19 safety precautions. In Study 2, we manipulated exposure to information about COVID-19 racial disparities (N = 1,505). Reading about the persistent inequalities that produced COVID-19 racial disparities reduced fear of COVID-19, empathy for those vulnerable to COVID-19, and support for safety precautions. These findings suggest that publicizing racial health disparities has the potential to create a vicious cycle wherein raising awareness reduces support for the very policies that could protect public health and reduce disparities.

Here is more from Skinner-Dorkenoo et.al.  Via D.  There may be broader lessons as well.

Crypto’s volatility premium

That is the topic of this week’s Bloomberg column, 2x the usual length, building on earlier work by Tyrone, see also this Megan McArdle column.  As for me, here is a key excerpt:

Much of the primary value of crypto assets is from their price volatility, which is part of their appeal. I raised this possibility some while ago, tongue in cheek, but upon further reflection it seems to me an actually useful (albeit counterintuitive) way of thinking about crypto assets. The general idea of price volatility as a value dates at least as far back as Fischer Black, one of the founders of options price theory.

In standard economic theory, investors are risk-averse, meaning they prefer more stable consumption patterns to less stable ones. That is usually true, but it does not mean investors always prefer more stable investment prices — a crucial distinction.

Consider this hypothetical: You are given an envelope containing one dollar. You are then offered the opportunity to exchange it for an envelope which contains either twice the money (that is, $2) or half the money (50 cents), each with 50% probability. In essence, you are accepting some exchange-rate volatility.

Most people will find this bet a pretty good one. The new expected value of your envelope is (0.5 x $2) + (0.5 x $0.5), or $1.25. That is a higher expected value than your original dollar.

If you are perched at the margin of subsistence, this bet might seem too risky. But for most investors, who have some level of wealth, it is an improvement in prospects, though with some additional risk.

Bitcoin and other crypto assets are essentially offering you a form of this bet. To be sure, this 50-50 bet does not exactly describe the price dynamics of crypto assets. But it is one way of illustrating that crypto prices, relative to the dollar, will either go up a lot or down a lot. The bet helps show that some investors might welcome price volatility — or, if you wish, call it exchange-rate volatility. And with even wilder swings in value, there is more extreme price volatility, which can be even more appealing.

So when Bitcoin and other crypto assets come along, they are a new source of expected gain — precisely due to their price volatility. It is like being invited into a casino where the odds favor you rather than the house! You won’t always win, but a lot of people will want to keep playing.

I’ve been pondering that argument since 2013, maybe now is the time to simply accept it.. Fischer Black and Jensen’s Inequality!

Factory Built Housing

Government concerns about great disparities in housing conditions, what are often called housing crises, date to at least the 1920s. These great disparities are, of course, still with us 100 years later. In this essay, we argue there will be no progress ending these great disparities until the residential construction industry adopts technology that other industries began adopting more than 100 years ago – factory production methods. There have been attempts to introduce these methods in residential construction for the last century, but they are always blocked and sabotaged by monopolies in the traditional construction sector, that is, the sector producing homes outside, on-site, using “stick-built” methods. Monopolies in traditional construction sabotage many types of factory-built homes. In this essay, we focus on the sabotage of particular types of such homes, what we call small-modular homes. These homes can be produced and sold at very-low prices, so that the sabotage of these homes has disproportionately hurt the low-income. The sabotage is the primary reason for the existence of, and perpetuation of, U.S. housing crises.

[Small-modular homes]…are blocked from most areas of the country – it’s simply illegal for a household to purchase such a home and place it on land owned by the household. In areas where they are “allowed,” they are often zoned for areas like manufacturing districts and dumps. Even then, regulations mean higher production costs for these homes in factories. They also mean the homes are financed as automobiles (with personal loans, or chattell loans) and not real estate loans. It’s clear why these homes are a threat to those constructing stick-built homes, especially in the lower-priced home market, and why monopolies in traditional construction have invested so heavily in blocking these small-modular homes. The homes are of high-quality, built to a strict national building code. They are manufactured at a cost per square foot that is one-third to one-half less than the cost per square foot to construct homes with traditional methods.

That’s from James Schmitz’s paper for the Minneapolis Fed, Solving the Housing Crisis will Require Fighting Monopolies in Construction.

Amazingly, a majority of the houses produced in the early 1970s were factory-built before these types of houses were driven out of the market. Capps at Bloomberg notes:

Manufactured homes briefly dominated the U.S. housing market during the 1960s. By 1972, these homes — not just mobile homes but small-scale modular houses — accounted for some 60% of all new single-family homes produced nationwide, according to census data. That number has diminished so much that the role of factories in building affordable housing has gone all but forgotten.

The Biden administration wants to put America’s house factories — those used to be a thing, really — back to work. A new housing plan by the White House offers a set of actions designed to close the nation’s massive affordability gap.

As Schmitz discusses at length in another paper, part of the reason economists have ignored the destruction of factory built housing is that economists came to think of the danger of monopoly as solely involving price (or, to put it the other way as Austrians do, they thought of the virtue of competition as only involving price.). In fact, monopolies reduce productivity and they use the political process to sabotage other firms. Competition isn’t just about price but about increased productivity and creative destruction.

P.S. I am in the process of building a factory-built house. The factory part was by far the easiest and most efficient part of the process.

Tim Harford covers *Talent* and parallel design

And Brian Eno.  Here is one opening bit from the FT:

Consider the advice for job interviewers in Talent, a new book by economist Tyler Cowen and venture capitalist Daniel Gross. They suggest asking a routine question, such as “give me an example of when you resolved a difficult challenge at work”. Then ask for another example. And another. The pat answers will be exhausted quickly, and the candidate will have to start improvising, digging deep — or perhaps admit to being stumped.

“If the candidate really does have 17 significant different work triumphs,” write Cowen and Gross, “maybe you do want to hear about what number 17 looks like.”

Indeed, one way to describe this tactic is that the interviewer is asking for answers in parallel rather than answers in series. Instead of stringing together a logical sequence of 17 questions, the interviewer is asking for 17 different answers to the same question.

Recommended, a great piece, subtle, and goes well beyond the topics of the book.

Those new (?) service sector jobs

The level of pay is new at least:

Who knew that LA lifeguards—who work in the sun, ocean surf, and golden sands of California— could reap such unbelievable financial reward?

It’s time we put Baywatch on pay watch. In 2019, we found top-paid lifeguards made up to $392,000.

Unfortunately, today, the pay and benefits are even more lucrative.

Daniel Douglas was the most highly paid and earned $510,283, an increase from $442,712 in 2020. As the “lifeguard captain,” he out-earned 1,000 of his peers: salary ($150,054), perks ($28,661), benefits ($85,508), and a whopping $246,060 in overtime pay.

The second highest paid, lifeguard chief Fernando Boiteux, pulled down $463,517 – up from $393,137 last year.

Our auditors at OpenTheBooks.com found 98 LA lifeguards earned at least $200,000 including benefits last year, and 20 made between $300,000 and $510,283. Thirty-seven lifeguards made between $50,000 and $247,000 in overtime alone.

And it’s not only about the cash compensation. After 30 years of service, LA lifeguards can retire as young as 55 on 79-percent of their pay.

Hard to believe?  The source is given as a FOIA request to LA County.  The details provided are so specific that if they are wrong, some kind of lawsuit would be forthcoming.  So maybe this is for real!  Here is the full article.  Via Anecdotal.

How the heritability of politics works?

Estimates from the Minnesota Twin Study show that sociopolitical conservatism is extraordinarily heritable (74%) for the most informed fifth of the public – much more so than population-level results (57%) – but with much lower heritability (29%) for the public’s bottom half.

Here is the research article by Nathan P. Kalmoe and Martin Johnson.  The reference is from Matt Yglesias, and one possibility is that you are born with inherited values, but you need to be educated to learn where those values ought to put you on the political spectrum.

Friday assorted links

1. Robert Armstrong at the FT directly addresses my eurozone inflation questions.  The results, to me, still do not discriminate against Fischer Black’s “inflation will be whatever people expect” hypothesis.

2. David A. Price of the Richmond Fed interviews me.

3. Life hacks (NYT).

4. Dressing the Queen, and with a nod toward Strauss.

5. The last Howard Johnson’s restaurant closed.

A Ross Douthat proposal on guns

So I would like to see experiments with age-based impediments rather than full restrictions — allowing would-be gun purchasers 25 and under the same rights of ownership as 40- or 60-year-olds, but with more substantial screenings before a purchase. Not just a criminal-background check, in other words, but some kind of basic social or psychological screening, combining a mental-health check, a social-media audit and testimonials from two competent adults — all subject to the same appeals process as a well-designed red-flag law.

Here is the full NYT Op-Ed.  And speaking of Ross, and guns, or rather gun, Ross gives the correct Straussian reading of Maverick, namely that Tom Cruise dies early in the movie, and the rest of the film is his pre-death fantasy.  This take is all the more plausible if you have seen Michael Powell’s Stairway to Heaven/Matter of Life or Death, where this is clearly the correct interpretation.

Data on IR scholars and their views on Russia/Ukraine

MR reader Edmund Levin sent me this very useful piece, based around a poll of IR scholars, with the poll opened on December 16 and if I understand correctly continuing through some point in January 2022.

Here is one question “In the next year, will Russia use military force against Ukrainian military forces or additional parts of the territory of Ukraine where it is not currently operating?”  The responses:

Yes 203 56.08%
No 73 20.17%
Do not know 86 23.76%

You will note that the question could simply be referring to some additional police action, which is in fact what many people were predicting at the time.  I find it striking that the researchers don’t ask about a full-scale invasion.  What percentage would have predicted a full-scale attack?

Here is the same question posed to the regional specialists, namely: “In the next year, will Russia use military force against Ukrainian military forces or additional parts of the territory of Ukraine where it is not currently operating?”  The responses are barely different, though slightly better:

Yes 36 (60.0%)

No 12 (20.0%)

Don’t know 12 (20.0%)

I take those results to be 60-40 that a modest majority of the specialists respondents expected further Russian military action in the next year, again noting that additional police action would suffice to generate a “yes” response.

Is that a good or bad performance relative to a full-scale invasion date of February 24, with the massing of Russian troops well underway?

If I turn to the December 3 Washington Post, I see a major article by journalists Shane Harris and Paul Sonne, titled “Russia planning massive military offensive against Ukraine involving 175,000 troops, U.S. intelligence warns.”  The piece offers plenty of detail, including photos, maps, and good sourcing.  Of course it turned out to be correct, and I am only one of many people who realized this at the time.  Furthermore, if you saw such a piece, you might have inquired with your network at the time (as I did), including sources in multiple relevant countries, and learned in response that the predictions of this article were no joke, no media excess, and in fact likely to happen.  Furthermore the rhetoric, demand, and logistics investments of Russia at the time strongly suggested “attack and blame Ukraine” as the equilibrium, rather than some kind of knife-edge bargaining strategy of “attack with p = 0.6” — that one can learn by reading Thomas Schelling.

So in my view the regional IR specialists were well behind the understanding of two Washington Post reporters, or for that matter well-connected newspaper readers. A lot of the experts don’t seem to have tracked the issue very closely.  Here is my previous (lengthy) post on the topic.

Addendum: Levin also points out to me that Sam Charap of Rand got it right as early as fall of 2021.

Those new service sector jobs

Free money for you, well…it’s not quite free:

I’m reaching out to see if you’d be willing to share an announcement about a contest for critically engaging with work in effective altruism. The total prize amount is $100,000, and the deadline for submissions is September 1. You can see the announcement of the contest here.

We (the contest organizers) would like to get submissions from people who aren’t very involved in effective altruism, and we can’t do that by posting on the Effective Altruism Forum. I would love to get submissions from your readers, and I’d be really grateful if you shared the announcement link with them.

Writing of course is the best way to figure out what you really think.

Rubin and Koyama on the Industrial Revolution

From Dylan Matthews:

The big question is what drove this transformation. Historians, economists, and anthropologists have proposed a long list of explanations for why human life suddenly changed starting in 18th-century England, from geographic effects to forms of government to intellectual property rules to fluctuations in average wages.

For a long time, there was no one book that could explain, compare, and evaluate these theories for non-experts. That’s changed: How the World Became Rich, by Chapman University’s Jared Rubin and George Mason University’s Mark Koyama, provides a comprehensive look at what, exactly, changed when sustained economic growth began, what factors help explain its beginning, and which theories do the best job of making sense of the new stage of life that humans have been experiencing for a couple brief centuries.

Here is the full coverage with interview.  And you can order the book here from Amazon.  I haven’t read it yet, but this is surely self-recommending…

Thursday assorted links

1. Noah Smith on ESG.

2. Is Queen Elizabeth II one of the great performance artists?

3. New results on whether science is getting harder.

4. Very good A.O. Scott review of Maverick Top Gun (NYT).

5. “The more a society is dedicated to the value of equality and the more choices it offers for individual self-determination, the higher its rates of functional mental illness.” (WSJ)

6. The progression of polygenic testing (Bloomberg).

Moving From Opportunity: The High Cost of Restrictions on Land Use

ImagePeople are more productive in cities. As a result, people move to cities to earn higher wages but some of their productivity and wages is eaten up by land prices. How much? In a new paper Philip G. Hoxie, Daniel Shoag, and Stan Veuger show that net wages (that is wages after housing costs) used to increase in cities for all workers but since around 2000 net wages actually fall when low-wage workers move to cities. The key figure is at right.

As I wrote earlier, it used to be that poor people moved to rich places. A janitor in New York, for example, used to earn more than a janitor in Alabama even after adjusting for housing costs. As a result, janitors moved from Alabama to New York, in the process raising their standard of living and reducing income inequality. Today, however, after taking into account housing costs, janitors in New York earn less than janitors in Alabama. As a result, poor people no longer move to rich places. Indeed, there is now a slight trend for poor people to move to poor places because even though wages are lower in poor places, housing prices are lower yet.

Ideally, we want labor and other resources to move from low productivity places to high productivity places–this dynamic reallocation of resources is one of the causes of rising productivity. But for low-skill workers the opposite is happening – housing prices are driving them from high productivity places to low productivity places. Furthermore, when low-skill workers end up in low-productivity places, wages are lower so there are fewer reasons to be employed and there aren’t high-wage jobs in the area so the incentives to increase human capital are dulled. The process of poverty becomes self-reinforcing.

Why has housing become so expensive in high-productivity places? It is true that there are geographic constraints (Manhattan isn’t getting any bigger) but zoning and other land use restrictions including historical and environmental “protection” are reducing the amount of land available for housing and how much building can be done on a given piece of land. As a result, in places with lots of restrictions on land use, increased demand for housing shows up mostly in house prices rather than in house quantities.

Moreover, as I also argued earlier, even though the net wage is still positive for college-educated workers a signficant share of the returns to education are actually going to land owners!  Enrico Moretti (2013) estimates that 25% of the increase in the college wage premium between 1980 and 2000 was absorbed by higher housing costs. Moreover, since the big increases in housing costs have come after 2000, it’s very likely that an even larger share of the college wage premium today is being eaten by housing. High housing costs don’t simply redistribute wealth from workers to landowners. High housing costs reduce the return to education, reducing the incentive to invest in education. Thus higher housing costs have reduced human capital and the number of skilled workers with potentially significant effects on growth.