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Posts by Patrick Coate

Space, the Final Frontier of Economics

When I was very small, I wanted to be an astronaut – or, better yet, to be Luke Skywalker. With the recent release of “Star Wars: Rogue One” (but I haven’t gone yet, no spoilers!), I thought it would be fun to take a look at the way economists have, in ways both serious and lighthearted, thought about outer space.

Star Wars is a big business here on Earth, as anyone doing their Christmas shopping in a big-box retail store this year will have noticed. But space can also provide an outlet for economists to practice their economic intuition in a playful way.

Last year, Washington University Assistant Professor Zachary Feinstein created an Internet sensation with his short paper written “to calibrate and simulate a model of the banking and financial systems within the galaxy” and “measure the level of systemic risk that may have been generated by … the destruction of the second Death Star.” And Paul Krugman, 30 years before his Nobel Prize, wrote “The Theory of Interstellar Trade,” in which he proves two “useless but true theorems” about how interest rates should respond to interstellar travel at relativistic speeds.

These papers each represent, as Krugman himself quips, “a serious analysis of a ridiculous subject, which is of course the opposite of what is usual in economics.” It’s easy to see how much fun they were to write (and read, for the right kind of audience), but they aren’t exactly relevant.

However, there are real-world intersections of space exploration and economics. This blog post from consulting firm Edgeworth Economics gives a brief nod to positive externalities from the Space Shuttle program. Private ventures are considering the feasibility of mining on other planets or asteroids. But my favorite application of using outer space in economic research comes from what we can see when we are in space and look down.

A 2012 study proposed and estimated a method for using lights seen from space at night to adjust estimates of economic growth. The basic idea is that for many developing countries, official GDP statistics may be of low quality due to a combination of factors, including larger informal markets, less economic integration over regions, and weak government statistical infrastructure.

With this in mind, even an imprecise signal of economic activity that can be measured objectively and easily (at least, easily to those with access to satellite imagery) may help improve estimates of GDP and economic growth. The authors show that changes in lights are useful signals of short and long-run GDP growth measures. They propose that (i) a weighting of official statistics and lighting data for countries with unreliable statistics may provide better growth estimates, and (ii) the lighting data can help us estimate growth at levels of geography smaller than countries. This is especially helpful, they say, in nations where such regional data is not readily available.

Economists may not be astronauts, but we can still learn from them.

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Budget Airlines Are Driving Competition For Everyone

United Airlines made headlines recently with its announced introduction of Basic Economy pricing – and mostly not in a good way. Most of the coverage has focused on the fact that this plan no longer includes complimentary carry-on baggage or choice of seating, with the baggage fee recently drawing the ire of Senator Chuck Schumer.  However, this new fare is just the latest example of increasing competition between name-brand airlines and budget airlines such as Spirit and Frontier airlines.

When I lived in Michigan, there were a number of cheap routes offered by Spirit Airlines, and I can personally attest to many of the inconveniences – less legroom and comfort in seats, individual fees for baggage and seat choice, lesser customer service and fewer re-routing options if flights were canceled – but I can also attest to the cheaper price. And I benefited from Spirit even when I decided on another airline.

Research has confirmed that low-cost carriers (LCCs) provide close enough competition to have an important impact on legacy carriers’ fares. That 2016 study finds “LCCs have a much larger fare impact than do legacies, but that their fare-reducing effect diminishes as they become dominant on a route. It also finds that legacy carriers primarily affect each other’s prices, whereas LCCs have a significant impact on pricing by both other LCCs and legacies.”

In other words, Spirit is having a lot more effect on United than United is having on Spirit. In fact, low-cost carriers have been effective enough that the two types of airlines are in fact becoming more similar to each other along operational dimensions like route choice as well as in pricing structures such as United’s introduction of Basic Economy. In a recent academic book chapter on the economics of airlines, the authors conclude that the “current competitive atmosphere improves efficiency as the distinctions between legacy and low-cost carriers have become less obvious.”

While we may like to complain about service, the evidence is that when making tradeoffs between price and service, U.S. air travelers largely choose price.

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Trump, and the Free-Trade Tradeoff

The biggest news story in the world this past week has been the election of Donald Trump, and the biggest reason for his election was the vote of working class white voters in the Rust Belt states of Pennsylvania, Michigan and Wisconsin, states that had been projected to vote for Hillary Clinton. One current explanation is that these voters felt Trump better understood their economic anxieties better than Clinton – or past Republican candidates. In particular, President-elect Trump spent a lot of time during the campaign talking about trade with China and immigration.

Economists’ research backs the conventional wisdom that free trade is good for the economy as a whole, but not good for everybody. There has always been an economic argument that free trade benefits all citizens, and even some argument that traditional price measures understate gains from trade. However, while the gains are distributed widely to all consumers who wish to buy imports (or wish to buy domestic products that have to compete with imports), the costs are borne mostly by certain types of workers.

Recent research attempts to measure this in the U.S. by comparing local labor markets by how much they hosted labor-intensive manufacturing industries. The idea was that these markets should be most affected by competition from Chinese imports. A study published in 2013 in the American Economic Review found imports contributed to reduced wages and higher unemployment in labor markets most exposed to the shock. It estimated that import competition explains a quarter of the decline in manufacturing employment in the U.S. Other authors have found similar results – trade increases overall economic well-being of U.S. residents, but not in all labor markets.

This is why internal migration can be so important, as many economists have studied and I have discussed in previous AIER work. If some geographic areas are harmed and some are helped, can’t people move to the less affected labor markets? Maybe not, if moving is expensive, if local ties are important, or if the workers have a lot of special skills in a declining industry.

In a 2010 paper, Abigail Wozniak of Notre Dame showed that college-educated workers are more likely to move toward labor markets with higher labor demand. Those more likely to stay are more likely to be the less-educated workers, such as those who broke heavily for Trump. The challenge for policymakers, including our future president, is how to look out for affected workers without destroying the trade that is beneficial to most Americans.

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Presidential Elections: There’s a Theorem for That

To follow up on my colleague Aaron Nathans’ roundup of AIER blogs on the election, I want to highlight some economic perspectives on elections as they may relate to this one.

One classic insight is Arrow’s impossibility theorem, originally published in a 1950 paper by Nobel Prize winning economist Kenneth Arrow. It shows that there is no way to ensure any voting system, will always simultaneously satisfy a list of seemingly desirable conditions. More generally, it makes the same point about the impossibility of aggregating individual preferences to societal preferences.

The biggest sticking point is usually the “independence of irrelevant alternatives” condition, which states that societal preferences between options A and B should only depend on individuals’ relative rankings of A and B – in other words, no spoiler third options.

In our general elections, this usually comes up in the possibility of a third party candidate taking votes primarily from one side and thus allowing the opposing candidate to win. It’s undesirable because the presence of the third candidate changes the voters’ rankings of the two leading candidates. Some people propose a preferential voting structure, such as “instant-runoff” voting, that would allow people to rank their choices and eliminate this spoiler effect.

But there are some “rock-paper-scissors” situations this can’t resolve. As a simple example, suppose there were only three types of Republican voters, with different preferences over the final three candidates. Type 1 prefers Trump, Cruz and Kasich in that order; Type 2 prefers Kasich, Trump, Cruz; and Type 3 prefers Cruz, Kasich, Trump. (These were the final primary vote tallies in North Carolina, Ohio, and Utah, respectively.)

No matter who is nominated, two of the types prefer someone else. Suppose, as in reality, Trump is nominated. Two of the three types, Types 2 and 3, would rather have Kasich than Trump. But then Types 1 and 3 prefer Cruz to Kasich, and Types 1 and 2 prefer Trump to Cruz. Any of them could win or lose a two-man race. There is no perfect choice.

Other studies look not only at who ends up winning, but how we got there. One much more recent paper than Arrow’s classic, measures the effects of political uncertainty, in particular uncertainty about presidential elections, on implied stock market volatility in the United States. They find “the presidential election process engenders market anxiety as investors form and revise their expectations regarding future macroeconomic policy.” Not only the policy but its changes can be important; as a 2013 cross-national study shows, policy volatility can lead to negative impacts on economic growth.

If instead of reading economists’ thoughts on politics, you want to cut out the middleman and simply vote for one, that’s an option too. FiveThirtyEight recently ran an interesting feature on Laurence Kotlikoff, a Boston University professor who will appear on the ballot in two states, and a man who I originally came across in his work on risk sharing within U.S. extended families.

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Why It’s Hard to Rate Teacher Training Programs

As discussed in our recent research brief, there is a continuing debate in public policy about the use of value-added measures to evaluate teachers. While the Department of Education has in many cases agreed to reduce the weight assigned to value-added measures in evaluating teachers and schools, they remain by government mandate part of the conversation. Recently, federal regulations were published regarding the use of value-added measures in teacher education programs – teach-the-teachers, as we say at AIER.

Value-added models demonstrate the difference between how students perform on a standardized test, and how they were expected to perform. Such models are intended to show the added value of particular teachers to their students’ achievement.

The new regulation calls for the states to publish ratings of teacher-prep programs, including those at colleges as well as independent programs such as Teach for America. One of the criteria for rating programs: you guessed it, value-added measures. Specifically, they track a teacher’s value-added score using test scores of the students of recently minted teachers. The regulations also call for the publication of other data, such as proportion of the program’s graduates who get jobs in their chosen specialties.

Although I generally believe value-added measures are useful, I am a little skeptical this will be helpful in evaluating teaching programs. The argument in favor is straightforward: If value-added measures do capture something important about an individual teacher’s performance, then shouldn’t the average value-added score of the teachers minted by a teaching program tell us something important about the program’s performance?

Not so fast. This is actually a point that gets to the heart of what value-added measures are meant to do. Recall that traditional value-added measures compare a student’s test scores to expected scores. If one teacher has a class full of students with high past scores and another teacher has a class of students with low past scores, can we measure the teachers’ value-added score based on whose students score higher? No: We would instead compare how each class scores compare to the typical performance of students with similar histories. There are legitimate questions about whether our controls work, but education researchers put a great deal of effort into trying to account for the student’s background before entering the teacher’s classroom.

The same logic must apply to evaluations of teaching programs. Suppose a state’s two largest education programs are the education departments of Flagship University and the less prestigious Safety State. The best aspiring teachers are probably going to attend Flagship U, and Flagship’s graduates are likely to have better value-added scores and place into their chosen fields. But that does not necessarily mean their training was any better. By the same logic used in value-added, to make that claim we would have to know not only that Flagship’s graduates are better teachers than Safety’s, but that they are better teachers than they would have been if they had attended Safety State. For students, we base our expectations partly on prior test scores, but for new teachers we don’t have the equivalent prior teaching evaluations. Ironically, one common attack against value-added scores, a perceived unfairness to teachers who mentor disadvantaged students, may more correctly apply to schools that train less well-prepared future teachers.

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Facing Criticism of ‘Value Added’ Teacher Evaluations

In light of my recent research brief about teacher value-added models, which estimate teachers’ contribution to student achievement, I was interested to read a recent Atlantic article by E.D. Hirsch.

Prof. Hirsch has been an influential voice in education reform for decades, and from the article’s title, “Don’t Blame the Teachers,” one can guess that he is not a fan of value-added models’ use in educational policy. His writing has always focused on cultural literacy and the use of a specific curriculum to improve education. Through this lens, he argues that value-added models are looking in the wrong place.

There are actually two separate criticisms here. One is that “current modes of testing cannot identify which student achievements and progress are the result of school instruction.” In other words, value-added models cannot separate teachers from other educational inputs as they claim.

The second is that the emphasis on value-added models implies that the best way to improve educational quality is by improving the caliber of teachers. Hirsch’s body of work fervently argues that a more cohesive curriculum and teacher environment would do far more than removing the worst teachers in the current system.

Policymakers must wrestle with both questions, but in studying the value-added research I can mainly speak to the first. Hirsch has previously argued that testing reading comprehension is particularly difficult because student understanding is not independent of the content of the material. Students may have trouble parsing an essay written on an unfamiliar subject, but have no trouble with an equally complex text on a topic about which they have more knowledge. What reading tests purport to measure, such as a general ability to find the main idea, Hirsch calls a “nonexistent general skill.”

While I do not agree this critique makes value-added measures invalid, it is certainly true that some subjects are easier to test than others. Imagine, for instance, that Hirsch’s critique of reading tests is half right. What if student test scores are affected by their prior knowledge of the subject matter of the tested passage, as he argues, but students also really do have a separate “critical reading” ability that they apply when reading about any subject?

Further, suppose that English teachers can help students improve their general “critical reading” skills, but whether their students happen to be assigned passages on familiar subjects on tests is random. If only one of these factors is even partly under the teacher’s control, how will that manifest in teachers’ value-added scores?

What will happen is that better teachers would, on average, still have higher value-added scores, but our estimates of teacher effects would be smaller and noisier than in subjects where the teacher can affect all relevant student skills. And indeed, studies with student math and reading scores regularly find stronger estimates for teacher effects on student scores in math.

Keeping in mind Hirsch’s critique, it might be that a perfect system would use value-added measures in teacher evaluations for some subjects but not others, or at least use different relative weights on value-added versus other measures of teacher effectiveness. However, I do not agree that the current system renders value-added measures useless; under the present regime, the teacher value-added model is still a useful tool for educational policy that we would be worse off for ignoring entirely.

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Income Inequality: Where Denmark Falls Short

America has long been called the land of opportunity. But in recent years, there has been a lot of discussion about whether that still describes our nation, comparing us both to other countries and our own past. Two measures people often look at are income inequality and intergenerational income mobility. How much difference is there between rich and poor? And how closely related is a person’s economic standing compared to that of his or her parents?

Interestingly, I recently saw two different types of pieces written by prominent economists comparing the United States to Denmark. One was a Bloomberg article by Tyler Cowen, and the other was a working paper written by Rasmus Landerso and Nobel laureate James Heckman. The themes were very similar. While Denmark and other Scandinavian countries are often held up as examples of wealthy and egalitarian societies, the full picture is more complex, and each author espouses an almost identical view that elements of both Denmark and the United States have value in promoting better economic outcomes.

Landerso and Heckman find that while intergenerational income mobility is smaller in Denmark than in the United States, educational mobility – comparing one’s top academic degree to one’s parents — is about the same.  This is despite a smaller gap in school test scores between relatively advantaged and disadvantaged Danish children than the same comparison in the United States. One would think this fact should lead to more educational mobility, but it does not

They suggest this is due to higher education leading to smaller financial returns in the Danish labor market. Cowen’s article supports this argument by noting that, despite Denmark’s relatively high standard of living, Danish-Americans make over 50 percent more than Danes in Denmark. While it may be that Danes that came to America were particularly able or ambitious and passed on these traits to their descendants, this disparity is consistent with the idea of higher financial returns for highly educated and skilled Americans.

Landerso and Heckman therefore explain the similarity in educational mobility as a balance of two competing dissimilarities. Denmark appears to promote mobility by effectively educating disadvantaged youth, but the United States has stronger incentives for continued education. As the authors conclude, “Policies that combine the best features of each system would appear to have the greatest benefit for promoting intergenerational mobility in terms of both income and educational attainment.”

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Why Many Don’t Settle Down Where They Went to College

I’ve written previously in this space and elsewhere for AIER about declining internal migration in the U.S. and what that might mean for our economy. I was therefore very interested when I got back from vacation and saw a recent story in The Boston Globe on declining migration.

As many others have, the Globe has interpreted declining migration as a bad sign for the economy’s dynamism. You can read my earlier work or watch my AIER talk to see I find the evidence more mixed, but today I want to call attention to an interesting side point the author made when talking about Massachusetts.

The author, Evan Horowitz, noted that the Bay State had very low in-migration, despite its strong local economy and higher education system. “We have some of the finest colleges in the world. Surely, some of the people who study in our slice of America should fall in love with local culture and decide to stay.”

This is an interesting point, and in fact this is exactly the intuition I had when I first started thinking about migration. However, some older evidence argues pretty convincingly that the link between where people attend college and where they ultimately work is not that strong, especially after accounting for where they lived before college.

Two 2004 papers, one written by economist Jeff Groen and the other by Groen and three co-authors, studied the link between attending college in a state and working in that state. The first paper looked directly at individuals, and the other looked at the relationship between the number of graduates from a state’s universities and the total stock of college-educated workers in that state. Neither method found the link to be very strong. They were interested in the efficacy of state-sponsored merit aid programs to recruit high-skill workers, but the point applies to the article as well. (Full disclosure: one co-author, John Bound, was later my postdoctoral mentor.)

Since overall migration has been decreasing since 2004, perhaps this has changed, but I doubt it. Attending a distant college is not exactly meant to be a permanent move like other migration. Those who do so are exactly the type of people who tend to be more open to distant job opportunities.

As an example, I graduated from the University of Dayton, Ohio. I saw nine of my old college friends at a wedding recently. Between the 10 of us, five were originally from Ohio; five (including myself) were not. Today, three of the Ohio guys are still in the state and none of the rest of us are. This is obviously not a scientific survey, but it’s a good illustration. Many college graduates make national job searches, and for those who don’t, where the college is has much less to do with location choice than family or other previous local ties.

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Are Americans Moving Less Because of Weaker Demand?

One interesting feature of the U.S. labor market over the last few decades is the decline in geographic migration, a topic I discussed in AIER’s March Research Brief. This is occurring during a period where other types of labor-market mobility (such as job changes) are also decreasing. A natural supposition is that these trends are linked, and many observers believe that this lower mobility is a sign of a less flexible and dynamic U.S. economy, and thus, a big problem.

Other researchers, including myself, find the evidence less clear, and are open to more optimistic explanations. If, for instance, information technology has made job matching easier, people may be making fewer moves because today’s workers can be quicker to find a good fit for their skills and preferences without as much hopping between jobs and cities.

A new paper, profiled last week in the The Wall Street Journal, comes down on the pessimistic side of the debate – but with a twist. Many people who are concerned about lower mobility blame changes in labor supply: occupational licensing, housing restrictions, or even just the aging population make it tougher for people to move to the place or job that would be best. The authors, however, argue that lower mobility since 2000 is instead a function of lessening labor demand. Maybe there just aren’t enough new jobs for people to find, and for which to move around.

I am interested to see close attention paid to the demand side, since most economists have focused on supply-based explanations – in fact, I mentioned exactly this point this week in my migration talk for the AIER Summer Speaker Series. However, I am skeptical this is truly the best explanation for mobility declines – especially for lower geographic migration.

Internal migration has been declining steadily since at least 1990, a decade before the recessions to which the authors point as primary contributors to lower mobility. This has held through periods of wage growth as well as stagnation.

In the WSJ article, Notre Dame economist Abigail Wozniak, a prominent researcher on this topic, makes a similar cautionary point. Still, while I may not agree with their conclusions, I found this an interesting study on an unanswered question that is very important to our economy.

Patrick Coate delivered a talk on this topic last Tuesday at our Great Barrington, Massachusetts headquarters. The video can be seen at this link.

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Did the Housing Bust Hurt College Enrollment?

The two biggest investments of many Americans’ lives are buying a home and sending their kids to college. For middle-class homeowners with kids approaching college, housing wealth is often their primary or only financial asset, and it not surprising that many of them look to their home values to help finance their children’s education.

This was especially common in the housing boom of the 2000’s. One researcher, Michael Lovenheim, studied this and found housing wealth affected college enrollment and college choice. This was especially true for those in families without many other resources, concluded Lovenheim and his co-author on the second paper, Lockwood Reynolds.  While his analysis was too early to say much about the aftermath of the housing bust, his results suggest it may have made it a lot tougher for everyday Americans to go to college, which has repeatedly been shown to be a good human capital investment.

Recently, I attended the Panel Study of Income Dynamics Conference, and this question was on a few presenters’ minds. In 2013, the panel added a question to the survey asking about transfers between family members, including whether family members helped young adults pay for college. Joe Hotz of Duke University (who was my thesis advisor) and Tom Laidley of New York University presented separate work on this topic. While both presentations were of preliminary work, early results suggested the effect of the housing bust may have affected other things more than college enrollment.

The study by Hotz and his colleagues replicated the relationship between housing wealth and college enrollment during the boom previously found by Lovenheim, but they did not find a similar decline during the bust. However, they did find slight increases in debt and declines in consumption for parents whose children attended college.

Lindley’s results also found a link between housing wealth and college attendance. In contrast to Lovenheim’s work using other data, however, it suggested the link was strongest for  children in more advantaged families, not less – those who might not have needed rising house values to finance college costs. Because their effects were smaller, this supports the notion that less wealthy youth’s college decisions might not have been greatly affected after the bust.

It was really enjoyable to see new data brought to bear on this important question. While still preliminary, I found these studies encouraging. It does not appear credit constraints due to the housing bust closed off college access for as many people as we might have suspected from behavior during the boom.

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