Month: October 2021

The First Nobel Prize for Marginal Revolution University!

The Nobel Prize in economics this year goes to David Card, Joshua Angrist and Guido Imbens. I describe their contributions in greater detail in A Nobel Prize for the Credibility Revolution.

It’s also fun to note that Joshua Angrist mostly teaches at MIT but he also teaches a course on Mastering Econometrics at Marginal Revolution University so this is our first Nobel Prize! Here is Master Joshua on instrumental variables.

A Nobel Prize for the Credibility Revolution

The Nobel Prize goes to David Card, Joshua Angrist and Guido Imbens. If you seek their monuments look around you. Almost all of the empirical work in economics that you read in the popular press (and plenty that doesn’t make the popular press) is due to analyzing natural experiments using techniques such as difference in differences, instrumental variables and regression discontinuity. The techniques are powerful but the ideas behind them are also understandable by the person in the street which has given economists a tremendous advantage when talking with the public. Take, for example, the famous minimum wage study of Card and Krueger (1994) (and here). The study is well known because of its paradoxical finding that New Jersey’s increase in the minimum wage in 1992 didn’t reduce employment at fast food restaurants and may even have increased employment. But what really made the paper great was the clarity of the methods that Card and Krueger used to study the problem.

The obvious way to estimate the effect of the minimum wage is to look at the difference in employment in fast food restaurants before and after the law went into effect. But other things are changing through time so circa 1992 the standard approach was to “control for” other variables by also including in the statistical analysis factors such as the state of the economy. Include enough control variables, so the reasoning went, and you would uncover the true effect of the minimum wage. Card and Krueger did something different, they turned to a control group.

Pennsylvania didn’t pass a minimum wage law in 1992 but it’s close to New Jersey so Card and Kruger reasoned that whatever other factors were affecting New Jersey fast food restaurants would very likely also influence Pennsylvania fast food restaurants. The state of the economy, for example, would likely have a similar effect on demand for fast food in NJ as in PA as would say the weather. In fact, the argument extends to just about any other factor that one might imagine including demographics, changes in tastes and changes in supply costs. The standard approach circa 1992 of “controlling for” other variables requires, at the very least, that we know what other variables are important. But by using a control group, we don’t need to know what the other variables are only that whatever they are they are likely to influence NJ and PA fast food restaurants similarly. Put differently NJ and PA are similar so what happened in PA is a good estimate of what would have happened in NJ had NJ not passed the minimum wage.

Thus Card and Kruger estimated the effect of the minimum wage in New Jersey by calculating the difference in employment in NJ before and after the law and then subtracting the difference in employment in PA before and after the law. Hence the term difference in differences. By subtracting the PA difference (i.e. what would have happened in NJ if the law had not been passed) from the NJ difference (what actually happened) we are left with the effect of the minimum wage. Brilliant!

Yet by today’s standards, obvious! Indeed, it’s hard to understand that circa 1992 the idea of differences in differences was not common. Despite the fact that differences in differences was actually pioneered by the physician John Snow in his identification of the causes of cholera in the 1840 and 1850s! What seems obvious today was not so obvious to generations of economists who used other, less credible, techniques even when there was no technical barrier to using better methods.

Furthermore, it’s less appreciated but not less important that Card and Krueger went beyond the NJ-PA comparison. Maybe PA isn’t a good control for NJ. Ok, let’s try another control. Some fast food restaurants in NJ were paying more than the minimum wage even before the minimum wage went into effect. Since these restaurants were always paying more than the minimum wage the minimum wage law shouldn’t influence employment at these restaurants. But these high-wage fast-food restaurants should be influenced by other factors influencing the demand for and cost of fast food such as the state of the economy, input prices, demographics and so forth. Thus, Card and Krueger also calculated the effect of the minimum wage by subtracting the difference in employment in high wage restaurants (uninfluenced by the law) from the difference in employment in low-wage restaurants. Their results were similar to the NJ-PA comparison.

The importance of Card and Krueger (1994) was not the result (which continue to be debated) but that Card and Krueger revealed to economists that there were natural experiments with plausible treatment and control groups all around us, if only we had the creativity to see them. The last thirty years of empirical economics has been the result of economists opening their eyes to the natural experiments all around them.

Angrist and Krueger’s (1991) paper Does Compulsory School Attendance Affect Schooling and Earnings? Is one of the most beautiful in all of economics. It begins with a seemingly absurd strategy and yet in the light of a few pictures it convinces the reader that the strategy isn’t absurd but brilliant.

The problem is a classic one, how to estimate the effect of schooling on earnings? People with more schooling earn more but is this because of the schooling or is it because people who get more schooling have more ability? Angrist and Krueger’s strategy is to use the correlation between a student’s quarter of birth and their years of education to estimate the effect of schooling on earnings. What?! What could a student’s quarter of birth possibly have to do with how much education a student receives? Is this some weird kind of economic astrology?

Angrist and Krueger exploit two quirks of US education. The first quirk is that a child born in late December can start first grade earlier than a child, nearly the same age, who is born in early January. The second quirk is that for many decades an individual could quit school at age 16. Put these two quirks together and what you get is that people born in the fourth quarter are a little bit more likely to have a little bit more education than similar students born in the first quarter. Scott Cunningham’s excellent textbook on causal inference, The Mixtape, has a nice diagram:

Putting it all together what this means is that the random factor of quarter of birth is correlated with (months) of education. Who would think of such a thing? Not me. I’d scoff that you could pick up such a small effect in the data. But here come the pictures! Picture One (from a review paper, Angrist and Krueger 2001) shows quarter of birth and total education. What you see is that years of education are going up over time as it becomes more common for everyone to stay in school beyond age 16. But notice the saw tooth pattern. People who were born in the first quarter of the year get a little bit less education than people born in the fourth quarter! The difference is small, .1 or so of a year but it’s clear the difference is there.

Ok, now for the payoff.  Since quarter of birth is random it’s as if someone randomly assigned some students to get more education than other students—thus Angrist and Krueger are uncovering a random experiment in natural data. The next step then is to look and see how earnings vary with quarter of birth. Here’s the picture.

Crazy! But there it is plain as day. People who were born in the first quarter have slightly less education than people born in the fourth quarter (figure one) and people born in the first quarter have slightly lower earnings than people born in the fourth quarter (figure two). The effect on earnings is small, about 1%, but recall that quarter of birth only changes education by about .1 of a year so dividing the former by the latter gives an estimate that implies an extra year of education increases earnings by a healthy 10%.

Lots more could be said here. Can we be sure that quarter of birth is random? It seems random but other researchers have found correlations between quarter of birth and schizophrenia, autism and IQ perhaps due to sunlight or food-availability effects. These effects are very small but remember so is the influence of quarter of birth on earnings so a small effect can still bias the results. Is quarter of birth as random as a random number generator? Maybe not! Such is the progress of science.

As with Card and Kruger the innovation in this paper was not the result but the method. Open your eyes, be creative, uncover the natural experiments that abound–this was the lesson of the credibility revolution.

Guido Imbens of Stanford (grew up in the Netherlands) has been less involved in clever studies of empirical phenomena but rather in developing the theoretical framework. The key papers are Angrist and Imbens (1994), Identification and Estimation of Local Treatment Effects and Angrist, Imbens and Rubin, Identification of Causal Effects Using Instrumental Variables which answers the question: When we use an instrumental variable what exactly is it that we are measuring? In a study of the flu, for example, some doctors were randomly reminded/encouraged to offer their patients the flu shot.  We can use the randomization as an instrumental variable to measure the effect of the flu shot. But note, some patients will always get a flu shot (say the elderly). Some patients will never get a flu shot (say the young). So what we are really measuring is not the effect of the flu shot on everyone (the average treatment effect) but rather on the subset of patients who got the flu shot because their doctor was encouraged–that latter effect is known as the local average treatment effect. It’s the treatment effect for those who are influenced by the instrument (the random encouragement) which is not necessarily the same as the effect of the flu shot on groups of people who were not influenced by the instrument.

By the way, Imbens is married to Susan Athey, herself a potential Nobel Prize winner. Imbens-Athey have many joint papers bringing causal inference and machine learning together. The Akerlof-Yellen of the new generation. Talk about assortative matching. Angrist, by the way, was the best man at the wedding!

A very worthy trio.

David Card on the return to schooling

Card is best known amongst intellectuals for his minimum wage work, but he also has been central in estimating the returns to higher education, using superior methods.  In particular, he has induced many economists to downgrade the import of the signaling model of education.  Here is one excerpt from his Econometrica paper, appropriately entitled “Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems:

A review of studies that have used compulsory schooling laws, differences in the accessibility of schools, and similar features as instrumental variables for completed education, reveals that the resulting estimates of the return to schooling are typically as
big or bigger than the corresponding ordinary least squares estimates. One interpretation of this finding is that marginal returns to education among the low-education subgroups typically affected by supply-side innovations tend to be relatively high, reflecting their high marginal costs of schooling, rather than low ability that limits their return to education.

The empirical problem arises of course because intrinsic talent and degree of schooling are highly correlated, so the investigator needs some recourse to superior identification.  How can you tell if apparent returns to schooling simply reflect a higher talented cohort in the first place?  So you might for instance look for an exogenous change to compulsory schooling laws that affects some children but not others (a few of those have come in the Nordic countries).  That likely will be uncorrelated with child talent, and so it will help you separate out the true causal return to additional schooling, because you can measure whether the kids with that extra year end up earning more, controlling for other relevant variables of course.  And see Alex’s discussion of the Angrist and Card paper on similar questions.

See also Card’s survey of this entire field, written for Handbook of Labor Economics.  One impressive feature of these pieces is they show how many disparate methods of measurement all point toward a broadly common conclusion.  Whether or not you agree, these papers have been extremely influential, and they are one reason why Claudia Goldin, in my recent CWT with her, asserted that very little of higher education was about the signaling premium.

The moderating role of culture on the benefits of economic freedom

By Johan Graafland and Eelkede Jong:

…we argue that successful implementation of pro-market policies and institutions requires that large parts of the population know how to use the resulting freedom in a way that can bring long term benefits. A panel analysis on a sample of 67 countries from 1970 to 2019 confirms this theoretical argument. We find that Long Term Orientation increases the effect of economic freedom on income per capita, whereas Uncertainty Avoidance weakens the positive relationship between economic freedom and income per capita. The policy implication is that the introduction of free market policies and institutions will particularly foster economic development in long-term oriented societies and in societies with low Uncertainty Avoidance.

Via the excellent Kevin Lewis.

Solve for the Kiwi Covid equilibrium?

Elimination was so popular with voters that every major political party backed it.

But over the past two weeks, the National, Act and Green parties have all peeled off from the government, vocally denouncing the new approach or offering new plans of their own. Ardern and her ministers continue to equivocate on whether elimination is over at all – a hemming and hawing that Smith says could hinder them from communicating a clear new vision for New Zealand’s path forward.

In one sense, Ardern could now be a victim of her own success, says Ben Thomas, a communications consultant and former National government staffer. The government’s elimination campaign was so compelling and its results so strong, that it won huge support – polling above 80% through most of the pandemic.

“Part of the prime minister’s problem is that she did such a good job of rallying New Zealanders to this cause, of convincing them – correctly – that elimination was an achievable goal, and of instilling a real fear of the virus. That’s a very hard thing to unwind from,” Thomas says.

Smith says: “Elimination was something that New Zealanders could be proud of, it brought us together and became a common goal.” And the challenge now is to find – what is the common goal during a suppression strategy? Probably vaccination rates – but to give us this same pride that we had last year in our Covid response again that is the big challenge facing Jacinda and her team now.”

The most likely candidate for that new vision is vaccination, but it’s harder to capture the urgency of that message while simultaneously arguing the country is still eliminating the virus.

Here is the full story, via Rich Dewey.

Economic recalculation is proceeding slowly

An engineer stuck on a cargo ship abandoned in a Black Sea port has waited four years to get paid and go home.

Off the coast of Somalia, a crew awaiting pay languishes on a pirate-trawled stretch of the Indian Ocean while their ship slowly takes on water. Another 14 seafarers, stuck on a cargo ship off the coast of Iran, have run out of food and fuel. Some contemplated suicide.

“We cannot survive here,” said an engineer aboard the MV Aizdihar, abandoned off the Iranian port city of Bandar Abbas. “Please help us.” He spoke via video earlier this year, his face drawn.

The $14 trillion shipping industry, responsible for 90% of world trade, has left in its wake what appears to be a record number of cargo-ship castaways. Abandonment cases are counted when shipowners fail to pay crews two or more months in wages or don’t cover the cost to send crew members home, according to the International Maritime Organization, a United Nations agency.

Last year, the number of such cases reported to the agency more than doubled to 85 from 40 in 2019. This year is on track to be worse.

More than 1,000 seafarers are currently abandoned on container ships and bulk carriers, according to estimates by the International Transport Workers’ Federation, a labor union. The true toll is likely higher because many crew members are reluctant to speak out for fear of being blacklisted, according to interviews with seafarers on abandoned vessels, shipowners, agents, maritime organizations and union officials.

Mohamed Arrachedi, the union’s Middle East coordinator, said he wakes up to dozens of WhatsApp messages from distraught sailors around the world: “It’s a global humanitarian crisis.”

Here is more from the WSJ, by Drew Hinshaw and Joe Parkinson.

Washington, D.C. facts of the day

The report released Thursday by the DowntownDC Business Improvement District painted a bleak picture of fall in downtown Washington, driven largely by the continued absence of nine-to-fivers. Office vacancies hit record highs, dozens of restaurants remain closed, and less than 25 percent of employees had returned to their downtown buildings by mid-September — up less than 2 percent from July. In a telltale sign of hard times in downtown Washington, it is difficult to find a shop open for coffee after 4 p.m.

Here is more from Emily Davies.

Is the revolving door a moderating force on politicians?

Board appointments represent highly lucrative career trajectories for former politicians. We investigate which types of legislators are more likely to gain board service. Leveraging comprehensive data on the board service of former Members of Congress, we show that ideological extremists are less likely to be appointed to a board after serving in Congress. Additionally, we use a difference-in-differences design to show that when the supply of legislators who are willing to take a directorship increases, firms become less likely to appoint extremist legislators to their board. The estimates are striking in magnitude, indicating a strong preference for appointing moderates to boards. Surprisingly, we find no evidence that a strong legislative record, service on powerful committees, or networks increase the probability of board service. The results show that extremist legislators are effectively shut out of one of the most lucrative post-elective career paths, placing a cost on radical behavior.

That is from a new paper by Benjamin C.K. Egerod and Hai Tran.

Servers are masked, the elites are unmasked

That is the main topic of my latest Bloomberg column.  Here is one bit:

Even if the attendees are wearing masks at the beginning, the masks come off once they start wining and dining — and they usually don’t go back on. Isn’t this a sign that mask-wearing is no longer so essential? At the very least, it sends a mixed message: If you want to be comfortable eating and drinking with your peers, it’s OK to take off your mask — but it’s not OK if you want to be comfortable serving food, carrying heavy trays and describing the dessert menu…

By now everyone must realize just how selective the enforcement of mask rules can be. If those same employees are drinking or eating together in the back room, their masks are off and everyone is fine with that. All of a sudden, the possibility of spreading a Covid infection is not such a big deal.

And:

Many public health intellectuals and pundits may be uncomfortable with mask apartheid. But they have so strongly promoted the mask-wearing norm it is hard for them to object. They could argue that the elite guests should be required to wear their masks before and after the food and drinks are served, or even between bites, but at this point such a recommendation would be ignored.

If you read the whole piece, some of my readers will notice I am actually presenting one of the versions of Sen’s Paretian Liberal Paradox.