That is the topic of my latest Bloomberg column. Here is one bit:
Most fundamentally, some key nerve centers of the world economy have been hit by a mix of Covid and bad luck, especially in the latter part of this year. Transportation, energy and high-quality semiconductor chips all are experiencing big problems at the same time, for reasons which are distinct yet broadly related.
This combination has fueled price inflation. The demand is hitting the market, and the supply can’t catch up. And it’s not just one problem that has an easy, direct fix, but rather a series of interlocking paths of economic chaos and delay.
Don’t expect all of your Christmas shopping to run smoothly!
It sounded like the ultimate COVID-era travel bargain: five-star hotels in Manhattan at a 60 percent discount. “I do not know exactly what hotel u would be place but I know it would be 5 star hotel … be cash app ready!!” read a Facebook post hyping the deal. A Cash App–only hotel promotion might raise a few red flags, but trust that the rooms were very much real — they were just supposed to be set aside for COVID patients and health-care providers. The scam was uncovered after four months of excellent business, and this week, federal prosecutors charged Chanette Lewis with fraudulently booking New York’s emergency COVID hotel rooms using health-care workers’ stolen personal information. Lewis, 30, and three other accomplices are alleged to have advertised the rooms on Facebook and to have made a whopping $400,000 by booking more than 2,700 nights’ worth of stays in the spring and summer of last year.
Lewis, whose actual job was to book quarantine rooms on behalf of the city, had access to health-care workers’ personal information through her work at the Office of Emergency Management. But she allegedly used their credentials to book stays for her guests instead, making it look like they had been exposed to COVID. “I stole some doctor numbers and emails … I was writing down they employed ID number lmao,” prosecutors say Lewis wrote in a Facebook message. The hotel rooms, which would normally run hundreds of dollars a night, went for only $50 a night and $150 for the week. She then took the cash, prosecutors say, and the city was billed for the rooms. The grift went so well that Lewis recruited others to help her out. “I wanna teach u the ropes of it,” she messaged her co-conspirator Tatiana Benjamin, 26, in June. Her guests did the opposite of quarantine; some threw parties and, as one special agent for the U.S. Attorney ominously put it, “engaged in violence.”
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.
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.
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.
…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.
I never get it right (except when I named Duflo, Banerjee, and Kremer!), but a few of you have been asking. I’ll predict David Card, Claudia Goldin, and Larry Katz for “work related to issues of poverty and inequality,” etc. In any case, we’ll know soon enough.
Do you have a better prediction?
I will be doing a Conversation with him. So what should I ask? And if you wish, here is his Wikipedia page.
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.
In Launching the Innovation Renaissance I said that “If total factor productivity had continued to grow at its 1957 to 1973 rate then we today would be living in the world of 2076 rather than in the world of 2014.” Sadly, the future is continuing to recede. Consider the graph below. If growth had continued at the rate expected by the CBO in 2005 then we today would be living in the world of 2037 rather than in the world of 2021. (n.b. I am eyeballing.)
By the way, don’t blame the forecasters. The forecast was reasonable, the reality is below expectation.
Here is my “Control C” from Greg Mankiw:
A new paper by Kevin Corinth, Bruce Meyer, Matthew Stadnicki, and Derek Wu finds the following (emphasis added).
The proposed change under the American Families Plan (AFP) to the Tax Cuts and Jobs Act (TCJA) Child Tax Credit (CTC) would increase maximum benefit amounts to $3,000 or $3,600 per child (up from $2,000 per child) and make the full credit available to all low and middle-income families regardless of earnings or income. We estimate the anti-poverty, targeting, and labor supply effects of the expansion by linking survey data with administrative tax and government program data which form part of the Comprehensive Income Dataset (CID). Initially ignoring any behavioral responses, we estimate that the expansion of the CTC would reduce child poverty by 34% and deep child poverty by 39%. The expansion of the CTC would have a larger anti-poverty effect on children than any existing government program, though at a higher cost per child raised above the poverty line than any other means-tested program. Relatedly, the CTC expansion would allocate a smaller share of its total dollars to families at the bottom of the income distribution—as well as families with the lowest levels of long-term income, education, or health—than any existing means-tested program with the exception of housing assistance. We then simulate anti-poverty effects accounting for labor supply responses. By replacing the TCJA CTC (which contained substantial work incentives akin to the EITC) with a universal basic income-type benefit, the CTC expansion reduces the return to working at all by at least $2,000 per child for most workers with children. Relying on elasticity estimates consistent with mainstream simulation models and the academic literature, we estimate that this change in policy would lead 1.5 million workers (constituting 2.6% of all working parents) to exit the labor force. The decline in employment and the consequent earnings loss would mean that child poverty would only fall by 22% and deep child poverty would not fall at all with the CTC expansion.
Worth a ponder.
During the pandemic a pasta restaurant launched on UberEats in Paris. Cala quickly attracted a top 1% rating for it’s high quality to price ratio. Only now has it been revealed that the chef is a robot.
“We wanted to make sure that the quality of the product was what was really driving customers to come to a restaurant,” says Ylan Richard, who founded Cala in 2019, when he was 19 . “No one knew there was a robot behind the restaurant on the platforms.”
The economics are interesting.
Most restaurants spend roughly 30% of their costs on food; 30% on labour and 30% on real estate (rent, maintenance, electricity, heating and cleaning.)
In Cala’s restaurant, the kitchen is entirely removed and replaced by the robot, which measures 3m2 — significantly reducing the space needed. The restaurant also doesn’t have any seating.
The robot also allows Cala to produce many more meals per hour per square metre than other restaurants.
“With three metres squared, we can serve 1.2k meals an hour,” says Richard. “A traditional McDonald’s restaurant is 125m2, and usually they can serve 550 meals an hour.”
The robot means Cala saves 60% on real estate costs, which it says it puts into spending more on the cost of food ingredients, allowing it, Richard says, to deliver higher quality meals at a better price.
More generally, one can see top chefs producing recipes that are then scaled not just to restaurants but also to home robot preparation services. Meals would be produced by a subscription service (“We have 10,000 recipes from the greatest chefs on every continent.”). Restaurants would compete even more on ambience.
In Germany, where one in four jobs depends on exports, the crisis gumming up the world’s supply chains is weighing heavily on the economy, which is Europe’s largest and a linchpin to global commerce.
Recent surveys and data point to a sharp slowdown of the German manufacturing powerhouse, and economists have begun to predict a “bottleneck recession.”
Almost everything that German factories need to operate is in short supply, not just computer chips but also plywood, copper, aluminum, plastics and raw materials like cobalt, lithium, nickel and graphite, which are crucial ingredients of electric car batteries.
The widespread assumption that suppliers close to home are more reliable has not always proved true. During the turmoil caused by the pandemic, some German companies had more trouble getting supplies from France or Italy, because of strict lockdowns, than they did from Asia.
Here is the full NYT piece by Jack Ewing, recommended.
Here is the transcript and audio. Here is part of the CWT summary:
Claudia joined Tyler to discuss the rise of female billionaires in China, why the US gender earnings gap expanded in recent years, what’s behind falling marriage rates for those without a college degree, why the wage gap flips for Black women versus Black men, theoretical approaches for modeling intersectionality, gender ratios in economics, why she’s skeptical about happiness research, how the New York Times wedding announcement page has evolved, the problems with for-profit education, the value of an Ivy League degree, whether a Coasian solution existed to prevent the Civil War, which Americans were most likely to be anti-immigrant in the 1920s, her forthcoming work on Lanham schools, and more.
Here is an excerpt:
COWEN: If you look at a school, say, like Duke or Emory, is it a long-run problem that if they admit people on their merits, there’ll be too many women in the school relative to men, and some kind of affirmative action will be needed for the males?
GOLDIN: These are private institutions, and they can generally accept whom they would like to accept for various reasons of diversity.
COWEN: Should they do that? Or should they just get in 76 percent women, say?
GOLDIN: I’m brought back to the original issues that were raised by a small number of liberal arts colleges and universities in the ’50s and the ’60s about why they should become coeducational institutions.
Those reasons were that their marginal student was not going to Princeton but going to Harvard, not going to Princeton but going to Penn, not going to Princeton but going to Cornell, because that student wanted an education that was more balanced in terms of what the world would look like when they got out. And that more balanced, then, was not necessarily Blacks, Hispanics, and Jews, but the one major thing that was missing from Princeton and Yale and Dartmouth and Amherst and Wesleyan and a whole bunch of places was women.
Those institutions, in a process that I’ve described in the origins of coeducation, led these institutions to move in the direction of accepting more women. Now what’s going through your mind, I think, is, “Yes, but they weren’t lowering quality. In fact, they were increasing quality.” Diversity, in any dimension, can be thought of as a plus for everyone.
It was about 10 years ago that some dean in a small liberal arts college in the Midwest admitted to the fact that they were accepting men with lower SAT, ACT, and grade point averages to increase diversity.
COWEN: Men, probably, are not less intelligent than women, on average. What’s the pipeline problem? Is it too much homework and too many extracurriculars in high school or something else? Where are we failing our young boys?
GOLDIN: We can go back to as early as we have data on high schools and know that girls attended high schools, graduated from high schools at far, far greater numbers than boys. If there is an issue here, it’s certainly not extracurriculars. It may have to do with what’s going on in your cells and this difference between this Y and this double X.
COWEN: The value of an Ivy League degree — what percentage of that value do you think comes from signaling as opposed to learning?
GOLDIN: Very little. I think that it’s not signaling. It’s probably networks.
The New York Public Library is proud to announce a major policy shift: Beginning today, all late fines have been eliminated going forward—and all prior late fines and replacement fees have been cleared—so that everyone gets a clean slate at the Library. Research shows that fines are not effective in ensuring book returns—New Yorkers are quite reliable and responsible, clearly respecting our collections and the need for them to be available for others to borrow. But, unfortunately, fines are quite effective at preventing our most vulnerable communities from using our branches, services, and books. That is the antithesis of our mission to make knowledge and opportunity accessible to all, and needed to change. As New York grapples with the inequities laid bare by the pandemic, it is all the more urgent that we ensure the public library is open and freely available to all.
Anthony W. Marx
President, The New York Public Library