Category: Science

An important new paper on the costs of climate change

by  Tyler Cowen in

Forthcoming in ReStud, I haven’t had the chance to read it yet:

To analyze climate change mitigation strategies, economists rely on simplified climate models — so-called climate emulators — that provide a realistic quantitative link between CO2 emissions and global warming at low computational costs. In this paper, we propose a generic and transparent calibration and evaluation strategy for these climate emulators that is based on freely and easily accessible state-of-the-art benchmark data from climate sciences. We demonstrate that the appropriate choice of the free model parameters can be of key relevance for the predicted social cost of carbon. The key idea we put forward is to calibrate the simplified climate models to benchmark data from comprehensive global climate models that took part in the Coupled Model Intercomparison Project, Phase 5 (CMIP5). In particular, we propose to use four different test cases that are considered pivotal in the climate science literature: two highly idealized tests to separately calibrate and evaluate the carbon cycle and temperature response, an idealized test to quantify the transient climate response, and a final test to evaluate the performance for scenarios close to those arising from economic models, and that include exogenous forcing. As a concrete example, we re-calibrate the climate part of the widely used DICE-2016, fathoming the CMIP5 uncertainty range of model responses: the multi-model mean as well as extreme, but still permissible climate sensitivities and carbon cycle responses. We demonstrate that the functional form of the climate emulator of the DICE-2016 model is fit for purpose, despite its simplicity, but its carbon cycle and temperature equations are miscalibrated, leading to the conclusion that one may want to be skeptical about predictions derived from DICE-2016. We examine the importance of the calibration for the social cost of carbon in the context of a partial equilibrium setting where interest rates are exogenous, as well as the simple general equilibrium setting from DICE-2016. We find that the model uncertainty from different consistent calibrations of the climate system can change the social cost of carbon by a factor of four if one assumes a quadratic damage function. When calibrated to the multi-model mean, our model predicts similar values for the social cost of carbon as the original DICE-2016, but with a strongly reduced sensitivity to the discount rate and about one degree less long-term warming.
The social cost of carbon in DICE-2016 is oversensitive to the discount rate, leading to extreme comparative statics responses to changes in preferences.

That is the abstract from Doris Folini,  Aleksandra Friedl,  Felix Kübler, and Simon Scheidegger,

What happened in 17th century England (a lot)

by  Tyler Cowen in

East India Company founded — 1600

Shakespeare – Hamlet published 1603

England starting to settle America – 1607 in Virginia, assorted, you could add Harvard here as well

King James Bible – 1611

The beginnings of steady economic growth – 1620 (Greg Clark, JPE)

Rule of law ideas, common law ideas, Sir Edward Coke – 1628-1648, Institutes of the Laws of England, four volumes

Beginnings of libertarian thought – Levellers 1640s

Printing becomes much cheaper, and the rise of pamphlet culture

John Milton, Aeropagitica, defense of free speech, 1644

King Charles I executed – 1649 (leads to a period of “Britain without a King,” ending 1660)

Birth of economic reasoning – second half of 17th century

Royal African Company and a larger slave trade – 1660

General growth of the joint stock corporation

Final subjugation of Ireland, beginnings of British colonialism and empire (throughout, mostly second half of the century)

Discovery of the calculus, Isaac Newton 1665-1666

Great Plague of London, 1665-1666, killed ¼ of city?

Great Fire of London, 1666

John Milton, Paradise Lost, 1667

Social contract theories – John Locke 1689

Bill of Rights (rights of Parliament) — 1689

Birth of modern physics – Newton’s Principia 1687

Bank of England — 1694

Scientific Revolution – throughout the 17th century, places empiricism and measurement at the core of science

The establishment of Protestantism as the religion of Britain, both formal and otherwise, throughout the century, culminating in the Glorious Revolution of 1688.

London – becomes the largest city in Europe by 1700 at around 585,000 people.

England moves from being a weak nation to perhaps the strongest in Europe and with the strongest navy.

Addendum: Adam Ozimek adds:

…first bank to print banknotes in Europe, 1661

Discovery of the telescope 1608

First patent for a modern steam engine 1602

Economic ornithology

by  Tyler Cowen in

But a hundred years ago, birds were seen as the best remedy for the weeds and insect pests that threatened the country’s food supply and cost farmers hundreds of millions of dollars every year. And in order to identify the precise impact that birds had on agriculture, a field called economic ornithology was born. According to one of its leading practitioners, economic ornithology was “the study of birds from the standpoint of dollars and cents … in short, it is the practical application of the knowledge of birds to the affairs of everyday life.”1 And from the 1880s to the 1930s, birds were widely seen as economic agents, working alongside farmers in the fight against the insect hordes.

By the 1940s, economic ornithology had become discredited and obsolete. Effective and affordable pesticides had entirely replaced the birds’ bug-killing role, while economic ornithologists could never prove that their methods actually increased the number of helpful birds. But before their role in agriculture was dismissed, there was a time when we believed that we depended on birds for our food, and for our very survival.

And here was the method of economic ornithology:

In 1916, Gilbert Trafton summarized the primary approach used by economic ornithologists: “The practical value of birds to man, whether helpful or harmful, depends chiefly on their food habits,” and by examining what they eat, “the exact economic status of a bird is determined.” Sometimes this was done by observing the behaviors of birds in the field, but it usually meant dissecting birds and seeing what they had in their stomachs.

Here is the full Substack, by Robert Francis.  Via Philip Wallach.

Department of Uh-Oh, economic research edition

by  Tyler Cowen in

We assess statistical power and excess statistical significance among 31 leading economics general interest and field journals using 22,281 parameter estimates from 368 distinct areas of economics research. Median statistical power in leading economics journals is very low (only 7%), and excess statistical significance is quite high (19%). Power this low and excess significance this high raise serious doubts about the credibility of economics research. We find that 26% of all reported results have undergone some process of selection for statistical significance and 56% of statistically significant results were selected to be statistically significant. Selection bias is greater at the top five journals, where 66% of statistically significant results were selected to be statistically significant. A large majority of empirical evidence reported in leading economics journals is potentially misleading. Results reported to be statistically significant are about as likely to be misleading as not (falsely positive) and statistically nonsignificant results are much more likely to be misleading (falsely negative). We also compare observational to experimental research and find that the quality of experimental economic evidence is notably higher.

That is from a new paper by Zohid Askarov, Anthony Doucouliagos, Hristos Doucouliagos, and T. D. Stanley.

Via my colleague Jonathan Schulz.

How women are perceiving the economics profession

by  Tyler Cowen in

Fewer women reported being satisfied with the climate in the economics profession in 2023 compared to five years ago, despite efforts during that time to improve conditions for women in the field, according to a new survey.

About 17% of women in economics said they strongly agreed or agreed with a statement about being satisfied in the profession, down from 20% in 2018, according to the topline results of a survey conducted in the fall. The preliminary findings were presented by University of Chicago Booth School of Business economist Marianne Bertrand Friday at the American Economic Association’s annual meeting in San Antonio.

The gap between women and men’s experience in economics widened slightly over the past five years, with 39% of men saying they were satisfied with the profession’s climate, compared to 40% in 2018.

Women made up just 17.8% of full economics professors in 2022. While representation is higher among students and associate professors, the share of new economics doctoral degree recipients that were women fell in 2023, Bertrand said Friday.

Here is more from Catarina Saraiva at Bloomberg.

The economics of dinosaur brand names

by  Tyler Cowen in

But unlike a full-grown T. rex, which would be about the size of a city bus, this dinosaur was more like the size of a pickup truck.

The specimen, which is now listed for sale for $20 million at an art gallery in London, raises a question that has come to obsess paleontologists: Is it simply a young T. rex who died before reaching maturity, or does it represent a different but related species of dinosaur known as a Nanotyrannus?

The dispute has produced reams of scientific research and decades of debate, polarizing paleontologists along the way. Now, with dinosaur fossils increasingly fetching eye-popping prices at auction, the once-esoteric dispute has begun to ripple through auction houses and galleries, where some see the T. rex name as a valuable brand that can more easily command high prices.

Here is more from the NYT.

Why Do Poor People Commit More Crime?

by  Alex Tabarrok in

It’s well known that people with lower incomes commit more crime. Call this the cross-sectional result. But why? One set of explanations suggests that it’s precisely the lack of financial resources that causes crime. Crudely put, maybe poorer people commit crime to get money. Or, poorer people face greater strains–anger, frustration, resentment–which leads them to lash out or poorer people live in communities that are less integrated and well-policed or poorer people have access to worse medical care or education and so forth and that leads to more crime. These theories all imply that giving people money will reduce their crime rate.

A different set of theories suggests that the negative correlation between income and crime (more income, less crime) is not causal but is caused by a third variable correlated with both income and crime. For example, higher IQ or greater conscientiousness could increase income while also reducing crime. These theories imply that giving people money will not reduce their crime rate.

The two theories can be distinguished by an experiment that randomly allocates money. In a remarkable paper, Cesarini, Lindqvist, Ostling and Schroder report on the results of just such an experiment in Sweden.

Cesarini et al. look at Swedes who win the lottery and they compare their subsequent crime rates to similar non-winners. The basic result is that, if anything, there is a slight increase in crime from winning the lottery but more importantly the authors can statistically reject that the bulk of the cross-sectional result is causal. In other words, since randomly increasing a person’s income does not reduce their crime rate, the first set of theories are falsified.

A couple of notes. First, you might object that lottery players are not a random sample. A substantial part of Cesarini et al.’s lottery data, however, comes from prize linked savings accounts, savings accounts that pay big prizes in return for lower interest payments. Prize linked savings accounts are common in Sweden and about 50% of Swedes have a PLS account. Thus, lottery players in Sweden look quite representative of the population. Second, Cesarini et al. have data on some 280 thousand lottery winners and they have the universe of criminal convictions; that is any conviction of an individual aged 15 or higher from 1975-2017. Wow! Third, a few people might object that the correlation we observe is between convictions and income and perhaps convictions don’t reflect actual crime. I don’t think that is plausible for a variety of reasons but the authors also find no statistically significant evidence that wealth reduces the probability one is suspect in a crime investigation (god bless the Swedes for extreme data collection). Fourth, the analysis was preregistered and corrections are made for multiple hypothesis testing. I do worry somewhat that the lottery winnings, most of which are on the order of 20k or less are not large enough and I wish the authors had said more about their size relative to cross sectional differences. Overall, however, this looks to be a very credible paper.

In their most important result, shown below, Cesarini et al. convert lottery wins to equivalent permanent income shocks (using a 2% interest rate over 20 years) to causally estimate the effect of permanent income shocks on crime (solid squares below) and they compare with the cross-sectional results for lottery players in their sample (circle) or similar people in Sweden (triangle). The cross-sectional results are all negative and different from zero. The causal lottery results are mostly positive, but none reject zero. In other words, randomly increasing people’s income does not reduce their crime rate. Thus, the negative correlation between income and crime must be due to a third variable. As the authors summarize rather modestly:

Although our results should not be casually extrapolated to other countries or segments of the population, Sweden is not distinguished by particularly low crime rates relative to comparable countries, and the crime rate in our sample of lottery players is only slightly lower than in the Swedish population at large. Additionally, there is a strong, negative cross-sectional relationship between crime and income, both in our sample of Swedish lottery players and in our representative sample. Our results therefore challenge the view that the relationship between crime and economic status reflects a causal effect of financial resources on adult offending.

Moving to Opportunity?

by  Tyler Cowen in

But inside the lab, Chetty and his colleagues have not always practiced what their research preaches, several former employees say. When hiring for their prestigious “pre-doctoral fellowship” program, for instance, the lab uses a rubric that explicitly favors students from the very colleges that its own research has called out for reinforcing elitist systems. Opportunity Insights didn’t have its first Black pre-doc until 2021. Seven former employees who spoke to The Chronicle about their experiences were bothered by what they saw as contradictions between the lab’s practices and its stated values.

After landing the fellowship, some employees said they were also disturbed to find a culture of overwork that left them fried but feeling forced to impress in order to secure a letter of recommendation to a top Ph.D. program. For some employees, it took a toll on their health. Harvard even reviewed the lab following claims of unsustainable working hours.

That is excerpted from the (gated) Chronicle of Higher Education.

Of course I am with Chetty here, noting I have no idea how good their personnel selections are (though a priori I would be surprised if they were not very good).  In any case, once again you can see the tension between the meritocratic elements of the top schools and the rhetoric they claim to live by.  This is reaching an absurd point.  “Culture of overwork”?  C’mon people, no one has to join up.  You don’t think Chetty “overworks” very very hard?  Isn’t that exactly the opportunity on tap, admittedly not for everyone?

How about “feeling forced to impress in order to secure a letter of recommendation to a top Ph.D. program”?  I am in fact opposed to this whole pre-doc thing, but I don’t blame Chetty and co.  “Forced to impress”?  On what basis are good letters supposed to be handed out?   Are we not also “forced to impress” the people we want to date and marry?  Do start-ups with?

Someone needs to “go the full Ayn Rand” on this whole thing.  Part of the real shame is that Chetty and co. are in no real position to do that.

John Stuart Mill on empirical economics and causal inference

by  Tyler Cowen in

Written by me, here is a passage from GOAT: Who is the Greatest Economist of All Time, and Why Should We Care?

A System of Logic covers many different topics, but for our purposes the most important discussion is Mill’s treatment “Of the Four Methods of Experimental Inquiry,” sometimes called “Mill’s Methods” and indeed receiving their own Wikipedia page. Mill outlines different manners in which causes and effects might be correlated, or not, and what we can infer from such patterns, and how difficult it can be to sort out actual cause and effect from the data. He refers to the “direct method of agreement,” the “method of difference,” “joint method of agreement difference,” the “method of residue,” and the “method of concomitant variations,” all as ways of trying to make correct or at least better inferences from the data.

I’ll spare you the details on the full argument, but in essence Mill was trying to figure out how to do causal inference econometrics, but with words only. That enterprise was doomed to fail, but it gives us insight into what Mill thought was by far the most important question in social science, namely causal inference when faced with complex underlying chains of cause and effect. For Mill, everything is what we would now call “an identification problem,” and this understanding is clearest in Mill’s chapters “Fallacies of Generalization” and “Fallacies of Ratiocination.” Mill also serves up a remarkably on-target discussion of how the different nature of social science problems, and their possibly greater complexity, can lead to identification problems that are not necessarily present in the natural sciences – see his chapter “Of the Chemical, or Experimental, Method in the Social Science.” That entire approach is remarkably 2020s in orientation, and you won’t find earlier history of thought books giving Mill much if any credit for this.

In a funny way, Mill was ahead of Milton Friedman in his understanding here. Friedman knew much more statistics, but in his economics he often presented causal inference as fairly straightforward. In his Monetary History of the United States, co-authored with Anna Schwartz, the reader does get the impression that the historical correlations, and ordinary least squares techniques, do in fact show that the money supply is a central driver of nominal income, given the relative stability of money demand. Later, the real business cycle theorists were to challenge that inference, and suggest that often it was income that was causing the money supply. That is a kind of complex challenge Mill seemed quite comfortable with in A System of Logic, whereas Friedman and Schwartz assigned higher power to common sense approaches to cause and effect.

Mill remains in my eyes one of the most underrated thinkers.

No Child Left Behind: Accelerate Malaria Vaccine Distribution!

by  Alex Tabarrok in

My post What is an Emergency? The Case for Rapid Malaria Vaccination, galvanized the great team at 1DaySooner. Here is Zacharia Kafuko writing at Foreign Policy:

Right now, enough material to make 20 million doses of a lifesaving malaria vaccine is sitting on a shelf in India, expected to go unused until mid-2024. Extrapolating from estimates by researchers at Imperial College London, these doses—enough for 5 million children—could save more than 31,000 lives, at a cost of a little more than $3,000 per life. But current plans by the World Health Organization to distribute the vaccine are unclear and have been criticized as lacking urgency.

…Vaccine deployment and licensure is an incredibly complex scientific, legal, and logistical process involving numerous parties across international borders. Roughly speaking, after the WHO recommends vaccines (such as R21), it must also undertake a prequalification process and receive recommendations from its Strategic Advisory Group of Experts before UNICEF is allowed to purchase vaccines. Then Gavi—a public-private global health alliance—can facilitate delivery by national governments, which must propose their anticipated demand to Gavi and make plans to distribute the vaccines.

Prequalification can take as long as 270 days after approval. However, the COVID-19 vaccines were rolled out within weeks of WHO’s approval, using the separate EUL process rather than the more standard prequalification process that R21 is now undergoing.

For COVID-19 vaccines, EUL was available because the pandemic was undeniably an emergency. Given the staggering scale of deaths of children in sub-Saharan Africa every year, shouldn’t we also be treating malaria vaccine deployment as an emergency?

The R21 malaria vaccine does not legally qualify for EUL because malaria already has a preventive and curative toolkit available. My concern is that this normalizes the deaths of hundreds of thousands of children each year in Africa.

We can move more quickly and save more lives, if we have the will.

Space Tourism Revisited, Again

by  Alex Tabarrok in

One of the advantages of writing a blog for 20 years is that you get a feel for what is new and for what seems new but is actually old. Space tourism falls into the latter category. I wrote my first piece on space tourism in 2004 when Burt Rutan was predicting 100,000 space tourists annually in 10 years. In contrast, I argued that rockets were far too unsafe a technology on which to build a tourism industry:

The problem is safety. Simply put, rockets remain among the least safe means of transportation ever invented. Since 1980 the United States has launched some 440 orbital launch rockets (not including the Space Shuttle). Nearly five percent of those rockets have experienced total failure, either blowing up or wandering so far from course as to be useless. The space shuttle has a slightly better record of safety — it was destroyed in two of 113 flights. There are lots of millionaires willing to spend one or two million dollars for a flight into space but how many will risk a two to five percent chance of death?

Ten years later there weren’t 100,000 space tourists but Richard Branson was predicting a more modest (!) 10,000 space tourists by 2022. Well, 2022 came and went and space tourism has yet to get off the ground. Overall, rockets still look very unsafe. Is anyone surprised? Blue Origin, for example has had 1 total failure in 22 flights, 4.5%. SpaceX has by far the best record with–generously not including test flights–1 total failure in 289 Falcon flights, .34%. That’s great and especially impressive given that Falcon flies much higher than other rockets! But wingsuit flying, no one’s ideas of a safe sport, is still safer than a SpaceX flight! (.2%) and commercial airlines are running at many orders of magnitude safer at .00034%.

Thus, after 20 years, I don’t see much reason to update. Like climbing Mount Everest or wingsuit flying, we might see a few flights a year catering to the rich and foolhardy but we have a long way to get before we get fat guys with cameras in space.

The Effect of Public Science on Corporate R&D

by  Tyler Cowen in

We study the relationships between corporate R&D and three components of public science: knowledge, human capital, and invention. We identify the relationships through firm-specific exposure to changes in federal agency R\&D budgets that are driven by the political composition of congressional appropriations subcommittees. Our results indicate that R&D by established firms, which account for more than three-quarters of business R&D, is affected by scientific knowledge produced by universities only when the latter is embodied in inventions or PhD scientists. Human capital trained by universities fosters innovation in firms. However, inventions from universities and public research institutes substitute for corporate inventions and reduce the demand for internal research by corporations, perhaps reflecting downstream competition from startups that commercialize university inventions. Moreover, abstract knowledge advances per se elicit little or no response. Our findings question the belief that public science represents a non-rival public good that feeds into corporate R&D through knowledge spillovers.

Emphasis added by me.  That is a new NBER working paper by Ashish Arora, Sharon Belenzon, Larisa C. Cioaca, Lia Sheer & Hansen Zhang.