Here is one notice. As a teenager I spent a great deal of time with his book Some Controversies in the Cambridge Theory of Capital. It was difficult, and seemed to contain so many secrets…and it was so elegantly presented with a lovely cover.
Here is Harcourt’s Wikipedia page — many of his children went into academia as well.
For the pointer I thank Alexander Millmow.
We evaluate the performance of artificial intelligence (AI)-powered mutual funds. We find that these funds do not outperform the market per se. However, a comparison shows that AI-powered funds significantly outperform their human-managed peer funds. We further show that the outperformance of AI funds is attributable to their lower transaction cost, superior stock-picking capability, and reduced behavioral biases.
The demographic transition –the move from a high fertility/high mortality regime into a low fertility/low mortality regime– is one of the most fundamental transformations that countries undertake. To study demographic transitions across time and space, we compile a data set of birth and death rates for 186 countries spanning more than 250 years. We document that (i) a demographic transition has been completed or is ongoing in nearly every country; (ii) the speed of transition has increased over time; and (iii) having more neighbors that have started the transition is associated with a higher probability of a country beginning its own transition. To account for these observations, we build a quantitative model in which parents choose child quantity and educational quality. Countries differ in geographic location, and improved production and medical technologies diffuse outward from Great Britain. Our framework replicates well the timing and increasing speed of transitions. It also produces a correlation between the speeds of fertility transition and increases in schooling similar to the one in the data.
Here is the link, enjoy!
My recent post, Air Pollution Reduces Health and Wealth drew some pushback in the comments, some justified, some not, on whether the results of these studies are not subject to p-hacking, forking gardens and the replication crisis. Sure, of course, some of them are. Andrew Gelman, for example, has some justified doubt about the air filters and classroom study. Nevertheless, I don’t think that skepticism about the general thrust of the results is justified. Why not?
First, go back to my post Why Most Published Research Findings are False and note the list of credibility checks. For example, my rule is trust literatures not papers and the new pollution literature is showing consistent and significant negative effects of pollution on health and wealth. Some might respond that the entire literature is biased for reasons of political correctness or some such and sure, maybe. But then what evidence would be convincing? Is skepticism then justified or merely mood affiliation? And when it comes to action should we regard someone’s prior convictions (how were those formed?) as more accurate then a large, well-published scientific literature?
It’s not just that the literature is large, however, it’s that the literature is consistent in a way that many studies in say social psychology were not. In social psychology, for example, there were many tests of entirely different hypotheses–power posing, priming, stereotype threat–and most of these failed to replicate. But in the pollution literature we have many tests of the same hypotheses. We have, for example, studies showing that pollution reduces the quality of chess moves in high-stakes matches, that it reduces worker productivity in Chinese call-centers, and that it reduces test scores in American and in British schools. Note that these studies are from different researchers studying different times and places using different methods but they are all testing the same hypothesis, namely that pollution reduces cognitive ability. Thus, each of these studies is a kind of replication–like showing price controls led to shortages in many different times and places.
Another feature in favor of the air pollution literature is that the hypothesis that pollution can have negative effects on health and cognition wasn’t invented yesterday along with the test (we came up with a new theory and tested it and guess what, it works!). The Romans, for example, noted the negative effect of air pollution on health. There’s a reason why people with lung disease move to the countryside and always have.
I also noted in Why Most Published Research Findings are False that multiple sources and types of evidence are desirable. The pollution literature satisfies this desideratum. Aside from multiple empirical studies, the pollution hypothesis is also consistent with plausible mechanisms and it is consistent with the empirical and experimental literature on pollution and plants and pollution and animals. See also OpenPhilanthropy’s careful summary.
Moreover, there is a clear dose-response effect–so much so that when it comes to “extreme” pollution few people doubt the hypothesis. Does anyone doubt, for example, that an infant born in Delhi, India–one of the most polluted cities in the world–is more likely to die young than if the same infant grew up (all else equal) in Wellington, New Zealand–one of the least polluted cities in the world? People accept that “extreme” pollution creates debilitating effects but they take extreme to mean ‘more than what I am used to’. That’s not scientific. In the future, people will think that the levels of pollution we experience today are extreme, just as we wonder how people could put up with London Fog.
What is new about the new pollution literature is more credible methods and bigger data and what the literature shows is that the effects of pollution are larger than we thought at lower levels than we thought. But we should expect to find smaller effects with better methods and bigger data. (Note that this isn’t guaranteed, there could be positive effects of pollution at lower levels, but it isn’t surprising that what we are seeing so far is negative effects at levels previously considered acceptable.)
Thus, while I have no doubt that some of the papers in the new pollution literature are in error, I also think that the large number of high quality papers from different times and places which are broadly consistent with one another and also consistent with what we know about human physiology and particulate matter and also consistent with the literature on the effects of pollution on animals and plants and also consistent with a dose-response relationship suggest that we take this literature and its conclusion that air pollution has significant negative effects on health and wealth very seriously.
We examine whether immigration causally affects the likelihood that the U.S.-born elderly live in institutional settings. Using a shift-share instrument to identify exogenous variation in immigration, we find that a 10 percentage point increase in the less-educated foreign-born labor force share in a local area reduces institutionalization among the elderly by 1.5 and 3.8 percentage points for those aged 65+ and 80+, a 26-29 percent effect relative to the mean. The estimates imply that a typical U.S-born individual over age 65 in the year 2000 was 0.5 percentage points (10 percent) less likely to be living in an institution than would have been the case if immigration had remained at 1980 levels. We show that immigration affects the availability and cost of home services, including those provided by home health aides, gardeners and housekeepers, and other less-educated workers, reducing the cost of aging in the community.
How many times have you read something like this, “Bitcoin uses as much electricity as Malaysia or Sweden or Denmark or Chile….”. What a bore. Have you ever wondered, however, why the comparison is to countries? Why don’t they ever tell you what would seem to be a more natural comparison which is how much “Bitcoin” spends on electricity?
The reason is that electricity is incredibly cheap so Bitcoin electricity expenditures priced in dollars don’t look very large. Bitcoin uses something like 100 terawatt hours (TWH) of electricity annually (depending on the price of Bitcoin) but a TWH costs less than $100 million (10 cents per KWH times 1000000000). Thus, Bitcoin spends say $10 billion on electricity annually. (In fact, it’s less than this since bitcoin miners can be located in places where electricity prices are especially cheap.)
$10 billion in spending isn’t a lot. It’s less than the world spends on toothpaste ($30b), much less than the US spends on cigarettes ($80b), and considerably less than the US Federal government spends in one day ($18.65 billion).
If we think of the $10 billion spent by Bitcoin as a security budget (as the spending secures the blockchain) it also compares reasonably to US bank spending on cybersecurity. Bank of America alone spent more than $1 billion on its cybersecurity budget and the total financial security budget is much larger.
None of this proves that Bitcoin spending is well spent but it puts things in context. It is also true, of course, that most of the new crypto platforms such as Elrond (I am an advisor) use proof of stake which uses much less electricity than proof of work.
Still, the next time you read that Bitcoin consumes as much electricity as Sweden substitute Bitcoin spends as much on electricity as Americans spend on Halloween costumes.
Photo Credit: MaxPixel.
I am long since tired of this debate, and I see that a lot of people are not joining it in the best of faith. I can pass along a few updates, namely this study, with some critical commentary attached. And here is more on the Bangladeshi mask RCT. With more data transparency, it does not seem to be holding up very well.
That said, I am not sure that either calculation really matters. Any good assessment of mask efficacy has to be radically intertemporal in nature, and I mean for the entirety of the pandemic. “Not getting infected” now may well raise your chance of getting infected later on, and that spans for longer than any feasibly designed RCT. And have you heard about the new “Nu” variant? It may turn out not to matter, but it does remind us that the pandemic is not over yet.
As a simple first approximation, think of the real value of masks as “a) how many infections are delayed for how long, plus improvements in treatment in the meantime, plus b) how many infections are avoided altogether.” Even a well-designed RCT is going to focus on a version of b), but only for a limited period of time. The extant studies don’t at all consider “plus improvements in treatment in the meantime,” or when some of those protected by masks for say a year or two might nonetheless later catch Covid later yet. So those RCTs, no matter what their results, are grabbing only one leg of the elephant.
To make matters more complicated yet, a “very small” efficacy for masks might (yes, might) translate into a much larger final effect, due to effective R (sometimes) being greater than 1. So finding a very small effect for masks doesn’t mean masks are only slightly effective. As the pandemic is ending, you might (again might) have had one less “pandemic cycle” than if you hadn’t tried masks at all. You can think of masks as a kind of lottery ticket on “one big gain,” paying off only when the timing is such that the masks have helped you choke off another Covid wave. Again, the RCT is not capable of estimating that probability or the magnitude of its effect.
Yet another part of my mental model of masks has evolved to be the following. You have two sets of countries, countries that manage Covid well and countries that don’t, argue all you want who goes into which bin but that isn’t the point right now.
Now consider the countries that don’t manage Covid well. They might wish to stretch out their epidemics over time, so that better treatments arrive, subject to economic constraints of course. But the countries that manage Covid well probably want the poorly-managed countries to reach herd immunity sooner rather than later, if only to lower the ongoing risk of transmission from a poorly-managed country to a well-managed country. And to lower the risk of those countries birthing new variants, just as southern Africa now seems to have birthed the Nu variant.
So we have two major points of view, represented by multiple countries, one wanting quicker resolution for the poorly managed countries but the other wanting slower resolution. Does any study of masks take those variables into account? No. Nor is it easy to see how it could.
To be clear, I am not arguing masks don’t work, nor am I making any claims about how much masks may or may not protect you individually, or the people you interact with. I am claiming that at the aggregate social level we are quite far from knowing how well masks work.
I say it is third doses we should be doubling down on, not masks. To be clear, I am fine with wearing masks myself, I am used to it, and I dislike it but I don’t hate it. On this issue, I am not one of those people translating his or her own snowflake-ism into some kind of biased policy view.
But the emerging science on third doses is much stronger, and most countries have been dropping the ball on that one.
Great piece by David Wallace-Wells on air pollution.
Here is just a partial list of the things, short of death rates, we know are affected by air pollution. GDP, with a 10 per cent increase in pollution reducing output by almost a full percentage point, according to an OECD report last year. Cognitive performance, with a study showing that cutting Chinese pollution to the standards required in the US would improve the average student’s ranking in verbal tests by 26 per cent and in maths by 13 per cent. In Los Angeles, after $700 air purifiers were installed in schools, student performance improved almost as much as it would if class sizes were reduced by a third. Heart disease is more common in polluted air, as are many types of cancer, and acute and chronic respiratory diseases like asthma, and strokes. The incidence of Alzheimer’s can triple: in Choked, Beth Gardiner cites a study which found early markers of Alzheimer’s in 40 per cent of autopsies conducted on those in high-pollution areas and in none of those outside them. Rates of other sorts of dementia increase too, as does Parkinson’s. Air pollution has also been linked to mental illness of all kinds – with a recent paper in the British Journal of Psychiatry showing that even small increases in local pollution raise the need for treatment by a third and for hospitalisation by a fifth – and to worse memory, attention and vocabulary, as well as ADHD and autism spectrum disorders. Pollution has been shown to damage the development of neurons in the brain, and proximity to a coal plant can deform a baby’s DNA in the womb. It even accelerates the degeneration of the eyesight.
A high pollution level in the year a baby is born has been shown to result in reduced earnings and labour force participation at the age of thirty. The relationship of pollution to premature births and low birth weight is so strong that the introduction of the automatic toll system E-ZPass in American cities reduced both problems in areas close to toll plazas (by 10.8 per cent and 11.8 per cent respectively), by cutting down on the exhaust expelled when cars have to queue. Extremely premature births, another study found, were 80 per cent more likely when mothers lived in areas of heavy traffic. Women breathing exhaust fumes during pregnancy gave birth to children with higher rates of paediatric leukaemia, kidney cancer, eye tumours and malignancies in the ovaries and testes. Infant death rates increased in line with pollution levels, as did heart malformations. And those breathing dirtier air in childhood exhibited significantly higher rates of self-harm in adulthood, with an increase of just five micrograms of small particulates a day associated, in 1.4 million people in Denmark, with a 42 per cent rise in violence towards oneself. Depression in teenagers quadruples; suicide becomes more common too.
Stock market returns are lower on days with higher air pollution, a study found this year. Surgical outcomes are worse. Crime goes up with increased particulate concentrations, especially violent crime: a 10 per cent reduction in pollution, researchers at Colorado State University found, could reduce the cost of crime in the US by $1.4 billion a year. When there’s more smog in the air, chess players make more mistakes, and bigger ones. Politicians speak more simplistically, and baseball umpires make more bad calls.
As MR readers will know Tyler and I have been saying air pollution is an underrated problem for some time. Here’s my video on the topic:
Tim Harford has a good piece on the virtues of a carbon tax:
A friend recently wrote to me agonising over an ethical question. He was pondering a long-haul trip to see his family but was all too aware that the flight would have a huge carbon footprint. Could the journey possibly be justified? I suggested that my friend find out what the carbon footprint was (a tonne of CO2, it turns out) and then imagine a hypothetical carbon tax. Would he still be willing to travel if he had to pay the tax? If not, the trip wasn’t worth it.
My advice raises the question of what this carbon tax should be. At a carbon tax of £5 per tonne of CO2 — plenty of carbon global emissions are taxed at less than that — the extra tax on that one-tonne return flight would be trivial. At a more serious £50, it would be noticeable but perhaps not decisive. (The emissions trading systems in the EU and the UK until recently implied a carbon price of around £50 per tonne of CO2; the UK price has since leapt. US Democrats are pondering their own carbon tax.) If the carbon tax were a deep-green £500 per tonne of CO2, my friend would have to be missing his family more than most of us ever do.
I realise it is quixotic to advise checking one’s personal consumption decisions against a completely hypothetical tax, but it gets to the core of what a carbon tax is for. It isn’t just an incentive to change behaviour; it’s a source of information about which behaviour we most urgently need to change.
Exactly right. Or as Tyler and I say in Modern Principles, a price is a signal wrapped up in an incentive. Put a price on carbon and every actor in the system will be incentivized to follow the signal and reduce carbon use in ways that no one can predict or plan.
…A carbon tax changes that by making the climate impact as real a cost as any other. It sends a signal along all those supply chains, nudging every decision towards the lower-carbon alternative. A shopper may decide that a carbon-taxed T-shirt is too costly, but meanwhile the textile factory is looking to save on electricity, while the electricity supplier is switching to solar. Every part of the value chain becomes greener.
I will be doing a podcast with him, specifically focusing on his decision to emigrate to Israel. Here are the suggestions that Russ solicited from Twitter. We will release the episode both on EconTalk and on CWT.
So what should I ask him? Keep in mind this is the Conversation with Russ I want to have…
Unveiled in October after Apple showed off its new line of gadgets, the soft, light gray square is made of “nonabrasive material” and embossed with Apple’s logo. During tests, the rag worked like other microfiber cloths that list for less than half that price. So…why $19?
As it happens, Apple’s pricing strategy rarely allows accessories to fall below that threshold. The 6.3-inch swatch of fabric sits beside 17 other Apple-branded items on the company’s website—a mélange of charging cables, dongles and adapters—each priced at $19. Some, such as the wired earbuds and charging adapter, were once included with new iPhones.
Those $19 Apple items—together with the Apple Watch, AirPods and other small gadgets—are part of the company’s growing Wearables, Home and Accessories category, which had more than $8 billion in revenue in the quarter that ended in October.
Almost every Apple price ends in the number “9.” Would it matter if we all carried around $30 bills? There is further discussion in this Galvin Brown WSJ piece.
Via the excellent Samir Varma.
That is the topic of my latest Bloomberg column, here is one excerpt:
Maybe the men, on average, did have greater ambition and thus promotion potential. One reason could be that women, on average, spend more time at home raising children than men. For very demanding executive jobs, even a small difference in time and travel availability could make a big difference in job performance.
And yet even if that’s the case, there could still be a discrimination problem. Even if women and men differ on average, there is a probability distribution for each group, and those distributions usually will overlap. That is, there will be many women who are willing and able to meet any workplace standard thrown at them, and many men with limited ambition.
If you think men and women are different on average, the unfairness can become all the more severe for the potential top performers. In this context, employers will look at the most talented women and, for reasons of stereotyping, dramatically underestimate their potential, including for leadership positions.
Economic reasoning suggests another subtle effect at play. Promotion to the top involves a series of steps along a career ladder, often many steps. If there is a discrimination “tax” at each step, even if only a small one, those taxes can produce a discouraging effect. It resembles the old problem of the medieval river that has too many tolls on it, levied by too many independent principalities. The net effect can be to make the river too costly to traverse, even if each prince is taking only a small amount.
With a citation to Zaua further below!
Hugh Rockoff does a 72 pp. deep dive on Milton Friedman on bailouts. This is an excellent paper, as he also considers Friedman’s columns and spoken words over the years and he also fleshes out Friedman’s thoughts on what we now call “shadow banks” (he worried about them). Friedman was willing to accept a fair number of bailouts, here is one excerpt:
In the bailout of Continental Illinois, a case that Friedman thought had been handled well, depositors and other creditors were protected, but shareholders were mostly wiped out and management was replaced. The protection of depositors and other creditors created an advantage for large banks: they could raise funds more easily because they, like Continental Illinois, were “too big to fail.” However, Friedman thought that as long as shareholders and managers were forced to pay dearly when a financial institution was bailed out there would still be an adequate incentive for bank managers to exercise prudence.
For Friedman this meant that in the case of financial institutions the benefits of a bailout might outweigh the costs.
And more speculatively:
No one can channel an economist as brilliant and creative as Milton Friedman. Nevertheless, having come this far I will make an attempt. I believe that it would have been consistent with his earlier views for Friedman to have been “reluctant to condemn” the program of bailouts undertaken in 2008, to use the phrase that he used when questioned about the rescue of Long–Term Capital Management. I think he would have recognized that the repos issued by Lehman Brothers and other investment banks were similar to uninsured deposits in commercial banks, thus making possible a destructive panic. In other words, he would have recognized the logic of the contention that 2008 was a “run on repos” and similar to earlier financial panics (Gorton, Laarits, and Metrick 2018). He might have reminded us of the consequences of the failure to provide help for the BoUS in 1930. However, he might well have been critical of the structure of the bailouts, especially with respect to how various classes of stakeholders were treated.
I recall being excoriated in 2009 for suggesting that Friedman would have endorsed some version of the bailouts of that time.
In the 1920s immigration to the United States was restricted with quotas which were designed to reduce the number of immigrants from Italy and Eastern Europe, then considered to be low-quality immigrants. One unintended consequence was that the number of immigrant scientists from these areas also declined. The awesome Petra Moser and Schmuel San have an excellent new paper documenting the cost on US innovation and patenting.
Naturalization data indicate a dramatic decline in the arrival of new ESE-born scientists after the quotas. Until 1924, arrivals of new ESE-born immigrant scientists were comparable to arrivals from Northern and Western Europe (WNE), who were subject to comparable pull and push factors of migration.1 After the quotas, arrivals of ESE-born scientists decline significantly while arrivals from Northern and Western Europe continue to increase. Combining data on naturalizations with information on scientists’ university education and career histories, we estimate that 1,165 ESE-born scientists were lost to US science under the quota system. At an annual level, this implies a loss of 38 scientists per year, equivalent to eliminating the entire physics department of a major university each year between 1925 and 1955. For the physical sciences alone, an estimated 553 ESE-born scientists were lost to US science.
To estimate the effects of changes in immigration on US inventions, we compare changes in patenting per year after 1924 in the pre-quota fields of ESE-born US scientists with changes in patenting in other research fields in which US scientists were active inventors before the quotas. This identification strategy allows us to control for changes in invention by US scientists across fields, for example, as a result of changes in research funding. Year fixed effects further control for changes in patenting over time that are shared across fields. Field fixed effects control for variation in the intensity of patenting across fields, e.g., between basic and applied research.
Baseline estimates reveal a large and persistent decline in invention by US scientists in the pre-quota fields of ESE-born scientists. After the quotas, US scientists produced 68 percent fewer additional patents in the pre-quota fields of ESE-born scientists compared with the prequota fields of other US scientists. Time-varying effects show a large decline in invention by US scientists in the 1930s, which persisted into the 1960s. Importantly, these estimates show no preexisting differences in patenting for ESE and other fields before the quotas.
Canada which did not implement quotas did not see a similar decline. One interesting case study which is quite astounding in its way:
A case study of co-authorships for the prolific Hungarian-born mathematician Paul Erdős illustrates how restrictions on immigration reduced collaborations between ESE-born scientists and US scientists. Erdős moved to the United States as a post-doctoral fellow at Princeton, and became a professor at Notre Dame, travelling and collaborating with many US scientists. As a Hungarian citizen, however, Erdős was denied a re-entry visa by the US immigration services in1954, and not granted re-entry until 1963. To examine how these denials affected Erdős’ collaborations with US scientists, we collect the location of Erdős top 100 coauthors at the time of their first collaboration. These data show that Erdős’ collaborations shifted away from the United States when he was denied re-entry. Between 1954 and 1963, 24 percent of Erdős’ new co-authors were US scientists, compared with 60 percent until 1954. These patterns are confirmed in a broader analysis of patents by co-authors and co-authors of co-authors of ESEborn scientists, which indicates a 26 percent decline in invention by scientists who were directly or indirectly influenced by ESE-born scholars.
As you might suspect from the Erdos example, scientists in the US became less not more productive without the benefits of cooperation with Eastern European scientists.
Some of the scientists denied entry to the US in the 1920s went to Israel instead and innovated there so their genius was not entirely lost to the world.
Photo: Paul Erdos with Terrence Tao. Attribution, either Billy or Grace Tao, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0>, via Wikimedia Commons