El millor economista de tots els temps

 GOAT: Who is the  Greatest Economist of all Time and Why Does it Matter? 

En Tyler Cowen és un paio sorprenent sempre. El seu blog esdevé inabastable, de vegades escriu també Alex Tabarrock. I ara la última que ha fet és un llibre descarregable lliurement per internet amb intel·ligència artificial acoplada.

Aquest és l'índex:

•  Introduction, and why this is the book of a fan
•  Milton Friedman as GOAT?
•  John Maynard Keynes  
•  Friedrich A. Hayek
•  Those who did not make the short list: Marshall, Samuelson, Arrow, Becker, and Schumpeter
•  Why won’t anyone nominate John Stuart Mill as GOAT?
•  Malthus as GOAT, and are we all doomed?
•  Is Adam Smith the obvious winner?
•  The winner(s): so who is the greatest economist of all time?

He de dir que el guanyador escollit per en Cowen està relacionat amb la seva ideologia. Res a dir, ho ha escollit lliurement segons les seves preferències. Després fa variacions i ajustos, i ofereix altres candidats però va per allà mateix. Jo en diria algun dels que diu, i d'altres que oblida. 

No us desvelo el final, així podeu tastar el llibre durant el cap de setmana.

Keynes

Efficient health insurance as a first best

 Sick Insurance: Adverse Selection and Regulation of Health Insurance Markets

When heterogeneity in consumer tastes and needs, and in cost and quality of products, are publically observable, markets can price, sort, and match these variations, and product choices made by consumers yield demand signals that foster efficient resource allocation. These conditions hold, roughly, for a broad swath of economic activity, allowing lightly regulated private markets to successfully approximate allocative efficiency. However, in health care systems around the globe today, participants do not necessarily see the big picture of lifetime health costs and quality of life, and in many systems the incentives that consumers and providers face do not promote efficient allocation of health care resources. Information asymmetries are the fundamental source of difficulties in health insurance markets and in efficient provision of health services. Additional factors contributing to poor performance of health markets include (1) government regulation that is intended to protect the disadvantaged and promote equity, but creates incentives antagonistic to allocative efficiency, (2) inefficient provider organizations and non-competitive conduct, sometimes sheltered by government policies, and (3) behavioral shortcomings of consumers in promoting their own self-interest, including inconsistent beliefs regarding low-probability future events, myopia, and inconsistent risk assessment.

The seminal contributions to economic analysis of Kenneth Arrow, George Akerlof, Joe Stiglitz, Mike Spence, Mike Rothschild, and John Riley establish that when there are information asymmetries between buyers and sellers, adverse selection, moral hazard, and counter-party risk can result, causing markets to operate inefficiently or unravel. Asymmetric information between buyers and sellers, or market regulations that restrict competitive underwriting and force common prices for disparate products, can induce adverse selection. Moral hazard occurs when effort to avoid risks cannot be observed by sellers and stipulated in insurance contracts, and buyers have less incentive for risk-reducing effort when some of their potential losses are covered. When the productivity and cost of medical interventions is not known to all parties, then buyers and third-party-payers may not make informed decisions on therapies. Counter-party risk occurs when sellers evade payment of benefits for losses, or fail as agents to respect the interests of the consumers who are their principals. Adverse selection of buyers with high latent risk or low risk-reducing effort, or sellers with high counter-party risk, make insurance less attractive to buyers, and may cause insurance markets to unravel. Administrative overhead will induce less than full insurance. By itself, this does not make insurance market outcomes inefficient, but increasing returns to scale in administrative costs may lead to an inefficient concentrated market.

In principle, the problems of asymmetric information can be overcome by government operation or regulation of health services; in practice, there remains a major mechanism design problem of designing incentives that handle the asymmetries; e.g., “single payer” systems permit additional levers of control, but information asymmetries cause principal-agent problems even in command organizations. Legal mandates and regulations can make adverse selection worse. Government policy on private health insurance markets often reflects a social ethic that individuals should not be denied health care because of inability to pay, expressed for example in requirements that hospitals admit uninsured patients with life-threatening conditions, and a social ethic that insurance contract underwriting should not be based on risk factors such as gender, race, and pre-existing conditions. When these requirements are not publically financed, they are implicit taxes on insurers and providers that are at least in part passed through to consumers as higher premiums that increase the effective load for low-risk consumers. Both the higher loads and the prospect of public assistance as a last resort reduce the incentive for consumers to buy insurance and to pay (or copay) for preventative care.

The United States has, more than any other developed country, relied on private markets for health insurance and health care delivery. These markets have performed poorly. Denials and cancellations, exclusion of pre-existing conditions, and actuarially unattractive premiums have left many Americans with no insurance or financially risky gaps in coverage. Administrative costs for health insurance in the United States are seven times the OECD average. These are symptoms of adverse selection. Delayed and inconsistent preventative and chronic care, arguably induced by incomplete coverage, have had substantial health consequences: the United States ranks 25th among nations in the survival rate from age 15 to age 60. This impacts the population of workers and young parents whose loss is a substantial cost to families and to the economy. If the U.S. could raise its survival rate for this group to that of Switzerland, a country that has mandatory standardized coverage offered by private insurers, this would prevent more than 190,000 deaths per year.

Given the damage that information asymmetries can inflict on private market allocation mechanisms, the obvious next question is what regulatory mechanisms can be used to blunt or eliminate these problems. This involves examining closely the action of adverse selection and moral hazard, and the tools from principal-agent theory and from regulatory theory that can blunt these actions. There is an extensive literature relevant to this analysis that can be focused on the regulatory design question. Less well investigated are the impacts of consumer behavior, particularly mistaken beliefs. This paper examines these issues, and studies the impacts of regulations intended to promote equity and efficiency. More practically, this paper investigates these issues with reference to the private market in the United States for prescription drug coverage for seniors, introduced in 2006 and subsidized and regulated as part of Medicare.

The efficient regulatory design is mandatory universal insurance, this is the answer. But it has to be eficient, otherwise appears duplicate insurance, paying twice for the same. This is the worst second best, a combined failure of mandatory and private coverage.

How plagues end

Apollo's Arrow. The Profound and Enduring Impact of Coronavirus on the Way We Live

From the book:

After its dramatic initial appearance, SARS-2 will ultimately become endemic; it will regularly circulate among us at some low, steady level. This is connected to the second kind of end, which we have already considered: herd immunity. Here, the pathogen is still around, but it has a much more difficult time reestablishing itself. This resembles a well-vaccinated population for any infectious disease; there are only occasional, small outbreaks among nonimmune people.

By 2022 or so, we will reach this outcome naturally or via vaccination. Of course, if we do rapidly develop and distribute a safe and effective vaccine, we could reach herd immunity with fewer deaths. Based on the fundamental R0 of SARS-2, as we saw in chapter 2, up to an estimated 60 to 67 percent of the population could be affected (or roughly two hundred million people in the United States). The necessary percentage could be lower, closer to 40 to 50 percent, given that social network structure means that different people spread the virus to different extents (as we also saw in chapter 2); or it could be higher, if the epidemic moves extremely fast and we overshoot the level required for herd immunity. Whatever the exact percentage, as a pathogen spreads, some people will die and others will recover and become immune, so eventually the virus will run out of places to go. This is the ordinary, natural way that, biologically speaking, epidemics end.

This is what we mean when we say that a pathogen is under control. But sometimes, plagues are so devastating that a society never recovers. It’s very important to emphasize that, as bad as COVID-19 is, it’s not remotely as bad as epidemics of bubonic plague, cholera, or smallpox that have killed much larger fractions of the population and that have had much larger and longer-lasting effects. Those types of plagues are even associated with the iconography of the Four Horsemen of the Apocalypse, Pestilence riding side by side with War, Famine, and Death. Those epidemics vindicated the adage that “too few of the living were left to bury the dead.”

N. Christakis says at the begining of the book 

The god Apollo, for example, was both a healer and the bringer of disease. During the Trojan War, with his silver bow and quiver of arrows, he rained a plague down on the Greeks to punish them for kidnapping and enslaving Chryseis, the daughter of one of his favored priests.

I found myself thinking again about Apollo and his vengeance as I contemplated our own twenty-first-century barrage more than three thousand years after the events described in The Iliad. It seemed to me that the novel coronavirus was a threat that was both wholly new and deeply ancient. This catastrophe called on us to confront our adversary in a modern way while also relying on wisdom from the past.

Excerpts from the last chapter, How plagues end: 

The pathogens evolve to respond to us, but we, at a slower pace, also evolve to respond to them. Infectious diseases have been a part of our evolutionary history for so long that they have left a mark on our genes. For instance, humans have evolved genetic changes that have proven useful in coping with malaria beginning over one hundred thousand years ago, tuberculosis over nine thousand years ago, cholera and bubonic plague over six thousand years ago, and smallpox over three thousand years ago.36

Infectious pathogens (even if nonepidemic) have arguably been a crucial selective pressure throughout the evolution of our species.37 The primary killers of human beings across evolutionary time are other human beings. Humans do not have any natural predators that substantially affect survival.38 Except for our microscopic enemies.

The SARS-2 virus is a lot less lethal to people of reproductive age and can be combated with the lifesaving tools of modern medicine, so the impact on human evolution is surely going to be minimal. But, at least in theory, another way epidemics end is that hosts evolve to be resistant. And in fact, we may already have naturally occurring genetic variation in our species that affects the severity of COVID-19 in different populations, which would lay the groundwork for such evolution. Over generations, this can result in changes to the genetic makeup of the afflicted populations.

 This social construction of COVID-19 means that the end of the pandemic can also be socially defined. In other words, plagues can end when everyone believes they are over or when everyone is simply willing to tolerate more risk and live in a new way. If everyone willingly risks infection and resumes a semblance of normal life (or, implausibly, if everyone decides to employ physical distancing forever), then the epidemic can be said to have ended, even if the virus is still circulating. We got a glimpse of this phenomenon as well in the summer of 2020 as different states, tired of the lockdowns, acted as if the epidemic were over, even though, biologically speaking, it was not. It was wholly understandable that everyone was eager to leave the epidemic behind as quickly as possible. But the epidemiological reality did not submit to our desires. The pandemic was still claiming roughly a thousand lives per day, although Americans seemed inured to it. Many people, and not just self-interested politicians, seemed to believe the SARS-2 epidemic could end by fiat.

Last paragraph:

 Microbes have shaped our evolutionary trajectory since the origin of our species. Epidemics have done so for many thousands of years. Like the myth of Apollo’s arrows, they have been a part of our story all along. We have outlived them before, using the biological and social tools at our disposal. Life will return to normal. Plagues always end. And, like plagues, hope is an enduring part of the human condition.

A must read. This is my preferred book reference on current pandemic.

 

Figure 16: The mortality impact of COVID-19 in the United States can be quantitatively compared to that of other modern epidemics.

The end of marginal revolution

Richard Thaler was awarded with the Nobel Prize some days ago. If you follow this blog you'll know his works on behavioral economics and nudging. Since many years I've been interested in this perspective, though it has still more to deliver.
Today I would suggest you to read JM Colomer blog. He has written an excellent post on him and its impact on economic science. Selected statements:

Marginalist microeconomics held that we could understand collective outcomes by assuming that they derive from free interactions among homines economici.
A first big counter-revolution was the reintroduction of institutions in the basic analysis, especially since the 1980 and 1990s (including by Nobel laureates related to the social choice and public choice schools such as Kenneth Arrow, James Buchanan, Ronald Coase, Douglass North, Amartya Sen, Thomas Schelling, Leonid Hurwicz, Roger Myerson, political scientist Elinor Ostrom, Oliver Williamson, and others).

The second is the reintroduction of realistic observations about people’s motivations and behavior, including emotions. This has been based on psychology, on the background of huge progress in neuroscience (while pioneers include political scientist Herbert Simon and psychologist Daniel Kahneman). That Richard Thaler professes at the University of Chicago, once the temple of the neoclassical school, shows the depth of the change.

Now we know again that the three pillars of social analysis are, together with people’s calculated self-interested choices, emotions and institutions, as Hume and Smith masterfully had already established.

And this is the return to the roots of economics with a new toolkit.

Parov Stelar

Arrow in memoriam

K. J. Arrow passed away this Wednesday. He is one of the giants of economics and the founder of health economics. It is difficult to summarise his works in few words. You'll find obituaries in the Post and NYT. Josep M. Colomer has written an interesting post in his blog (social choice perspective). Tony Culyer has published also his obituary (health economics perspective)

An interview in a recent book reviews his works and opinions. Regarding healt economics, he says:

I was asked to study, as a theoretical economist, health care. This was a paper that I regard very highly, one of the best things I ever did. I think I mentioned that in fact, afair amount of my research is the result of people asking me these kinds of questions. I studied Social Choice because somebody asked me a question. A now retired professor, Victor Fuchs, was then at the Ford Foundation, and they wanted to get studies done of social problems. They wanted studies of welfare—in the ordinary sense of the word—of medical care and of education. For each of these areas, they wanted one study by somebody who had worked in the field and one by a theorist, and I’m a theorist who had not necessarily worked with people.

In my case, I was asked to work on medical care. I read up on the literature, and gradually a pattern emerged that essentially the parties know different things. The physician knows a lot that the patient doesn’t, and therefore the patient can’t check on the quality of medical care in the same way we buy a loaf of bread. It’s not like I’ll buy that loaf again. But with medical care, you can’t be sure because you don’t know that much. It’s the same thing between the insurer and the physician or the patient. So I said that with medical care, noneconomic factors, essentially ethical codes, play a role in keeping the system together. But I didn’t have a theory at the time, I just had a statement. It was pretty clear to me that non-economic factors do play a major role. What is considered good practice, that’s what keeps the system going. The trouble is that I’ve seen the limits of economic analysis. I could see one solution, but it was very different from market kinds of solutions. But I did have a theory about it.

When I look at other people, they don’t have theories either, or they have rather vague theories. When I try to impart this to students, of course it’s a very confusing message. That’s one of the reasons I don’t think I’ve been a great teacher. I’ve perhaps had students who did appreciate what I was doing, although they tended to pick up the more technical parts of it. I’m a little disappointed they haven’t tried to tackle the broader picture. If they’re working with it, they’ve done very fine work, going well beyond what I did. So I’d say that would be a rather lengthy answer to your question. I see myself primarily as a scholar, as a thinker about things, trying to enlist others in this thinking. Yes, I think I would say that more so than others.

PS. Arrow in my posts

Quatre dècades d'economia de la salut

Four decades of health economics through a bibliometric lens
Qui vulgui saber què ha passat durant els darrers quaranta anys en economia de la salut -fins el 2010- hauria de llegir fins a 33.000 articles, altrament cal fer una ullada al treball de Wagstaff i Culyer que publica el Journal of Health Economics. O també a la llista sencera d'articles més citats. Avui voldria destacar la que es refereix a Medical Insurance. És clar que l'encapçala Arrow amb la seva contribució decisiva al naixement de l'economia de la salut com disciplina. Aquesta és la llista:


Els llistats els han fet des d'Econlit i els mateixos autors alerten que aquí no hi ha tots els articles que s'han publicat. Al final mostren com els autors més citats tenen articles clau en revistes mèdiques. Cal tenir-ho en compte.

La bola de vidre

Predictive risk and health care: an overview
L'ofici de predir el futur té ganxo però falla més que una escopeta de fira. Almenys això és el que succeeix sovint amb les prediccions meteorològiques. L'excusa habitual és que la geografia és diversa i cal fer prediccions molt acurades territorialment. D'acord, salvem els homes i les dones del temps per un moment, que fa uns mesos ja vaig carregar sobre el tema en aquest blog.
Si traslladem l'argument a l'entorn salut què ens agradaria més que predir els risc d'emmalaltir i quan costarà?. L'Arrow hauria de reescriure el seu article de fa gairebé 50 anys. Sabem que això no serà així almenys per ara. El document de Nuffield Trust revisa l'estat de la qüestió de l'ajust de risc al NHS. No us feu ilusions, no hi ha predicció de risc de veritat. Ens diu on són i cap on van. Està bé fer-hi una ullada.

PD. L'especulació sobre la medicina predictiva segueix dia rera dia, avui a LV. No hi ha altra tema més suggerent?