Le pied dans l’plat

31 Dec, 2021 at 16:08 | Posted in Varia | Comments Off on Le pied dans l’plat

… and a Happy New Year!

Sweden’s corona strategy — a success story

30 Dec, 2021 at 17:28 | Posted in Politics & Society | 3 Comments

Kan vara en bild av text där det står ”Cumulative Excess Death 2021 "The Economist" Estimates per 100k population, Data 2020/12/21 2021/12/21 200 Sweden Denmark 300 400 Norway 500 Switzerland 39 Finland Belgium France Ireland Cyprus Germany 80 Austria United Kingdom Netherlands 109 Portugal Greece Estonia 29 Czechia Poland Hungary 253 Albania 258 Croatia 279 Latvia Slovakia Belarus Ukraine Bosnia Herzegovina 374 Lithuania Macedonia 470 Serbia Bulgaria”

Conspiracy Theorist Anonymous

30 Dec, 2021 at 14:59 | Posted in Varia | Comments Off on Conspiracy Theorist Anonymous

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Models and reality

30 Dec, 2021 at 13:36 | Posted in Economics | 8 Comments

One of the limitations with economics is the restricted possibility to perform experiments, forcing it to mainly rely on observational studies for knowledge of real-world economies.

But still — the idea of performing laboratory experiments holds a firm grip of our wish to discover (causal) relationships between economic ‘variables.’If we only could isolate and manipulate variables in controlled environments, we would probably find ourselves in a situation where we with greater ‘rigour’ and ‘precision’ could describe, predict, or explain economic happenings in terms of ‘structural’ causes, ‘parameter’ values of relevant variables, and economic ‘laws.’

Galileo Galilei’s experiments are often held as exemplary for how to perform experiments to learn something about the real world. Galileo’s heavy balls dropping from the tower of Pisa, confirmed that the distance an object falls is proportional to the square of time and that this law (empirical regularity) of falling bodies could be applicable outside a vacuum tube when e. g. air existence is negligible.

The big problem is to decide or find out exactly for which objects air resistance (and other potentially ‘confounding’ factors) is ‘negligible.’ In the case of heavy balls, air resistance is obviously negligible, but how about feathers or plastic bags?

One possibility is to take the all-encompassing-theory road and find out all about possible disturbing/confounding factors — not only air resistance — influencing the fall and build that into one great model delivering accurate predictions on what happens when the object that falls is not only a heavy ball but feathers and plastic bags. This usually amounts to ultimately state some kind of ceteris paribus interpretation of the ‘law.’

Another road to take would be to concentrate on the negligibility assumption and to specify the domain of applicability to be only heavy compact bodies. The price you have to pay for this is that (1) ‘negligibility’ may be hard to establish in open real-world systems, (2) the generalization you can make from ‘sample’ to ‘population’ is heavily restricted, and (3) you actually have to use some ‘shoe leather’ and empirically try to find out how large is the ‘reach’ of the ‘law.’

In mainstream economics, one has usually settled for the ‘theoretical’ road (and in case you think the present ‘natural experiments’ hype has changed anything, remember that to mimic real experiments, exceedingly stringent special conditions have to obtain).

In the end, it all boils down to one question — are there any Galilean ‘heavy balls’ to be found in economics, so that we can indisputably establish the existence of economic laws operating in real-world economies?

As far as I can see there are some heavy balls out there, but not even one single real economic law.

Economic factors/variables are more like feathers than heavy balls — non-negligible factors (like air resistance and chaotic turbulence) are hard to rule out as having no influence on the object studied.

Galilean experiments are hard to carry out in economics, and the theoretical ‘analogue’ models economists construct and in which they perform their ‘thought-experiments’ build on assumptions that are far away from the kind of idealized conditions under which Galileo performed his experiments. The ‘nomological machines’ that Galileo and other scientists have been able to construct have no real analogues in economics. The stability, autonomy, modularity, and interventional invariance, that we may find between entities in nature, simply are not there in real-world economies. That’s are real-world fact, and contrary to the beliefs of most mainstream economists, they won’t go away simply by applying deductive-axiomatic economic theory with tons of more or less unsubstantiated assumptions.

By this, I do not mean to say that we have to discard all (causal) theories/laws building on modularity, stability, invariance, etc. But we have to acknowledge the fact that outside the systems that possibly fulfill these requirements/assumptions, they are of little substantial value. Running paper and pen experiments on artificial ‘analogue’ model economies is a sure way of ‘establishing’ (causal) economic laws or solving intricate econometric problems of autonomy, identification, invariance and structural stability — in the model world. But they are pure substitutes for the real thing and they don’t have much bearing on what goes on in real-world open social systems. Setting up convenient circumstances for conducting Galilean experiments may tell us a lot about what happens under those kinds of circumstances. But — few, if any, real-world social systems are ‘convenient.’ So most of those systems, theories and models, are irrelevant for letting us know what we really want to know.

To solve, understand, or explain real-world problems you actually have to know something about them — logic, pure mathematics, data simulations or deductive axiomatics don’t take you very far. Most econometrics and economic theories/models are splendid logic machines. But — applying them to the real world is a totally hopeless undertaking! The assumptions one has to make in order to successfully apply these deductive-axiomatic theories/models/machines are devastatingly restrictive and mostly empirically untestable– and hence make their real-world scope ridiculously narrow. To fruitfully analyze real-world phenomena with models and theories you cannot build on patently and known to be ridiculously absurd assumptions. No matter how much you would like the world to entirely consist of heavy balls, the world is not like that. The world also has its fair share of feathers and plastic bags.

Most of the ‘idealizations’ we find in mainstream economic models are not ‘core’ assumptions, but rather structural ‘auxiliary’ assumptions. Without those supplementary assumptions, the core assumptions deliver next to nothing of interest. So to come up with interesting conclusions you have to rely heavily on those other — ‘structural’ — assumptions.

Economic models frequently invoke entities that do not exist, such as perfectly rational agents, perfectly inelastic demand functions, and so on. As economists often defensively point out, other sciences too invoke non-existent entities, such as the frictionless planes of high-school physics. But there is a crucial difference: the false-ontology models of physics and other sciences are empirically constrained. If a physics model leads to successful predictions and interventions, its false ontology can be forgiven, at least for instrumental purposes – but such successful prediction and intervention is necessary for that forgiveness. The
idealizations of economic models, by contrast, have not earned their keep in this way. So the problem is not the idealizations in themselves so much as the lack of empirical success they buy us in exchange. As long as this problem remains, claims of explanatory credit will be unwarranted.

A. Alexandrova & R. Northcott

In physics, we have theories and centuries of experience and experiments that show how gravity makes bodies move. In economics, we know there is nothing equivalent. So instead mainstream economists necessarily have to load their theories and models with sets of auxiliary structural assumptions to get any results at all in their models.

So why then do mainstream economists keep on pursuing this modeling project?

Mainstream ‘as if’ models are based on the logic of idealization and a set of tight axiomatic and ‘structural’ assumptions from which consistent and precise inferences are made. The beauty of this procedure is, of course, that if the assumptions are true, the conclusions necessarily follow. But it is a poor guide for real-world systems. As Hans Albert has it on this ‘style of thought’:

A theory is scientifically relevant first of all because of its possible explanatory power, its performance, which is coupled with its informational content …

Clearly, it is possible to interpret the ‘presuppositions’ of a theoretical system … not as hypotheses, but simply as limitations to the area of application of the system in question. Since a relationship to reality is usually ensured by the language used in economic statements, in this case the impression is generated that a content-laden statement about reality is being made, although the system is fully immunized and thus without content. In my view that is often a source of self-deception in pure economic thought …

The way axioms and theorems are formulated in mainstream economics often leaves their specification without almost any restrictions whatsoever, safely making every imaginable evidence compatible with the all-embracing ‘theory’ — and theory without informational content never risks being empirically tested and found falsified. Used in mainstream ‘thought experimental’ activities, it may, of course, ​be very ‘handy,’ but totally void of any empirical value.

Some economic methodologists have lately been arguing that economic models may well be considered ‘minimal models’ that portray ‘credible worlds’ without having to care about things like similarity, isomorphism, simplified ‘representationality’ or resemblance to the real world. These models are said to resemble ‘realistic novels’ that portray ‘possible worlds’. And sure: economists constructing and working with that kind of models learn things about what might happen in those ‘possible worlds’. But is that really the stuff real science is made of? I think not. As long as one doesn’t come up with credible export warrants to real-world target systems and show how those models — often building on idealizations with known to be false assumptions — enhance our understanding or explanations about the real world, well, then they are just nothing more than just novels.  Showing that something is possible in a ‘possible world’ doesn’t give us a justified license to infer that it therefore also is possible in the real world. ‘The Great Gatsby’ is a wonderful novel, but if you truly want to learn about what is going on in the world of finance, I would recommend rather reading Minsky or Keynes and directly confront real-world finance.

Different models have different cognitive goals. Constructing models that aim for explanatory insights may not optimize the models for making (quantitative) predictions or deliver some kind of ‘understanding’ of what’s going on in the intended target system. All modeling in science have tradeoffs. There simply is no ‘best’ model. For one purpose in one context model A is ‘best’, for other purposes and contexts model B may be deemed ‘best’. Depending on the level of generality, abstraction, and depth, we come up with different models. But even so, I would argue that if we are looking for what I have called ‘adequate explanations’ (Syll, Ekonomisk teori och metod, Studentlitteratur, 2005) it is not enough to just come up with ‘minimal’ or ‘credible world’ models.

The assumptions and descriptions we use in our modeling have to be true — or at least ‘harmlessly’ false — and give a sufficiently detailed characterization of the mechanisms and forces at work. Models in mainstream economics do nothing of the kind.

Coming up with models that show how things may possibly be explained is not what we are looking for. It is not enough. We want to have models that build on assumptions that are not in conflict with known facts and that show how things actually are to be explained. Our aspirations have to be more far-reaching than just constructing coherent and ‘credible’ models about ‘possible worlds’. We want to understand and explain ‘difference-making’ in the real world and not just in some made-up fantasy world. No matter how many mechanisms or coherent relations you represent in your model, you still have to show that these mechanisms and relations are at work and exist in society if we are to do real science. Science has to be something more than just more or less realistic ‘story-telling’ or ‘explanatory fictionalism.’ You have to provide decisive empirical evidence that what you can infer in your model also helps us to uncover what actually goes on in the real world. It is not enough to present your students with epistemically informative insights about logically possible but non-existent general equilibrium models. You also, and more importantly, have to have a world-linking argumentation and show how those models explain or teach us something about real-world economies. If you fail to support your models in that way, why should we care about them? And if you do not inform us about what are the real-world intended target systems of your modeling, how are we going to be able to value or test them? Without giving that kind of information it is impossible for us to check if the ‘possible world’ models you come up with actually hold also for the one world in which we live — the real world.

Hästskitsteoremet i svensk tappning

29 Dec, 2021 at 09:52 | Posted in Economics | 2 Comments

Daniel Waldenström om kapitalbeskattningens förutsättningar - SNS1980- och 1990-talen var avregleringens årtionden. Som en följd växte ekonomin, med undantag för krisåren i början av 90-talet, snabbt. Och det ledde till att den ekonomiska ojämlikheten ökade.

– Skillnaden i inkomster mellan grupper har ökat – det har varit en medveten strategi sedan 1980-talet, säger Daniel Waldenström.

– I takt med att vi fick upp farten i ekonomin så skapade det förutsättningar för personer med drivkraft och det skapar välstånd.

DN

Ja, vad ska man säga om denna, med både hängslen och livrem försedda, ekonomistiska ojämlikhetsapologetik? Ibland säger en bild mer än tusen ord …

7360f8a6_Reagonomics-1200x959

Utbildning — ett exempel på spårbundenhet

28 Dec, 2021 at 09:06 | Posted in Education & School | 2 Comments

How to resolve "Microsoft.Data.SqlClient is not supported on this  platform." in an Azure Function App?Hur fria är unga egentligen att välja sin väg i livet? Föräldrarnas bakgrund har stort genomslag både när unga väljer utbildning och ska ta sig in på arbetsmarknaden, visar uppföljningen av två hela årskullar av Malmöungdomar.

Professor Jonas Olofsson vid Malmö universitet … har följt upp dem som var sjätteklassare och niondeklassare i Malmö 2008 för att se var de befann sig vid 22 respektive 26 års ålder. Några exempel på hur den sociala bakgrunden spelar in:

♦ Sannolikheten för att en tonåring ska välja högskoleförberedande gymnasieutbildning ökar 290 procent om minst en av föräldrarna har eftergymnasial utbildning.

♦ Sannolikheten för att fullfölja gymnasieutbildningen är 130 procent högre för unga med svenskfödda föräldrar än för unga med föräldrar som är födda utomlands.

♦ Sannolikheten för att som 22-åring leva på försörjningsstöd ökar dramatiskt om föräldrarna varit utan jobb och fått försörjningsstöd.

Cecilia Kintö/SvD

Adorno in 60 Minuten

27 Dec, 2021 at 17:21 | Posted in Politics & Society | Comments Off on Adorno in 60 Minuten

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Lars Jonung och Daniel Waldenström — Sveriges egna Dr Pangloss

24 Dec, 2021 at 14:17 | Posted in Economics | 3 Comments

Sagan om Karl-Bertil Jonssons julaftonNär Karl-Bertil Jonsson i Tage Danielssons klassiska julsaga lägger undan några rika människors julklappar och delar ut dem till fattiga brister han i respekt för den privata äganderätten, skriver Lars Jonung och Daniel Waldenström på DN Debatt idag. Enligt dessa ekonomiprofessorer är det kapitalism och marknadsliberalism som är den ’okände välgöraren’ som gynnat oss alla med “god ekonomisk tillväxt och demokratiska fri- och rättigheter.”

Och detta ankors plask och grodors plums tror dessa herrar att de kan lura i folk. De måste tagit fel på årstiden. Det är inte första april idag. Det är julafton!

Trots att båda herrar vet bättre, nämner de inte med ett ord att Sverige under de senaste fyra decennierna är ett av de länder i världen där ökningstakten i ojämlikhet vad avser rikedom och inkomster varit som störst.

Jag säger som Fabian Månsson: Vet hut! Vet sjufalt hut!

O holy night

24 Dec, 2021 at 08:44 | Posted in Varia | Comments Off on O holy night

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Jussi Björling — the greatest of them all.

Snön faller och vi med den

23 Dec, 2021 at 13:50 | Posted in Varia | Comments Off on Snön faller och vi med den

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How scientists manipulate research

23 Dec, 2021 at 11:15 | Posted in Statistics & Econometrics | Comments Off on How scientists manipulate research

All science entails human judgment, and using statistical models doesn’t relieve us of that necessity. Working with misspecified models, the scientific value of significance testing is actually zero — even though you’re making valid statistical inferences! Statistical models and concomitant significance tests are no substitutes for doing real science.

In its standard form, a significance test is not the kind of ‘severe test’ that we are looking for in our search for being able to confirm or disconfirm empirical scientific hypotheses. This is problematic for many reasons, one being that there is a strong tendency to accept the null hypothesis since they can’t be rejected at the standard 5% significance level. In their standard form, significance tests bias against new hypotheses by making it hard to disconfirm the null hypothesis.

And as shown over and over again when it is applied, people have a tendency to read ‘not disconfirmed’ as ‘probably confirmed.’ Standard scientific methodology tells us that when there is only say a 10 % probability that pure sampling error could account for the observed difference between the data and the null hypothesis, it would be more ‘reasonable’ to conclude that we have a case of disconfirmation. Especially if we perform many independent tests of our hypothesis and they all give ​the same 10% result as our reported one, I guess most researchers would count the hypothesis as even more disconfirmed.

Statistics is no substitute for thinking. We should never forget that the underlying parameters we use when performing significance tests are model constructions. Our p-values mean next to nothing if the model is wrong. Statistical​ significance tests do not validate models!

In many social sciences, p-values and null hypothesis significance testing (NHST) is often used to draw far-reaching scientific conclusions — despite the fact that they are as a rule poorly understood and that there exist alternatives that are easier to understand and more informative.

Not the least using confidence intervals (CIs) and effect sizes are to be preferred to the Neyman-Pearson-Fisher mishmash approach that is so often practiced by applied researchers.

Running a Monte Carlo simulation with 100 replications of a fictitious sample having N = 20, confidence intervals of 95%, a normally distributed population with a mean = 10 and a standard deviation of 20, taking two-tailed p-values on a zero null hypothesis, we get varying CIs (since they are based on varying sample standard deviations), but with a minimum of 3.2 and a maximum of 26.1, we still get a clear picture of what would happen in an infinite limit sequence. On the other hand p-values (even though from a purely mathematical-statistical sense more or less equivalent to CIs) vary strongly from sample to sample, and jumping around between a minimum of 0.007 and a maximum of 0.999 doesn’t give you a clue of what will happen in an infinite limit sequence!

Dangerous physics envy in economics

22 Dec, 2021 at 13:40 | Posted in Economics | 5 Comments

Unlike in physics, there are no universal and immutable laws of economics. You can’t will gravity out of existence. But as the recurrence of speculative bubbles shows, you can unleash ‘animal spirits’ so that human behaviour and prices themselves defy economic gravity. Change the social context – in economic parlance, change the incentive structure – and people will alter their behaviour to adapt to the new framework …

Andrew Lo quote: Economists suffer from a deep psychological disorder that  I call...

The apogee of economic ‘scientism’ came in the 1990s … Hindsight has revealed the misplaced hubris of that decade, one during which Greenspan helped to fuel a speculative bubble that nearly destroyed the world economy, and the Soviet Union’s failed reform knocked seven years off its life expectancy. Many economists, Sachs included, defend themselves on the grounds that their advice was not actually taken: bad politics got in the way of good economics.

Given this willful blindness, the current reaction against economists is understandable. In response, a ‘data revolution’ has prompted many economists to do more grunt work with their data, while engaging in public debates about the practicality of their work. Less science, more social. That is a recipe for an economics that might yet redeem the experts.

John Rapley

Sarah Palin — the anti-vax rocket scientist

22 Dec, 2021 at 08:39 | Posted in Politics & Society | 9 Comments

Sarah Palin, rocket scientist, offered her thoughts on the coronavirus vaccine at a far-right conference in Arizona over the weekend. “It will be over my dead body that I’ ll have to get a shot,” she proclaimed.

Sarah Palin Hazed for Not Getting COVID Vaccination: 'I Bet She Is Horse  Dewormed'Unlikely, governor. Phase III trials have shown that the vaccines fail to generate a robust immune response when administered to dead people.

But Palin’s talk of dead bodies is on point. By discouraging vaccination, she and Tucker Carlson and the rest of the anti-science right are quite literally getting people killed. Studies show that those living in the most pro-trump counties in the United States are dying from covid-19 at a rate more than five times higher than in the most antiTrump counties …

Back in September, Palin had boasted on Fox News: “I am one of those White, common-sense conservatives, I believe in science, and I have not taken the shot.” And now she says she won’t take it — unless and until she’s a dead body.

Thanks to Palin and other death-cult leaders, countless Republicans have become exactly that.

Dana Milbank / Washington Post

The Endurance — a legendary story of survival and courage

21 Dec, 2021 at 14:37 | Posted in Varia | Comments Off on The Endurance — a legendary story of survival and courage

In science, courage is to follow the motto of enlightenment and Kant’s dictum — Sapere Aude!  To use your own understanding, having the ​courage to think for yourself and question ‘received opinion,’ authority or orthodoxy.

In our daily lives, courage is a capability to confront fear, as when in front of the powerful and mighty, not to step back, but stand up for one’s right not to be humiliated or abused.

Courage is to do the right thing in spite of danger and fear.

As when Ernest Shackleton and Frank Worsley, in April 1916, aboard the small boat ‘James Caird’, spent 16 days crossing 1,300 km of ocean to reach South Georgia, then trekked across the island to a whaling station, and finally could rescue the remaining men from the crew of ‘Endurance’ left on the Elephant Island. Not a single member of the expedition died.

What we do in life echoes in eternity.

Analytical strategies in mediation analysis

21 Dec, 2021 at 12:01 | Posted in Statistics & Econometrics | Comments Off on Analytical strategies in mediation analysis

5. Example of a Basic Test of Mediation – Dr Martin LeaCase 3: Does cognitive skill (M) mediate the relation between college attendance (Z) and earnings (Y)? Case 3 involves one causal variable — college attendance — and two outcomes — one proximal (cognitive skill) and the other distal (earnings). College attendance is causal because a person may or may not go to college. Cognitive skill is not causal because one cannot be assigned or choose to have high skill; it is instead a “surrogate marker” for earnings. If prior research indicates that cognitive skill (which can be measured early) is a good predictor of later earnings, we may infer the impact of college attendance on earnings even before participants are old enough to work.

Case 3 poses a tough inferential problem even though it entails only one causal variable. To say that cognitive skill accounts for the impact of college attendance on earnings is to dismiss the possibility that college attendance can have a large effect on earnings even for people whose cognitive skill is unaffected by college attendance.

This idea cannot be tested via regression but can be tested through principal stratification … We might classify participants into three principal strata — one for those whose cognitive skill would increase a great deal if they attend college, a second for those whose skill would not increase much even as a result of attending college, and a third for those whose skill would increase even without attending college. If we find that college attendance strongly increases the earnings of persons in the second and third strata, that evidence will falsify the claim that college attendance improves earnings solely by increasing cognitive skill.

But unless we have a crystal ball, we can’t know a priori which stratum to put a person in. Nevertheless, by collecting pretreatment variables that predict college attendance and cognitive skill, we may be able to identify causal effects within each stratum … This approach does not constitute a full mediation analysis, but it does put some strong claims of mediation to an important test.

Stephen Raudenbush & Guanglei Hong

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