Lönesänkarna

9 May, 2015 at 15:52 | Posted in Economics | Comments Off on Lönesänkarna

 

Random walks model thinking

9 May, 2015 at 09:48 | Posted in Statistics & Econometrics | 1 Comment

 

Hög tid skrota överskottsmålet!

8 May, 2015 at 16:34 | Posted in Economics | 3 Comments

Sverige har haft massarbetslöshet i över 20 år. Varken de reformer som rekommenderas av etablerad nationalekonomisk teori, såsom lägre a-kassa, skatter och facklig organisering, eller andra förändringar, såsom låg realränta eller expansiv penningpolitik, verkar vara tillräcklig för att motverka denna massarbetslöshet. Än mindre för att skapa full sysselsättning.

Om regeringen ska klara av att nå full sysselsättning verkar det därför som att kraftigt expansiv finanspolitik är enda lösningen. Regeringen bör därför rikta om finanspolitiken i generellt mer expansiv inriktning. Detta innebär per definition att statsskulden bör öka …

frn-massarbetslshet-till-full-sysselsttning-katalys-no-20-1-638Alla partier i riksdagen arbetar i dag för att den sparsamma finanspolitik som bedrivits i Sverige sedan början av 1990-talet ska fortsätta. Men det finns ingen enkel relation mellan statsskuldens nivå och samhällsekonomins funktionssätt. Den skepsis som i dag verkar vara utbredd mot ökningar av statsskulden bygger till stora delar på helt omotiverade farhågor om dess inverkan på samhällsekonomin i övrigt. Delvis grundar sig detta i missförstånd om svensk ekonomi under 1990-talet.

Det finns ingen mening att ha ett mål för låg statsskuld om detta inte bidrar till sysselsättning, tillväxt eller något annat önskvärt. Mycket tyder på att sparsam finanspolitik kan skapa stora problem för samhällsekonomin, både vad gäller sysselsättning och tillväxt. Och mycket tyder på att expansiv finanspolitik är det nödvändiga verktyg som regeringen måste använda för att nå full sysselsättning. Regeringen bör därför avskaffa överskottsmålet.

I den mån det överhuvudtaget bör finnas explicita långsiktiga mål för statens finanser och statsskulden talar mycket för att detta bör vara någon form av expansionsmål eller underskottsmål. Om det finns lediga resurser, såsom arbetslösa, bör regeringen se till att deras arbete kommer till nytta — genom att öka statsskulden om så krävs.

Bra rutet!

8 May, 2015 at 14:30 | Posted in Economics | Comments Off on Bra rutet!

Fredrik Andersson, 43, är lärare på Slottsskolan i Vingåker.
Efter flera år i yrket har han tröttnat på slappa elever. Därför tänker han ta i med hårdhandskarna …
Han har infört helt nya regler – för att komma tillrätta med sena ankomster och dåliga prestationer.
dagens_ungdom_531a7a94ddf2b379f462494f– Allt ska gå så himla lätt och smidigt för elever nu. Det ska vara roligt och lattjo. De är så slappa. Det får inte kosta någon ansträngning och jag har varit så frustrerad över det så länge, säger Fredrik Andersson …
De nya reglerna går bland annat ut på att vara extra hård och noggrann med elever som beter sig illa …
Han nämner också att han kommer registrera alla sena ankomster som överstiger en minut och att han, dagligen om så behövs, kommer att kontakta föräldrar om saker går fel.
– Jag kommer att ställa och formulera krav på ett sådant sätt att det inte kommer att vara möjligt att smita undan längre, säger Fredrik Andersson.

Expressen

Seven principles to guard you against economics silliness

8 May, 2015 at 09:01 | Posted in Economics | 3 Comments

argueIn the increasingly contentious world of pop economics, you … may find yourself in an argument with an economist. And when this happens, you should be prepared, because many of the arguments that may seem at first blush to be very powerful and devastating are, in fact, pretty weak tea …

Principle 1: Credentials are not an argument.

Example: “You say Theory X is wrong…but don’t you know that Theory X is supported by Nobel Prize winners A, B, and C, not to mention famous and distinguished professors D, E, F, G, and H?”

Suggested Retort: Loud, barking laughter.

Alternative Suggested Retort: “Richard Feynman said that ‘Science is the belief in the ignorance of experts.’ And you’re not going to argue with HIM, are you?”

Reason You’re Right: Credentials? Gimme a break. Nobody accepts received wisdom from sages these days. Show me the argument!

Principle 2: “All theories are wrong” is false.

Example: “Sure, Theory X fails to forecast any variable of interest or match important features of the data. But don’t you know that all models are wrong? I mean, look at Newton’s Laws…THOSE ended up turning out to be wrong, ha ha ha.”

Suggested Retort: Empty an entire can of Silly String onto anyone who says this. (I carry Silly String expressly for this purpose.)

Alternative Suggested Retort: “Yeah, well, when your theory is anywhere near as useful as Newton’s Laws, come back and see me, K?”

Reason You’re Right: To say models are “wrong” is fatuous semantics; philosophically, models can only have degrees of predictive power within domains of validity. Newton’s Laws are only “wrong” if you are studying something very small or moving very fast. For most everyday applications, Newton’s Laws are very, very right.

Principle 3: “We have theories for that” is not good enough.

Example: “How can you say that macroeconomists have ignored Phenomenon X? We have theories in which X plays a role! Several, in fact!”

Suggested Retort: “Then how come no one was paying attention to those theories before Phenomenon X emerged and slapped us upside the head?”

Reason You’re Right: Actually, there are two reasons. Reason 1 is that it is possible to make many models to describe any phenomenon, and thus there is no guarantee that Phenomenon X is correctly describe by Theory Y rather than some other theory, unless there is good solid evidence that Theory Y is right, in which case economists should be paying more a lot attention to Theory Y. Reason 2 is that if the profession doesn’t have a good way to choose which theories to apply and when, then simply having a bunch of theories sitting around gathering dust is a little pointless.

Principle 4: Argument by accounting identity almost never works.

Example: “But your theory is wrong, because Y = C + I + G!”

Suggested Retort: “If my theory violates an accounting identity, wouldn’t people have noticed that before? Wouldn’t this fact be common knowledge?”

Reason You’re Right: Accounting identities are mostly just definitions. Very rarely do definitions tell us anything useful about the behavior of variables in the real world. The only exception is when you have a very good understanding of the behavior of all but one of the variables in an accounting identity, in which case of course it is useful. But that is a very rare situation indeed.

Principle 5: The Efficient Markets Hypothesis does not automatically render all models useless.

Example: “But if your model could predict financial crises, then people could use it to conduct a riskless arbitrage; therefore, by the EMH, your model cannot predict financial crises.”

Suggested Retort: “By your logic, astrophysics can never predict when an asteroid is going to hit the Earth.”

Reason You’re Right: Conditional predictions are different than unconditional predictions. A macro model that is useful for making policy will not say “Tomorrow X will happen.” It will say “Tomorrow X will happen unless you do something to stop it.” If policy is taken to be exogenous to a model (a “shock”), then the EMH does not say anything about whether you can see an event coming and do something about it.

Principle 6: Models that only fit one piece of the data are not very good models.

Example: “Sure, this model doesn’t fit facts A, B, and C, but it does fit fact D, and therefore it is a ‘laboratory’ that we can use to study the impact of changes in the factors that affect D.”

Suggested Retort: “Nope!”

Reason You’re Right: Suppose you make a different model to fit each phenomenon. Only if all your models don’t interact will you be able to use each different model to study its own phenomenon. And this is highly unlikely to happen. Also, it’s generally pretty easy to make a large number of different models that fit any one given fact, but very hard to make models that fit a whole bunch of facts at once. For these reasons, many philosophers of science claim that science theories should explain a whole bunch of phenomena in terms of some smaller, simpler subset of underlying phenomena. Or, in other words, wrong theories are wrong.

Principle 7: The message is not the messenger.

Example: “Well, that argument is being made by Person X, who is obviously just angry/a political hack/ignorant/not a real economist/a commie/stupid/corrupt.”

Suggested Retort: “Well, now it’s me making the argument! So what are you going to say about me?”

Reason You’re Right: This should be fairly obvious, but people seem to forget it. Even angry hackish ignorant stupid communist corrupt non-economists can make good cogent correct arguments (or, at least, repeat them from some more reputable source!). Arguments should be argued on the merits. This is the converse of Principle 1.

There are, of course, a lot more principles than these … The set of silly things that people can and will say to try to beat an interlocutor down is, well, very large. But I think these seven principles will guard you against much of the worst of the silliness.

Noah Smith

Why the ergodic theorem is not applicable in economics

6 May, 2015 at 15:06 | Posted in Economics | 5 Comments

At a realistic level of analysis, Keynes’ claim that some events could have no probability ratios assigned to them can be represented as rejecting the belief that some observed economic phenomena are the outcomes of any stochastic process: probability structures do not even fleetingly exist for many economic events.

7107nQVwWOLIn order to apply probability theory, one must assume replicability of the experiment under the same conditions so that, in principle, the moments of the random functions can be calculated over a large number of realizations …

For macroeconomic functions it can be claimed that only a single realization exists since there is only one actual economy; hence there are no cross-sectional data which are relevant. If we do not possess, never have possessed, and conceptually never will possess an ensemble of macroeconomic worlds, then the entire concept of the definition of relevant distribution functions is questionable. It can be logically argued that the distribution function cannot be defined if all the macroinformation which can exist is only a finite part (the past and the present) of a single realization. Since a universe of such realizations must at least conceptually exist for this theory to be germane, the application of the mathematical theory of stochastic processes to macroeconomic phenomena is therefore questionable, if not in principle invalid.

Paul Davidson

To understand real world “non-routine” decisions and unforeseeable changes in behaviour, ergodic probability distributions are of no avail. In a world full of genuine uncertainty — where real historical time rules the roost — the probabilities that ruled the past are not necessarily those that will rule the future.

hicksbbcWhen we cannot accept that the observations, along the time-series available to us, are independent … we have, in strict logic, no more than one observation, all of the separate items having to be taken together. For the analysis of that the probability calculus is useless; it does not apply … I am bold enough to conclude, from these considerations that the usefulness of ‘statistical’ or ‘stochastic’ methods in economics is a good deal less than is now conventionally supposed … We should always ask ourselves, before we apply them, whether they are appropriate to the problem in hand. Very often they are not … The probability calculus is no excuse for forgetfulness.

John Hicks, Causality in Economics, 1979:121

To simply assume that economic processes are ergodic — and a fortiori in any relevant sense timeless — is not a sensible way for dealing with the kind of genuine uncertainty that permeates open systems such as economies.

Den svenska skolkatastrofen

6 May, 2015 at 09:16 | Posted in Economics | Comments Off on Den svenska skolkatastrofen

skola_mellin
Aftonbladet

Why minimum wage has no discernible effect on employment

5 May, 2015 at 17:11 | Posted in Economics | 3 Comments

Economists have conducted hundreds of studies of the employment impact of the minimum wage. Summarizing those studies is a daunting task, but two recent meta-studies analyzing the research conducted since the early 1990s concludes that the minimum wage has little or no discernible effect on the employment prospects of low-wage workers.

01The most likely reason for this outcome is that the cost shock of the minimum wage is small relative to most firms’ overall costs and only modest relative to the wages paid to low-wage workers. In the traditional discussion of the minimum wage, economists have focused on how these costs affect employment outcomes, but employers have many other channels of adjustment. Employers can reduce hours, non-wage benefits, or training. Employers can also shift the composition towardhigher skilled workers, cut pay to more highly paid workers, take action to increase worker productivity (from reorganizing production to increasing training), increase prices to consumers, or simply accept a smaller profit margin. Workers may also respond to the higher wage by working harder on the job. But, probably the most important channel of adjustment is through reductions in labor turnover, which yield significant cost savings to employers.

John Schmitt/CEPR

Krugman lectures Taylor on his rule

5 May, 2015 at 16:50 | Posted in Economics | 2 Comments

6-Oct-2010-taylor-rule-equationIn fact … Taylor’s central claim about the alleged errors of monetary policy is bizarre. The Taylor rule was and is a clever heuristic for describing how central banks try to steer between unemployment and inflation, and perhaps a useful guide to how they ought to behave in normal times. But it says nothing at all about bubbles and financial crises; financial instability is impossible in the models usually used to justify the rule, and the rule wasn’t devised with such possibilities in mind. It makes no sense, then, to claim that following the rule just so happens to be exactly what we need to avoid crises. It slices! It dices! It prevents housing bubbles and stabilizes the financial system! No, I don’t think so.

Paul Krugman

The limits of statistical inference

5 May, 2015 at 15:25 | Posted in Statistics & Econometrics | Comments Off on The limits of statistical inference

causationCausality in social sciences — and economics — can never solely be a question of statistical inference. Causality entails more than predictability, and to really in depth explain social phenomena require theory. Analysis of variation — the foundation of all econometrics — can never in itself reveal how these variations are brought about. First when we are able to tie actions, processes or structures to the statistical relations detected, can we say that we are getting at relevant explanations of causation.

For more on these issues — see the chapter “Capturing causality in economics and the limits of statistical inference” in my On the use and misuse of theories and models in economics.

Demand theory gobbledygook

4 May, 2015 at 11:27 | Posted in Economics | 2 Comments

Back in 1992, New Jersey raised the minimum wage by 18 per cent while its neighbour state, Pennsylvania, left its minimum wage unchanged. Unemployment in New Jersey should — according to mainstream economic theory — have increased relative to Pennsylvania. However, when economists Alan Krueger and David Card gathered information on fast food restaurants in the two states, it turned out that unemployment had actually decreased in New Jersey relative to that in Pennsylvania. Counter to neoclassical demand theory we had an anomalous case of a backward-sloping supply curve.

Lo and behold!

But of course — when facts and theory don’t agree, it’s the facts that have to be wrong …
 
copy-Reality-bats-last-final-blk-ledge

buchC6The inverse relationship between quantity demanded and price is the core proposition in economic science, which embodies the pre-supposition that human choice behavior is sufficiently rational to allow predictions to be made. Just as no physicist would claim that “water runs uphill,” no self-respecting economist would claim that increases in the minimum wage increase employment. Such a claim, if seriously advanced, becomes equivalent to a denial that there is even minimal scientific content in economics, and that, in consequence, economists can do nothing but write as advocates for ideological interests. Fortunately, only a handful of economists are willing to throw over the teaching of two centuries; we have not yet become a bevy of camp-following whores.

James M. Buchanan in Wall Street Journal (April 25, 1996)

Two-envelope paradox explained (wonkish)

3 May, 2015 at 20:40 | Posted in Statistics & Econometrics | 1 Comment

 

När vinden vände

3 May, 2015 at 16:45 | Posted in Politics & Society | 3 Comments

Efterkrigstidens inkomstutjämning till följd av beskattning, utbildningsreformer, löneutjämning och ökat kvinnlig arbetskraftsdeltagande förbyttes under 1980-talet i ökade inkomstskillnader. Sedan dess har förmögenheterna vuxit i takt och aktiemarknadens tillväxt har tydligt gynnat de kapitalägande grupperna. Bilden bekräftar detta och särskilt notabelt är alltså att toppens andelar idag är så höga även när vi inte räknar in realiserade kapitalvinster.

Vi behöver veta mera om vad som drivit fram denna utveckling. Arbete pågår med att förstå skattereformernas inverkan som under de senaste åren kraftigt minskat skatterna på kapitalinkomster, förmögenhet, fastigheter, arv och gåvor och nu senast företagares utdelningar.

Daniel Waldenström

reagan-thatcherAllt ändrades på 1980-talet. Jäpp. Undrar om Margaret Thatcher, Ronald Reagan, normpolitik, nyliberalism, avregleringar och “den enda vägens politik” kan ha varit med och “drivit fram denna utveckling” …

My Madeleine cookie (private)

3 May, 2015 at 15:55 | Posted in Varia | Comments Off on My Madeleine cookie (private)

 

Marcel Proust had his Madeleine cookies. To me this song always brings back sweet summer memories of flipper playing and jukebox listening at the Luhrsjön beach café in the beginning of the ’70s …

Hypothesis and significance tests — the art of asking the wrong questions

3 May, 2015 at 09:39 | Posted in Statistics & Econometrics | 3 Comments

Most scientists use two closely related statistical approaches to make inferences from their data: significance testing and hypothesis testing. Significance testers and hypothesis testers seek to determine if apparently interesting patterns (“effects”) in their data are real or illusory. They are concerned with whether the effects they observe could just have emanated from randomness in the data.

null-hypothesis1The first step in this process is to nominate a “null hypothesis” which posits that there is no effect. Mathematical procedures are then used to estimate the probability that an effect at least as big as that which was observed would have arisen if the null hypothesis was true. That probability is called “p”.

If p is small (conventionally less than 0.05, or 5%) then the significance tester will claim that it is unlikely an effect of the observed magnitude would have arisen by chance alone. Such effects are said to be “statistically significant”. Sir Ronald Fisher who, in the 1920s, developed contemporary methods for generating p values, interpreted small p values as being indicative of “real” (not chance) effects. This is the central idea in significance testing.

Significance testing has been under attack since it was first developed … Jerzy Neyman and Egon Pearson argued that Fisher’s interpretation of p was dodgy. They developed an approach called hypothesis testing in which the p value serves only to help the researcher make an optimised choice between the null hypothesis and an alternative hypothesis: If p is greater than or equal to some threshold (such as 0.05) the researcher chooses to believe the null hypothesis. If p is less than the threshold the researcher chooses to believe the alternative hypothesis. In the long run (over many experiments) adoption of the hypothesis testing approach minimises the rate of making incorrect choices.

Critics have pointed out that there is limited value in knowing only that errors have been minimised in the long run – scientists don’t just want to know they have been wrong as infrequently as possible, they want to know if they can believe their last experiment!

Today’s scientists typically use a messy concoction of significance testing and hypothesis testing. Neither Fisher nor Neyman would be satisfied with much of current statistical practice.

Scientists have enthusiastically adopted significance testing and hypothesis testing because these methods appear to solve a fundamental problem: how to distinguish “real” effects from randomness or chance. Unfortunately significance testing and hypothesis testing are of limited scientific value – they often ask the wrong question and almost always give the wrong answer. And they are widely misinterpreted.

Consider a clinical trial designed to investigate the effectiveness of new treatment for some disease. After the trial has been conducted the researchers might ask “is the observed effect of treatment real, or could it have arisen merely by chance?” If the calculated p value is less than 0.05 the researchers might claim the trial has demonstrated the treatment was effective. But even before the trial was conducted we could reasonably have expected the treatment was “effective” – almost all drugs have some biochemical action and all surgical interventions have some effects on health. Almost all health interventions have some effect, it’s just that some treatments have effects that are large enough to be useful and others have effects that are trivial and unimportant.

So what’s the point in showing empirically that the null hypothesis is not true? Researchers who conduct clinical trials need to determine if the effect of treatment is big enough to make the intervention worthwhile, not whether the treatment has any effect at all.

A more technical issue is that p tells us the probability of observing the data given that the null hypothesis is true. But most scientists think p tells them the probability the null hypothesis is true given their data. The difference might sound subtle but it’s not. It is like the difference between the probability that a prime minister is male and the probability a male is prime minister! …

Significance testing and hypothesis testing are so widely misinterpreted that they impede progress in many areas of science. What can be done to hasten their demise? Senior scientists should ensure that a critical exploration of the methods of statistical inference is part of the training of all research students. Consumers of research should not be satisfied with statements that “X is effective”, or “Y has an effect”, especially when support for such claims is based on the evil p.

Rob Herbert

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