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och åtstramningspolitik ges ett skimmer av vetenskaplighet om man skalar bort den övliga tekniska mumbo jumbon som vi nationalekonomer så ofta tyvärr hänger oss åt kvarstår det enkla faktum att det som verkligen styr den på vaga och ifrågasättbara grunder beräknade jämviktsarbetslösheten är den faktiskt uppmätta arbetslösheten varför konjunkturinstitut riksbank och finansdepartement då ska lägga en massa pengar och datortid på att kalkylera denna jämviktsarbetslöshet är svårt att förstå bland de som likväl försvarar jämviktsarbetsbegreppet brukar det ofta framföras att det trots sina brister är en bra tankeram verkligen nairu och andra jämviktsarbetslöshetsbegrepp fungerar i själva verket inte alls bra som tankeram i själva verket fungerar de som inte minst senare tids forskning av exempelvis engelbert stockhammer 2011 özlem onaran 2012 roger farmer 2010 storm naastepad 2012 och andra övertygande visat monumentalt dåligt som just tankeram så varför detta fasthållande vid ett begrepp som alla inklusive dess försvarare medger är dåligt teoretiskt underbyggt och empiriskt nästintill omöjligt att skatta i vanliga fall brukar vi inom vetenskapen kassera icke falsifierbara teorier det är hög tid att också göra det med jämviktsarbetslöshetsbegreppet share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky nancy cartwright s contributions to the philosophy of science 16 jun 2026 at 11 21 posted in theory of science methodology 1 comment alongside roy bhaskar and tony lawson nancy cartwright is probably the philosopher of science who has had the greatest influence on my thinking in this area cartwright and yours truly firmly committed to critical realism share a deep commitment to depth realism we reject the flat humean empiricism that reduces causality to nothing more than regular associations between observable events instead we maintain that reality is stratified beneath observable phenomena lie real causal powers and mechanisms that may or may not be activated depending on the circumstances cartwright s distinction between closed and open systems closely parallels my own understanding in closed systems such as carefully controlled experiments regularities can be artificially generated and sustained the open systems that characterise most of the real world however are governed by the interaction of multiple mechanisms making simple law like predictions difficult and often impossible like critical realists more generally cartwright sees the task of science as identifying the generative mechanisms that produce observed phenomena rather than searching for universal constant conjunctions her conception of a dappled world in which laws are local contingent and context dependent is entirely compatible with my own ontological commitments and my rejection of reductionism we also share a methodological affinity in our use of transcendental reasoning asking what the world must be like for scientific knowledge and practice to be possible in the first place in this respect cartwright has always been an important intellectual ally in the effort to move beyond positivism and towards a genuinely realist philosophy of science as cartwright states in the interview her interest in science lies not only in understanding the world but also in transforming it i wholeheartedly share this commitment as a staunch defender of bhaskar s concept of explanatory critique i would argue that identifying social structures that generate suffering and harm is never a neutral act of understanding rather it is the recognition of conditions that violate human flourishing and intrinsic worth a recognition that carries with it an imperative to act this is not merely an ethical recommendation but a rational necessity scientific critique therefore must form an integral part of any emancipatory project demanding active intervention to remedy the injustices and harms it reveals share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky what does randomisation guarantee nothing 15 jun 2026 at 17 15 posted in statistics econometrics leave a comment the point of conducting a randomised experiment is often said to be that it allows researchers to make stronger causal inferences from observed correlations this claim rests on the idea that randomisation reduces systematic differences between treatment and control groups thereby limiting the influence of confounding factors the problem with this rather simplistic view of randomisation is that the claims made on its behalf are both exaggerated and strictly speaking false even if the assignment of subjects to treatment and control groups is perfectly random sample selection almost certainly is not except in extremely rare cases proper random assignment does not compensate for a biased sample if experimental results are derived from a non representative sample there is always a risk that the findings will not generalise beyond that setting what works there may not work here randomisation therefore does not guarantee or ensure that we arrive at the correct causal conclusion while it may help us rule out certain alternative explanations randomisation in itself guarantees nothing even if both sampling and assignment were carried out in an ideally random manner standard randomised experiments provide only average effects the difficulty is that an estimate of the true average causal effect may conceal important heterogeneity the average treatment effect may be zero while some individuals experience effects of 100 and others effects of 100 for anyone considering whether to undergo treatment such heterogeneity is likely to be far more informative than the average effect alone there is almost always a trade off between bias and precision in real world settings a small amount of bias may be more than offset by a substantial gain in precision more importantly when a population exhibits significant heterogeneity the average treatment effect estimated from a sample may differ considerably from the average treatment effect in the wider population if that is the case the value of extrapolating findings from trial samples to other populations becomes highly questionable since most real world experiments and trials involve only a single randomisation the properties that would emerge over an infinite sequence of repeated randomisations provide little reassurance about the validity of the one experiment that is actually conducted it is difficult to see why knowledge of what would happen under circumstances that will never occur should make us more confident about the conclusions drawn from the single study we have performed the problem faced by many randomistas when they underestimate heterogeneity and interaction effects is not merely one of external validity when attempting to transfer regression results across periods contexts or populations it is also an internal problem affecting the millions of regression estimates produced by economists each year ideally controlled experiments can tell us with certainty what causes what effects but only given the appropriate closure assumptions making valid extrapolations from experiments whether ideal accidental natural or quasi experimental to different settings populations or target systems is far from straightforward since trials are rarely repeated properties such as unbiasedness and balance in repeated sampling tell us nothing directly about any particular trial it works there is not evidence that it will work here causes identified in an experimental setting must still be shown to possess an export warrant to the target population or system the underlying causal assumptions must be justified and without such a warrant the value of even the most rigorous and precise methods and of knowledge that holds only on average is severely limited rcts have little reach beyond describing what has occurred in the past from the perspective of future decision making and public policy they are often of limited value because they cannot tell us which background factors were held constant when the intervention was implemented nor do rcts generally provide evidence that their results are exportable to other target systems they cannot simply be assumed to yield generalisable findings the fact that something works somewhere for someone is no guarantee that it will work for us here or indeed that it works more generally randomisation may in the right circumstances help us draw causal conclusions however it is neither necessary for establishing causality nor sufficient for securing scientific validity randomisation guarantees nothing just as observational studies are vulnerable to various forms of bias so too are randomised studies and trials share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky model reification in statistics and econometrics 15 jun 2026 at 10 57 posted in economics statistics econometrics 2 comments conventional statistical methods assume that within measured covariate levels the data come from surveys with purely random selection from a sharply defined population or from experiments with purely random treatment assignment in which the researcher has complete control and understanding of every important aspect of those who entered the analysis and how they were treated the methods thus provide logically sound inferences only in ideal cases rather than correct answers for our actual imperfect applications they are however widely applied and misinterpreted in studies that fall far short of their assumptions we may view each statistical analysis as a thought experiment in a fictional small world or toy example sharply restricted by its simplifying assumptions the questions that motivated the study must be translated properly into this fictional world statistical methods then answer the questions via mathematical deductions from the assumptions but those answers apply with logical force only within a fictional world in which all the assumptions hold sander greenland since econometrics aspires to explain things in terms of causes and effects it needs many assumptions limiting model assumptions in economic science always have to be closely examined because if we are to show that the mechanisms or causes we isolate in our models are stable in the sense that they do not change when we export them to our target systems we must also demonstrate that they do not hold only under ceteris paribus conditions if they do they are of limited value to explanations and predictions of real economic systems unfortunately real world social systems are usually not governed by stable causal mechanisms or capacities the kinds of laws and relations that econometrics has established are about entities in models that presuppose invariant atomistic and additive causes but when causal mechanisms operate in the real world they mostly do so in ever changing and unstable ways if economic regularities obtain they do so as a rule only because we have engineered them for that purpose outside man made nomological machines they are rare or even non existent model reification in econometrics is evident when properties that belong strictly to models are treated as if they were real world features econometricians standardly aim to export model based mechanisms to target systems without change this metaphor itself implies reification the gap between model assumptions and messy reality is a gap that econometrics often forgets treating engineered regularities as inherent economic laws is reification pure and simple share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky why democracy outperforms dictatorship 14 jun 2026 at 10 35 posted in statistics econometrics 1 comment the power of diversity is not merely a social ideal it is a mathematical fact the diversity prediction theorem demonstrates that bringing together people who think differently generates a measurable performance advantage imagine that you fill a transparent jar with 1000 jelly beans and ask a group of students to estimate how many are inside amanda guesses 980 bob 1050 clark 1020 and david 940 individually their estimates are relatively inaccurate however when we treat the students as a collective the average of their guesses is 997 5 remarkably close to the true number the diversity prediction theorem explains why collective error average individual error prediction diversity the accuracy of a group depends both on the average quality of its members judgements and on the extent to which their predictions differ from one another if the students are sufficiently diverse making independent estimates based on different information experiences or perspectives much of the individual error effectively cancels out producing a collective estimate that is closer to the truth from an economic perspective the diversity prediction theorem challenges conventional models of rationality and efficiency diversity is not simply a matter of fairness or representation it generates a tangible advantage the most resilient and innovative economies are those that harness the power of a wide range of perspectives experiences and ways of thinking the diversity prediction theorem also provides a theoretical argument for why democratic systems often have an advantage over dictatorships they can harness the knowledge and perspectives of many different people reducing collective error and improving decision making it does not prove that democracy is always superior but it helps explain why inclusive and participatory political systems standardly outperform rule by a single leader or a narrow elite share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky tidöregeringens dårhusmässiga ekonomiska visdom 11 jun 2026 at 15 57 posted in economics politics society 4 comments det är inte troligt att alla som med gillande upprepa talet om att vi måste spara och därför inte kunnat skaffa arbete åt de arbetslösa ha gjort klart för sig vad deras ståndpunkt innebär den leder nämligen till den fantastiska slutsatsen att arbete är en lyx att arbete åt alla medborgare är något man kan kosta på sig i rika och välmående samhällen men som långt överstiger krafterna hos ett fattigt land som sverige och överhuvudtaget hos alla de av krisen drabbade länderna ju svårare krisen blir ju mera arbetslösheten växer desto mindre blir inkomsterna i hela samhället desto mer måste alltså kravet på sparsamhet skärpas och desto omöjligare skulle det bli att sätta igång arbeten inför den växande arbetslösheten skulle medborgarna alltså bekymrat men undergivet säga till varandra vi äro för fattiga för att kunna arbeta och ju fattigare vi bli desto mindre har vi råd att 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