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ens situation är så klart att man skulle ha ett underskotts mål och möjliggöra ännu större nödvändiga investeringar i välfärd och underhåll av vår infrastruktur och att nu låta skuldankaret ligga kvar på 35 är fel väg att gå med de problem svensk ekonomi brottas med idag inte minst den höga arbetslösheten duger inga sådana harhjärtade halvmesyrer share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky how credible is the credibility revolution in econometrics 17 jun 2026 at 16 32 posted in statistics econometrics leave a comment the so called credibility revolution in econometrics rests on the non parametric and robust nature of its inferences which are seen as independent of incredible or at best doubtful economic theories such approaches including natural experiments and regression discontinuity designs like well designed rcts often do carry conviction over their domain of validity but as is often the case they estimate an ate that is valid only in the limited circumstances under which it was obtained an aspect of the familiar trade off between getting a result that is likely to be accurate in itself versus getting one that is likely to be useful as evidence for other claims put differently the special requirements of rcts regression discontinuity methods and instrumental variables often give us an estimate of a local magnitude that is not what we originally set out to measure credibility is fine but it would be better if it did not so often involve a shift away from what we want to measure to some other quantity that is dictated by the design indeed if we focus on design we run the risk of prioritizing method over substance and of ranking results not by what we learn about the world but according to a hierarchy of method which is one of our main targets economics is about a great deal more than estimating average treatment effects it is not epidemiology and the design analysis framework will not make economics more credible but turn it into the kind of alchemy that turns gold into lead not the reverse angus deaton nancy cartwright especially when it comes to questions of causality randomisation is nowadays considered something of a gold standard everything has to be evidence based and the evidence must come from randomised experiments funding bodies demand it journals prefer it policymakers trust it but just like econometric modelling randomisation is basically a deductive method given the assumptions such as manipulability transitivity separability additivity linearity and so forth these methods deliver deductive inferences the problem of course is that we will never completely know when the assumptions are correct and although randomisation may contribute to controlling for confounding it does not guarantee it genuine randomness presupposes infinite experimentation or at least a well defined superpopulation and a perfectly executed randomisation protocol yet we know that all real experimentation is finite in finite samples randomisation only ensures balance on average across hypothetical repetitions for any given actual trial substantial imbalances may remain and even if randomisation helps to establish average causal effects it says nothing about individual effects unless homogeneity is added to the list of assumptions which defeats the whole point of allowing for heterogeneity real target systems are seldom epistemically isomorphic to our axiomatic deductive models or systems that is the structure of reality rarely matches the neat closed mathematically tractable worlds we construct on paper or in our statistical software and even if they were isomorphic we would still have to argue for the external validity of the conclusions reached from within these epistemically convenient models causal evidence generated by randomisation procedures may be valid in closed models models in which all relevant variables are measured no unobserved confounding exists no interactions alter the effect when exported and no feedback loops distort the system but what we are usually interested in is causal evidence in the real target system in which we happen to live an open dynamic adaptive and only partly measurable world when does a conclusion established in population x hold for target population y only under very restrictive conditions when the distributions of all effect modifiers are the same when the causal structure is identical when the measurement procedures are commensurable and when no new causal pathways emerge these conditions are rarely if ever met in social science ideally controlled experiments tell us with certainty what causes what effects but only given the right closures that is a big only making appropriate extrapolations from ideal accidental natural or quasi experiments to different settings populations or target systems is not easy it works there is no evidence for it will work here causes deduced in an experimental setting still need to demonstrate that they come with an export warrant to the target population or system the causal background assumptions made must be justified without licences to export the value of rigorous and precise methods and of on average knowledge is despairingly small our statistical precision may be high but if we are precisely wrong about the wrong target we have achieved nothing of worth in the end a precise estimate of the wrong quantity is far worse than a rough estimate of the right one and randomisation for all its technical sophistication often gives us the former experiments are most effective when evaluating isolated causal relationships with clear separable effects they rely on a simplifying premise a policy intervention either produces an identifiable effect or it does not and the role of the experiment is to estimate that effect however many of the central questions in economics such as long run growth human capital formation inequality or institutional development do not conform to this structure these are inherently multi dimensional processes shaped by interdependent variables and feedback mechanisms that evolve over time in such settings the assumptions underlying experimental methods such as stable treatment effects sutva become harmfully restrictive what is often abstracted away is the complexity of real economic systems agents interact strategically expectations adjust and policies propagate through networks and markets in non linear ways outcomes are path dependent and causal effects may vary across contexts and over time as a result interventions rarely yield binary stable effects to address these phenomena we need an ontology and conceptualisation more in tune with real world economies we have to move beyond a narrow experimental paradigm towards an analytical framework that treats the economy as the complex adaptive system it really is so no i find it hard to share the enthusiasm and optimism about the value of quasi natural experiments and all the statistical econometric machinery that comes with them the fanfare surrounding the credibility revolution strikes me as disproportionate to the actual achievements we have perhaps traded one set of naive assumptions the assumption that our structural models are correctly specified for another the assumption that randomisation solves everything in both cases we evade the hard work of understanding causal heterogeneity testing for external validity and grounding our inferences in substantive social theory i suppose i am still waiting for the export warrant until it arrives and i suspect it never will except in the most trivial of cases i shall remain a sceptic not of empiricism per se but of the uncritical almost religious devotion to a particular set of empirical techniques that are no less assumption laden than the theories they were meant to replace share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky randomness and representativeness are not the same 17 jun 2026 at 14 24 posted in statistics econometrics leave a comment in research and everyday reasoning the terms random and representative are often used interchangeably many people assume that if a sample is selected at random it will automatically reflect the wider population and conversely that a representative sample must have been chosen randomly this is however a misunderstanding randomness and representativeness are two fundamentally distinct properties one describes a procedure the other describes an outcome they can and often do come apart let us consider a simple classroom experiment a teacher has a group of 20 children 10 boys and 10 girls the teacher asks the children whether they prefer sausages or ice cream and wants to select a small group of six children whose opinions can stand for the whole class she first uses a purely random method she writes all 20 names on slips of paper places them in a hat and draws six names without looking by chance the draw yields five boys and only one girl suppose that in this class the boys strongly prefer sausages while the girls overwhelmingly prefer ice cream the randomly selected group with its heavy male majority ends up favouring sausages by a large margin even though the class as a whole given its equal gender split might actually prefer ice cream this sample was random in every technical sense each child had an equal chance of being selected and chance alone determined the outcome yet it is clearly not representative of the class as a whole because it fails to reflect the crucial gender balance that shapes the underlying preference distribution randomness provides no guarantee of representativeness it merely ensures equal probability of selection and probabilities do not always produce proportional outcomes in a single draw now consider the opposite case the teacher adopts a different approach instead of drawing names from a hat she deliberately selects three boys and three girls to match the class s 50 50 gender split to do this she simply chooses the first three boys and the first three girls who raise their hands this sample perfectly mirrors the class in terms of gender and since gender is the main factor influencing food preference the sample s opinions will closely track those of the whole class it is in other words highly representative yet the procedure is clearly not random the teacher does not use chance she applies a conscious non probabilistic rule furthermore she only samples children who happen to raise their hands quickly which may exclude distracted or shy pupils thus a representative sample has been produced through a non random method demonstrating that representativeness does not require randomness what these two cases show is that randomness and representativeness answer different questions randomness tells us how a sample is selected it is a feature of the selection mechanism representativeness tells us how the sample looks like it is a feature of the sample s composition a random sample may by chance fail to capture those characteristics conversely a carefully constructed quota sample may capture them accurately without any use of chance in the end both properties are valuable for different reasons randomness is important because it enables statistical inference we can calculate margins of error and confidence intervals because the selection process is known representativeness is important because it gives us confidence that our conclusions apply to the population of interest the ideal study combines both but as my sausage and ice cream example shows in practice these are two distinct ideals a random sample can be unrepresentative and a representative sample can be non random confusing the two is a common error but it remains an error nonetheless share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky lönebildning och arbetslöshet i sverige 17 jun 2026 at 09 51 posted in economics 14 comments i ekonomisk debatt 4 2026 lyfter ekonomiprofessor pär österholm den höga svenska arbetslösheten som han menar har strukturella orsaker konjunkturinstitutet uppskattar den så kallade jämviktsarbetslösheten till 7 3 procent det är denna nivå österholm tar som utgångspunkt jämviktsarbetslösheten är avgörande i österholms text eftersom det är den som påvisar problemets vidd ändå accepterar han okritiskt begreppet och konjunkturinstitutets nivå det är alltså så lågt arbetslösheten antas kunna pressas innan den blir inflationsdrivande men jämviktsarbetslösheten går inte att observera det är en teoretisk konstruktion på mycket skakig grund felmarginalen är därtill stor och antagandena har svag verklighetsförankring värre är att en felaktigt hög uppskattning kan bli självuppfyllande om finans och penningpolitiken stramar åt för tidigt skapas hysteresis att arbetslösheten biter sig fast frågan är om en viktigare förklaring till den höga arbetslösheten snarare är just den teoribildning som österholm bekänner sig till daniel lind german bender med tanke på det nästintill obefintliga empisk statistiska stöd som jämviktsarbetslöshets begreppet med rötter i milton friedmans naturliga arbetslöshet och nairu har är det minst sagt anmärkningsvärt att mainstreamekonomer fortsätter använda det det är svårt att undgå misstanken att det till stor del handlar om ideologiska motiv med inställningen att full sysselsättning är omöjlig att uppnå kan högerpolitiska förslag om fortsatt nedmontering av det svenska välfärdssamhället 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 contr...
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