by The radical façade of randomistas do not help us fight poverty Perhaps the most concerning aspect of the randomista enterprise is their claim to neutrality and objectivity. While knowledge generated by RCTs may be able to generate useful insights in some instances, evidence always requires interpretation … The findings of the randomistas do not speak for themselves; they require interpretation. The randomistas’ interpretation of these results through a neoclassical lens limits their understanding of social phenomena because it fails to understand how structures constrain individual behavior. Particularly in light of covid-19, this theoretical and methodological narrowing of the field and of what counts as evidence is a problem for our ability to build
Topics:
Lars Pålsson Syll considers the following as important: Economics
This could be interesting, too:
Lars Pålsson Syll writes Daniel Waldenströms rappakalja om ojämlikheten
Peter Radford writes AJR, Nobel, and prompt engineering
Lars Pålsson Syll writes MMT explained
Lars Pålsson Syll writes Statens finanser funkar inte som du tror
The radical façade of randomistas do not help us fight poverty
Perhaps the most concerning aspect of the randomista enterprise is their claim to neutrality and objectivity. While knowledge generated by RCTs may be able to generate useful insights in some instances, evidence always requires interpretation … The findings of the randomistas do not speak for themselves; they require interpretation. The randomistas’ interpretation of these results through a neoclassical lens limits their understanding of social phenomena because it fails to understand how structures constrain individual behavior.
Particularly in light of covid-19, this theoretical and methodological narrowing of the field and of what counts as evidence is a problem for our ability to build a more just and resilient society, given the structural fragilities the pandemic has exposed (Alves and Kvangraven 2020). The laureates draw attention to the massive disparities and poverty in the world, and in many instances also the problems with relying on market forces to fix these issues. However, their solutions center on patching the system here and there – with vaccines and social safety nets – rather than addressing the underlying systemic problems that give rise to poverty and inequality …
The randomista enterprise tends to delegitimize other ways of knowing, thereby excluding centuries of insights and research in the social sciences from across the world. While in line with the marginalization of alternative economic theories since the 1970s, the randomistas have helped cement a hierarchical, positivist and Eurocentric field. To decolonize economics, it is nec- essary to challenge RCTs’ claim to objectivity, while pushing to open up space in the field for epistemologies that originate from outside of the West.
Most ‘randomistas’ underestimate the heterogeneity problem. It does not just turn up as an external validity problem when trying to ‘export’ regression results to different times or different target populations. It is also often an internal problem to the millions of regression estimates that are produced every year.
Just as econometrics, randomization promises more than it can deliver, basically because it requires assumptions that in practice are not possible to maintain. And just like econometrics, randomization is basically a deductive method. Given the assumptions, these methods deliver deductive inferences. The problem, of course, is that we will never completely know when the assumptions are right. And although randomization may contribute to controlling for confounding, it does not guarantee it, since genuine randomness presupposes infinite experimentation and we know all real experimentation is finite. And even if randomization may help to establish average causal effects, it says nothing of individual effects unless homogeneity is added to the list of assumptions. Causal evidence generated by randomization procedures may be valid in ‘closed’ models, but what we usually are interested in, is causal evidence in the real-world target system we happen to live in.
‘Ideally controlled experiments’ tell us with certainty what causes what effects — but only given the right ‘closures.’ 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 have to show that they come with an export-warrant to the target population/system. The causal background assumptions made have to be justified, and without licenses to export, the value of ‘rigorous’ and ‘precise’ methods — and ‘on-average-knowledge’ — is despairingly small.
Apart from these methodological problems, there is also a rather disturbing kind of scientific naïveté in the randomista approach to combatting poverty. The way randomistas present their whole endeavour smacks of not so little ‘scientism’ where fighting poverty becomes a question of applying ‘objective’ quantitative ‘techniques.’ But that can’t be the right way to fight poverty! Fighting poverty and inequality is basically a question of changing the structure and institutions of our economies and societies.