I'm a PhD student in economics at MIT, originally from New Zealand.
My (fairly uninformed) impression is that Israel is somewhat unique in that economic policy is not a major axis of differentiation between the main political parties. It might therefore be much easier for bloggers/commentators to influence economic policy because it is not very politicized. Would you disagree with that impression?
Strongly agreed with both of the comments above. One additional strategy to consider would be looking into whether any of the professors in your school's economics department typically hire part-time undergrad research assistants. If so, taking a class with one of those professors and impressing them is a good way to get a job, which can then serve as a stepping-stone to RA jobs at other institutions (since you'll pick up skills and get someone who can write you a recommendation letter).
Great post; I'd note that while you focus on arguing that the correlation between Ivy admissions and intelligence is lower than one might naively expect, the statement "most Ivy-smart students aren't at Ivy-tier schools" is also a trivial consequence of the fact that these schools collectively admit very few people and there are plausibly at least twice as many "Ivy-smart" applicants each year as there are spots at Ivies.
Economists have thought a bit about automation taxes (which is essentially what you're suggesting). See, e.g., this paper.
I'm currently working as a predoc so am happy to chat if you have any questions. Honestly, I doubt RA jobs at EA orgs can achieve that in the foreseeable future, since so much of the value of a predoc comes in the form of a letter from a professor who's tightly integrated into the network of top academic economists. Unless EA orgs can attract senior researchers with tight connections to faculty at top schools, and clout with those faculty, that won't happen.
Amazing and super informative post! A few more thoughts on "predocs" (1-2 year post-BA full-time research assistantships focused on empirical work), which have exploded in popularity since the 80k article was written:
This is a really interesting post. A few points of pushback:
It's not clear that your claim that "[mathematics has] commonly accepted, rigorous methodologies for determining what counts as 'domain knowledge' [while morality] does not" is true. See this paper for relevant counterarguments: http://www.pgrim.org/philosophersannual/34articles/clarkedoanemoral.pdf
In brief: the methodology used by mathematicians (postulate axioms and derive theorems from those axioms, in the long-run engaging in a process of reflective equilibrium to narrow down to the right set of axioms and theorems) can also be applied in moral philosophy (and it appears to be exactly what modern moral philosophers do). Moreover, it's not at all clear that commonly-used mathematical axioms are less controversial than commonly-used moral axioms.
Hi everyone! I’m Shakked, a PhD student in economics at MIT, and this comment summarizes The Short-Termism of ‘Hard’ Economics, a chapter in Essays on Longtermism that I coauthored with my dad Ilan, a Professor of Economics at Victoria University of Wellington in New Zealand.
The chapter is about the academic economics profession. We think the profession has not yet, and will not in the foreseeable future, produce much useful longtermist research as part of the mainstream research it systematically produces, publishes in top journals, and rewards professionally. (Individual economists might still produce useful longtermist research as voluntary side-projects, as we note below.) In the chapter, we argue that this is because the profession is subject to a constricting set of methodological norms that preclude the kinds of research that might be useful from a longtermist perspective.
Specifically, we think that useful longtermist research - by virtue of its speculative nature and the thin historical evidence base it has to rely on - will tend to have a few attributes. It will draw on a variety of sources of empirical evidence, including expert forecasts, narrative historical commentaries, quantitative projections, interviews and focus groups, and so on. It will make theoretical arguments that are often institutionally-specific or draw on a diversity of concepts. As a consequence of these diversities of evidence and argument, it will tend to be multi-disciplinary.
Almost all of the above are ruled out by the current methodological norms of the academic economics profession. An obsession with methodological “hardness” - a concept which George Akerlof says is used to “classify sciences according to a hard–soft hierarchy, with physics at the top and sociology, cultural anthropology, and history at the bottom” - results in the imposition of severe restrictions on the kinds of work economists accept. On the empirical side, this means that only carefully identified causal estimates are permissible forms of empirical evidence. This rules out descriptive, explanatory, or predictive work and narrows attention to topics where sufficiently large micro datasets are available, implicitly narrowing the geographic and temporal scope of research. On the theoretical side, this involves a focus on mathematical generality and technical sophistication and difficulty, which precludes both arguments that are impossible to formalize mathematically and arguments that are trivial to formalize.
The chapter goes into a lot of detail about the exact shape of the norms and applies them to three areas of longtermist interest: long-term decision-making, climate change, and AI.
The chapter was written in mid-2022, before the release of ChatGPT, so the past 3 years have constituted an interesting out-of-sample test of the arguments and predictions we make. We think the chapter has held up pretty well. There’s been an enormous growth in research on AI in economics; the majority of this new research has taken the form of the kind of short-termist empirical or backwards-looking theoretical work we describe in the chapter. There’s also been an increase in interest in longtermist perspectives on AI, including an upcoming NBER conference on the Economics of Transformative AI, as well as some examples of genuinely useful research. But so far our sense is these developments show no sign of penetrating the top journals or professional reward processes.