I’m a PhD student in economics at MIT, originally from New Zealand.
Shakked Noy
Karma: 83
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:
They’re increasingly a soft requirement for entry into top PhD programs. My impression from looking at the admit pools at the top ~5 schools this year is that virtually everyone either (a) did a predoc or (b) is an international student with a Master’s degree. The few students who got in straight out of undergrad were mostly students from very elite institutions (Harvard/MIT) interested in doing economic theory
I think doing a predoc at a top institution dominates doing a Master’s degree at a top institution, unless you’re interested in theory: you get paid fairly well, the skills you learn are much more relevant for research (unless you’re interested in theory), and you get more face-time with the faculty who write you recommendation letters.
Doing a predoc lets you “test run” doing a PhD, which I think is very valuable for undergrad EAs interested in economics:
You can figure out whether you actually enjoy/are good at the style of research that academic economists do.
It’s a very educational experience (you’re forced to quickly pick up technical skills, you get exposed to what cutting-edge economics research looks like, etc.). You might do a predoc and decide you’ve already learned enough to dive directly into an EA research or policy job.
One factor I severely underestimated when applying to predoc jobs is the importance of being a good “fit” in the sense of being personally interested in the kind of research you’d work on in that predoc job. If you don’t ultimately care about or aren’t interested in the research you’re assisting with, you end up being much less motivated and productive and both the educational and credentialing value of the predoc decrease substantially.
Derek Parfit has a good discussion of “prioritarian” views that place greater weight on the welfare of the less well-off: https://www.philosophy.rutgers.edu/joomlatools-files/docman-files/3ParfitEqualityorPriority2000.pdf
This is a really interesting post. A few points of pushback:
You object that facts about objective value would be causally inert (wouldn’t have a causal influence on people’s motivations or actions, or on anything else for that matter). Two things:
This isn’t true if you’re a naturalist moral realist, who holds that facts about objective value are reducible to natural facts that are causally efficacious.
Lots of other arguably unproblematic classes of facts are causally inert—including facts about mathematics, conscious experience (under some views of consciousness), and practical normativity. For example, we may think it’s objectively true that I ought not to stick my hand in a fire if doing so would hurt me and not help me at all. But this objective normative fact has no causal effect on my motivations or actions.
I don’t follow your discussion of why qualia anti-realism would undermine the idea that pleasure has intrinsic value. It seems possible to be a hedonist and also a naturalist or reductionist about conscious experience. The quote from Dr Drescher is pointing more at an evolutionary debunking argument against the belief that pleasure is intrinsically desirable than an argument that hedonism is incompatible with qualia anti-realism.
You claim your prioritization of goals other than pleasure is a counterexample to hedonism. But this is begging the question by assuming your priorities are reasonable or correct. Hedonists would simply argue in response that you’re mistaken about what to value and pursue. Similar remarks apply to your two thought experiments, which channel anti-hedonist intuitions. Hedonists would respond by arguing that these intuitions are mistaken (John’s comment gives some strong counter-intuitions). In fact, I think an evolutionary debunking argument much like the one Dr Drescher suggests would work very well at undermining these anti-hedonist intuitions.
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.
I’m skeptical that there’ll be a consensus 5 or 10 years from now on whether market power was a key contributor to 2021 inflation; there are just too many confounding factors blocking an inference like “inflation decreased without changes in competitiveness, so market power must not have been causing inflation.” It’s true that retrospective consensus sometimes emerges (for example, that financial deregulation was at least partly responsible for the Global Financial Crisis), but this is pretty rare.
I’m not sure how the forum started. I assume a key factor in making the participants interested was that the panel was started by a reputable institution (Chicago Booth) that could guarantee a wide audience. I wouldn’t guess that economists are more willing than academics from other fields to express views on policy-relevant issues; I suspect political scientists, sociologists, etc. would be similarly willing.
I think the IGM panel is great, and expanding it within economics or extending it to other fields would be great. Some quick thoughts:
It’s impossible to objectively retrospectively evaluate panel responses, because all of the interesting questions are about causal effects of X on Y, where the answer doesn’t become clear over time because the counterfactual is never observed. Maybe the best you could do is run a retrospective survey where you ask panel members (or other economists) whether they think the panel responses were right in hindsight.
I’m not surprised that Tyler Cowen has a negative view of many of the panel results; he’s a libertarian and the panel is broadly left-leaning, as is the broader economics profession. I think a better critique is one that Cowen expresses, e.g., in his interview on Spencer Greenberg’s podcast: that the views of economists seem to covary pretty closely with the political beliefs of their demographic group (highly educated urban dwellers), suggesting that their views aren’t purely reflective of professional expertise.
I also don’t think it’s concerning that the IGM forum “excludes certain schools of economics, like the Austrian school.” The economics profession doesn’t really have “schools” anymore, and I don’t think the “Austrian school” has ever had much mainstream credibility. The first and third bullet points from the 2016 paper are more concerning.
I don’t think the IGM panel influences future research topics, as the questions are typically about topics already highly salient to academic economists. I don’t know whether it influences public discourse or policymakers. The panel has high buy-in from academics because its members are all highly regarded in the profession.
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.