Does this mean that even if all of the funds that FTX-related entities granted to EVF-related entities were clawed back, donations to EA Funds (or otherwise through GWWC) would be unaffected (i.e., would still end up in their intended recipients’ hands)?
HStencil
- GWWC Should Require Public Charity Evaluations by 9 Jan 2023 20:11 UTC; 93 points) (
- GWWC Should Require Public Charity Evaluations by 9 Jan 2023 20:10 UTC; 28 points) (LessWrong;
- 4 Jan 2023 16:03 UTC; 6 points) 's comment on StrongMinds should not be a top-rated charity (yet) by (
New Harvest is also listed as a standout charity in spite of (my impression is) an even narrower focus on cell-cultured product innovation than GFI (which also supports plant-based meat substitutes). I too would love some clarity from ACE on this.
[Question] What would “doing enough” to safeguard the long-term future look like?
[Question] Did Fortify Health receive $1 million from EA Funds?
Credit Cards for EA Giving
I think this is a really hard question, and the right answer to it likely depends to a very significant degree on precisely what you’re likely to want to do professionally in the near and medium-term. I recently graduated from a top U.S. university, and my sense is that the two most significant benefits I reaped from where I went to school were:
Having that name brand on my resume definitely opened doors for me when applying for jobs during my senior year. I’m actually fairly confident that I would not have gotten my first job out of college had I gone to a less prestigious school, though I think this only really applies to positions at a fairly narrow set of financial services firms and consulting firms, as well as in certain corners of academic research.
I think I personally benefited from a significant peer effect. My specific social circle pushed me to challenge myself academically more than I likely otherwise would have (in ways that probably hurt my GPA but served me well all things considered). That said, I know that the academic research on peer effects in education is mixed to say the least, so I’d be hesitant to extrapolate much from my own experience.
I’m not sure how to weigh the importance of the first of those considerations. On the one hand, your first job is just that: your first job. It doesn’t necessarily mean anything about where you’ll end up at age 35. On the other hand, I do feel like I have observed this phenomenon of smart people graduating from relatively unknown universities and really struggling to find interesting work during their first several years out of college and then eventually resigning themselves to getting a master’s degree from a more well-known school (sometimes in a field where the educational benefit of the degree is relatively low) just so that they can get in the door to interview for jobs in their field of choice. This obviously comes at a significant cost, both in terms of time and—often but not always—in terms of money. That said, in some fields, you just do need a master’s to get in the door for a lot of roles, no matter where you went to undergrad or what you did while you were there, and maybe that’s all that’s really behind this.
Another thing potentially worth noting is that, in my experience, it seems as if U.S. research universities are most usefully divisible into three categories with respect to their undergraduate job placement: universities that “high-prestige” employers are unlikely to have heard of, universities that “high-prestige” employers are likely to have heard of and have vaguely positive associations with, and finally, the set of Harvard, Princeton, Yale, MIT, and Stanford (these are distinguished not only by their name brands but also by the extent of their funding and support for undergraduate research and internships, the robustness of their undergraduate advising, and other more “experiential” factors). There are certainly exceptions to this breakdown (the financial services and consulting firms mentioned above definitely differentiate between Penn and Michigan), but by and large, my sense has been that controlling for “ability,” the difference in early-career outcomes between a Harvard graduate and a Penn graduate is significantly larger than the difference in early-career outcomes between a Penn graduate and a Michigan graduate (note: the specific schools chosen as examples within each cohort here are completely arbitrary). Accordingly, I don’t think that very many people generally have a strong professional reason to transfer from UCLA to Brown or from the University of Virginia to Dartmouth, etc. However, I buy that those at lesser-known schools may, in many circumstances, have a strong professional reason to transfer to their flagship state school.
Other good reasons to transfer, I think, include transferring for the purpose of getting to a particular city where you know you want to work when you graduate, with an eye toward spending a portion of the remainder of your time in college networking or interning in your field of choice. In particular, I think that if you want to work in U.S. (national) policy after graduation, transferring to a school in the Washington, DC Metropolitan Area can be hugely beneficial. The same goes for financial services in the New York City Metropolitan Area, entertainment in Los Angeles, and (perhaps, though I am less sure about this) tech in the San Francisco Bay Area. In your case, it might be worthwhile to submit a transfer application to Georgetown with the aim of trying to forge some connections at the Center for Security and Emerging Technology (or perhaps the Center for Global Health Science and Security if you are interested in biosecurity policy), both of which are housed there. One other very strong reason to transfer, it seems to me, would be if you wanted to work on AI, but your current school didn’t have a computer science department, like a local state school near where I grew up. I assume from your post that that isn’t your situation, though.
Finally, I wouldn’t underestimate the importance of mental health considerations, to the extent that those may be at all relevant to your choice. Mental health during college can have a huge impact on GPA, and while where you go to undergrad will only really be a factor in determining your grad school prospects for a relatively narrow set of programs (mainly, I think, via the way it affects the kinds of research jobs you can get during and post-college), GPA is a huge determinant of grad school admissions across basically every field, so that is important to bear in mind. The transfer experience, from what I have heard, is not always easy, especially, I imagine, in academic environments that are already very high-pressure.
If you’d like to talk through this at greater length, feel free to DM me. To the extent that my perspective might be useful, I’d be more than happy to offer it, and if you’d just like someone to bounce ideas off of, I’d be happy to fill that role, as well.
For the last few years, I’ve been an RA in the general domain of ~economics at a major research university, and I think that while a lot of what you’re saying makes sense, it’s important to note that the quality of one’s experience as an RA will always depend to a very significant extent on one’s supervising researcher. In fact, I think this dependency might be just about the only thing every RA role has in common. Your data points/testimonials reasonably represent what it’s like to RA for a good supervisor, but bad supervisors abound (at least/especially in academia), and RAing for a bad supervisor can be positively nightmarish. Furthermore, it’s harder than you’d think to screen for this in advance of taking an RA job. I feel particularly lucky to be working for a great supervisor, but/because I am quite familiar with how much the alternative sucks.
On a separate note, regarding your comment about people potentially specializing in RAing as a career, I don’t really think this would yield much in the way of productivity gains relative to the current state of affairs in academia (where postdocs often already fill the role that I think you envision for career RAs). I do, however, think that it makes a lot of sense for some RAs to go into careers in research management. Though most RAs probably lack the requisite management aptitude, the ones who can effectively manage people, I think, can substantially increase the productivity of mid-to-large academic labs/research groups by working in management roles (I know J-PAL has employed former RAs in this capacity). A lot of academic research is severely management-constrained, in large part because management duties are often foisted upon PIs (and no one goes into academia because they want to be a manager, nor do PIs typically receive any management training, so the people responsible for management often enough lack both relevant interest and relevant skill). Moreover, productivity losses to bad management often go unrecognized because how well their research group is being managed is, like, literally at the very bottom of most PIs’ lists of things to think about (not just because they’re not interested in it, also because they’re often very busy and have many different things competing for their attention). Finally, one consequence of this is that bad RAs (at least in the social sciences) can unproductively consume a research group’s resources for extended periods of time without anyone taking much notice. On the other hand, even if the group tries to avoid this by employing a more active management approach, in that case a bad RA can meaningfully impede the group’s productivity by requiring more of their supervisor’s time to manage them than they save through their work. My sense is that fear of this situation pervades RA hiring processes in many corners of academia.
At least until quite recently, there was a fairly uniform consensus in mainstream Anglo-American economics that the convergence thesis was true. I think this was mainly because it was based on fundamental theoretical insights that were believed to be relatively unimpeachable, like the Solow Model and the Stolper-Samuelson Theorem.
The Solow Model uses a formal representation of the idea that capital can be put to better use (yielding a higher economic return) in places where it is more scarce to demonstrate that, all other things being equal, places further from a given steady-state output level will grow toward that level faster than places nearer to it. In other words, ceteris paribus, places where capital stock is lower will grow faster than places where capital stock is higher because adding a marginal unit of capital in a capital-poor economy will generate a greater return than adding a marginal unit of capital in a capital-rich economy, where all the high-yielding capital investment opportunities have already been funded. (Bear in mind, though, that “ceteris paribus” is doing a lot of work in that sentence. You might reasonably claim that the traditional Solow Model holds constant nearly everything we ought to care about in trying to explain development outcomes.) To the extent that it’s true, though, in a world with open cross-border capital flows, one would expect capital to flood from low-return investment opportunities in wealthier countries to high-return investment opportunities in poorer countries. Alas, the evidence that this is actually taking place on a large scale is mixed at best, and other factors excluded from the neoclassical theories of international trade and finance likely play a large role in determining the global allocation of capital.
The productivity term in the Solow Model also often comes up in discussions of convergence. This term, representing an economy’s efficiency at deploying its factors of production to make things, is frequently treated—for the purpose of simplification—as a representation of an economy’s level of technological advancement alone. Traditional growth economists tend to treat rates of technological advancement as largely exogenous (whether this assumption is realistic is the subject of considerable debate). However, separate models of global technological advancement are typically built around the idea that it’s cheaper to copy a technology that was developed in another country and put it to use in one’s domestic industries than it is to develop a wholly new technology from scratch, thereby advancing the technological frontier. As a result, economists often conclude that countries not yet at the technological frontier will enjoy faster productivity growth than counties that are at the technological frontier, in accordance with the convergence paradigm.
The Stolper-Samuelson Theorem shows that when a national economy specializes in the production of a good in which it has a comparative advantage and then the relative price of that good rises on global markets, the return on investment in the factor of production that most contributes to making that good will rise. For example, if a country has a comparative advantage in making blue jeans, and it specializes in making blue jeans, and labor is the most important factor of production in making blue jeans, if the relative price of blue jeans on globals markets rises, then the return on investment in labor in that country will rise. This is equivalent to saying that the marginal product of labor in that country will rise, and in a competitive labor market, the price of labor (the wage) should equal its marginal product, so producer wages should rise with, for instance, a relative increase in global demand for blue jeans (which would push up the price).
There is vigorous debate over the extent to which the Stolper-Samuelson Theorem is applicable to world in which we live today. It requires making a number of assumptions in order for its conclusion to hold (constant returns to scale, perfect competition, an equal number of factors and products). One famous counterexample to Stolper-Samuelson was proposed Raúl Prebisch and Hans Singer and was embraced by the anti-trade left of the postwar years. Prebisch and Singer propose that because complex manufactured goods (like computers) exhibit greater income elasticity of demand than simple commodities (like wheat or coffee), if a country specializes in exporting wheat (consistent with its comparative advantage), and relies on imports from foreign manufacturers to get computers, as global incomes rise, it will suffer declining terms of trade (i.e. as time passes, each imported computer will cost more and more wheat). Today, the Prebisch-Singer Hypothesis, as it’s called, has received some degree of very qualified acceptance by mainstream economists. Its fundamental proposal that it doesn’t always make sense to treat comparative advantages as destiny is quite widely accepted, though more on the basis of Paul Krugman’s work in New Trade Theory (demonstrating, e.g., that comparative advantages can arise from economies of scale in addition to from initial actor endowments) than on the basis of Prebisch and Singer’s work. However, the specifics of the hypothesis are regarded as an extremely special case, an exception to what is generally true of developing countries. There are two main reasons for this. The first is that many developing countries specialize in the extraction of metals and minerals that are necessary inputs in making complex manufactured goods, like copper and silicon. These commodities likely violate Prebisch-Singer’s assumption that simple commodity goods necessarily exhibit lower income elasticity of demand than complex manufactured goods. The second reason is that many of the complex manufactured goods that the poorest countries import from wealthier countries actually probably increase those countries’ productivity in producing basic commodities (consider, for instance, the way organizations like Precision Agriculture for Development deliver scientific agricultural guidance to farmers throughout South Asia and Subsaharan Africa via their cell phones).
I’m not sure to what extent this theoretical background will be helpful to you as you think about convergence, but regarding the facts on the ground, with very few exceptions (like Botswana), almost all of the progress toward convergence in the last four decades has taken place in East Asia. While the “Asian Miracle” is very much real, it may itself prove to be a special case, specific to the region or the historical period in which it took place. As premature deindustrialization begins to take its toll on those countries that are not yet rich, there are, I think, a number of serious concerns about the continued viability of the export-led growth models that lifted countries like South Korea and Japan out of poverty. While the theoretical insights on which those models were based are robust, it remains to be seen to what extent they continue to apply in our 21st-century economy. Similarly, the traditional convergence thesis assumes increasing liberalization of international trade and capital flows, a premise that has grown increasingly untenable over the last five years.
[UPDATED June 30, 10:00 pm EDT to reflect substantial improvements to the statistical approach and corresponding changes to the results]
I spent some time this weekend looking into the impact of COVID-19 on the 2020 U.S. presidential election, and I figured I might share some preliminary analysis here. I used data from The COVID Tracking Project and polling compiled by FiveThirtyEight to assemble a time series of Biden’s support head-to-head against Trump in 41 states (all those with adequate polling), along with corresponding COVID-19 data. I then implemented a collection of panel models in R evaluating the relationship between Biden’s performance against Trump in state polling and the severity of the pandemic in each state. My data, code, and regression output are on GitHub, and I’ve included some interpretive commentary below.
Interpretation of Results
When appropriately controlling for state-level fixed effects, time fixed effects, and heteroscedasticity, total COVID-19 cases and deaths are not significantly associated with support for Biden, nor are the number of ongoing COVID-19 hospitalizations (see Models A, B, and C in the 6.30 R script). However, controlling for state-level fixed effects, greater daily increases in cases and in deaths are significantly associated with higher support for Biden (see Models 2 and 3 in Table I above). Breusch-Pagan tests indicate that we must also control for heteroscedasticity in those models, and when we do so, the results remain significant (see Models 2 and 3 in Table II above), though only at a 90 percent confidence level.
These results do come with a caveat. While Lagrange FF multiplier tests indicate that there is no need to control for time fixed effects in Table I models 2 and 3, F-tests suggest the opposite conclusion. I lack the statistical acumen to know what to make of this, but it’s worth noting because when you control for both time fixed effects and heteroscedasticity, the results cease to be statistically significant, even at a 90 percent confidence level.
Interestingly, the state-level fixed effects identified by Table I models 2 and 3 are strikingly powerful predictors of support for Biden everwhere except for Arkansas, Florida, Georgia, North Carolina, Nevada, Ohio, and Texas, all of which (except for Arkansas) are currently considered general election toss-ups by RealClearPolitics. This makes sense — in a society as riven by political polarization as ours, you wouldn’t necessarily expect the impacts of the present pandemic to substantially shift political sympathies on the aggregate level in most states. The few exceptions where this seems more plausible would, of course, be swing states. In the case of Arkansas, the weak fixed effect identified by the models is likely attributable to the inadequacy of our data on the state.
A Hausman test indicates that when regressing the amount of COVID-19 tests performed in a state on the support for Biden there, a random effects model is more appropriate than a fixed effects model (because the state-level “fixed effects” identified are uncorrelated with the amount of tests performed in each state). Implementing this regression and controlling for heteroscedasticity yields the result featured under Model 1 in Table II above: statistically significant at a 99 percent confidence level.
This is striking, both for the significance of the relationship and for how counterintuitive the result of the Hausman test is. One would assume that the fixed effects identified by a model like this one would basically reflect a state’s preexisting, “fundamental” partisan bent, and my prior had been that the more liberal a state was, the more testing it was doing. If that were true, one would expect the Hausman test to favor a fixed effects model over a random effects model. However, it turns out that my prior wasn’t quite right. A simple OLS regression of states’ post-2016 Cook Partisan Voting Indexes on the amount of testing they had done as of June 26 (controlling for COVID-19 deaths as of that date and population) reveals no statistically significant relationship between leftist politics and tests performed (see Model 1 in Table III), and this result persists even when the controls for population and deaths are eliminated (see Model 2 in Table III).
This is odd. Hausman tests on Models A, B, and C in the 6.30 R script favor fixed effects models over random effects models, indicating that state-level fixed effects (i.e. each state’s underlying politics) are correlated with COVID-19 cases, hospitalizations, and deaths, but those same fixed effects are not correlated with COVID-19 tests. Moreover, when applying appropriate controls (e.g. for heteroscedasticity, time fixed effects, etc.), we find that while cases, hospitalizations, and deaths are not associated with support for Biden, testing is associated with support for Biden (basically the opposite of what I would have expected, under the circumstances). We can run a Breusch-Pagan Lagrange multiplier test on Table I’s Model 1 just to confirm for sure that a random effects model is appropriate (as opposed to an OLS regression), and it is. At that point, we are left with the question of what those random effects are that are associated with support for Biden but not with COVID-19 testing, as well as it’s corollary: Why aren’t the fixed effects in Models A, B, and C associated with testing (given that they are associated with cases, hospitalizations, and deaths)? Without the answers to these questions, it’s hard to know what to make of the robust association between total COVID-19 testing and support for Biden revealed by Table I’s Model 1.
The puzzling nature of the Table I, Model 1 results might incline some to dismiss the regression as somehow erroneous. I think jumping to that conclusion would be ill-advised. Among other things that speak in its favor, the random effects identified by the model, however mysterious, are remarkably consistent with common intuitions about partisanship at the state level, even more so, in fact, than the fixed effects identified by Models 2 and 3 in Table I. Unlike those fixed effects models, Model 1′s state-level random effects explain a considerable amount of Biden’s support in Georgia and Texas. I consider this a virtue of the model because Georgia and Texas have not been considered swing states in any other recent U.S. presidential elections. They are typically quite safe for the Republican candidate. Furthermore, Model 1 identifies particularly weak random effects in a few swing states not picked up by Models 2 and 3 — notably, Wisconsin, Pennsylvania, and Arizona. Wisconsin and Pennsylvania are genuine swing states: They went blue in 2008 and 2012 before going red in 2016. Arizona has been more consistently red over the last 20 years, but the signs of changing tides there are clear and abundant. Most notably, the state elected Kyrsten Sinema, an openly bisexual, female Democrat, to the Senate in 2018 to fill the seat vacated by Jeff Flake, who is none of those things.
It’s worth noting that there is an extent to which the above is really an oversimplification of “swinginess.” As FiveThirtyEight explains, state-level opinion elasticity is not the same as being a swing state. While how close a state’s elections are is determined by the proportion of Democrat relative to Republican voters in the state (with states closer to 50⁄50 obviously being “swingier” in this sense), the extent to which events out in the world lead to shifts in polling in a given state is determined largely by how many people in the state are uncommitted to a particular partisan camp. A state being full of such voters without strong partisan commitments might well express itself in close elections, but it also might not, and by the same token, another way a state might end up with close elections is by being 50 percent composed of die-hard, party-line Republicans and 50 percent composed of die-hard, party-line Democrats. We would not expect new developments in current events to particularly shift voter sentiment in such a state. As a result, FiveThirtyEight proposes the metric of state-level opinion elasticity—measured using the extent to which shifts in national polling correspond to shifts in state-level polling in each state—as an alternative concept of “swinginess” that is potentially more appropriate for analyses such as this one. This is important because FiveThirtyEight has found that a number of states where there are frequently close elections actually exhibit extremely low elasticity. The paradigm case of this is Georgia, which has an elasticity of 0.84 (meaning a one-point shift in national polling corresponds to a 0.84-point shift in Georgia polling).
On this basis, a better way of comparing the fixed effects identified by Table I models 2 and 3 with the random effects identified by Model 1 would be to compare the average elasticities (calculated by FiveThirtyEight) of the “swing states” identified by each model. In the case of Models 2 and 3, the average is an uninspiring 0.994, or weakly un-swing-y. In the case of Model 1, however, the average is 1.025: pretty swing-y. That’s what happens when you swap out Georgia (0.84) for Arizona (1.07) and Wisconsin (1.06).
I have one outstanding technical question about Table I, Model 1. When controlling for heteroscedasticity in R’s plm package, I understand that the “arellano” covariance estimator is generally preferred for data exhibiting both heteroscedasticity and serial correlation but is generally not preferred for random effects models (as opposed to fixed effects models). The “white” covariance estimators, on the other hand, are preferred for random effects models, though “white1” should not be used to control for heteroscedasticity in data that also exhibit serial correlation. A Breusch-Godfrey test indicates that Model 1 requires an estimator compatible with serial correlation, but it is of course a random effects model, not a fixed effects model. Would it be better to control for heteroscedasticity here with “white2“ or with “arellano?” Ultimately, it doesn’t matter much because both approaches yield a result that is statistically signicant at at least a 95 percent confidence level, but only the “white2” estimator is statistically significant at a 99 percent confidence level.
Thanks for this great post! I’m curious whether you’ve looked into any of the other developing world COVID-19 initiatives for which The Life You Can Save is currently raising money (beyond Development Media International and GiveDirectly). These include programs by TLYCS top charities D-Rev, Evidence Action, Living Goods, Population Services International, and Project Healthy Children, several of which are also, as you know, highly regarded by GiveWell.
I think the right kind of feedback here depends mainly on whether you mean to propose that EA underestimates the extent to which treating people with dignity improves their welfare or you mean to propose that EA fails to consider the importance of dignity as an intrinsically and independently valuable element of a life lived well. If dignity is only important on account of its instrumental role in improving welfare, I very much doubt that a thorough evaluation of that role would lead many EAs to conclude that they should redirect their charitable giving. Even if treating someone with dignity were associated with a truly striking increase in their welfare, it seems unlikely to me that, for instance, global health interventions with such an emphasis would outperform distributing insecticide-treated mosquito nets or deworming pills. Among other things, I imagine that most parents of young children would agree to an arbitrarily large amount of undignified treatment in exchange for preventing their child from dying of malaria (revealed preferences suggest this is true). This suggests that the AMF would outperform a hypothetical charity with a dignity focus even without accounting for the positive impact of saving children’s lives unless promoting dignity were extraordinarily cheap per person affected. Similarly, I doubt that integrating concern for the role dignity may play in determining welfare into longtermist perspectives would do much to shift people’s ideas about the best giving opportunities to safeguard the long-term future of humanity.
If on the other hand you take dignity to be valuable in itself (apart from any role it might have in bringing about another good, like improving welfare), I wonder whether the philosophical foundation for your view is really fully compatible with EA. From what I’ve read, it seems as if most of the philosophers who take treating people with respect to be a good in itself view dignity as the sort of thing that each of us has a reason to accord to others when we interact with them. They do not, however, by and large view dignity as the sort of thing that we have a reason to impartially maximize (i.e. while it’s very important for me to treat you with dignity, it’s nowhere near as important—and may not even be valuable at all—for me to counterfactually enable you to treat someone else with dignity). In their view, the obligation to treat others with dignity “spring[s] from an agent’s special relationship to his own actions” and “the claims of those with whom we interact to be treated by us in certain ways” (Korsgaard 1993, emphasis mine), not from the objective value of the world having more dignity in it (or anything like that). As a result, some (see, for example, Taurek 1977) go so far as to argue that it is not necessarily any better for more people to be treated well than for fewer. Following Korsgaard, we might think of the value of treating people with dignity as similar to the value of keeping promises — while I have reason to keep my own promises, I likely do not have reason to promote a world in which more promises are kept. Doing so would suggest that I misunderstood the way in which keeping promises is valuable. If dignity is the kind of moral good that most clearly has a place in non-consequentialist moral views that oppose interpersonal aggregation wholesale, I suspect that at least our present philosophical concept of it may be unsuited to sit among what we might conventionally refer to as “EA values.”
That said, I should note: Like surprisingly many EAs, I am not a utilitarian. I am, however, some kind of consequentialist, and I would love for EA folks to invest more effort in developing a thorough conception of human flourishing, of what it means for a person’s life to go well for them. Without such a theory, we cannot ensure that we are actually improving others’ lives to the greatest extent possible (because we lack a robust understanding of what it means for a life to be improved). For that reason, I personally welcome posts like this that seek to draw attention in those kinds of directions and propose some less conventional ideas about what flourishing might involve.
The political scientist Yuen Yuen Ang has some great work addressing this neighborhood of intuitions. Her view is basically that “corruption” is decomposable into a several distinct types of phenomena, and some of these can be growth-promoting (as in China during the period between, approximately, Deng and Hu), whereas others can be fairly extreme impediments to growth.
Answering the question of whether a candidate is “good,” might well (at least on certain EA world views) be sufficient to answer the question of whether donating to the candidate would be (sufficiently) cost-effective (given evidence that 1) donations matter for getting elected, and 2) getting elected allows one to influence policy). Consider the case of a candidate running on a longtermist platform. My impression is that when longtermist grantmakers evaluate giving opportunities in existential risk mitigation, their decision process is much closer to “determine whether the opportunity in question has a reasonable chance of improving humanity’s longterm trajectory within a range of broadly acceptable costs” than to “conduct a thorough, systematic, GiveWell-style cost-effectiveness analysis.” I would think that roughly the same principles that apply to donations to organizations that lobby Congress for better biosecurity policy apply to donations to candidates for Congress who strongly favor better biosecurity policy. This seems to be the thinking behind OP’s post. The back-of-the-envelope intuition here is pretty straightforward; insisting on a GiveWell-style CEA in its place reads like an isolated demand for rigor.
And this isn’t even to mention Kremer’s substantial contributions to growth theory (which are still his second- and third-most-cited papers), much less the major theoretical contribution he made to the modeling of HIV transmission in his free time back in the late 1990s...
I’m in general perfectly willing to draw distinctions in between different “tiers” of universities, but I have to say, as an ~Ivy League Person~, the notion that students at Georgetown (or Northwestern, UCLA, Johns Hopkins, Duke, WashU, UMich, UVA, etc.) might generally be of lower caliber than students on Ivy League campuses has literally never crossed my mind, nor is it one I would have guessed that more than a trivial number of other people in highly educated communities would endorse. I may be wrong, but I’ve really never thought I was particularly progressive in this respect. I’ve always understood the view supported by the data/graphs in this post to be the conventional one.
If the concern is that donations don’t have any impact on electoral outcomes, there is a good bit of high-quality social science research indicating that television advertising, at least, does, particularly (as OP notes) in down-ballot races. If the concern is that it nonetheless isn’t worth its cost, that’s plausible, but I don’t think OP said anything to suggest strong grounds to believe campaign donations beat GiveWell’s Maximum Impact Fund, nor (I assume) would most readers leap to that conclusion, given the unique depth and rigor of GiveWell’s research process and the far greater difficulty of modeling cost-effectiveness in politics. The thrust of this post seems to be more that this is something worth considering, which seems like a fair assessment, particularly given the extent of preexisting EA activity in this area (and the reasonable argument that there are decreasing returns to scale).
As someone who first encountered EA through Slate Star Codex, this is also my sense.
I actually think full-time RA roles are very commonly (probably more often than not?) publicly advertised. Some fields even have centralized job boards that aggregate RA roles across the discipline, and on top of that, there are a growing number of formalized predoctoral RA programs at major research universities in the U.S. I am actually currently working as an RA in an academic research group that has had roles posted on the 80,000 Hours job board. While I think it is common for students to approach professors in their academic program and request RA work, my sense is that non-students seeking full-time RA positions very rarely have success cold-emailing professors and asking if they need any help. Most professors do not have both ongoing need for an (additional) RA and the funding to hire one (whereas in the case of their own students, universities often have special funding set aside for students’ research training, and professors face an expectation that they help interested students to develop as researchers).
Separately, regarding the second bullet point, I think it is extremely common for even full-time RAs to only periodically be meaningfully useful and to spend the rest of their time working on relatively low-priority “back burner” projects. In general, my sense is that work for academic RAs often comes in waves; some weeks, your PI will hand you loads of things to do, and you’ll be working late, but some weeks, there will be very little for you to do at all. In many cases, I think RAs are hired at least to some extent for the value of having them effectively on call.
This is a fascinating argument — thank you for sharing it! I think it’s particularly interesting to consider it in the context of metaethical theories that don’t fall neatly within the realist paradigm but share some of its features, like R.M. Hare’s universal prescriptivism (see Freedom and Reason [1963] and Moral Thinking [1981]). However, I also think this probably shouldn’t lead most discounting realists to abandon their moral view. My biggest issue with the argument is that I suspect (though I am still thinking this through) that there exist parallel arguments of this form that would purport to disprove all of philosophical realism (i.e. including realism about empirical descriptions of the natural world). I think statements rejecting philosophical realism are pretty epistemically fraught (maybe impossible to believe with justification), which leaves me suspicious of your argument. (It’s worth noting here that special relativity itself is an empirical description of the natural world.)
I have a feeling that the right way of thinking about this is that the rise relativistic physics changed the conventional meaning of a “fact” into something like: a true statement for which its truth cannot depend upon the person thinking it within a particular inertial frame of reference. Otherwise, I think we would be forced to admit that there are not facts about the order in which events occur in time, and that seems quite obviously inconsistent with the ordinary language meanings of several common concepts to me. I know that relativity teaches that statements about time and duration are not objective descriptions of reality but are instead indexical reports of “where the speaker is” relative to a particular object, similar to “Derek Parfit’s cat is to my left,” but (for basically Wittgensteinian reasons) I do not think that this is actually what these statements mean.
Ultimately, if you’re someone who, like me, believes that a correct analysis of the question, “What is the right thing to do?” must start with a correct analysis of the logical properties of the concepts invoked in that sentence (see R.M. Hare, especially Sorting Out Ethics [1997]), and you believe that those logical properties are determined by the way in which those concepts are used (see Wittgenstein’s Philosophical Investigations [1953]), then I think this argument is mainly good evidence that the proper understanding of what moral realism means today is the following: “Moral realism holds that moral statements are facts, and the truth of a fact must be universal within the inertial frame of reference in which that fact exists; that is, that truth cannot depend upon the person thinking the fact within that inertial frame of reference.”
I do work in this academic neighborhood, so maybe I’ll comment a bit. There’s a part of me that feels like caricaturing Linch’s question as akin to asking: What were all the other physicists doing with their time in 1905, when Einstein was sitting around churning out paradigm-shifting papers without a PhD or even access to a decent academic library? But that’s probably unhelpful, so I’ll try to give a bit more color on Kremer’s context.
First, it’s important to understand that until very recently, experimental work in LMIC settings was really prohibitively difficult to 1) organize and 2) get funded. Prior to the founding and (recently explosive) growth of the J-PAL/IPA network (in which Kremer himself played no small part), as well as the emergence of the Gates Foundation as a major funder of global health RCTs, these studies were just really, really hard to do. Moreover, even if you could put one together, until the last 10-15 years, you were very unlikely to be able to publish your results in a top-5 economics journal (and top-10 economics departments assess their tenure-track faculty almost exclusively on the basis of their publications in top-5 economics journals). If you look at Duflo’s early work on healthcare in India, a lot of it published, well, badly, by the standards of the MIT Economics Department. Miguel & Kremer’s original deworming paper did make a top-5 journal (Econometrica), but only because they spun their contribution as principally methodological in nature. The title is “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities;” the paper basically pretends to be about approaches to accounting for potential interference/spillovers between treated and untreated units in an experiment, rather than about deworming itself. I’m too young, but folks I know who were around those circles at the time recall being told that empirical research on health in LMICs “wasn’t economics,” that it was “just reduced-form” and that committing to it was a terrible professional decision. This assessment of the professional consequences wasn’t without justification, either. Seema Jayachandran was denied tenure at Stanford as recently as 2011 due to prejudice against this sort of research. What Banerjee, Duflo, and Kremer won the Nobel for, more than anything else, was changing the field’s conception of what counts as economics in a fairly dramatic way.
So how did folks like Banerjee, Duflo, Kremer, Karlan, Miguel, etc. overcome those obstacles? In the cases of Banerjee and Kremer, both had established themselves as theorists (and received tenure at MIT) before they turned to doing experimental work. In the cases of Duflo, Karlan, and Miguel, 1) they were Banerjee/Kremer students, so they had access to that source of encouragement and support, and 2) they were unusually principled about their interests and willing to take gigantic professional risks.
The consequence of this is that those five names—in one permutation or another—are on a huge proportion of the papers evaluating health & development interventions with credible identification that were published prior to ~2010, and as for interventions that were first evaluated more recently, well, 1) the evidence base about those interventions is often still too thin to be the basis of a GiveWell recommendation, and 2) running these RCTs may be easier today than it was 20 years ago, but it’s still administratively challenging and (more importantly) difficult to fund. If you’re going to persuade Gates (or someone similar) to give you a seven- (or eight-) figure sum of money to figure out if sending people text messages gets them to vaccinate their children, it really helps if you can say that you’ll be drawing on someone like Michael Kremer’s network and expertise in putting your study together. This isn’t just empty signaling, either. There’s a lot that can go wrong in organizing an RCT like this, and having someone on the study team who has loads of experience navigating the challenges that tend to arise is genuinely very valuable. Moreover, if you’re a junior researcher, you may need someone like Kremer’s network in order to get the approvals you need to launch your intervention from the government wherever you’re working, to access J-PAL/IPA resources in-country, and to connect with other reliable implementation partners on the ground (e.g., local non-profits, survey firms, etc.).
In conjunction with the fact that Banerjee, Duflo, Kremer, Karlan, Miguel have all skyrocketed to (global) prominence in the last 15 years, this means that much of the best (particularly experimental) work happening in health and development even today still has one of their names on it. The best work, after all, requires a lot of money and excellent contacts, and the distinguished stature of those five economists has left them with money and contacts in spades. If you go on Kremer’s lab’s website, you can see that it employs, like, a shocking number of people (for a social science research center in a university setting). His personal contribution to the clean water paper under discussion here was probably not enormous, but it’s nonetheless no coincidence that he led it. If I’m a promising young economist with ambitions to do impactful work on these topics, then Kremer is going to be among the people I’d be most eager to collaborate with; I’m going to do everything I can to build a working relationship with him (especially given that he is also, reputedly, a nice guy). I think that’s why you see him behind so many of GiveWell’s recs.