Maybe more standing variation and population resilience which could have indirect effects on welfare over the long-term?
cole_haus
Karma: 804
I’m struggling to think of non-human-animal-relevant examples of this, but it seems in principle possible to have welfare effects which depend on total population rather than density. Ideas and technological innovation are (roughly) an example of this in humans. That is, more total humans means more freely shareable ideas/innovations means more welfare. (See e.g. Population growth and technological change: One million BC to 1990.)
Thought I’d just throw it out there in case someone can think of a way to make this relevant to animals.
Some related things that come to mind:
Challenges to Bayesian Confirmation Theory outlines some conceptual potential issues arising from the use of explicit probabilities in a Bayesian framework.
Gerd Gigerenzer likes to claim that “fast and frugal” heuristics often just perform better than more formal, quantitative models. These claims can be linked to the bias-variance tradeoff and extreme priors.
The optimizer’s curse can be generalized to the satisficer’s curse. This generalization doesn’t obviously seem to differentially affect explicit probabilities though.
Having looked at the paper now, I definitely have a different take as to how definitive it is. My maximally contrarian take would be that it’s a non-systematic review in which many (most?) of the works reviewed are in favor of an important causal link running from health to income. I do agree that the overall macro-scale evidence is weak (which is distinct from strong evidence of a weak effect), but this is exactly why people like RCTs over national development! Causal inference at a macro scale is hard!
(Health and Economic Growth: Reconciling the Micro and Macro Evidence also looks like a good source that I’ll look at.)
It’s not binary, though.
Yup, agreed.
The link I was thinking of is that migration loans are relevant for urbanization, agglomeration effects, and generally the distribution and density of humans (which seem like they fit into human geography—”Human geography attends to human patterns of social interaction, as well as spatial level interdependencies”—and urban geography).
Deworming doesn’t change the geography of a place in itself but it mediates the impact of that geography on humans/the economy/society (supposing that we consider the disease environment a part of geography similar to the way we might consider forestation and the endowment of domesticable animals as geographic factors).
Similarly, fertilization mediates the impact of poor soils which are prevalent in equatorial regions.
Not sure if that makes the examples and their connection to geography more scrutable.
What are you thinking of when you emphasize geography as a determinant of growth? Stuff like shipping access and which countries are neighbors?
The Easterlin paradox notwithstanding, economic growth does buy you a lot of subjective wellbeing improvement in a country.
I might not be understanding you, but it seems like this tries to smuggle in causation and assume away the problem. As I see things, there are two conflicting pieces of correlational evidence:
Cross-country regressions show strong correlation at a point in time between income and SWB (what the post highlights)
Time series regressions within countries show a weak correlation between income and SWB (Easterlin paradox)
I don’t currently know of a fully convincing resolution of this conflict, but the second correlation actually seems a bit more central for the question of the causal effect of growth over time on SWB.
It would be interesting to explore how far increasing growth in a country would improve subjective wellbeing in LMICs.
Easterlin on LMIC and the paradox:
Some analysts assert that in less developed countries happiness and economic growth are positively related “up to some point,” beyond which the association tends to become nil, but time series data do not support this view. The most striking contradiction is China where, despite a fourfold multiplication in two decades in real GDP per capita from a low initial level, life satisfaction has not improved.
(For the record, I would be surprised if the Easterlin paradox turned out to 100% correct and rising national income over time had 0 positive effect on subjective well-being. But I am significantly uncertain about this, could imagine the effect being quite substantial, and knowing the magnitude of the effect seems very important.)
I updated in the direction that health and education are not that important for growth at least for very poor countries
Yeah, I will have to look into this perspective more.
But it’s a different question on whether we can do anything about that by ramping up health spending and ameliorate these differences and whether that’s important for growth.
I do think it’s an open question though.
Yup, agreed that none of the linked things are on growth per se. I just think the link to the systemic change objection is useful because it gives hints as to what problems there might be with the growth-focus argument, how people are likely to react to the growth-focus argument, which arguments are persuasive, etc.
Yup, agree that the argument I outline is not definitive and thoughtful work in this area is worthwhile. I think I may be more pessimistic on the politics aspect (i.e. I may think it’s a more tightly-binding constraint and harder for outsiders to work on), but my sense of that is kind of inchoate and not worth much at the moment.
not really understanding how ordinal moral theories are really meant to work
Yeah, I think this is where I’m at too. It seems inescapable that ordinal preferences have cardinal implications when combined with empirical uncertainty (e.g. if I prefer a 20% chance of A to an 80% chance of B, that implies I like A at least four times as much). The only choice we really have is whether the corresponding cardinal implications are well-formed (e.g. Dutch bookable). The best distinctions I can come up with are:
In a purely deterministic world without lotteries, there wouldn’t be an obvious mechanism forcing the cardinalization of ordinal preferences. So their overlap is only a contingent feature of the world we find ourselves in. (Though see A Theory of Experienced Utility and Utilitarianism for an alternate basis for cardinalization.)
Ordinal preferences only specify a unique cardinalization in the limit of an infinite sequence of choices. Since we aren’t likely to face an infinite sequence of choices any time soon, they’re distinct in practice.
P.S. Thanks for the Tarsney link. I have it open in a tab and should get around to reading it at some point.
Plus there’s the roadblock of me not having in-depth understanding of how the vNM utility theorem is meant to work.
Not sure if it’ll help but I have a short explanation and interactive widget trying to explain it here.
China’s growth acceleration from 1977 onwards produced $14 trillion NPV in cumulative economic output. Thus, if the only thing the economics profession achieved in 50 years was to increase by 4 percentage points the probability that the Chinese government shifted to this new economic strategy, then it would have had greater economic benefits than the Graduation approach.
I think this argument equivocates between the probability of any reform and the probability of a particular reform. Because the reform policy was academic-economist-inflected, denying the influence of economists sounds silly. But I think we should instead think about two separate chunks:
the wedge between the status quo growth trajectory and the reform trajectory that would have obtained had Chinese reformers only had e.g. 1960s economics knowledge
the wedge between the 1960s-style-reform trajectory and the actual reform trajectory
Economists since the 1960s get 100% of the credit for the second wedge, but I think it’s plausibly extremely small (especially since Chinese reforms didn’t hew particularly closely to economic orthodoxy in the details). Economists since the 1960s only get any credit for the first wedge insofar as they were the impetus for major economic reforms. My limited knowledge of Chinese economic history suggests that this probability could easily be very small.
Phrased differently, we can model this as:
Without post-1960s economists:
Status quo had X% chance of continuing
Reform had (1-X)% chance of happening and represented a $Z gain
With post-1960s economists:
Status quo had X-A% chance of continuing
Reform had (1-X)+A% chance of continuing and represented a $Z+B gain
I can easily imagine A and B both being quite small but the original presentation disguises that.
Here’s a central argument against focusing on growth per se that I find fairly plausible:
Obviously terrible growth-related policies are at historic lows. Our ability to produce more detailed/refined policy prescriptions is weak (see Pritchett’s acknowledgement of the lost decades and the transition depression). In fact, many of the greatest successes of development (China, Singapore, etc.) defied the economic orthodoxy in the details. Rather, they implemented policies that were tailored to and required deep understanding of local conditions. The key barrier to increased economic growth is not the absence of knowledge or advocacy but mundane implementation issues and the indifference or antipathy of the relevant political actors.
Wild speculation:
I think one reason this area may get less attention in EA is that if you’re willing to sign up for high-risk high-return scenarios that are more theory-driven and less retrospective-data-driven (like economic growth), you’re also more sympathetic to long-termist areas like x-risk. And once you’re comparing x-risk to economic growth, there’s no guarantee that growth wins.
In other words, I think economic growth may be competing against x-risk—not RCTs—among EAs.
(Though certain ethical views may argue against long-termist interventions like x-risk reduction. A focus on economic growth may be the best fit for people that are “epistemically permissive” but “ethically conservative”, if that makes sense.)
RD has moved in an entirely different direction. Instead of replicating this success, it asks: among interventions that we can test with RCTs, what is most impactful? In the wake of the period with by far the greatest progress in human welfare of all time, this change in strategy is difficult to justify.
I think one possible explanation (I’ve not heard this anywhere explicitly; it’s just me making things up.) that I find moderately persuasive is:
Development RCTs rose to prominence in the wake of the ‘“lost decades” in Latin America and the “transition depression” in some (not all) former Soviet dominated countries’. These failures and the broader (perceived) failures of the Washington Consensus provoked a crisis of confidence in economic policy prescriptions. In particular, large-scale economic theory is hard to make accountable to empirical evidence. RCTs shore up this epistemic weakness—they allow economists to test their theories against reality and build up more certain knowledge which can perhaps eventually be applied to big questions of national development. Without RCTs, economic prescriptions are much more reliant on theory (even non-RCT causal inference from empirical data is more theory-laden) and it doesn’t seem great to cut out (almost) one whole category of evidence. (See What randomization can and cannot do for a good discussion of the interplay of theory and RCTs.)
I think epistemics is plausibly a crux for in the randomista vs big national development debate.
This should lead us to be sceptical about RD. Growth is arguably the major driver of human progress, but proponents of RD rarely argue that the interventions that they recommend do increase growth.
To the extent that this is true, I think there are pretty benign possible explanations:
The data on growth (e.g. GDP) and RD interventions typically operate at different scales. Even if GiveDirectly substantially increases the long-term growth in a village, that’s not going to show up in national aggregate data.
Making empirical growth claims requires data accumulated over time. The randomista trend is pretty recent. Using Randomized Controlled Trials to Estimate Long-Run Impacts in Development Economics mentions that looking at long-run impacts will become more and more possible as more early RCTs reach the age of maturity.
Thus, many RCT-backed interventions do not seem to explain much of the cross-national variation in GDP per capita. What does? There are a range of factors including:
Growth-friendly policies
Geography
Natural resources
Human capital
Culture
I’m confused here. It seems like there are examples of RCTs addressing at least:
Geography: It seems like No Lean Season, Duflo’s fertilizer nudges, and deworming (edit: I try to make the link between these and geography more obvious in a grandchild comment.) are all examples of RCTs targeting various aspects of geography depending on how you want to interpret the term.
Human capital: Education and health are clear focus areas for development RCTs.
Culture: It seems likely that many RCTs have flow-through effects on culture.
Is the argument not that RCTs can’t address things in these categories but that they aren’t good at it?
The reason these things are unlikely to be the best way to increase growth is that they play no role in the causal story of the huge differences in GDP per capita across space and time.
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But given the story above, it would be very surprising if this was the case: differences in rates of deworming explain a miniscule fraction of the variation in individual economic outcomes across the world. No-one argues that deworming is among the top 1000 causes of the huge economic transformation documented above.
These seem like clearly insufficient arguments. They strike me as analogous to:
Don’t focus on fixing your broken leg. What separates the top 20 bicyclists from the next 40 is the kind of bike they ride. Focus on getting the right bike.
Different circumstances apply in different places and focusing on and importing salient characteristics of the winners is not likely to be successful because it’s almost never the case in complex domains like this that a single factor is sufficient for success on its own.
More concretely, while no one argues that deworming is a top cause of the huge economic transformation, plenty of people argue that disease environment/burden (or geography more broadly) is an important cause of differential economic success. For example, Historical Development references arguments that the tsetse fly had substantial impacts on Africa’s path of development (“by inhibiting the development of intensive agriculture using draft animals, resulted in lower populations, less urbanization, and less state development”).
It seems like one of the key implicit claims in the post is that growth effects are better/more important than level effects (e.g. The post says “Moreover, the vast majority of proponents of RD do not tackle the question of whether the interventions they assess increase economic growth.” which is true, but RD proponents often focus on level effects) . I think it would be good to state and argue for this point explicitly.
Relatedly, I think the anti-RD perspective advocated in this post require the claim that level effects don’t affect growth rates. If boosting someone’s assets or income leads to a persistent increase in their income growth, the RD-caused level effect also gets the growth benefits this post argues for. The low-level equilibrium trap is a pretty popular model which describes just this dynamic.
How Poverty Ends: The Many Paths to Progress—and Why They Might Not Continue is from Abhijit V. Banerjee and Esther Duflo (the recent econ Nobel Laureates who won for their RCT work) and I think can reasonably be read as a response to the criticisms of Lant Pritchett (probably the most vocal advocate of the line of thinking this post represents).
Key excerpts:
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