On 3., is it worth trying to be more Bayesian? Yes, we face data limitations because there’s <200 countries in the world, and the data from most countries is pretty crap. But it feels intuitive (to me, at least) that growth should have some positive effect on happiness, and we have some data from areas, like cash transfers, that suggests more money makes people a bit more happy. And then Vadim suggests that the data we do have suggests a small but slightly positive effect of growth on happiness. So my belief that the studies he refers to are picking up on a real effect rather than pure chance is higher than it would be based on the study’s error bars alone.
Personally, I find 7. a compelling response to 5. and 6. We don’t need to imagine reductio scenarios of counterfactual effects lasting for a 500 years or 1000x increases in world GDP because even short-lived growth accelerations have large aggregate effects because they affect so many people. Relatedly, I think in practice growth interventions in practice will look less like “increasing economic growth by 0.0001 percentage points” and more like x% chance of sparking a growth acceleration for years or decades a la Pritchett et al. 2016.
What kind of evidence you refer to in 8. would actually change your mind? Why does expected value reasoning not work here?
On 3., is it worth trying to be more Bayesian? Yes, we face data limitations because there’s <200 countries in the world, and the data from most countries is pretty crap. But it feels intuitive (to me, at least) that growth should have some positive effect on happiness, and we have some data from areas, like cash transfers, that suggests more money makes people a bit more happy. And then Vadim suggests that the data we do have suggests a small but slightly positive effect of growth on happiness. So my belief that the studies he refers to are picking up on a real effect rather than pure chance is higher than it would be based on the study’s error bars alone.
Personally, I find 7. a compelling response to 5. and 6. We don’t need to imagine reductio scenarios of counterfactual effects lasting for a 500 years or 1000x increases in world GDP because even short-lived growth accelerations have large aggregate effects because they affect so many people. Relatedly, I think in practice growth interventions in practice will look less like “increasing economic growth by 0.0001 percentage points” and more like x% chance of sparking a growth acceleration for years or decades a la Pritchett et al. 2016.
What kind of evidence you refer to in 8. would actually change your mind? Why does expected value reasoning not work here?