Michael, thanks so much for really engaging with the post. I think we are now very close in our big-picture views on the subject, but would love to continue the discussion on the more interesting areas of disagreement (I will respond to those points below). I agree that we don’t have enough data to say if the Easterlin paradox holds. I am also somewhat hesitant about prioritizing economic growth as an intervention, although my concerns are less about effect sizes directly, and more about whether generating growth is tractable, and whether potential interventions carry large risks.
I agree with Stephen Clare’s response that we can try to be more Bayesian here. I think it’s reasonable to start with a prior based on the very statistically significant cross-sectional correlation between a country’s GDP and its well-being. In order to believe that this correlation does not generalize to changes in one country across time, we would need to believe that Ethiopia could grow to have the current US GDP but remain as unhappy as a low income country. That would make it an extreme outlier in the cross-sectional data, and would imply that there was some kind of idiosyncratic problem with the country (and I don’t think the argument about people comparing themselves to peers deals with this problem). So I think there is some burden of proof on providing evidence that there actually is a paradox. If we start with a prior based on the cross sectional data, we would initially expect a 0.5 life satisfaction point increase for an income doubling. Then we can update on HLI’s meta-analysis results, suggesting that the impacts of cash transfers only have an impact that is a quarter of that. So now we would believe that the impact is somewhere between those two values. Then we get Easterlin and O’Connor’s regression results, which are not in themselves statistically significant. However, they are pretty much the same as the HLI results, so there is no reason to move below the range we believed the effect to be in before. It does not seem to make sense to update all the way to 0 based on results that are non-zero. So even though Easterlin and O’Connor’s regressions do not in themselves have enough statistical power to provide any evidence for their being an impact of growth in happiness, the coefficients they provide should not update us away from what we believed to be the effects of income doubling before.
That being said, we have very small datasets here, the individual countries are correlated to each other (making the amount of independent information we have even smaller than it seems), and all of this is simply correlation. We have not done anything here to control for omitted variables, to try to run lagged regressions, or to try quasi-experimental designs. So overall I agree that we should not expect to learn very much about causal impacts from these types of regressions.
I agree with this. And I think the amount of data we would really need would be much higher than it initially seems. Since Easterlin and O’Connor’s are running multiple different statistical tests (deciding exactly how many years of data a country needs before it counts as full-cycle, and separately deciding which countries are transition countries), we would need even more data to make up for the multiple hypotheses.
If we accept the results from the 2020 data, or alternatively assign a probability of 50% to there being no Easterlin paradox, then it would really only be 3-4 doublings to get an additional point of life satisfaction. If we accept the results from HLI’s analysis, I believe it would be about 6 income doublings (starting with 0.1 standard deviations, converting to life satisfaction points, and then discounting for decreased benefits for non-recipient household members)? A country like Ethiopia could have about 6 GDP doublings before getting to United States GDP levels. I would like to thank Matt Lerner for pointing this out.
I agree that we have very little evidence so far about the tractability of economic growth interventions. I just think that Easterlin and O’Connor’s work should not make us think that economic growth interventions are any less useful than we would have otherwise thought. Since these sorts of regressions seem to show smaller impacts for health and pollution than GDP, maybe they should (very very slightly) update us towards thinking a little more of economic growth interventions than whatever our prior beliefs were.
I agree that all of the increases in regression coefficients are not that large in some absolute sense, and are in some sense luck. But the increases do seem to be large enough to flip us towards rejecting rather than accepting the Easterlin Paradox. This is statistical luck in some sense, but that just seems to show that the results are very sensitive to that sort of luck. So, as we both seem to agree, we don’t really have enough data to say if the Easterlin paradox holds.
I would love to see more work around estimating the expected costs and impacts of national health, pollution, social safety net, and growth policy on life satisfaction. I suspect that these sorts of change-on-change regressions would not end up being a large part of the evidence on which we based these estimates. Since there is so little data here, we might end up having to rely on judgements about individual policies’ chances of success. My point in the post was simply that Easterlin and O’Connor’s analysis does not seem to give us any evidence to suggest that GDP is likely to be less impactful than health or pollution.
I agree that we can and should try to be Bayesian but, if we do, we still don’t get a slam-dunk result that economic growth will increase average happiness (at least, in already rich countries).
The story that often gets told to explain why the Easterlin Paradox holds refers to hedonic adaptation, social comparison, and evolution. We are very good at getting used to lots of things but we do continue to notice our status relative to others. How much material prosperity do we really need, given humans are basically naked apes who evolved to live in the savannah? We might imagine getting richer would make a difference to us, but think about the last thing you were really excited to buy, then think about how you’ve stopped paying attention to it. Therefore, we can explain both why money would matter in the cross-section and why it wouldn’t matter in the time-series. So noticing that money makes individuals happier at a time does not, by itself, require us to conclude that economic growth would increase average happiness.
What’s more, there are some reasons to worry that modernity is not good for humans. As I said in my earlier post:
Notably, Hidaka (2012) argues that depression is rising as a result of modernity, and points to the fact that “modern populations are increasingly overfed, malnourished, sedentary, sunlight-deficient, sleep-deprived, and socially-isolated”.
In other words, you can’t just assume that economic growth increases happiness—that’s exactly the point. If you’re going to already take it as given, then there’s no purpose in having the debate.
That is entirely fair. It’s reasonable to not accept the cross-sectional results as having any information value for your prior. So I should have have said we can start with a prior from the HLI meta-analysis results (which if I remember correctly are pretty statistically significant). Then when we get the information from the Easterlin and O’Connor paper, where the results are the same as our prior, but not statistically significant, to say that the new information does not shift our prior results at all. So even though the Easterlin and O’Connor paper does not give us much information one way or the other, it still seems reasonable to say there is no reason to think that the results are likely to be much lower than the HLI results?
I don’t think this makes sense, no, sorry. The HLI meta-analysis results are from cash transfers, which make a few individuals happier over time, not looking at the average of an entire society. It’s well-studied that people care about their relative income, not just their absolute income. So we should be particularly worried about extrapolating from what works for individuals to what works for societies—especially where we think the benefit to the individual could be from comparisons. Hence, I think it is not justified to start from the HLI numbers.
IIRC, in the HLI cash transfer meta-analysis, we found that cash transfers had no effect on those in nearby villages (‘across-village’ effect). In other words, there was, on average, no relative income effect. I was puzzled by it and I find it hard to believe—our CEA does, however, despite my disbelief, assume there are no negative spillovers from cash transfers. I was puzzled by this because there’s such consistent evidence of a relative income effect in rich countries. I also thought it was weird the effect from cash transfers was zero. To put this in context, imagine a bunch of people down the road from you get given $40,000 for each household. Would you expect that to have no effect on you? It wouldn’t make you envious? Or, it wouldn’t make you excited that this might happen to you? I’d expect the effect of income to be (almost) wholly relative in rich countries, but not that there was no relative income effect in the very poor. However, there wasn’t loads of across-village data in the HLI meta-analysis, so I didn’t update much. It would be good to have a bigger and better analysis of the relative income effect in very poor contexts.
Michael, thanks so much for really engaging with the post. I think we are now very close in our big-picture views on the subject, but would love to continue the discussion on the more interesting areas of disagreement (I will respond to those points below). I agree that we don’t have enough data to say if the Easterlin paradox holds. I am also somewhat hesitant about prioritizing economic growth as an intervention, although my concerns are less about effect sizes directly, and more about whether generating growth is tractable, and whether potential interventions carry large risks.
I agree with Stephen Clare’s response that we can try to be more Bayesian here. I think it’s reasonable to start with a prior based on the very statistically significant cross-sectional correlation between a country’s GDP and its well-being. In order to believe that this correlation does not generalize to changes in one country across time, we would need to believe that Ethiopia could grow to have the current US GDP but remain as unhappy as a low income country. That would make it an extreme outlier in the cross-sectional data, and would imply that there was some kind of idiosyncratic problem with the country (and I don’t think the argument about people comparing themselves to peers deals with this problem). So I think there is some burden of proof on providing evidence that there actually is a paradox. If we start with a prior based on the cross sectional data, we would initially expect a 0.5 life satisfaction point increase for an income doubling. Then we can update on HLI’s meta-analysis results, suggesting that the impacts of cash transfers only have an impact that is a quarter of that. So now we would believe that the impact is somewhere between those two values. Then we get Easterlin and O’Connor’s regression results, which are not in themselves statistically significant. However, they are pretty much the same as the HLI results, so there is no reason to move below the range we believed the effect to be in before. It does not seem to make sense to update all the way to 0 based on results that are non-zero. So even though Easterlin and O’Connor’s regressions do not in themselves have enough statistical power to provide any evidence for their being an impact of growth in happiness, the coefficients they provide should not update us away from what we believed to be the effects of income doubling before. That being said, we have very small datasets here, the individual countries are correlated to each other (making the amount of independent information we have even smaller than it seems), and all of this is simply correlation. We have not done anything here to control for omitted variables, to try to run lagged regressions, or to try quasi-experimental designs. So overall I agree that we should not expect to learn very much about causal impacts from these types of regressions.
I agree with this. And I think the amount of data we would really need would be much higher than it initially seems. Since Easterlin and O’Connor’s are running multiple different statistical tests (deciding exactly how many years of data a country needs before it counts as full-cycle, and separately deciding which countries are transition countries), we would need even more data to make up for the multiple hypotheses.
If we accept the results from the 2020 data, or alternatively assign a probability of 50% to there being no Easterlin paradox, then it would really only be 3-4 doublings to get an additional point of life satisfaction. If we accept the results from HLI’s analysis, I believe it would be about 6 income doublings (starting with 0.1 standard deviations, converting to life satisfaction points, and then discounting for decreased benefits for non-recipient household members)? A country like Ethiopia could have about 6 GDP doublings before getting to United States GDP levels. I would like to thank Matt Lerner for pointing this out.
I agree that we have very little evidence so far about the tractability of economic growth interventions. I just think that Easterlin and O’Connor’s work should not make us think that economic growth interventions are any less useful than we would have otherwise thought. Since these sorts of regressions seem to show smaller impacts for health and pollution than GDP, maybe they should (very very slightly) update us towards thinking a little more of economic growth interventions than whatever our prior beliefs were.
I agree that all of the increases in regression coefficients are not that large in some absolute sense, and are in some sense luck. But the increases do seem to be large enough to flip us towards rejecting rather than accepting the Easterlin Paradox. This is statistical luck in some sense, but that just seems to show that the results are very sensitive to that sort of luck. So, as we both seem to agree, we don’t really have enough data to say if the Easterlin paradox holds.
I would love to see more work around estimating the expected costs and impacts of national health, pollution, social safety net, and growth policy on life satisfaction. I suspect that these sorts of change-on-change regressions would not end up being a large part of the evidence on which we based these estimates. Since there is so little data here, we might end up having to rely on judgements about individual policies’ chances of success. My point in the post was simply that Easterlin and O’Connor’s analysis does not seem to give us any evidence to suggest that GDP is likely to be less impactful than health or pollution.
I agree that we can and should try to be Bayesian but, if we do, we still don’t get a slam-dunk result that economic growth will increase average happiness (at least, in already rich countries).
The story that often gets told to explain why the Easterlin Paradox holds refers to hedonic adaptation, social comparison, and evolution. We are very good at getting used to lots of things but we do continue to notice our status relative to others. How much material prosperity do we really need, given humans are basically naked apes who evolved to live in the savannah? We might imagine getting richer would make a difference to us, but think about the last thing you were really excited to buy, then think about how you’ve stopped paying attention to it. Therefore, we can explain both why money would matter in the cross-section and why it wouldn’t matter in the time-series. So noticing that money makes individuals happier at a time does not, by itself, require us to conclude that economic growth would increase average happiness.
What’s more, there are some reasons to worry that modernity is not good for humans. As I said in my earlier post:
In other words, you can’t just assume that economic growth increases happiness—that’s exactly the point. If you’re going to already take it as given, then there’s no purpose in having the debate.
Michael,
That is entirely fair. It’s reasonable to not accept the cross-sectional results as having any information value for your prior. So I should have have said we can start with a prior from the HLI meta-analysis results (which if I remember correctly are pretty statistically significant). Then when we get the information from the Easterlin and O’Connor paper, where the results are the same as our prior, but not statistically significant, to say that the new information does not shift our prior results at all. So even though the Easterlin and O’Connor paper does not give us much information one way or the other, it still seems reasonable to say there is no reason to think that the results are likely to be much lower than the HLI results?
I don’t think this makes sense, no, sorry. The HLI meta-analysis results are from cash transfers, which make a few individuals happier over time, not looking at the average of an entire society. It’s well-studied that people care about their relative income, not just their absolute income. So we should be particularly worried about extrapolating from what works for individuals to what works for societies—especially where we think the benefit to the individual could be from comparisons. Hence, I think it is not justified to start from the HLI numbers.
IIRC, in the HLI cash transfer meta-analysis, we found that cash transfers had no effect on those in nearby villages (‘across-village’ effect). In other words, there was, on average, no relative income effect. I was puzzled by it and I find it hard to believe—our CEA does, however, despite my disbelief, assume there are no negative spillovers from cash transfers. I was puzzled by this because there’s such consistent evidence of a relative income effect in rich countries. I also thought it was weird the effect from cash transfers was zero. To put this in context, imagine a bunch of people down the road from you get given $40,000 for each household. Would you expect that to have no effect on you? It wouldn’t make you envious? Or, it wouldn’t make you excited that this might happen to you? I’d expect the effect of income to be (almost) wholly relative in rich countries, but not that there was no relative income effect in the very poor. However, there wasn’t loads of across-village data in the HLI meta-analysis, so I didn’t update much. It would be good to have a bigger and better analysis of the relative income effect in very poor contexts.