Hi Vadim, please see my response to your points below. Even if we don’t come to agreement, I appreciate your intentions. I appreciate anyone who wants to make the world a better place, and especially those who are actually trying to do something. I operate mostly from the ivory tower.
1. Thank you for sending this. It’s encouraging that we may have levers to move that have larger impacts than economic growth. It definitely updates me away from believing the results of the social safety net regression I outline in my post (although as I mentioned in the post, those results were never that compelling). I used OurWordInData’s “Adequacy of Social Safety Net Programs”. There were only 30 countries, and they were mostly LMICs, so I am not surprised that the results differ from yours. I would be curious what you think of that dataset, and whether the data you use looks like it avoids some of the noise in mine. I would definitely love to see more analysis on this with bigger datasets than both of ours’ if there are any ways to create them. I wonder if the implication might be something like: large social safety nets are effective in European states which have a lot of state capacity to deliver services, and less effective in the LMICs in my dataset.
Response: A brief look at the “Adequacy of Social Safety Net Programs” data indicates it is an expenditure measure. The challenge with expenditure measures are that they mechanically increase as people age or the unemployment rate increases. A better indicator is based on the policies themselves, like the indicator in the referenced PNAS paper. There are approximately 30 countries available in that data set now too, but not for the same duration that we wanted to work with. I published a paper on 104 countries using safety net expenditures and controls to account for the mechanical relation mentioned, however, it only provides a cross-sectional relation (https://www.researchgate.net/publication/321948496_Happiness_and_Welfare_State_Policy_Around_the_World). A positive cross-sectional relation exists in each country group, including the less developed.
There are causal studies on the expansion of the safety net, though they are all likely confined to developed countries.
2. I think this is an interesting point. If we believe in hedonic adaptation, then we would expect the results of cash transfer RCTs to be much higher than the results over 14 or 40 years like in the two datasets you use in your paper. So the fact that the implied impacts seem to be very similar seems to be (very weak) evidence against adaptation in this context? Am I right in thinking that the results in your two sets of regression implicitly factor in adaptation, since those countries became wealthier slowly? If so, I think we should be comfortable applying the results with 14-40 years (gallup-wvs/evs) of adaptation factored in, to an estimate that looks at benefits spanning from 1-40 years for Ethiopia?
Response: I don’t agree that the results are similar in size between the RCTs and long-run trend analysis in Easterlin and O’Connor. It takes 71 years of one percent additional GDPpc growth to increase life satisfaction by 0.142 points, over which time there were 32 income doublings. 0.142 points for 32 income doublings is 0.142/32 = 0.004 points per doubling.
However, you are right that the trend analysis does allow for adaptation. We explain why the relationship is smaller in trend analysis than in cross-sectional relations, and this logic could be extended to short run gains. Briefly, in the short run, your reference point hasn’t changed, while it does in the long run. The reference point is set by both your past experience (adaptation) and peers (comparison).
To look at Ethiopia using my results, you should look at interventions that increase growth, e.g., education, innovation, capital investment, trade… I wouldn’t use your conversion as it add confusion, hence our disagreement.
The results of RCTs can be evaluated as they are currently, but we should not assume the results last or scale to the full population. We need longer-term (than the typical) studies of interventions to assess the long-run impacts, and to assess at the population level, there’s a whole science developing on scaling interventions.
3. But those coefficients seem to be close to what we would expect from the cross-sectional data? If that is the case, are you suggesting that even if the Easterlin paradox turned out to not hold, we would not update towards thinking more of economic growth? That would imply that a low income country could increase their life satisfaction from around 4 to around 6 if they could figure out a way to enable the kind of catch-up growth that some East-Asian countries have managed.
Response: I don’t agree that the results are similar in size. See my other response above. There are low-income countries that have similar subjective well-being as high income countries. The Latin American countries, especially Costa Rica, seem to have figured it out. The primary explanation for their success is in their focus on social relations (including family).
4. Thank you for sending this. Reducing fear of violent crime stands out as especially promising to me as a potential intervention. However, it does look like doubling income is still one of the larger results here, and is not obviously harder to achieve than some of the other large-effect interventions. I definitely hope that there are more tractable interventions than boosting growth that we can find. Also, even if we don’t, I think we can probably find ways to do a lot of good by just saving lives, rather than boosting well-being.
Response: Doubling income may be very effective in certain settings. I do not disagree with this, but that’s not the same as increasing long-run growth. Full employment is my go to conceptually though I would need to look more into this to make a more definitive statement. Saving lives surely increases well-being. Self-reported health has one of the strongest relations with SWB, but the relations are prone to significant bias and difficult to estimate. Saving lives also contributes to WELLBYs or happy life years, which are technically the same, but the former is referenced by the UK government.
5. I agree. I only meant to try a couple of easy alternative specifications to see how sensitive the results are to them. The Gallup World Poll Data had more countries and was easier to download so I just decided to look at that dataset. If my results are correct, they are not meant to invalidate the Easterlin Paradox. I just think we should be aware that it seems sensitive to specification (even after accepting the exclusion of transition economies, and countries with less than 12 years of data).
Response: The implications of your narrative would be an invalidation of the Paradox—saying the cross-sectional result is the same as the long-run time series. However, I disagree with your conversion as mentioned above. Maintaining the interpretation in growth terms, the results are not so sensitive.
Hi Vadim, please see my response to your points below. Even if we don’t come to agreement, I appreciate your intentions. I appreciate anyone who wants to make the world a better place, and especially those who are actually trying to do something. I operate mostly from the ivory tower.
1. Thank you for sending this. It’s encouraging that we may have levers to move that have larger impacts than economic growth. It definitely updates me away from believing the results of the social safety net regression I outline in my post (although as I mentioned in the post, those results were never that compelling). I used OurWordInData’s “Adequacy of Social Safety Net Programs”. There were only 30 countries, and they were mostly LMICs, so I am not surprised that the results differ from yours. I would be curious what you think of that dataset, and whether the data you use looks like it avoids some of the noise in mine. I would definitely love to see more analysis on this with bigger datasets than both of ours’ if there are any ways to create them. I wonder if the implication might be something like: large social safety nets are effective in European states which have a lot of state capacity to deliver services, and less effective in the LMICs in my dataset.
Response: A brief look at the “Adequacy of Social Safety Net Programs” data indicates it is an expenditure measure. The challenge with expenditure measures are that they mechanically increase as people age or the unemployment rate increases. A better indicator is based on the policies themselves, like the indicator in the referenced PNAS paper. There are approximately 30 countries available in that data set now too, but not for the same duration that we wanted to work with. I published a paper on 104 countries using safety net expenditures and controls to account for the mechanical relation mentioned, however, it only provides a cross-sectional relation (https://www.researchgate.net/publication/321948496_Happiness_and_Welfare_State_Policy_Around_the_World). A positive cross-sectional relation exists in each country group, including the less developed.
There are causal studies on the expansion of the safety net, though they are all likely confined to developed countries.
2. I think this is an interesting point. If we believe in hedonic adaptation, then we would expect the results of cash transfer RCTs to be much higher than the results over 14 or 40 years like in the two datasets you use in your paper. So the fact that the implied impacts seem to be very similar seems to be (very weak) evidence against adaptation in this context? Am I right in thinking that the results in your two sets of regression implicitly factor in adaptation, since those countries became wealthier slowly? If so, I think we should be comfortable applying the results with 14-40 years (gallup-wvs/evs) of adaptation factored in, to an estimate that looks at benefits spanning from 1-40 years for Ethiopia?
Response: I don’t agree that the results are similar in size between the RCTs and long-run trend analysis in Easterlin and O’Connor. It takes 71 years of one percent additional GDPpc growth to increase life satisfaction by 0.142 points, over which time there were 32 income doublings. 0.142 points for 32 income doublings is 0.142/32 = 0.004 points per doubling.
However, you are right that the trend analysis does allow for adaptation. We explain why the relationship is smaller in trend analysis than in cross-sectional relations, and this logic could be extended to short run gains. Briefly, in the short run, your reference point hasn’t changed, while it does in the long run. The reference point is set by both your past experience (adaptation) and peers (comparison).
To look at Ethiopia using my results, you should look at interventions that increase growth, e.g., education, innovation, capital investment, trade… I wouldn’t use your conversion as it add confusion, hence our disagreement.
The results of RCTs can be evaluated as they are currently, but we should not assume the results last or scale to the full population. We need longer-term (than the typical) studies of interventions to assess the long-run impacts, and to assess at the population level, there’s a whole science developing on scaling interventions.
3. But those coefficients seem to be close to what we would expect from the cross-sectional data? If that is the case, are you suggesting that even if the Easterlin paradox turned out to not hold, we would not update towards thinking more of economic growth? That would imply that a low income country could increase their life satisfaction from around 4 to around 6 if they could figure out a way to enable the kind of catch-up growth that some East-Asian countries have managed.
Response: I don’t agree that the results are similar in size. See my other response above. There are low-income countries that have similar subjective well-being as high income countries. The Latin American countries, especially Costa Rica, seem to have figured it out. The primary explanation for their success is in their focus on social relations (including family).
4. Thank you for sending this. Reducing fear of violent crime stands out as especially promising to me as a potential intervention. However, it does look like doubling income is still one of the larger results here, and is not obviously harder to achieve than some of the other large-effect interventions. I definitely hope that there are more tractable interventions than boosting growth that we can find. Also, even if we don’t, I think we can probably find ways to do a lot of good by just saving lives, rather than boosting well-being.
Response: Doubling income may be very effective in certain settings. I do not disagree with this, but that’s not the same as increasing long-run growth. Full employment is my go to conceptually though I would need to look more into this to make a more definitive statement. Saving lives surely increases well-being. Self-reported health has one of the strongest relations with SWB, but the relations are prone to significant bias and difficult to estimate. Saving lives also contributes to WELLBYs or happy life years, which are technically the same, but the former is referenced by the UK government.
5. I agree. I only meant to try a couple of easy alternative specifications to see how sensitive the results are to them. The Gallup World Poll Data had more countries and was easier to download so I just decided to look at that dataset. If my results are correct, they are not meant to invalidate the Easterlin Paradox. I just think we should be aware that it seems sensitive to specification (even after accepting the exclusion of transition economies, and countries with less than 12 years of data).
Response: The implications of your narrative would be an invalidation of the Paradox—saying the cross-sectional result is the same as the long-run time series. However, I disagree with your conversion as mentioned above. Maintaining the interpretation in growth terms, the results are not so sensitive.