First off, very interesting. This is my first exposure to the EA community. My friends / colleagues have rightly encouraged me to learn more about your work.
Essentially, my argument is this: you cannot believe that the relations we estimate are consistent with cross-sectional results or experimental results because it takes 71 years for income to double when increasing growth. I further explain this below.
I think we agree that GDP is not a good measure of wellbeing. I also strongly believe it is not a good policy target. We should target wellbeing directly.
For alternative policies that similarly cover a long period of time, see recent work by me and Easterlin, “Explaining happiness trends in Europe” (https://doi.org/10.1073/pnas.2210639119). We show the best predictor of long-run changes in life satisfaction is the generosity of the social safety net – more generous, greater happiness – in ten European countries. At the same time, we argue economic growth does not have a meaningful influence on life satisfaction in the long-run.
For my more substantive comment: increasing growth from two to three percent takes 71 years to double income. This is a very long time in my view. I’m not coming from the EA framework. Perhaps the EA community disagrees with me. It matters a great deal however, for both conceptual and empirical reasons.
Fundamentally, you cannot compare doubling one’s income at a point of time (e.g., due to lottery and investment returns or cash transfers) to doubling one’s income in 71 years. 71 is greater than life expectancy in numerous countries. Empirically, the growth-happiness relation depends upon on the time horizon; it gets smaller as the duration increases. We discuss this in the paper conceptually and in reference to the two data sets we use. The longer period in the WVS/EVS data results in lower growth- subjective well-relations. For further support, see Bartolini S, Sarracino F (2014) Happy for how long? How social capital and economic growth relate to happiness over time. Ecol Econ 108:242–256. https://doi.org/10.1016/j.ecolecon.2014. 10.004.
Your replication / robustness tests are not so surprising. As you point out, our results include larger coefficient estimates using different specifications, yet we still argue they are not economically significant, implying we would argue your alternative results are still too small to prioritize growth. Here’s the quote: “Based on the largest magnitude across all estimations [larger than what you estimate using 2020 or excluding India], it would still take 100 years for a one percentage point increase in the growth rate to raise happiness by one point.”
To your point, however, even a small increase in subjective well-being for a large number of people is meaningful, but we’re talking about a long time to achieve even these small changes. I’m reasonably assured you can find much more effective policies for short-run gains. See Table 1 of P. Frijters, A. E. Clark, C. Krekel, R. Layard, A happy choice: Wellbeing as the goal of government. Behav. Public Policy, 1–40 (2020).
This table inspired a similar one used by the U.K. government in the Green Book. See:
MacLennan, S., Stead, I., 2021a. Wellbeing Guidance for Appraisal: Supplementary Green Book Guidance, His Majesty’s Treasury: Social Impact Task Force.
MacLennan, S., Stead, I., 2021b. Wellbeing discussion paper: monetisation of life satisfaction effect sizes: A review of approaches and proposed approach, His Majesty’s Treasury: Social Impacts Task Force.
Your robustness test results do not overturn our results; they fall within the range we estimate and only apply to one data set, indeed the one that is based on a shorter period, which is less preferred for reasons explained in the text and implied by the Bartolini Sarracino paper referenced above.
We need more research on wellbeing. Increasing consumption does not necessarily increase wellbeing, especially in highly developed countries.
See instead the MacLennan references above to see a derivation of the monetary value of a life satisfaction point per year.
Unfortunately, I have not had time to go through the comments, and will be slow to respond due to family concerns. I’ll do my best to respond and keep up with future posts. Thanks for the lively discussion. I wish we could do it in person.
Lastly, you all probably know the Easterlin Paradox has come under fire for years upon years and in different fields. See his article Easterlin RA (2017) Paradox lost? Rev Behav Econ 4:311–339. https://doi.org/10.1561/105. 00000068. You can also find the working paper version for free on google scholar.
Thanks so much for taking the time to engage in this discussion! I am going to try to reply to where we have interesting areas of disagreement, and to number the points for easier response.
“For alternative policies that similarly cover a long period of time, see recent work by me and Easterlin, “Explaining happiness trends in Europe.” ”
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.
2. “Fundamentally, you cannot compare doubling one’s income at a point of time (e.g., due to lottery and investment returns or cash transfers) to doubling one’s income in 71 years… Empirically, the growth-happiness relation depends upon the time horizon; it gets smaller as the duration increases. We discuss this in the paper conceptually and in reference to the two data sets we use. The longer period in the WVS/EVS data results in lower growth- subjective well-relations.”
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?
3. “Your replication / robustness tests are not so surprising. As you point out, our results include larger coefficient estimates using different specifications, yet we still argue they are not economically significant, implying we would argue your alternative results are still too small to prioritize growth.”
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.
4) ” I’m reasonably assured you can find much more effective policies for short-run gains. See Table 1 of P. Frijters, A. E. Clark, C. Krekel, R. Layard, A happy choice: Wellbeing as the goal of government. Behav. Public Policy, 1–40 (2020). “
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.
5) “Your robustness test results do not overturn our results; they fall within the range we estimate and only apply to one data set, indeed the one that is based on a shorter period, which is less preferred for reasons explained in the text”
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).
6) “Perhaps you can explain to me how the GiveWell team determined the “Value assigned to increasing ln(consumption) by one unit for one person for one year” and why this is used in determining the value of subjective well-being benefits.”
GiveWell assigns one unit to an income doubling, so boosting ln(consumption) by one unit is simply =1/ln(2). They then try to estimate the value of saving a life relative to an income doubling by looking at surveys of recipients, Global Burden of Disease estimates, value of statistical life approaches, internal surveys, and other sources. For the purposes of my estimation, you wouldn’t need to accept any of their assumptions except for the fact that it is difficult to find ways to help people that is more than ten times more cost-effective than simply giving cash to the very poorest people in the world.
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.
First off, very interesting. This is my first exposure to the EA community. My friends / colleagues have rightly encouraged me to learn more about your work.
Essentially, my argument is this: you cannot believe that the relations we estimate are consistent with cross-sectional results or experimental results because it takes 71 years for income to double when increasing growth. I further explain this below.
I think we agree that GDP is not a good measure of wellbeing. I also strongly believe it is not a good policy target. We should target wellbeing directly.
For alternative policies that similarly cover a long period of time, see recent work by me and Easterlin, “Explaining happiness trends in Europe” (https://doi.org/10.1073/pnas.2210639119). We show the best predictor of long-run changes in life satisfaction is the generosity of the social safety net – more generous, greater happiness – in ten European countries. At the same time, we argue economic growth does not have a meaningful influence on life satisfaction in the long-run.
For my more substantive comment: increasing growth from two to three percent takes 71 years to double income. This is a very long time in my view. I’m not coming from the EA framework. Perhaps the EA community disagrees with me. It matters a great deal however, for both conceptual and empirical reasons.
Fundamentally, you cannot compare doubling one’s income at a point of time (e.g., due to lottery and investment returns or cash transfers) to doubling one’s income in 71 years. 71 is greater than life expectancy in numerous countries. Empirically, the growth-happiness relation depends upon on the time horizon; it gets smaller as the duration increases. We discuss this in the paper conceptually and in reference to the two data sets we use. The longer period in the WVS/EVS data results in lower growth- subjective well-relations. For further support, see Bartolini S, Sarracino F (2014) Happy for how long? How social capital and economic growth relate to happiness over time. Ecol Econ 108:242–256. https://doi.org/10.1016/j.ecolecon.2014. 10.004.
Your replication / robustness tests are not so surprising. As you point out, our results include larger coefficient estimates using different specifications, yet we still argue they are not economically significant, implying we would argue your alternative results are still too small to prioritize growth. Here’s the quote: “Based on the largest magnitude across all estimations [larger than what you estimate using 2020 or excluding India], it would still take 100 years for a one percentage point increase in the growth rate to raise happiness by one point.”
To your point, however, even a small increase in subjective well-being for a large number of people is meaningful, but we’re talking about a long time to achieve even these small changes. I’m reasonably assured you can find much more effective policies for short-run gains. See Table 1 of P. Frijters, A. E. Clark, C. Krekel, R. Layard, A happy choice: Wellbeing as the goal of government. Behav. Public Policy, 1–40 (2020).
This table inspired a similar one used by the U.K. government in the Green Book. See:
MacLennan, S., Stead, I., 2021a. Wellbeing Guidance for Appraisal: Supplementary Green Book Guidance, His Majesty’s Treasury: Social Impact Task Force.
MacLennan, S., Stead, I., 2021b. Wellbeing discussion paper: monetisation of life satisfaction effect sizes: A review of approaches and proposed approach, His Majesty’s Treasury: Social Impacts Task Force.
Your robustness test results do not overturn our results; they fall within the range we estimate and only apply to one data set, indeed the one that is based on a shorter period, which is less preferred for reasons explained in the text and implied by the Bartolini Sarracino paper referenced above.
We need more research on wellbeing. Increasing consumption does not necessarily increase wellbeing, especially in highly developed countries.
Perhaps you can explain to me how the GiveWell team determined the “Value assigned to increasing ln(consumption) by one unit for one person for one year” and why this is used in determining the value of subjective well-being benefits (cf. the value: https://docs.google.com/spreadsheets/d/1lTX-qNY1cSo-L3yZCNzMbzIM1kqWC1vSEhbyFAYr6E0/edit#gid=1362437801, which is used in this calculation: https://docs.google.com/spreadsheets/d/1aDUPvizGsgT6rLtIf8RkT8LNTmZyXjlXa7Kddc-UeWM/edit#gid=135302151)?
See instead the MacLennan references above to see a derivation of the monetary value of a life satisfaction point per year.
Unfortunately, I have not had time to go through the comments, and will be slow to respond due to family concerns. I’ll do my best to respond and keep up with future posts. Thanks for the lively discussion. I wish we could do it in person.
Lastly, you all probably know the Easterlin Paradox has come under fire for years upon years and in different fields. See his article Easterlin RA (2017) Paradox lost? Rev Behav Econ 4:311–339. https://doi.org/10.1561/105. 00000068. You can also find the working paper version for free on google scholar.
Thanks so much for taking the time to engage in this discussion! I am going to try to reply to where we have interesting areas of disagreement, and to number the points for easier response.
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.
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?
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.
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.
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).
GiveWell assigns one unit to an income doubling, so boosting ln(consumption) by one unit is simply =1/ln(2). They then try to estimate the value of saving a life relative to an income doubling by looking at surveys of recipients, Global Burden of Disease estimates, value of statistical life approaches, internal surveys, and other sources. For the purposes of my estimation, you wouldn’t need to accept any of their assumptions except for the fact that it is difficult to find ways to help people that is more than ten times more cost-effective than simply giving cash to the very poorest people in the world.
Thanks again for the interesting exchange.
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.