Kelsey J. O'Connor

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“If I understand correctly, it sounds like we now agree on the math of my post, and on my arguments around which coefficients from cross-sectional vs longitudinal regressions seem to match? But I think we still disagree about whether the impacts of a gradual increase in gdp across time should be compared to cross-sectional differences?”

Response: Not quite, I agree with some of the calculations you did in the last post, but not with the overall post and conclusions. The quickest justification for this response is that the EVS/WVS coefficients are smaller and even negative for certain country groups. There’s also the statistical significance to consider, which we have not discussed. There is a large amount of uncertainty in the estimates, and while they could be larger, they could also be zero. But regardless of coefficient, yes, we disagree about the implications of time-series  (TS) and cross-sectional (CS) differences.

"My first thought on our disagreement is that an income doubling is a fairly arbitrary metric. I think it would be equally reasonable to zoom in on the cross sectional graph, and look at the impact of a 1% increase in income. We can imagine country Y on the cross-section graph which lies a little higher than Ethiopia on the regression line in my post. This country would have \$1010 per capital GDP and a SWB of 4+1*.007=4.007, versus Ethiopia at \$1000 and 4. If we compare this to what we would expect from a .007 coefficient in one of your alternative regressions, it looks like it’s exactly what we would expect from one year of 1% growth vs the counterfactual for Ethiopia? In this case we don’t need to worry about the amount of time it takes to double income, and TS and CS become more intuitively comparable?"

Response: You’re right that in that case we do not need to worry about the time involved, but what you’re pointing out is how small the relationship actually is in the cross-section. From the figure, the cross-sectional relationship is: y = -2.955 + 0.342*ln(x). Then a 1 percent increase in income is related to an increase in SWB of approximately 0.342*0.01 = 0.003, which of course is actually smaller than the 0.007 coefficient (note the previous post, 0.007 applies to developed not less developed countries. Also, it is statistically insignificant and could also be zero).

"My second thought is that if we assume that TS results are not comparable to CS results because they take a long time, wouldn’t that make the existence of the Easterlin Paradox irrelevant for making any judgements about the world? Isn’t the Easterlin Paradox a paradox precisely because we expect the coefficients to match between CS and TS, but they don’t seem to in some specifications?"

Response: You’re right that the Paradox is about the contrast between the two types of results. However, it’s not just whether the coefficients match. The CS results are statistically significant and the TS results are generally not statistically significant. A second aspect is that the TS results, even if statistically significant, make it clear how long it would take for SWB to increase. The period necessary is not clear in CS results, which makes it look like the CS results are much larger than the TS results. Your calculation above suggests that the CS results are actually quite small too, or that we need larger changes in income to have meaningful changes in SWB, which as the TS results point out, will take a long time. Another aspect, the Paradox is about the contrast, but the surprising result is how small the TS relation is. Whether there's a contrast or not, this relation is important for thinking about the world.

“we are talking about the Gallup results and ignoring the EVS/WVS results. They are preferred for long-run periods.”

Agreed. I haven’t looked at the EVS/WVS results at all, so there is a good chance that they are less sensitive to the kinds of alternative specifications I tried for the Gallup results."

Response: the sensitivity isn’t too important, because the relationships are all small. As stated above, even the CS results are quite small, or we need larger changes in income to have meaningful changes in SWB, which as the TS results point out, will take a long time.

“It’s possible that many people on the lower end of the income distribution benefit greatly – indeed many economists, even happiness ones, believe this in their bones. We just need more evidence at scale.”

"I share the same intuition, and find this an interesting area for further exploration. I would be curious to hear your thoughts on why the “Growth X LDC” coefficients in all of your regressions are negative (which is a surprise to me). This seems to imply that people lower down the income distribution are actually benefiting less from % income increases? Re-running your regressions on just the less-developed countries in your Gallup dataset, I also get smaller coefficients than those for the whole dataset."

Response: The Growth X LDC coefficient applies to lower income countries not strictly lower income people. The distinction is important because we can expect the mechanisms to be different. The impacts of income within a country are absolute and relative, due to social comparison as well as cost of living. Recall that poverty is usually defined in relative terms, i.e., as 60% of the median. While at the country level, I expect income to operate more in absolute terms. It’s not clear to me why growth does not help more in these countries.

What I can suggest are two papers that discuss the quality of growth: (1) https://www.sciencedirect.com/science/article/abs/pii/S0305750X17300049

(2) Helliwell, J. (2016). Life satisfaction and quality of development. In Bartolini, S., Bilancini, E., Bruni, L., and Porta, P., editors, Policies for Happiness, chapter 7, page 149. Oxford University Press.

There should be others too. For partial explanations (off the top of my head), think income inequality, quality of jobs (security), corruption, work hours, and materialism.

Vadim, discussing with you will cause me to be more explicit about the time involved in future writing. Thank you. As I first argued, 71 years is a long time, which cross-sectional results do not consider. I respond more completely below.

“Yes, I am definitely talking about WELLBYs. I meant to say that there are two ways of looking at both income and SWB, a level at a point in time, and the sum of the levels per year (we can think of those as the area under the curve plotted across time). We can call the summed versions INCYs and WELLBYs, and the point in time estimates Income and SWB. So I think in year 13.5, we can say that we get .2 WELLBYs for 1 INCY. Or alternatively, we can say that we get .027 SWBs for 14% Income gain. I don't think that we should be comparing SWBs (a point in time estimate) to INCYs (a summation estimate).”

Response I agree with part, in year 13.5 we get .2 WELLBYs for 1 INCY (when INCY equals an income doubling),

and adjusted this statement slightly, 0.027 SWB points for a 14% increase in growth for one year.

However, a 14% increase in growth is rarely observed and never sustained. .002 does not apply to such changes.

"To illustrate I’ll try to go back to the example of boosting Ethiopia’s growth by 1pp, using your coefficient of 0.002. For simplicity, let’s say that Ethiopia starts with a per capita GDP of \$1000, a SWB of 4, and a real growth rate of 0%. It seems like we agree that “The population in year 13.5 reports .027 greater SWB points after an increase in growth by one percent.” So if we boost growth to 1% I think we agree that in year 71 Ethiopia would have a per capita GDP of \$2000 (versus the counterfactual \$1000) and a SWB = 4 + 71*.002=4.14."

Agreed

"Now to address our discussion on (3) in the below thread, you say: "As you point out, our results include larger coefficient estimates using different specifications, yet we still argue they are not economically significant," and then in response to my comment that "those coefficients seem to be close to what we would expect from the cross-sectional data," you comment "I don’t agree that the results are similar in size."

Let’s assume we accept the coefficient from your regression in table 3, column 5: 0.007. That would imply that in year 71 Ethiopia would have roughly twice the GDP than it would have had counterfactually (compared to the 0% growth world), and a SWB = 4+71*.007 = 4.5. This is 0.5 points higher than the counterfactual."

Agreed with the math, however, the coefficient is wrong. You should add the negative coefficient on the interaction term to get the correct relationship 0.007 - 0.005. The largest relationship applies in the transition countries 0.007 + 0.003, but as argued in the paper, they are exceptional cases. And, the WVS/EVS results show smaller coefficients.

"Now let's imagine that in the cross section regression Ethiopia and country X are both exactly on our regression line. Ethiopia is at \$1000 and SWB of 4, country X is at \$2000 and SWP of 4.5 (That is roughly where the cross sectional regression lines fall as I argue in my post, and as you can see from the graph I include). If there were no Easterlin Paradox, we would expect that if Ethiopia gradually got to \$2000 GDP, it would move up the regression line to where country X currently is. But it seems like that is exactly what the .007 regression coefficient implies in the preceding paragraph? If so, is this at odds with your response on discussion (3) in the below thread?"

Although the math is correct (assuming 0.007), it took 71 years to achieve the change in SWB, whereas the cross-sectional results do not consider the time involved. As mentioned before 71 years is greater than life expectancy in many countries, and as the WVS/EVS results show, the coefficients are smaller in longer time series.

The coefficient estimates of .002 or .007 apply to long-run sustained growth after accounting for adaptation and social comparison. If you were to double the income of Ethiopians in one year, neither coefficient would apply. We are not sure what applies. Short run growth typically has a larger relationship, the effect of which diminishes over time, possibly to zero, and the benefits of cash transfers do not accrue to the population as a whole.

I don’t think you want to apply our results to what you’re evaluating, especially considering we are talking about the Gallup results and ignoring the EVS/WVS results. They are preferred for long-run periods (still fare less than 71 years) and they reveal smaller, even negative growth relations.

We should be looking at long run outcomes of interventions and then assessing them at scale. It’s possible that many people on the lower end of the income distribution benefit greatly – indeed many economists, even happiness ones, believe this in their bones. We just need more evidence at scale.

"Alternatively, don’t the coefficients from Sacks, Stevenson, and Wolfers 2012 roughly correspond to the larger coefficient estimates in your regressions (since both include 10 year short-term fluctuations)? So if Sacks et. al. convincingly reran their analysis to focus on the same countries and longer time series that you use, and got the same coefficients they did in their paper, would that not update us towards thinking that longitudinal and cross-sectional results might be similar?"

Honestly, I haven’t read the SSW paper in quite some time, in part because of what you reference here, they shorten the length of the time-series and also because they leave out data that was available to them at the time.

My disagreement about the TS and CS studies is the time involved as mentioned above. The CS studies make it seem like we could magically double income and ignore the time involved. Even if the coefficients are the same, the implications are not. Discussing with you brought this to my attention, and I will discuss it more explicitly in future writing.

"I think we could also use a similar argument about the Ethiopian counterfactual SWB = 4 + 71*.002=4.14 to argue that it matches the cash transfer results that I cite in my post."

See discussion above about time. I simply don’t think my results apply to what you’re assessing.

I see the confusion now and introduce a term that should help. Discussed in response to your points below.

“In your spreadsheet, you multiplied 0.002 by the number of years, assuming a larger increase in SWB per year (i.e., 0.004 in year 2), which is not correct.

I meant the .004 to represent how much happier a person is after two years of faster growth than they would have been counterfactually (if growth had been 1pp lower). Since their annual change in SWB would have been .002 higher, they would have gotten .004 better off by year 2.

In other words, I think your formulas (4)-(3) represent the impact of additional growth (versus the counterfactual) on life satisfaction at time t (SWBt). So using your: 0.002*(∆G)t = .0021*2=.004 happier than the counterfactual. This is only .002 happier than the counterfactual after 1 year, but .004 happier than the counterfactual if there had been no additional growth at all. So since the person was .002 happier in year 1 and .004 happier in year 2, I would consider that a cumulative .006 happier across the two years."

Response:  We’re discussing different things. You’re discussing SWB points for one year. The U.K. uses the term WELLBYs, well-being points for one year.

So my statement is correct, subjective well-being increases by 0.027 points for a cumulative doubling of annual income in 13.5 years.

Your statement is also correct if we modify it slightly, from, “when you sum them you get a total of 0.2 life satisfaction points”, to “you get a total of 0.2 WELLBYs for a cumulative doubling of annual income in 13.5 years.”

In hopes of clarifying,  assume ɑ=0 and SWB_0= 5

Under baseline growth

Year 1: SWB2_1 = 5+.002*2*1 = 5.004

Year2: SWB2_2 = 5+.002*2*2 = 5.008

Year 3: SWB2_3 = 5+.002*2*3 = 5.012

With 3 percent growth

Year 1: SWB3_1 = 5+.002*3*1 = 5.006

Year2: SWB3_2 = 5+.002*3*2 = 5.012

Year 3: SWB3_3 = 5+.002*3*3 = 5.018

By year 3, the difference in SWB is 5.018 – 5.012. In year 13.5 the total increase is 0.027.

Taking the WELLBY approach:

In year three, the total increase in SWB is .006, but people experienced .004 higher SWB for one year in the preceding year and .002 for one year in year one. Each differential lasts one year. Thus the WELLBYs in year 3 is .002+.004 +.006 = .012 WELLBYs. The total accrued WELLBYs in year 13.5 is 0.2.

0.027 SWB points are `permanent’ whereas 0.2 WELLBYs only last a year. Unfortunately, there are few studies that look at WELLBYs. The best resource on WELLBYs was just made open access “A Handbook for Wellbeing Policy-Making: History, Theory, Measurement, Implementation, and Examples” (https://global.oup.com/academic/product/a-handbook-for-wellbeing-policy-making-9780192896803?cc=gb&lang=en&#)

"I think for the cumulative life satisfaction gain to be .027, you would have to expect the person in year 13.5 to only be .002 happier than he would have been without the additional growth (that way he would only be .002 happier each year, for a total of .027 life satisfaction points summed across the 13.5 years). But that would imply that our SWB measure wasn’t annualized, and that it shouldn’t matter whether you’ve been growing for one year or 1000, you would still be happier by the same amount?"

Response: The population in year 13.5 reports .027 greater SWB points after an increase in growth by one percent (see above). SWB increases by .002 points per year. In year 1000, the population would have experienced 1000 years of additional SWB at .002 per year, which is 1000*.002 = 2 points. However, as I discuss above there are differences in each year that you could call WELLBYs.

"Perhaps our difference is in how we are using the word cumulative? By cumulative, I mean actually summing across the counterfactual SWB gains in each of the 13.5 years. I think this is the correct thing to look at if we are comparing it to the income gains in each year summed across the 13.5 years. Perhaps by cumulative you meant just the total counterfactual impact on life satisfaction in year 13.5? But then it seems like we need to add the counterfactual impacts at each of the preceding years?"

Response: see above

"Perhaps one useful intuition pump would be to compress the whole income doubling into 1 year. Lets say annual growth increases by 100pp. Then we counterfactually double income in the first year. The impact on SWB is 100*.002=.2 life satisfaction points. Which is a bit higher than the estimates from cash transfers."

Response: I mostly agree. The calculation is correct, but the estimated relation is based on long run growth rates, not a one time change in GDPpc. We don’t have the “support” from the data to assess your hypothetical. It is plausible that the growth relation is non-linear and diminishes at higher rates, but we do not know for sure, because we do not observe such growth rates. China and India who had the highest sustained growth rates benefited little from growth as far as we can observe (reported in the paper).

Also, it may be plausible to increase GDP pc for one year by 100 percent but not growth trends, which of course last over many years.

I’m not sure about the coefficient on cash transfers, but the cross-country association between GDPpc and life satisfaction is still much higher.

and so this does very well known paper: https://pubs.aeaweb.org/doi/pdf/10.1257/jep.22.2.53?utm_source=link_newsv9&utm_campaign=item_182843&utm_medium=copy

Though be careful with the last paper. The author is not distinguishing between time-series and cross-sectional relations.

Great question. The difference between the cross-sectional (CS) positive relation between GDP pc and life satisfaction and the nill time-series (TS) relation is best explained by social comparison. See the article for details.

However, people also surely adjust their expectations over time, Bernard Van Praag used the term preference drift to refer to the phenomenon described (https://www.nowpublishers.com/article/DownloadSummary/MIC-026), and psychologists use the term hedonic treadmill. This is important, but not enough to explain the difference between the CS and TS results. There are other explanations too, essentially that it is the quality of growth that matters, see (https://www.sciencedirect.com/science/article/abs/pii/S0305750X17300049)

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.

Vadim, I don’t agree with your calculation. In the second year you do not get 0.004 more Cantril Ladder points (SWB), you get 0.002. The estimated equation is ∆SWB = ɑ + β*GDPpcGrowth. ∆SWB is the average annual change in SWB. A one percentage point increase in GDP growth is associated with β additional SWB points per year, every year. In your spreadsheet, you multiplied 0.002 by the number of years, assuming a larger increase in SWB per year (i.e., 0.004 in year 2), which is not correct.

The cumulative increase in the Cantril Ladder by year 13.5 is the number of years multiplied by the increase (13.5*0.002 = .027). As before, you get 0.027 subjective well-being points for a cumulative doubling of annual income in 13.5 years.

I guess that's clear, but it might also help to look at SWB in levels.

SWB at time t equals baseline SWB (SWB_0) plus the change in SWB multiplied by t periods in which it changed.

(1) SWBt = SWB_0 + ∆SWB*t

The average annual change in SWB was estimated in Easterlin O’Connor as

(2) ∆SWB = ɑ + β*GDPpcGrowth

Assuming β=0.002, G_0 is baseline growth, and there is no change in growth, we have:

(3) SWBt = SWB_0 + (ɑ + 0.002*G_0)*t

Assuming a change in growth

(4) SWBt = SWB_0 + (ɑ + 0.002*(G_0+∆G))*t

The difference between (3) and (4) gives us the cumulative effect of additional growth at time t, which equals 0.002*(∆G)*t. The average annual change is always 0.002*(∆G).

After  13.5 years, the cumulative increase is  0.002 * 1 *13.5. = 0.027, assuming a one percentage point increase in growth.

I forgot to add a very important feature. I don't think income doubling is a helpful conversion in this case. In fact, I think it's quite misleading.

Increasing growth by one percent for 71 years costs a lot more than doubling one's income for a year. In the 71st year, the cost is the same, but there are the preceding  transfers that were also necessary to increase subjective well-being.  In year 70, the cost was nearly a doubling of income.

I estimate there are nearly 32 annual income doublings  when you sum them across the 71 years. You get a portion of an income doubling every year, which can be calculated as (1.03/1.02)^n - 1. In the first year (n=1) it costs you 0.01 of an income doubling. In year 70, it costs 0.98 of a doubling. Summing them from n = 1 to n=71 you get  31.9 doublings.

Your estimated impact should then be the coefficient * 71 /32. If we use .002 as the coefficient on growth, that equals .004 - hardly worth it. The same is true for the lager estimated coefficients. Alternatively, you could look at my calculation to see when the costs add up to a the first cumulative annual income doubling. I estimate this happens between year 13 and 14. Let's say 13.5, then 13.5 years * .002 = .027. You get 0.027 subjective well-being points for a cumulative doubling of annual income in 13.5 years. Again, I think this is not worth it.

Essentially, you need to consider the time involved to double income, for the reasons mentioned in my earlier post, and because it simply costs more. I wouldn't compare these results with those of interventions of cross-sectional studies.

Oh, and thanks for the transparency. I appreciate being able to follow the calculations. Great practice.

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.