The phenomenon you describe as “rescaling” is generally known as a (violation of) measurement invariance in psychometrics. It is typically tested by observing whether the measurement model (i.e., the relationship between the unobservable psychological construct and the measured indicators of that construct) differ across groups (a comprehensive evaluation of different approaches is in Millsap, 2011).
I would interpret the tests of measurement invariance you use.…
If people are getting happier over time — but reporting it on a stretched or stricter scale — then the link between how happy someone says they are, and what they do when they’re unhappy, should weaken over time.
In other words: if life satisfaction is increasing, but the reporting scale is stretching, then big life decisions — like leaving a job or ending a relationship — should become less predictable from reported happiness
....to actually be measures of “prediction invariance”: which holds when a measure has the same regression coefficient with respect to an external criterion across different groups or time.
But as Borsboom (2006) points out, prediction invariance and measurement invariance might actually be in tension with each other under a wide range of situations. Here’s a relevant quotation:
In 1997 Millsap published an important paper in Psychological Methods on the relation between prediction invariance and measurement invariance. The paper showed that, under realistic conditions, prediction invariance does not support measurement invariance. In fact, prediction invariance is generally indicative of violations of measurement invariance: if two groups differ in their latent means, and a test has prediction invariance across the levels of the grouping variable, it must have measurement bias with regard to group membership. Conversely, when a test is measurement invariant, it will generally show differences in predictive regression parameters.
This is stretching my knowledge of the topic beyond its bounds, but this issue seems related to the general inconsistency between measurement invariance and selection invariance, which has been explored independently in psychometrics and machine learning (e.g., the chapters on facial recognition and recidivism in The Alignment Problem).
Thanks a lot for this. I hadn’t actually come across these terms; that’s super useful. I’ll have to read both these articles when I get a chance, will report back.
To synthesize a few of the comments on this post—This comment sounds like a general instance of the issue that @geoffrey points out in another comment: what @Charlie Harrison is describing as a violation of “prediction invariance” may just be a violation of “measurement invariance”; in particular because happiness (the real thing, not the measure) may have a different relationship with GMEOH events over time.
I basically agree with this critique of the results in the post, but want to add that I nonetheless think this is a very cool piece of research and I am excited to see more exploration along these lines!
One idea that I had—maybe someone has done something like this? -- is to ask people to watch a film or read a novel and rate the life satisfaction of the characters in the story. For instance, they might be asked to answer a question like “How much does Jane Eyre feel satisfied by her life, on a scale of 1-10?”. (Note that we aren’t asking how much the respondent empathizes with Jane or would enjoy being her, simply how much satisfaction they believe Jane gets from Jane’s life.) This might allow us to get a shared baseline for comparison. If people’s assessments of Jane’s life go up or down over time, (or differ between people) it seems unlikely that this is a result of a violation of “prediction invariance”, since Jane Eyre is an unchanging novel with fixed facts about how Jane feels. Instead, it seems like this would indicate a change in measurement: i.e. how people assign numerical scores to particular welfare states.
haha, yes, people have done this! This is called ‘vignette-adjustment’. You basically get people to read short stories and rate how happy they think the character is. There are a few potential issues with this method: (1) they aren’t included in long-term panel data; (2) people might interpret the character’s latent happiness differently based on their own happiness
Anchoring vignettes may also sometimes lack stability within persons. That said, it’s par for the course that any one source of evidence for invariance is going to have its strengths and weaknesses. We’ll always be looking for convergence across methods rather than a single cure-all.
Always enjoy your posts, you tend to have fresh takes and clear analyses on topics that feel well-trodden.
That said, I think I’m mainly confused if the Easterlin paradox is even a thing, and hence whether there’s anything to explain. On the one hand there are writeups like Michael Plant’s summarising the evidence for it, which you referenced. On the other hand, my introduction to Easterlin’s paradox was via Our World in Data’s happiness and life satisfaction article, which summarised the evidence for happiness rising over time in most countries here and explain away the Easterlin paradox here as being due to either survey questions changing over time (in Japan’s case, chart below) or to inequality making growth not benefit the majority of people (in the US’s case).
The reply that Easterlin and O’Connor (2022) make is that Stevenson, Wolfers, and co. are looking over too short a time horizon. They point out that the critique looks at segments of ten years and to really test the paradox requires looking over a longer time period, which is what Easterlin and O’Connor (2022) do themselves. Easterlin and O’Connor (2022) write that they don’t really understand why Stevenson and Wolfers are using these short time segments rather than the longer ones.
But the chart for Japan above, which is from Stevenson and Wolfers (2008), spans half a century, not ten years, so Easterlin and O’Connor’s objection is irrelevant to the chart.
This OWID chart (which is also the first one you see on Wikipedia) is the one I always think about when people bring up the Easterlin paradox:
But Plant’s writeup references the book Origins of Happiness (Clark et al., 2019) which has this chart instead:
So is the Easterlin paradox really a thing or not? Why do the data seem to contradict each other? Is it all mainly changing survey questions and rising inequality? On priors I highly doubt it’s that trivially resolvable, but I don’t have a good sense of what’s going on.
I wish there was an adversarial collaboration on this between folks who think it’s a thing and those who think it isn’t.
First, there are differences in the metrics used – the life satisfaction (0-10) is more granular than the 4 category response questions.
Additionally, the plot from OWID, a lot of the data seems quite short-term – e.g., 10 years or so. Easterlin always emphasises that the paradox is across the whole economic cycle, but a country might experience continuous growth in the space of a decade.
My overall view – several happiness economists I’ve spoken to basically think the Easterlin Paradox is correct (at least, to be specific: self-reported national life satisfaction is flat in the long-run), so I defer to them.
It’s worth saying that the fact that most arrows go up on the OWiD chart could just point to two independent trends, one of growth rising almost everywhere and another of happiness rising almost everywhere, for two completely independent reasons. Without cases where negative or zero growth persists for a long time, it’s hard to rule this out.
It could in theory, but OWID’s summary of the evidence mostly persuades me otherwise. Again I’m mostly thinking about how the Easterlin paradox would explain this OWID chart:
I’m guessing Easterlin et al would probably counter that OWID didn’t look at a long-enough timeframe (a decade is too short), and I can’t immediately see what the timeframe is in this chart, so there’s that.
At the same time, my hunch is that all three of these exit actions have gotten easier to do and more common from 1990 to 2022. I believe divorce has gotten less stigmatized, the job market rewards more hopping around, and (I think) hospitalization has been recommended more.
If that “easier-to-do” effect is large enough, then it’d be compatible with a very wide range of happiness trends (rising/falling/stable + rescaling/no rescaling) Wondering if you have any thoughts on that.
It’s possible that these 3 exit actions have gotten easier to do, over time. Intuitively, though, this would be pushing in the same direction as rescaling: e.g., if getting a divorce is easier, it takes less unhappiness to push me to do it. This would mean the relationship should (also) get flatter. So, still surprising, that the relationship is constant (or even getting stronger).
Ah I missed the point about the relationship getting flatter before. Thanks for flagging that.
I think I’m more confused about our disagreement now. Let me give you a toy example to show you how I’m thinking about this. So there’s three variables here:
latent life satisfaction, which ranges from 0 to infinity
reported life satisfaction, which ranges from 0 to 10 and increases with latent life satisfaction
probability of divorce, which ranges from 0% to 100% and decreases with latent life satisfaction
And we assume for the sake of contradiction that rescaling is true. One example could be:
In t=1, latent life satisfaction = 1 * reported life satisfaction
Both are bounded from [0,10]
In t=2, latent life satisfaction = 2 * reported life satisfication.
Reported life satisfaction still ranges from [0,10]
But latent life satisfaction now ranges from [0,20]
Let’s say that’s true. Let’s also assume people divorce less as they get happier (and let’s ignore my earlier ‘divorce gets easier’ objection). One example could be:
In t=1 and t=2, probability of divorce = 0.40 - latent life satisfaction/100. That implies:
In t=1, probability of divorce ranges from [0.40, 0.30]
In t=2, probability of divorce ranges from [0.40, 0.20]
And so if I got the logic right, rescaling should accentuate (make steeper) the relationship between probability of divorce and reported life satisfaction. But I think you’re claiming rescaling should attenuate (make flatter) the relationship. So it seems like we’re differing somewhere. Any idea where?
I think rescaling could make it steeper or flatter, depending on the particular rescaling. Consider that there is nothing that requires the rescaling to be a linear transformation of the original scale (like you’ve written in your example). A rescaling that compresses the life satisfaction scores that were initially 0-5 into the range 0-3, while leaving the life satisfaction score of 8-10 unaffected will have a different effect on the slope than if we disproportionately compress the top end of life satisfaction scores.
Sorry if I expressed this poorly—it’s quite late :)
Hi Zachary, yeah, see the other comment I just wrote. I think stretching could plausibly magnify or attenuate the relationship, whilst shifting likely wouldn’t.
2. Scale shifting should always lead to attenuation (if the underlying relationship is negative and convex, as stated in the piece)
Your linear probability function doesn’t satisfy convexity. But, this seems more realistic, given the plots from Oswald/Kaiser look less than-linear, and probabilities are bounded (whilst happiness is not).
Again consider:
P(h)=1/h
T=1: LS = h ⇒ P(h) =1/LS
T=2: LS = h-5 ⇔ h = LS+5 ⇒ P(h) = 1/(LS+5)
Overall, I think the fact that the relationship stays the same is some weak evidence against shifting – not stretching. FWIW, in the quality-of-life literature, shifting occurs but little stretching.
Hello Charlie. This looks like an interesting research question. However, I do have a few comments on the interpretation and statistical modelling. Both statistical comments are on subtle issues, which I would not expect an undergraduate to be aware of. Many PhD students won’t be aware of them either!
On interpretation with respect to the Easterlin paradox: your working model, as far as I can tell, appears to assume that quit rates decrease in a person’s latent happiness, but not in their reported happiness. However, if shifts in reporting are caused by social comparisons (i.e., all the other jobs or relationships you see around you improving) then from that direction rescaling no longer implies a flatter relationship, as the quality of the jobs or relationships available upon quitting have increased. However, your results are indicative that other forms of rescaling are not occurring e.g., changes in culture. I think this distinction is important for interpretation.
The first of the statistical comments that these changes in probabilities of making a change could also be explained by a general increase in the ease of or tendency to get a hospital appointment/new job. This stems from the non-linearity of the logistic function. Logistic regression models a latent variable that determines the someone’s tendency to quit and converts this into a probability. At low probabilities, an increase in this latent variable has little effect on the probability of separation due to the flatness of the curve mapping the latent variable into probabilities. However, the same increase at values of the variable that give probabilities closer to 0.5 will have a big effect on the probability of separation as the curve is steep in this region. As your research question is conceptual (you’re interested in whether life satisfaction scales map to an individual’s underlying willingness to quit in the same way over time), rather than predicting the probability of separations, the regression coefficients on time interacted with life satisfaction should be the parameter of interest rather than the probabilities. These effects can often go in different directions. A better explanation of this issue with examples is available here: https://datacolada.org/57 I also don’t know whether results will be sensitive to assuming a logit functional form relative to other reasonable distributions, such as a probit.
Another more minor comment, is that you need to be careful when adding individual fixed effects to models like this, which you mentioned you did as a robustness check. In non-linear models, such as a logit, doing this often creates an incidental parameters problem that make your regression inconsistent. In this case, you would also be dealing with the issue that it is impossible to separately identify age, time, and cohort effects. Holding the individual constant, the coefficient of a change in time with life satisfaction would include both the time effect you are interested in and an effect of ageing that you are not.
I’d be happy to discuss any of these issues with you in more detail.
If people are getting happier (and rescaling is occuring) the probability of these actions should become less linked to reported LS — but they don’t.
When I first read this sentence I thought that your argument makes perfect sense, but then when I read
Surprisingly, the relationship appears to get stronger over time. That’s the opposite of what rescaling predicts!
in the Overall happiness section and my first thought was: “well, I guess people are getting more demanding”. And now I am confused.
I could imagine thinking about “people don’t settle for half-good any more” as a kind of increased happiness (even if calling it “satisfaction” would be strange).
Independent of this, my personal impression about economic wealth ever since childhood has been that my physical needs are essentially saturated and my social environment is massively more important for my subjective well-being. And although the latter is influenced by wealth, it is much more strongly affected by culture. And although I can think of plenty cultural developments that I am thankful for, I can also think of many that don’t push me in a healthy direction.
“If people are getting happier (and rescaling is occuring) the probability of these actions should become less linked to reported LS”
This means, specifically, a flatter gradient (i.e., ‘attenuation’) – smaller in absolute terms. In reality, I found a slightly increasing (absolute) gradient/steeper. I can change that sentence.
I could imagine thinking about “people don’t settle for half-good any more” as a kind of increased happiness
This feels similar to Geoffrey’s comment. It could be that it takes less unhappiness for people to take decisive life action now. But, this should mean a flatter gradient (same direction as rescaling)
And yeah, this points towards culture/social comparison/expectations being more important than absolute £.
This means, specifically, a flatter gradient (i.e., ‘attenuation’) – smaller in absolute terms. In reality, I found a slightly increasing (absolute) gradient/steeper. I can change that sentence.
I don’t think that is necessary—my confusion is more about grasping how the aspects play together :)
I’m afraid I will have to make myself a few drawings to get a better grasp.
The phenomenon you describe as “rescaling” is generally known as a (violation of) measurement invariance in psychometrics. It is typically tested by observing whether the measurement model (i.e., the relationship between the unobservable psychological construct and the measured indicators of that construct) differ across groups (a comprehensive evaluation of different approaches is in Millsap, 2011).
I would interpret the tests of measurement invariance you use.…
....to actually be measures of “prediction invariance”: which holds when a measure has the same regression coefficient with respect to an external criterion across different groups or time.
But as Borsboom (2006) points out, prediction invariance and measurement invariance might actually be in tension with each other under a wide range of situations. Here’s a relevant quotation:
This is stretching my knowledge of the topic beyond its bounds, but this issue seems related to the general inconsistency between measurement invariance and selection invariance, which has been explored independently in psychometrics and machine learning (e.g., the chapters on facial recognition and recidivism in The Alignment Problem).
Thanks a lot for this. I hadn’t actually come across these terms; that’s super useful. I’ll have to read both these articles when I get a chance, will report back.
To synthesize a few of the comments on this post—This comment sounds like a general instance of the issue that @geoffrey points out in another comment: what @Charlie Harrison is describing as a violation of “prediction invariance” may just be a violation of “measurement invariance”; in particular because happiness (the real thing, not the measure) may have a different relationship with GMEOH events over time.
I basically agree with this critique of the results in the post, but want to add that I nonetheless think this is a very cool piece of research and I am excited to see more exploration along these lines!
One idea that I had—maybe someone has done something like this? -- is to ask people to watch a film or read a novel and rate the life satisfaction of the characters in the story. For instance, they might be asked to answer a question like “How much does Jane Eyre feel satisfied by her life, on a scale of 1-10?”. (Note that we aren’t asking how much the respondent empathizes with Jane or would enjoy being her, simply how much satisfaction they believe Jane gets from Jane’s life.) This might allow us to get a shared baseline for comparison. If people’s assessments of Jane’s life go up or down over time, (or differ between people) it seems unlikely that this is a result of a violation of “prediction invariance”, since Jane Eyre is an unchanging novel with fixed facts about how Jane feels. Instead, it seems like this would indicate a change in measurement: i.e. how people assign numerical scores to particular welfare states.
haha, yes, people have done this! This is called ‘vignette-adjustment’. You basically get people to read short stories and rate how happy they think the character is. There are a few potential issues with this method: (1) they aren’t included in long-term panel data; (2) people might interpret the character’s latent happiness differently based on their own happiness
Oh, great, thanks so much! I’ll check this out.
Anchoring vignettes may also sometimes lack stability within persons. That said, it’s par for the course that any one source of evidence for invariance is going to have its strengths and weaknesses. We’ll always be looking for convergence across methods rather than a single cure-all.
Always enjoy your posts, you tend to have fresh takes and clear analyses on topics that feel well-trodden.
That said, I think I’m mainly confused if the Easterlin paradox is even a thing, and hence whether there’s anything to explain. On the one hand there are writeups like Michael Plant’s summarising the evidence for it, which you referenced. On the other hand, my introduction to Easterlin’s paradox was via Our World in Data’s happiness and life satisfaction article, which summarised the evidence for happiness rising over time in most countries here and explain away the Easterlin paradox here as being due to either survey questions changing over time (in Japan’s case, chart below) or to inequality making growth not benefit the majority of people (in the US’s case).
Plant’s writeup says that
But the chart for Japan above, which is from Stevenson and Wolfers (2008), spans half a century, not ten years, so Easterlin and O’Connor’s objection is irrelevant to the chart.
This OWID chart (which is also the first one you see on Wikipedia) is the one I always think about when people bring up the Easterlin paradox:
But Plant’s writeup references the book Origins of Happiness (Clark et al., 2019) which has this chart instead:
So is the Easterlin paradox really a thing or not? Why do the data seem to contradict each other? Is it all mainly changing survey questions and rising inequality? On priors I highly doubt it’s that trivially resolvable, but I don’t have a good sense of what’s going on.
I wish there was an adversarial collaboration on this between folks who think it’s a thing and those who think it isn’t.
Hey Mo, thanks so much!
I don’t have a particularly strong view on this.
I guess:
First, there are differences in the metrics used – the life satisfaction (0-10) is more granular than the 4 category response questions.
Additionally, the plot from OWID, a lot of the data seems quite short-term – e.g., 10 years or so. Easterlin always emphasises that the paradox is across the whole economic cycle, but a country might experience continuous growth in the space of a decade.
My overall view – several happiness economists I’ve spoken to basically think the Easterlin Paradox is correct (at least, to be specific: self-reported national life satisfaction is flat in the long-run), so I defer to them.
It’s worth saying that the fact that most arrows go up on the OWiD chart could just point to two independent trends, one of growth rising almost everywhere and another of happiness rising almost everywhere, for two completely independent reasons. Without cases where negative or zero growth persists for a long time, it’s hard to rule this out.
It could in theory, but OWID’s summary of the evidence mostly persuades me otherwise. Again I’m mostly thinking about how the Easterlin paradox would explain this OWID chart:
I’m guessing Easterlin et al would probably counter that OWID didn’t look at a long-enough timeframe (a decade is too short), and I can’t immediately see what the timeframe is in this chart, so there’s that.
This is really neat analysis idea.
At the same time, my hunch is that all three of these exit actions have gotten easier to do and more common from 1990 to 2022. I believe divorce has gotten less stigmatized, the job market rewards more hopping around, and (I think) hospitalization has been recommended more.
If that “easier-to-do” effect is large enough, then it’d be compatible with a very wide range of happiness trends (rising/falling/stable + rescaling/no rescaling) Wondering if you have any thoughts on that.
Hi Geoffrey,
Thank you!
It’s possible that these 3 exit actions have gotten easier to do, over time. Intuitively, though, this would be pushing in the same direction as rescaling: e.g., if getting a divorce is easier, it takes less unhappiness to push me to do it. This would mean the relationship should (also) get flatter. So, still surprising, that the relationship is constant (or even getting stronger).
Ah I missed the point about the relationship getting flatter before. Thanks for flagging that.
I think I’m more confused about our disagreement now. Let me give you a toy example to show you how I’m thinking about this. So there’s three variables here:
latent life satisfaction, which ranges from 0 to infinityreported life satisfaction, which ranges from 0 to 10 and increases withlatent life satisfactionprobability of divorce, which ranges from 0% to 100% and decreases withlatent life satisfactionAnd we assume for the sake of contradiction that rescaling is true. One example could be:
In t=1,
latent life satisfaction = 1 * reported life satisfactionBoth are bounded from [0,10]
In t=2,
latent life satisfaction = 2 * reported life satisfication.Reported life satisfaction still ranges from [0,10]
But latent life satisfaction now ranges from [0,20]
Let’s say that’s true. Let’s also assume people divorce less as they get happier (and let’s ignore my earlier ‘divorce gets easier’ objection). One example could be:
In t=1 and t=2,
probability of divorce = 0.40 - latent life satisfaction/100. That implies:In t=1, probability of divorce ranges from [0.40, 0.30]
In t=2, probability of divorce ranges from [0.40, 0.20]
And so if I got the logic right, rescaling should accentuate (make steeper) the relationship between
probability of divorceandreported life satisfaction. But I think you’re claiming rescaling should attenuate (make flatter) the relationship. So it seems like we’re differing somewhere. Any idea where?I think rescaling could make it steeper or flatter, depending on the particular rescaling. Consider that there is nothing that requires the rescaling to be a linear transformation of the original scale (like you’ve written in your example). A rescaling that compresses the life satisfaction scores that were initially 0-5 into the range 0-3, while leaving the life satisfaction score of 8-10 unaffected will have a different effect on the slope than if we disproportionately compress the top end of life satisfaction scores.
Sorry if I expressed this poorly—it’s quite late :)
Hi Zachary, yeah, see the other comment I just wrote. I think stretching could plausibly magnify or attenuate the relationship, whilst shifting likely wouldn’t.
While I agree in principle, I think the evidence is that the happiness scale doesn’t compress at one end. There’s a bunch of evidence that people use happiness scales linearly. I refer to Michael Plant’s report (pp20-22 ish): https://wellbeing.hmc.ox.ac.uk/wp-content/uploads/2024/02/2401-WP-A-Happy-Probability-DOI.pdf
Thanks for this example, Geoffrey. Hm, that’s interesting! This has gotten a bit more complicated than I thought.
It seems:
Surprisingly, scale stretching could lead to attenuation or magnification depending on the underlying relationship (which is unobserved)
Let h be latent happiness; let LS be reported happiness.
Your example:
P(h)=0.40−h/100
t=1,h≡LS=>dP/dLS=−1/100
t=2,h=2LS=>dP/dh∗dh/dLS=2∗−1/100=−1/50
So yes, the gradient gets steeper.
Consider another function. (This is also decreasing in h)
P(h)=1/h
t=1,h=LS=>dP/dLS=dh/dLS=dP/dh∗dh/dLS=−1/h2∗1=−1/(LS2)
t=2,h=2LS=>dp/dLS=dP/dh∗dh/dLS=−1/h2∗2=−2/(4LS2)=−1/(2LS2)
i.e., the gradient gets flatter.
2. Scale shifting should always lead to attenuation (if the underlying relationship is negative and convex, as stated in the piece)
Your linear probability function doesn’t satisfy convexity. But, this seems more realistic, given the plots from Oswald/Kaiser look less than-linear, and probabilities are bounded (whilst happiness is not).
Again consider:
P(h)=1/h
T=1: LS = h ⇒ P(h) =1/LS
T=2: LS = h-5 ⇔ h = LS+5 ⇒ P(h) = 1/(LS+5)
Overall, I think the fact that the relationship stays the same is some weak evidence against shifting – not stretching. FWIW, in the quality-of-life literature, shifting occurs but little stretching.
Interesting! I think my intuition going into this has always been stretching so that’s something I could rethink
Hello Charlie. This looks like an interesting research question. However, I do have a few comments on the interpretation and statistical modelling. Both statistical comments are on subtle issues, which I would not expect an undergraduate to be aware of. Many PhD students won’t be aware of them either!
On interpretation with respect to the Easterlin paradox: your working model, as far as I can tell, appears to assume that quit rates decrease in a person’s latent happiness, but not in their reported happiness. However, if shifts in reporting are caused by social comparisons (i.e., all the other jobs or relationships you see around you improving) then from that direction rescaling no longer implies a flatter relationship, as the quality of the jobs or relationships available upon quitting have increased. However, your results are indicative that other forms of rescaling are not occurring e.g., changes in culture. I think this distinction is important for interpretation.
The first of the statistical comments that these changes in probabilities of making a change could also be explained by a general increase in the ease of or tendency to get a hospital appointment/new job. This stems from the non-linearity of the logistic function. Logistic regression models a latent variable that determines the someone’s tendency to quit and converts this into a probability. At low probabilities, an increase in this latent variable has little effect on the probability of separation due to the flatness of the curve mapping the latent variable into probabilities. However, the same increase at values of the variable that give probabilities closer to 0.5 will have a big effect on the probability of separation as the curve is steep in this region. As your research question is conceptual (you’re interested in whether life satisfaction scales map to an individual’s underlying willingness to quit in the same way over time), rather than predicting the probability of separations, the regression coefficients on time interacted with life satisfaction should be the parameter of interest rather than the probabilities. These effects can often go in different directions. A better explanation of this issue with examples is available here: https://datacolada.org/57 I also don’t know whether results will be sensitive to assuming a logit functional form relative to other reasonable distributions, such as a probit.
Another more minor comment, is that you need to be careful when adding individual fixed effects to models like this, which you mentioned you did as a robustness check. In non-linear models, such as a logit, doing this often creates an incidental parameters problem that make your regression inconsistent. In this case, you would also be dealing with the issue that it is impossible to separately identify age, time, and cohort effects. Holding the individual constant, the coefficient of a change in time with life satisfaction would include both the time effect you are interested in and an effect of ageing that you are not.
I’d be happy to discuss any of these issues with you in more detail.
Very nice, thanks!
When I first read this sentence I thought that your argument makes perfect sense, but then when I read
in the Overall happiness section and my first thought was: “well, I guess people are getting more demanding”. And now I am confused. I could imagine thinking about “people don’t settle for half-good any more” as a kind of increased happiness (even if calling it “satisfaction” would be strange).
Independent of this, my personal impression about economic wealth ever since childhood has been that my physical needs are essentially saturated and my social environment is massively more important for my subjective well-being. And although the latter is influenced by wealth, it is much more strongly affected by culture. And although I can think of plenty cultural developments that I am thankful for, I can also think of many that don’t push me in a healthy direction.
Sorry – this is unclear.
This means, specifically, a flatter gradient (i.e., ‘attenuation’) – smaller in absolute terms. In reality, I found a slightly increasing (absolute) gradient/steeper. I can change that sentence.
This feels similar to Geoffrey’s comment. It could be that it takes less unhappiness for people to take decisive life action now. But, this should mean a flatter gradient (same direction as rescaling)
And yeah, this points towards culture/social comparison/expectations being more important than absolute £.
Thanks for engaging!
I don’t think that is necessary—my confusion is more about grasping how the aspects play together :) I’m afraid I will have to make myself a few drawings to get a better grasp.
All good. Easy to tie yourself in knots with this …