AI Use Note: Main body text entirely human written. Claude (Opus 4.8) helped develop models of animal life histories in the appendix.
Cross-posted from Good Structures.
Executive Summary
* Animal advocates sometimes make claims like “there are X of this animal...
“How long have you been v*g*n?”
This is one of the most common icebreakers at animal protection events. It’s a baseline assumption, and it mostly holds true: if you’re out advocating for animals not to be tortured or abused, realistically these days you are v**n, or close. And it makes for good conversation. It seems fairly safe to assume when you meet strangers.
But this assumption is hurting the movement in a way which we don’t always notice: someone new comes into the sp...
Summary
Back in November 2023 I posted here to launch Spiro and raise our first $198k. Two and a half years later this is an update and a fundraiser for the next step.
The short version: we've now reached over-5,900 people with TB preventive medicine, including over 3,000 children under five years old. Our early results have held up well an...
もしこれが従来のキャリアガイドなら、あなたが仕事に求める最も重要な要点のリストを書かせることから始めるだろう。「屋外で仕事する」とか「野心的な人と働く」など。ベストセラーとなったキャリアアドバイス本『What Color Is Your Parachute』（邦題『あなたのパラシュートは何色？』）は、まさにこれを勧めている。これは、誰しも心の底では自分が本当に望んでいるものを知っている、という期待に基づいている。
線：人生の満足度 ポジティブ感情軸：Fraction of people:人々の割合 Life satisfaction from 1-10:1‐10の人生の満足度 Household income：世帯収入邦訳「高給は生活の評価は改善するが、精神的安定には寄与しない」D. Kahneman・A. Deaton 2010年 リンク
There has been an extensive programme of research into how good humans are at predicting the effects of future events on their emotional wellbeing. It began with Kahneman and Snell in the early 1990s, and was led in the 2000s by Harvard psychologist Daniel Gilbert. Much of the research is summarised in Gilbert’s book Stumbling on Happiness. A 2009 review is in Gilbert and Wilson (2009). One takeaway is that we are bad at predicting how we will feel in the future, and we don’t realise this. We’ve previously written about our failures to predict how we will feel here.
We’re unsure how robust all of these findings are be in light of the replication crisis, and haven’t found any more recent work replicating these findings.
So we expect Gilbert has overstated the findings. Nevertheless, we think the broader point that we can’t simply trust our intuition about these matters is correct.↩
While many studies have found support for the “peak–end rule” — the idea that we judge experiences based on how we feel at the most intense point (i.e. the peak, and at the end) — some have cast doubt on it.
For instance, Kemp et al. (2008) found that the peak–end rule was “not an outstandingly good predictor,” and that the most memorable or unusual moments (not necessarily the most intense moments) were more relevant predictors of recalled happiness. This suggests the “peak” is less relevant than the end.
The peak–end rule predicts approximately the correct level of happiness, but the correlations of the peak–end average with the overall recalled happiness are generally lower than those obtained by considering the participants’ happiness in the most memorable or most unusual 24-h period… Remembered overall happiness seems to be better predicted by end happiness than by peak or trough happiness, and the comparative failure of the peak–end rule appears to stem more from the peak than from the end.
That said, a 2022 meta-analysis looking at 58 independent studies, with a cumulative sample size of around 12,500 people, found “strong support” for the peak–end rule.
The peak-end effect on retrospective summary evaluations was: (1) large (r = 0.581, 95% Confidence Interval = 0.487–0.661), (2) robust across boundary conditions, (3) comparable to the effect of the overall average (mean) score and stronger than the effects of the trend and variability across all episodes in the experience, (4) stronger than the effects of the first (beginning) and lowest intensity (trough) episodes, and (5) stronger than the effect of the duration of the experience (which was essentially nil, thereby supporting the idea of duration neglect).↩
We’re using CareerCast’s 2015 rankings because we want to compare these rankings with measurements of job satisfaction and meaningfulness. The most recent large survey on job satisfaction and meaningfulness that we could find was conducted by Payscale (archived link, retrieved 17-August-2022), with data collected from 2.7 million people between 6-November-2013 and 6-November-2015.
CareerCast’s 2021 methodology is largely unchanged and described here.
The most recent national survey by the UK’s Cabinet Office on life satisfaction by occupation was conducted in 2014 (and published by the University of Kent). The survey found “actuaries, economists and statisticians” ranked 64th out of 274 job titles, putting them in the top 23%.
In the more recent 2021 CareerCast survey, actuary was no longer top, but it was still ranked in the top 10.
Payscale’s surveys cover millions of workers. The most recent survey which asked about job satisfaction and job meaningfulness was conducted from 2013 to 2015. The survey found that only 36% of actuaries found their work meaningful. Job satisfaction was also high at 80%, but a significant number of jobs were rated over 80%.
A Gallup survey in October 2021 asked 13,085 US employees what was most important to them when deciding whether to accept a new job offered by a new employer. 64% of people said that “a significant increase in income or benefits” was “very important,” compared to 61% for “greater work-life balance and better personal well-being,” 58% for “the ability to do what they do best” and 53% for “greater stability and job security.”
The survey was a self-administered web survey. There are a number of possible sources of bias. For example, if this survey was paid (it’s unclear from the Gallup methodology), this may have introduced bias. That said, Gallup did weight samples to correct for nonresponse by adjusting the sample to match the national demographics of gender, age, race, Hispanic ethnicity, education, and region.
When individuals are asked what would most improve the quality of their lives, the most common response is a higher income
Judge, Timothy A., et al. “The relationship between pay and job satisfaction: A meta-analysis of the literature.” Journal of Vocational Behavior 77.2 (2010): 157-167.
A 2021 study found that wellbeing does rise with income, even above $75,000 a year. However, it found that this, like life satisfaction, was approximately logarithmic — as an individual’s income increases, their wellbeing increases at a slower and slower rate. As a result, above $75,000 a year, the wellbeing increases are very small. The study also found that, as income increased, wellbeing rose more slowly than life satisfaction (which was also approximately logarithmic). Read a critical review of the study. A later analysis of the data revealed that the happier people were, the less their returns diminished. The happiest people continued to become happier with incomes of over $100,000, but the least happy people see almost no benefit of this income on their happiness.↩
“The critical observation is that Fig. 1A shows the distribution of happiness to be markedly lopsided. In the range of high incomes, in particular, the average reported positive affect is 89% of a perfect score (equivalent to 2.67 on a 0 to 3 scale) and the average of two not-blue items is 81% of the maximum. For example, on average, each dichotomous positive affect item distinguishes between a happier 89% of people and a less happy 11% of people, while the composite report combines three such items. We know, of course, that happy people are not all equally happy. In MK’s experience sampling data, for example, the distribution of happiness was approximately normal. The high density of maximum scores in the KD items indicates that the items do not adequately discriminate among degrees of happiness—there is a ceiling effect.
An example illustrates the relevance of a ceiling effect to the naming of a variable. Imagine a test of cognitive functioning which consists of items that most elderly patients pass easily, with a few exceptions due to inattention or momentary confusion. Such a test would rightly be considered a measure of dementia: the number of items failed is an indication of the severity of dementia, but the scale does not discriminate among levels of normal cognitive functioning because most normal people get the same perfect score. A similar argument implies that KD’s affect items are best interpreted as measures of unhappiness.”↩
The average household in the US has 2.5 people, but of course this is just an average across a wide range of family structures. Larger households enjoy “economies of scale” by sharing houses, cars, and so on. This makes it tricky to say what the equivalent of a household income is for a single individual.
Standard conversion rates are the following:
A single individual has an equivalence score of 1.
A single extra adult adds another equivalence score of 0.5.
Adding a young child to this adds an equivalence score of 0.3, while a teenager costs another 0.5.
As a result, a couple can achieve the same lifestyle as an individual with 50% more income; a couple with a young child can achieve the same lifestyle as an individual with 80% more income; a couple with a teenager require an income twice as high.
These are approximations, but reasonable ones used by international organisations. They are described here by the UK Institute for Fiscal Studies. (The IFS table gives conversion rates from a childless couple to a household. Dividing by 0.67 — the conversion rate to a single individual — will give you the numbers we listed above.)
For the sake of simplicity we will assume that on average across their adult lives people are in a household with an adult couple and a child. This is just an average — some people will be single, while some will be supporting multiple children, at least for some of their lives.
Using this approximation means that a single individual requires about 1/1.9 = 53% as much as a typical household, averaged over their adult lives, to achieve the same standard of living.
In this case, 53% of $90,000 for a household represents $47,700 for an individual.↩
A bachelor’s degree holder earns, at the median, $2.8 million during a lifetime, which translates into average annual earnings of about $70,000.
This figure was for 2021, but wages have grown since then. In January 2021, average wages per hour in the US were $29.92 and were $33.03 in January 2023. That’s growth of 10%, which suggests that the average college graduate now earns $77,000. This is likely an overestimate, because college graduate earnings have been growing slower than average earnings since 2021. Source: FRED Economic Data, “Average Hourly Earnings of All Employees: Total Private (CES0500000003)”, retrieved 5-Feb-2017. This growth matches roughly what we’d expect from inflation, which was about 10% over the period, and wages tend to lag inflation in high-inflation periods like 2021–23.
Mean earnings for US Ivy League graduates
It’s hard to find comparable data for just Ivy League graduates. Payscale found a median mid-career salary of over $135,000, where mid-career is defined as over 10 years.
Our guess is that the Payscale data is too high, because people with higher incomes will be more likely to fill out the survey. On the other hand, income only peaks after 20–30 years, so the figure for 10+ years probably underreports the overall average. Moreover, the median will be less than the mean.
Overall, we’re pretty confident that the mean lifetime average for Ivy League graduates is over $120,000.
If you want to estimate your future income, then you should also account for future wage growth (which has been about 2% historically). We ignore this here, and only estimate current income.↩
A 2018 meta-analysis found that there is a causal link between performing acts of kindness and wellbeing. They included 27 experimental studies included in the review, with a total sample size of 4,045 people.
These 27 studies, some of which included multiple control conditions and dependent measures, yielded 52 effect sizes. Multi-level modeling revealed that the overall effect of kindness on the well-being of the actor is small-to-medium (δ = 0.28). The effect was not moderated by sex, age, type of participant, intervention, control condition or outcome measure. There was no indication of publication bias.↩
This is the study we quoted: Aknin, Lara, Christopher P. Barrington-Leigh, Elizabeth W. Dunn, John F. Helliwell, Robert Biswas-Diener, Imelda Kemeza, Paul Nyende, Claire Ashton-James, Michael I. Norton (2010). “Prosocial Spending and Well-Being: Cross-Cultural Evidence for a Psychological Universal.” Harvard Business School Working Paper 11-038. Link.
Though there is some evidence that part of the reason for the correlation is that happier people give more. See: Boenigk, S. & Mayr, M.L. J Happiness Stud (2016) 17: 1825. doi:10.1007/s10902-015-9672-2. Link.
For a more comprehensive review of the question, see Giving without sacrifice, by Andreas Mogensen, Giving What We Can Research, Archived Link, retrieved 6 April 2017.↩
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