#seth stephens-davidowitz

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Don't Trust Your Gut: Using Data to Get What You Really Want in Life (Seth Stephens-Davidowitz, 2022)

“Among more than 11,000 long-term couples, machine learning models found that the traits listed below, in a mate, were among the least predictive of happiness with that mate. 

Let’s call these traits the Irrelevant Eight, as partners appear about as likely to end up happy in their relationship when they pair off with people with any combo of these traits: 

Race/ethnicity 

Religious affiliation 

Height 

Occupation 

Physical attractiveness 

Previous marital status 

Sexual tastes 

Similarity to oneself 

Recall that I had previously discussed the qualities that make people most desirable as romantic partners, according to Big Data from online dating sites. 

It turns out that that list —the qualities that are most valued in the dating market, according to Big Data from online dating sites—almost perfectly overlaps with the list of traits in a partner that don’t correlate with long-term relationship happiness, according to the large dataset Joel and her coauthors analyzed. (…)

If I had to sum up, in one sentence, the most important finding in the field of relationship science, thanks to these Big Data studies, it would be something like as follows (call it the First Law of Love): 

In the dating market, people compete ferociously for mates with qualities that do not increase one’s chances of romantic happiness.”

[random-bookquotes]

In fact, at the risk of sounding grandiose, I have come to believe that the new data increasingly available in our digital age will radically expand our understanding of humankind. The microscope showed us there is more to a drop of pond water than we think we see. The telescope showed us there is more to the night sky than we think we see. And new, digital data now shows us there is more to human society than we think we see. It may be our era’s microscope or telescope—making possible important, even revolutionary insights.

Seth Stephens-Davidowitz, Everybody Lies

[naheemc]

Best Books Of 2018

[pouchythekidd]

Never compare your insides to everyone else's outsides

Seth Stephens-Davidowitz, Everybody Lies

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Don't Trust Your Gut: Using Data to Get What You Really Want in Life (Seth Stephens-Davidowitz, 2022)

“Massively Undervalued Groups in the Dating Market 

Short men 

Extremely tall women 

Asian men 

African-American women 

Men who are students or work in less desirable fields, such as education, hospitality, science, construction, or transportation 

Conventionally less attractive men and women (…)

Overall, perhaps the most striking finding in the data is the difficulties African-American women face in the dating market. 

Note the second row of the first chart. Men of just about every racial group are less likely to respond to messages from Black women. 

The second column of the second chart shows how African-American women respond to this harsh treatment by men: they become far less picky. 

For just about any group of men sending messages, Black women are the most likely to respond. (…)

Among males, the racial groups that receive the lowest response rates are Blacks and Asians.

Perhaps some of the reason that some racial groups, on average, perform better or worse in the dating market is that some racial groups earn, on average, different incomes. 

Hitsch, Hortaçsu, and Ariely try to correct for these factors. 

They found that, when you take into account these other factors, the bias against Asian men becomes even more severe. 

Since Asian men in the United States have above-average incomes, which tends to be attractive to women, the low response rates to their messages is even more striking. 

The researchers determined that an Asian man would have to earn a staggering $247,000 more in annual income to be as attractive to the average white woman as he would if he were white.”

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Don't Trust Your Gut: Using Data to Get What You Really Want in Life (Seth Stephens-Davidowitz, 2022)

“When you ask other people about work—at cocktail parties, at networking events, on social media—some will make statements such as “I live for my work,” “I love my job,” or at least “I like my job.” 

But when Mappiness asks people how much they are enjoying themselves, at the moment they are at work, with no other human beings able to see their answer, people report a more grim view of work. 

They report that work, on average, makes them more miserable than doing chores, taking care of old people, or waiting in line. 

This suggests many people may be lying—to themselves or to you—when they report that they like or love their job. (…)

In the Mappiness dataset, individuals who were at work with people they considered their friends were far happier than others at work. 

Being with friends can give such a large boost in happiness that it may make work a pleasurable experience. 

People can turn the daily, painful grind into the daily, pleasurable grind with a little help from their friends. (…)

My estimate, based on MacKerron and Bryson’s numbers, imply that the average person working with their friends is about as happy as they would be if they were relaxing alone. 

Recall from the previous chapter that relaxing doesn’t produce as much happiness as people would have guessed. 

But it is still far better than the misery of the average worker. 

The average person working from home, listening to music, with their friends (perhaps on Zoom or perhaps because the friend had come over for a work session), would be about as happy as the average person playing sports—which, you may recall, is one of the single happiest activities.”

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Don't Trust Your Gut: Using Data to Get What You Really Want in Life (Seth Stephens-Davidowitz, 2022)

“Basketball, in which success depends so much on the heavily genetic height, is a sport with a striking prevalence of identical twins playing at the highest level. 

Since the advent of the National Basketball Association, ten pairs of twin brothers have made the NBA. At least nine of them have been identical. 

In fact, assuming parents of NBA players birth identical twins at a roughly average rate, these numbers imply that an identical twin of an NBA player has more than a 50 percent chance of making the NBA himself. 

The average American male has a roughly 1 in 33,000 chance of making the NBA. 

I built a model based on the twins equations that behavioral geneticists use to study other traits. 

My best estimate is that variation in one’s ability to play basketball is 75 percent nature. 

The ability to play basketball so well to make the NBA is really, really, really, really, really genetic. (…)

Three times scouts, perhaps by analyzing the responses of mothers, have been convinced that one identical twin prospect was clearly better than the other, ranking one at least twenty spots higher in the NBA draft. 

Each time, the twin judged inferior performed far more similar to his brother than their draft spots would have predicted. 

Perhaps the scouts would have been better served ignoring the cheering patterns of the twins’ mothers and merely assuming the twins would end up roughly the same. After all, they had the same DNA.”

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Don't Trust Your Gut: Using Data to Get What You Really Want in Life (Seth Stephens-Davidowitz, 2022)

“In fact, for the large majority of parenting decisions, I might even condone a method of tackling them that you probably thought you would never hear from me: trust your gut. 

This is not because your gut has some magical powers that guide you to the correct answers. It is because the decisions just really aren’t that big a deal. 

Doing what feels right and moving on is perfectly fine. The data, in some sense, justifies a simple, gut-driven approach. 

Be confident that, as long as you make some reasonable choices, you’re doing about as well as parents can do. 

But there is one area of parenting that, the data says, you may want to pay more attention to: the people you expose your kids to. (…)

Kids who score well on math at an early age are more likely to end up having patents as adults. 

In the depressing and not-all-that-surprising domain, the researchers found that a child’s gender and socioeconomic status can influence the odds of becoming a successful inventor. 

Sadly, African-American and female kids are less likely to become inventors than white and male kids with the same childhood test scores. 

But a factor that played a surprisingly large role in predicting becoming an inventor: the adults in a neighborhood a child grew up in. 

Kids who, when they were young, moved to neighborhoods with many adult inventors were more likely to become inventors when they grew up. 

The effect is highly concentrated in the industry in which these adult inventors worked. 

Move to a neighborhood with lots of people who invent medical devices, and a kid is more likely to grow up and invent medical devices. 

Strikingly, the effects of living near adult inventors were gender-specific. 

Chetty and his coauthors found that, for females, the chances of becoming an inventor rose if they grew up near adult female inventors. 

There was no effect on a female’s chance of becoming an inventor from growing up near adult male inventors. 

Little girls who see a lot of successful female inventors around them try—and often succeed—to emulate these women.”