Crémieux Profile picture
Dec 19 25 tweets 11 min read Twitter logo Read on Twitter
Poverty and crime.

In the public imagination, these things go hand-in-hand.

But the link between poverty and crime is much weaker than people might imagine. It might not even be causal.

A new lottery study shows us just that:

🧵 Image
To understand the causes of crime, there are other things you need to understand first.

For example, you need to understand the roles of sex and age.

In the whole country the lottery study results came from, you get this result when you plot both variables. Image
The collapse in criminal offending from adolescence is the crux of the "age-crime curve". The gap between men and women that declines with age is another important part.

Unlike age and crime, income and crime are nonlinearly related: after a certain level, income barely matters. Image
To understand why crime and income relate, we must realize they aren't related randomly.

For example, schizophrenia is marked by premorbid cognitive deficits and it increases criminality while reducing socioeconomic attainment
Moreover, other crime-disposing factors like intelligence appear to be causal.

As an example, consider how within families, the less intelligent sibling is more likely to become a criminal.


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This is where lotteries come in: they create a quasi-random sample of people whose traits are unrelated to their wealth.

This leads to good causal identification because winning the lottery, among players, is random, and playing the lottery doesn't seem to be selective either:
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The other condition we need for causal inference is that lottery winnings aren't rapidly dissipated. As it turns out, these practically random samples have durably increased wealth as a result of winning the lottery.

So all are conditions are met for powerful causal inference. Image
As you can see in the OP, the effect of lottery wealth is distinct from the one for wealth in the general pop.

Lottery wealth was not significantly related to any category of crime or sentencing and it significantly differed from the gen pop effect in all but one case (traffic).
This effect is not especially related to time.

Consider the effect on perpetrating any crime over a time period of ten whole years.

It's bupkis, in terms of significance, scale, and trend. Image
If you read the conditional random assignment table, you might have seen that there were also intergenerational results: results for effects on kids' risk of crime.

Those results were ambiguous due to low power, so it's not clear what to make of them. Image
With that said, it's not like this is the only time the relationship between crime and poverty, wealth, income, neighborhood quality, recidivism—anything like that—has been investigated.

So let's look through some other designs and results.
In Moving to Opportunity (MTO), families were given vouchers to move to good neighborhoods.

The result for people who moved at a young age? Not much, but marginally higher violent crime perpetration. Image
If we watch the same people over time, we can see why, for example, neighborhood deprivation and the risk of being a violent criminal are associated.

Between persons, bad neighborhoods, more criminals.

Within persons, going to a bad or good neighborhood doesn't affect risk. Image
If we apply this same logic to property crimes, we get a similar result. Image
You can even do within-family designs, where siblings with different levels of neighborhood deprivation exposure are compared.

The confounding of the effect of neighborhood deprivation becomes obvious with these designs. Image
The same method has been applied to family income.

Siblings are exposed to different income levels because they aren't generally the same ages and parents' incomes vary over the lifespan.

So, with big registers, we see the cross-sectional relationship disappears. Image
It's not always genetic confounding that matters.

For example, in the case of teen motherhood predicting someone's own criminal conviction, that's due to shared environmental confounding. Image
The apparent effect of having a young mother on children's adolescent offending seems to also be driven by familial confounding. Image
What about when someone has a parent who goes to jail? That's obviously related to socioeconomic status, and some have suggested it's actually good for kids when a bad parent or sibling is arrested.

But there really doesn't seem to be an effect in large studies. Image
This replicates.

The effect of paternal conviction on the risk of violent crime disappears for men and women, while an effect on property crime remains for males. Image
The sibling-controlled effects of many types of other socioeconomic status measures also don't seem relevant. Image
The effects of exposure to paternal criminal offending (Scandinavia) and high-crime counties (America) are estimated to be similarly low as well. Image
The majority of many types of crime is just recidivism.

Thanks to Scandinavia's monitoring of their population with registers, we also know that recidivism's association with socioeconomic status and neighborhood deprivation is at least majorly driven by self-selection. Image
The absence of a strong link between poverty and crime is replicable and unsurprising.

Those who believe in a strong link are engaging in what seems to be wishful thinking.

All it takes to really get this is statistics like these.
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More from @cremieuxrecueil

Dec 17
The ACT has released their scores for 2023, so I thought I'd put everything in familiar terms and make some plots.

This thread will include lots of pictures!

So, how did everyone do nationally? Image
Differences were fairly typical.

Assuming Whites had a mean of 100 and an SD of 15

- Asians had a mean of 107.26 and an SD of 21.58
- Blacks had a mean of 86.20 and an SD of 12.54
- Hispanics had a mean of 90 and an SD of 15.21

Here are their scaled densities: Image
But we know that state-level participation rates and scores are correlated, so taking the ACT is selective.

However, in a few states, every kid took the ACT. These were Alabama, Kentucky, Louisiana, Mississippi, Nevada, Oklahoma, Tennessee, and Wyoming.

So, how did they do? Image
Read 17 tweets
Dec 16
What happens when you assign groups of people to

- Exercise without steroids
- Exercise with steroids
- Not exercise without steroids
- Not exercise with steroids

The guys who take steroids and don't exercise gain more muscle than the guys who exercise without steroids!Image
The above is the familiar outcome from this study. But they measured more things than just fat-free mass gains.

For example, they also measured bench presses.

People who exercised without steroids gained about the same amount as people who didn't exercise, with steroids. Image
Squats were also measured, and in that one, the people who exercised beat the people who didn't exercise but did take steroids, but both beat the ones who neither exercised nor took roids. Image
Read 7 tweets
Dec 9
The effect of adding together normal distributions is unintuitive.

If there's a 1 d difference between two groups with equal variances, you might think the result would be a bimodal distribution.

You would be wrong.

Short🧵 Image
So if we widen the gap, say, to 2 d, then we get a distribution that looks rather flat.

Even though the groups were strongly differentiated, the result of throwing them together still wasn't. Image
Once we make the difference very large, say, 3 d, that's when we finally get to a bimodal combined distribution. Image
Read 8 tweets
Dec 7
December 7th, a day that will live in infamy, is the day Japan decided to ensure the Axis Powers would lose World War II.

Prior to today's date, many Americans were wishy-washy on joining the war, but Japan changed their tune.

Let's go through the timeline, starting with Sept.: Image
Initially, America seems narrowly against getting involved.

After Poland fell, sentiment shifted strongly against involvement. Image
The nadir of American sentiment was when the airwaves became flooded with news about German assaults on the Netherlands, Belgium, and France.

America wanted nothing to do with the war. Image
Read 13 tweets
Dec 6
Do European countries appear to perform worse than White Americans in standardized tests because of low-performing immigrants?

Probably not. Removing 1st- and 2nd-generation immigrants from the 2022 PISA sample, America still outperforms.

🧵

First, mean scores: Image
Now, let's take a look at mathematics scores.

The Netherlands did jump ahead of American Whites here, but that's about it. Notably, U.S. Asians jumped from 4th to 2nd place! Image
What about reading? That should be the most impacted, since immigrants are language learners.

While true, this also impacted U.S. Whites. I assume this is due to "White Hispanics" who are English second-language learners, but it's hard to tell. Image
Read 7 tweets
Dec 5
With the latest PISA results, America has proven once again that it has one of the smartest populations and, perhaps, the best education systems.

American Asians and Whites topped the charts; American Hispanics beat all other Hispanics; American Blacks did well.

🧵

Means first Image
Some people think the most important scale is mathematics, while others think it's reading, and a select few think it's science.

They all indicate g, so there's no a priori rationale for favoring any particular scale.

Regardless, look at how America did in mathematics! Image
America's reading performance is perhaps its most laudable this year.

American Asians came in first in this test, and American Whites came in third. Image
Read 8 tweets

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