The first empirical evaluation of New York's congestion pricing has just been published.
Spoiler: It worked really, really wellđź§µ
First, what is congestion pricing?
It's an added fee sent to drivers when they drive on certain roads, at certain times, in order to dissuade people from using the road when they don't need to.
The initiative aims to cut down on needless overuse, leading to slow roads.
Congestion pricing, in New York, acts as a sort of redistribution:
Because people pay to get into Manhattan, fewer go in, and the payments that would go to paid parking lot owners are effectively redistributed to the city government.
Congestion pricing can improve land use!
Congestion pricing should also increase the use of public transit, like the subway and buses and such.
This also helps with the redistribution from inefficient land users in Manhattan to the city government, and it's fine because transit has lots of excess capacity.
The way congestion pricing was evaluated was by using a "synthetic control".
The data from NYC was compared to the data from a counterfactual NYC based on data from other, comparable cities.
The estimate is New York (real) vs New York (projected without congestion pricing).
Some of the data underlying this model looks like this.
In this example, we can see average daily speeds within New York's Central Business District (CBD) in red and in comparison cities in gray.
Notice the jump around congestion pricing being introduced?
With that data, we can compare real New York to the ensembled New York and get this result, our treatment effect of interest.
On average, road speeds went up by a whopping 16%!
But here's something interesting:
Speeds on highways went up 13%, arterial road speeds went up by 10%, and local road speeds increased by 8%.
None of that's 16%, and that's important: This means congestion pricing sped roads up, but also sorted people to faster roads.
In response to having to pay a toll, people not only got off the road, they also made wiser choices about the types of roads they used!
Now let's look at the times of day, as a check on the model
It works: Congestion pricing just boosts speed when it's active and shortly after:
As another check, let's look at the effects by location.
In the CBD, trips are faster. Going to the CBD, trips are faster. Leaving it, trips are faster, but not much. And outside of it, where congestion pricing is irrelevant? No effect.
This policy has economic benefits and incentive benefits, but it also helps residents of New York who aren't directly paying the fee.
This is because vehicle emissions are down!
They're down the most in the areas with the highest rate exposure (co-occurrence), too.
The policy is also fair: The impacts do not fall on particularly low- or high-class neighborhoods, and the distributional impacts are thus pretty much neutral, with some regional differences.
The big effect is really just that people are able to get into the city more reliably.
In short, congestion pricing, though only briefly in place, has been a rousing success.
But New Yorkers don't seem to mind if the policy goes. They seem to prefer being able to freely waste time in traffic, even though it's inefficient and boring.
As an added note, subway ridership was increasing and, more interestingly, with congestion pricing, more people were choosing to take the express service buses.
Because of the reduced traffic, those buses were also making their trips much faster.
World War I devastated Britain and likely slowed down its technological progressđź§µ
The reason being, the youth are the engine of innovation.
Areas that saw more deaths saw larger declines in patenting in the years following the war.
To figure out the innovation effects of losing a large portion of a generation's young men who were just coming into the primes of their lives, the authors needed four pieces of data.
The first were the numbers and pre-war locations of soldiers who died.
The next components were the numbers and locations of patent filings.
If you look at both graphs, you see obvious total population effects. So, areas must be normalized.
You know how most books on Amazon are AI slop now? If you didn't, look at the publication numbers.
Compare those to the proportion Pangram flags as AI-generated. It's fully aligned with the implied numbers based on the rise over 2022 publication levels!
Similarly, the rise of pro se litigants has come with a rise in case filings detected as being AI-generated, and with virtually zero false-positives before AI was around.
For reference, the French Revolution ushered in a number of egalitarian laws.
A major example of these had to do with inheritance, and in particular with partibility.
In some areas of France, there was partible inheritance, and in others, it was impartible.
Partible inheritance refers to inheritance spread among all of a person's heirs, sometimes including girls, sometimes not.
Impartible inheritance on the other hands refers to the situation where the head of an estate can nominate a particular heir to get all or a select portion.
In terms of their employment, religion, and sex, people who joined the Nazi party started off incredibly distinct from the people in their communities.
It's only near the end of WWII when they started resembling everyday Germans.
Early on, a lot of this dissimilarity is due to hysteresis.
Even as the party was growing, people were selectively recruited because they were often recruited by their out-of-place friends, and they were themselves out-of-place.
It took huge growth to break that.
And you can see the decline of fervor based on the decline of Nazi imagery in people's portraits.
And while this is observed by-and-large, it's not observed among the SS, who had a consistently higher rate of symbolic fanaticism.
I simulated 100,000 people to show how often people are "thrice-exceptional": Smart, stable, and exceptionally hard-working.
I've highlighted these people in red in this chart:
If you reorient the chart to a bird's eye view, it looks like this:
In short, there are not many people who are thrice-exceptional, in the sense of being at least +2 standard deviations in conscientiousness, emotional stability (i.e., inverse neuroticism), and intelligence.
To replicate this, use 42 as the seed and assume linearity and normality
The decline of trust is something worth caring about, and reversing it is something worth doing.
We should not have to live constantly wondering if we're being lied to or scammed. Trust should be possible again.
I don't know how we go about regaining trust and promoting trustworthiness in society.
It feels like there's an immense level of toleration of untrustworthy behavior from everyone: scams are openly funded; academics congratulate their fraudster peers; all content is now slop.
What China's doing—corruption crackdowns and arresting fraudsters—seems laudable, and I think the U.S. and other Western nations should follow suit.
Fraud leads to so many lives being lost and so much progress being halted or delayed.