Remember that time economists used a gravity model to find ancient lost cities from the Bronze Age?
If you do or you don't, check out this threadđź§µ
The authors gained access to a collection of almost 12,000 deciphered and edited texts that were excavated primarily at the archaeological site of Kültepe, ancient Kaneš.
The ruins (pictured) are located in central Turkey, in the province of Kayseri.
The texts look like this.
They were inscribed on clay tablets in the Old Assyrian dialect of Akkadian in cuneiform by ancient Assyrian merchants, business partners, and their family members.
This tablet is dated to between 1930 and 1775 B.C.
The tablets were all from between 1930 and 1775 B.C., and 90% of the sample came from just one generation of traders, between 1895 and 1865 B.C.
The reason is that Kaneš experienced a major fire in 1840 B.C. and the commercial archives in the city were sealed off.
Tablets were largely business letters, shipment documents, accounting records, seals, and contracts.
A typical shipment document or expense account in which a merchant would inform partners about their cargo and expenses would read like this:
Some business letters would contain information about market and transport conditions, like this:
The tablets are spread across the world in museums and institutions, but many have been transcribed.
The transcribed ones mentioned 79 cities distributed across modern-day Iraq, Syria, and Turkey and 2,806 mentioned at least two Anatolian city names simultaneously, like so:
That tablet identified three shipments: Durhumit to Kaneš, Kaneš to Wahšušana, and Durhumit to Wahšušana.
So the itinerary is A→B→C, and there were 227 of these, with 391 examples of travel between city pairs.
Specifically, 25 city pairs: 15 known (gray), 10 lost (black).
Using trade among known cities, they estimated the distance elasticity of trade (how sensitive trade btwn cities is to the distance btwn them), so they could estimate the prbblity of shipments from city i to city j given their distance
Thus, probable locations for 10 lost cities
These estimates largely concurred with those of historians, and since the historians' conjectures weren't used in the model, this suggests people should start pursuing those estimations.
In fact, this modeling exercise might help to decide among the different proposals made by historians.
But the authors weren't done. They supplemented their analysis with data from merchant itineraries. For example, consider this letter:
That letter was submitted to the Assyrian port authorities at Kaneš from emissaries in Wahšušana, and it described how missives would travel through two different routes:
Wahšušana→Ulama→Purušhaddum
W→Šalatuwar→P
But only Wahšušana, Ulama, and Šalatuwar are known cities.
Using every multistop itinerary, a model with just two constraints offers a lot of info. The constraints are simple:
1. When deciding itineraries, merchants like direct routes. 2. Caravans have to make stops to rest, replenish supplies, feed pack animals, and make side trades.
With estimates constrained to regions that are admissible given those constraints (dashed lines), the locations of the newly-identified lost cities are now more certain!
With the exception of Purušhaddum.
But how do we know this method works?
Easy! Just lose known cities and see if the method rediscovers them.
As the picture shows, the average distance between estimated and known city locations wasn't huge. In fact, estimates were a median of 33km away (mean = 40km).
This method also helps to identify the names of sites that people have continued living in, like Kırşehir Kalehöyük, which might have been located under where the Alaaddin Mosque and a high school were later built.
There are other interesting findings here, too.
Consider this: geography has deep and persistent impacts on the economy of the area, and cities tend to show up where there are "natural roads".
Ancient cities were estimated to be larger when the natural roads were better!
And, modern cities are larger when nearby ancient cities were estimated to be larger as well.
The deep geographic reasons for cities to crop up in certain locations are still powerful forces today!
And for the real nerds, Zipf's law looks to basically hold for ancient city populations.
There you have it: economists might have discovered the locations of ancient lost cities from the Bronze Age, and supported a number of other fun facts while they were at it.
Only time will tell if these discoveries end up being true 🤞
Link:
The model the authors used was the gravity model: the workhorse model of trade.
Are White women the primary beneficiaries of affirmative action?
That's a real claim that's commonly advanced by journalists, and the claim has gone so far that it's even made its way into academic publications and policy.
But the claim is completely falseđź§µ
This claim doesn't make a lot of sense. After all, shouldn't the primary beneficiaries of affirmative action be the people who the policies primarily target?
In America, that's African Americans and, among them, women get an added benefit. How could it be Whites?
To figure out where the claim comes from, I started reading supposed sources.
Often enough, journalists will just take the claim for granted without providing *any* source.
It's just tacit knowledge now, and that's not good!
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.
"Food deserts" are an example of social scientists getting causality backwards
They saw poor people eating unhealthy foods and blamed local supply
They should have blamed demand!
Using data from 13 years of supermarket entries, there's basically no effects on healthy eatingđź§µ
The significant effects are probably not meaningful. They're more likely under the null with this gigantic dataset (p's of 0.003 and 0.005 with a total sample size of ~2.9m)
Entry did affect sales for new stores, but not existing ones. It also affected more local places more.
When new supermarkets open up, they do nab a share of local grocery sales, but the effect on healthy eating in total, among low-income households, and in food deserts, just isn't there.