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.
This research directly militates against modern blood libel.
If people knew, for example, that Black and White men earned the same amounts on average at the same IQs, they would likely be a lot less convinced by basically-false discrimination narratives blaming Whites.
Add in that the intelligence differences cannot be explained by discrimination—because there *is* measurement invariance—and these sorts of findings are incredibly damning for discrimination-based narratives of racial inequality.
So, said findings must be condemned, proscribed.
The above chart is from the NLSY '79, but it replicates in plenty of other datasets, because it is broadly true.
For example, here are three independent replications:
A lot of the major pieces of civil rights legislation were passed by White elites who were upset at the violence generated by the Great Migration and the riots.
Because of his association with this violence, most people at the time came to dislike MLK.
It's only *after* his death, and with his public beatification that he's come to enjoy a good reputation.
This comic from 1967 is a much better summation of how the public viewed him than what people are generally taught today.
And yes, he was viewed better by Blacks than by Whites.
But remember, at the time, Whites were almost nine-tenths of the population.
Near his death, Whites were maybe one-quarter favorable to MLK, and most of that favorability was weak.
The researcher who put together these numbers was investigated and almost charged with a crime for bringing these numbers to light when she hadn't received permission.
Greater Male Variability rarely makes for an adequate explanation of sex differences in performance.
One exception may be the number of papers published by academics.
If you remove the top 7.5% of men, there's no longer a gap!
The disciplines covered here were ones with relatively equal sex ratios: Education, Nursing & Caring Science, Psychology, Public Health, Sociology, and Social Work.
Because these are stats on professors, this means that if there's greater male variability, it's mostly right-tail
Despite this, the very highest-performing women actually outperformed the very highest-performing men on average, albeit slightly.
The percentiles in this image are for the combined group, so these findings coexist for composition reasons.