#QJE Nov 2025, #1, “The Economics of Spatial Mobility: Theory and Evidence Using Smartphone Data,” by Miyauchi, Nakajima, and Redding (@reddingecon.bsky.social): doi.org/10.1093/qje/...

True, but on the other hand I like to be able to ctrl-f for the exact number written in the text in order to find a table.

this paper is nice www.nber.org/papers/w32124

> we propose a methodology that only reports decimal digits in a parameter estimate that reject a hypothesis of statistical equivalence.

This means that for other procedural things, like how voting is done, how bills get sent to the floor, etc, the mayor can weigh in and break ties?

h) Miyauchi, @ecmaeditors.bsky.social. Using Japanese firm‐to‐firm transactions panel data, shows rematching following unexpected supplier bankruptcy improves with spatial density of alternative suppliers; this can account for big share of agglomeration benefits t.ly/0TTgV

Thanks! Hope they can come to an agreement. I've been worried there will be lots of tenuous procedural disputes in the opening months

Wait, so Portland created a 12 person council, but the charter didn't explicitly outline how to deal with ties?

Yes that is a good example. Hard to do these specialized joins outside of SQL.

Ultimately, I'm not sure what I want, tbh. I kind of *want* a big binary blob of files that's managed exclusively by a database, but I also want to be able to inspect particular parquet files. It's a trade-off.

So aside from data integrity things (ensuring a structure, matches work seamlessly), it's not obvious how a SQL workflow is good when data is local.

The main problem I have is that our workflow is having a big Dropbox folder holding the data, and a separate git repo with all the code. That is, the data is local.

The main drawback to me is that an SQL database, when local, is just some big binary blog on your hard drive? Seems kind of opaque.

I'm still not sure I understand the benefits.

Is it that the WHERE commands are so intuitive in SQL? I could do a similar query very easily in dplyr.

Is it the size of the data? I could use feather in R or just work on the cluster.

Honestly that would make a current workflow I use so much better... maybe for the next project.

Or, rather, what econ data sets do I not have access to by not using databases.

I wouldn't mind this, but only as a way to motivate the use of SQL tools in econ. I've never used SQL (and it's probably hurting me on the industry market) because I've never seen a reason to. Would be nice to see a scenario where there's actually an advantage.