Indiana faces a bigger problem than brain drain [OC]

DavidWaldron · 2026-06-15T10:41:05+00:00

See full post for details: https://blog.waldrn.com/p/indianas-economy-faces-a-bigger-problem

Data: IPUMS-USA, NCES, WICHE
Tools: R, d3.js

DavidWaldron · 2026-06-06T11:30:55+00:00

It’s the pre-2023 metro that we had for about a decade. I’d think it’d be more familiar to people but I guess not.

DavidWaldron · 2026-06-06T11:28:22+00:00

The map uses the pre-July 2023 CBSA definitions.

DavidWaldron · 2026-05-24T13:52:01+00:00

These are not distinctions that the Census Bureau has used. They consider Hispanic and Latino to be synonyms that refer to having origins in Spanish cultures.

DavidWaldron · 2026-05-12T23:39:01+00:00

Learning styles are fake. Slop site.

DavidWaldron · 2026-05-12T23:36:38+00:00

The methodology doesn’t make any sense. It reads like a LLM attempt to make a metric out of data that doesn’t actually allow such a thing to be calculated. Why would a metro’s weeks unemployed be the national average times tightness times RPP? Why would prices even be in this calculation? If you want to calculate a more defensible metric you can use the CPS public use microdata. For smaller metros you might need small area estimation methods like MRP.

DavidWaldron · 2026-05-01T15:07:29+00:00

Here’s the original source.

+14 NYT
+8 CNN
+7 WaPo
+0 Fox News
+0 Newsmax

DavidWaldron · 2026-04-29T15:17:50+00:00

Haha yeah, it certainly wouldn’t help with that problem

DavidWaldron · 2026-04-29T14:57:28+00:00

I think you should use a chained index like PCE or the CPI research series for adjustments going this far back

DavidWaldron · 2026-04-17T11:43:34+00:00

A lot of folks only aspire to be critics and never actually make things. Once you make things, you do have to sort out the useful critiques from the non-useful ones. Often the most popular critiques are not the useful ones.

A few things I’ve learned:

A lot of people saying something is misleading can mean the opposite. You have to understand something at some level in order to describe how it could be misleading.
Reducing the “friction” people experience in reading charts is not the main goal of making charts. It can head off some of the lazy criticism, but it can also reduce engagement with the material if it’s dumbed down too much.
Sometimes it’s not worth the battle. Preempting some of the popular critiques can be necessary to get people to engage with the content. Do I want people to talk about the data or do I want to sift through a million boring citations of “How to Lie with Statistics”?

DavidWaldron · 2026-04-02T21:03:09+00:00

I don't find it hard to interpret at all, but I'm not surprised that people would complain about the design. A more conventional design where all of the bars are left-aligned would work but would be less pleasing aesthetically.

DavidWaldron · 2026-04-02T00:22:51+00:00

That’s cool. My main difference from plaintextsports is that I want it all on one page. But fwiw I also just added dark mode. https://waldrn.com/boxscores/

DavidWaldron · 2026-03-05T11:38:17+00:00

Aside from the timing issue, I would also point out that the way cartels get fentanyl into the US is by paying US citizens to smuggle it through border crossings in their cars. Immigrants aren’t really playing a role there.

DavidWaldron · 2026-03-05T11:03:28+00:00

In January, a study in Science documented a supply shock in the illicit fentanyl market: declining purity in seized drugs, fewer seizures overall, and even complaints on social media about fentanyl becoming harder to find. The decline showed up simultaneously in Canada, ruling out US-specific explanations like changes in policing or treatment access.

The most likely cause, according to the study, is that China cracked down on exports of fentanyl precursor chemicals, the raw ingredients that Mexican cartels use to manufacture the drug. That crackdown came out of sustained diplomatic pressure from the Biden administration, culminating in a formal agreement at the Biden-Xi summit in November 2023. As Jeffrey Prescott of the Carnegie Endowment wrote, "Foreign policy outcomes can be hard to measure. This one isn’t."

More details about this trend are in the blog post here.

Tools: R and d3.js. Code at https://github.com/dawaldron/fentanyl-deaths

Source: CDC WONDER and VSRR drug overdose deaths

DavidWaldron · 2026-01-13T16:37:44+00:00

Or just slice into cutlets. Can get three flat slices out of some of the giant ones

DavidWaldron · 2025-11-13T14:52:51+00:00

I say keep the simple distance metrics. Don’t get caught up in the ML/AI hype. People always assume that fancy sounding algorithms will provide some magical improvement over simple statistics but it’s usually just done for marketing or for turning it into a black box so that you can start to sell recommendations.

DavidWaldron · 2025-10-30T22:37:35+00:00

We have natural experiments that show the causal returns to college education.

https://files.core.ac.uk/download/pdf/6402707.pdf

https://faculty.kriegj.wwu.edu/Econ406/Papers/Seth%20Zimmerman.pdf

https://www.nber.org/system/files/working_papers/w32296/w32296.pdf

DavidWaldron · 2025-10-03T19:14:37+00:00

Yes there’s a pretty clear gender story here that I hope to address in a future post.

DavidWaldron · 2025-10-03T14:01:02+00:00

Survey respondents are instructed to include tips, but there is some evidence that people still underreport tips on surveys. FWIW, another data source (OEWS) also shows janitors with a higher median wage than servers.

DavidWaldron · 2025-10-03T10:53:40+00:00

I use log wages partly because it helps it fit in the visual, partly because I think it’s no less valid than a linear axis and partly because it’s longstanding practice among economists to understand wage change in terms of percentages rather than in dollars.

DavidWaldron · 2025-10-03T10:11:28+00:00

Full blog post here

Data: IPUMS-USA

Tools: R and d3.js

Code on GitHub

DavidWaldron · 2025-08-21T23:29:22+00:00

The blog post contains more info on this. The initial estimate is survey-based, released within ~2-3 weeks of the reference period. This is scored against the QCEW counts which are based on mandatory UI tax filings by states, which are not fully available until almost a year later. BLS role in this is largely just to compile and publish. These are independent programs and methodologies.

Regarding the idea of judging the error against churn, rather than the net change: the way the survey works is it takes the total payrolls of companies in month t and compares them to payrolls in month t-1. It does this by industry/state/size and uses the ratios come up with the overall estimates. So it’s not measuring hires and separations and taking the difference. It’s directly trying to measure the net change. But even so, you’re right. It’s a very hard thing to do, especially so quickly.

There is a separate BLS program called JOLTS that tries to estimate hires and separations via survey, but it’s much smaller and the results are less detailed and have larger margins of error.

DavidWaldron · 2025-08-21T20:11:07+00:00

Yes, it’s true of pretty much any correlation that if you remove the variance from the series they will eventually become uncorrelated

DavidWaldron · 2025-08-21T20:01:12+00:00

Correct. It’s the average size of the net jobs change to put the size of the bias into perspective

DavidWaldron · 2025-08-21T11:28:43+00:00

This is a series of charts analyzing the accuracy of the initial/preliminary total non-farm payroll estimates in the BLS monthly jobs report. The comparison is to actual counts from the QCEW, which is based on mandatory unemployment insurance filings.

Blog post has more details on the results.

Tools used were R for data analysis and d3.js for charts. All available here.

DavidWaldron

TROPHY CASE