Economics on Big Muddy

Colorado advances ban on algorothmic price and wage discrimination

Fri, 27 Mar 2026 17:48:00 -0400

The Colorado House voted today to ban the use of personal data to algorithmically set the price of a product or determine a wage. The legislation will now advance to the Colorado Senate for consideration. The summary of the bill, HB26-1210, reads:

Surveillance data is defined in the bill as data that is obtained through observation, inference, or surveillance of consumers or workers and that is related to personal characteristics, behaviors, or biometrics of an individual or group. The bill prohibits discrimination against a consumer or worker through the use of automated decision systems used to engage in:

Individualized price setting based on surveillance data regarding a consumer; or

Individualized wage setting based on surveillance data regarding a worker.

Obviously, the bill enumerates exceptions to the above rules, as it is not intended to ban, for example, charging a customer more to deliver an item a longer distance nor to prohibit schemes like discounts for students or seniors. One of the challenges of writing laws like this is to ensure they are written narrowly enough to target dystopian hyper-individualized pricing based on tracking of Internet and phone activity rather than normal business practices like pricing insurance policies according to demographic risk factors.

Colorado is one of at least a dozen American states considering similar bans. I don’t believe any of these proposed broad-based bans have been signed into law yet. I wrote about algorithmic price discrimination (surveillance pricing) last week in the context of proposed legislation in the Canadian province of Manitoba.

Manitoba introduces bill to ban algorithmic price discrimination

Wed, 18 Mar 2026 07:30:00 -0400

The Canadian province of Manitoba has introduced a bill to ban algorithmic price discrimination (also known as surveillance pricing), i.e., the use of personal data to set prices for individual consumers:

New Democrats announced in December they would begin cracking down on what’s known as differential or predatory pricing. That is when retailers charge different amounts for the same products based on the timing of customer purchases, where they live or other personal data. […] The proposed legislation would render the use of “personalized algorithmic pricing,” both online or in store, an unfair business practice.

Okay, I guess there’s a lot of different names for this particular practice. Whatever we call it, I believe bills cracking down on algorithmic price discrimination will be very popular, as it constitutes a very clear example of companies using our data against us to rip us off. The most famous recent exposé of this practice is Groundwork Collaborative’s report on how grocery delivery service Instacart charges users different prices depending on who they are.

Manitoba isn’t the only jurisdiction introducing bills targeting this practice, but I don’t believe anywhere in the US or Canada has actually managed to ban it yet. However, New York has made in mandatory for companies to disclose when they use personal data to set prices.

Using Claude Claude for cross-package statistical audits

Sun, 15 Mar 2026 22:49:00 -0400

Economist Scott Cunningham shared an important example of why we should always report the statistical package and version used in our analyses, as he used Claude Code to produce six versions of the exact same analysis using six different packages in R, Python, and Stata. In a difference-in-differences analysis of the mental health hospital closures on homicide using the standard Callaway and Sant’Anna estimator (for DiD with multiple time periods), he got very different results for some model specifications.

Since the specifications and the data were identical between packages, he discovered the divergences occurred due to how the packages handled problems with propensity score weights. Packages were not necessarily transparent about issues with these weights. If you were not running multiple analyses and comparing results across packages, or else carefully checking propensity score diagnostics, you might never have realized how precarious your results were.

Prof. Cunningham closes with the following advice:

The fifth point, and the broader point, is that this kind of cross-package, cross-language audit is exactly what Claude Code should be used for. Why? Because this is a task that is time-intensive, high-value, and brutally easy to get wrong. But just one mismatched diagnostic across languages invalidates the entire comparison, even something as simple as sample size values differing across specifications, would flag it. This is both easy and not easy — but it is not the work humans should be doing by hand given how easy it would be to even get that much wrong.

The other half of the ATM–bank teller story

Wed, 11 Mar 2026 23:49:00 -0400

David Oks had a great post yesterday on the classic parable of how the adoption of ATMs did not lead to the predicted job losses among bank tellers. In fact, the opposite occurred: the number of bank tellers rose. I heard this story recounted several times in early discussions I had about the anticipated effect of AI on labour. I think I first heard it from Ryan Khurana. More recently it has been trotted out by US Vice President JD Vance.

The problem with this story is that the key statistic quoted alongside it, namely that there are more bank tellers than ever before, is no longer true. The famous graph supporting this assertion stops in 2010, and with good reason: the number of bank tellers has sharply fallen since then.

I think I had come across this fact before, this second half of the famous ATM–bank teller story, but it wasn’t until I read David Oks’s post that I understood the reason behind it. Quite simply, mobile banking ate physical banks. The ATM didn’t reduce the demand for bank tellers because it simply changed the kind of labour they did inside the bank. The iPhone made it so we didn’t need to go to the bank at all. It changed the paradigm. Explained this way, it seems obvious. Many new banks (including my own) do not have physical locations and never did.

Canada exports a lot of coal, but not for power generation

Sun, 08 Mar 2026 14:05:00 -0400

This provocatively titled piece in the The Hub (“Why the world needs even more Canadian coal”) made me realize I know very little about one of Canada’s most important exports: coal.

Coal is often villainized because it is incredibly dirty way of generating power. I vaguely recall an article from maybe 20 years ago claiming something along the lines of “if everyone in Canada replaced their incandescent bulbs with energy-efficient ones, the greenhouse gas savings would be cancelled out by a single coal plant that China is building every [some shockingly short amount of time]”. Although, China’s dependence on coal for power has been falling for the past two decades.

It turns out LLM-assisted search is fantastic for finding these half-remembered quotes. Here is the exact article and quote I was remembering, from a 2008 Macleans magazine article (I was pretty close):

Even if every household in the U.S. screwed in an energy-efficient light bulb today, the savings in greenhouse gas emissions would be wiped out by fewer than two medium-sized coal plants - the kind of plant that is being built in China at a rate of one a week.

But coal is also used to make most of the world’s steel (“metallurgical coal”), and this is the kind of coal that Canada (or specifically, British Columbia) overwhelmingly exports. The article goes on to claim that Canada’s production of metallurgical coal is among the cleanest (by greenhouse gas emissions) in the world.

Homeownership rate doesn't mean what you think it does

Wed, 04 Mar 2026 20:15:00 -0500

This thread from demographer Lyman Stone on the definition of the US homeownership rate has stuck in my head for a couple of years now. Reading it produced a pretty profound “oh” for why this particular metric didn’t line up with my perception of the issue.

To put it simply, the definition of the homeownership rate is:

Take the number of households where the home is owned by the household head, divide by the total number of households.

The homeownership rate is based on households, not individuals. If an adult child lives with their parents (and their parents own their own home), they are counted as “homeowners” for the purpose of the homeownership rate. If more and more people in their 20s and their 30s move in with their parents (or never move out in the first place) rather than renting an apartment, this has the effect of increasing the homeownership rate, because you have reduced the denominator (number of households) without changing the numerator (number of owner-occupied households).

Canada uses the same definition:

The homeownership rate refers to the proportion of all households that are owner occupied.