There's an important paper on automation and inequality out today in AEJ:Macro.
It's elegant, it has kickstarted research on automation, and yet it's also under-appreciated.
So here's an appreciation thread:
Before Hemous & Olsen, we thought about skill-biased technological change (SBTC) as something that enhanced the productivity (and increased the wages) of high-skill workers.
Productivity growth among a subset of workers raises inequality.
This could explain a rising wage premium for high-skill workers, but there was a huge hole:
How to explain falling low-skill wage levels?
Another big gap was that technological innovation was a bit too Deus ex machina--computers arrived, labor or capital supplies shifted, etc.
Why did those things happen?
Hemous & Olsen propose an elegant explanation: a thermostat model, where there's a negative feedback loop between demand for low-skill (LS) labor and automation.
In the paper they show how this model can rationalize striking patterns in the labor share, wage premia, etc. in the US economy over the second half of the 20th century.
Prior models could explain parts of these dynamics, but would have to assume exogenous processes or insert little hacks to fit with the full picture.
Here's how the model works:
When wages are low, firms focus their resources on inventing new stuff. Those products require LS labor, and the growth of new products increases LS labor demand. LS wages rise.
As LS labor becomes more expensive, it becomes worthwhile to invest more resources in automating existing production (reducing the now-hefty wage bill) instead of inventing new products, which rely on that expensive LS labor.
How much LS work gets automated? It can't go too far because LS wages would fall so much that it is no longer to be profitable to automate.
So automation cools off the LS labor market, and LS wages feed back into decisions to automate.
That means LS wages may decrease temporarily, but not permanently.
Since LS wages grow in the long-run, automation becomes an important source of economic growth over time.
Pretty cool, right?
If you think, "Hey, that sounds familiar" you're probably right: in the past few years there's been an explosion in research on automation.
They often build on the work horse that Hemous & Olsen built in 2013.
Good to see it in print at last!
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In my 2015 AER, I examined *when* regulation distorts costs by looking at the prices power plants paid for fuel.
I found it depends on cost-reducing opportunities and focused on the difference between natural gas and coal: Gas is homogeneous and traded on open markets, while coal varies along dimensions of heat, ash, sulfur, etc. and tends to be sold in bilateral contracts.
I'm glad to see a discussion of the culture in economics seminars. A little history (and possibly folklore) on how we got here: Why "We don't clap."
The possible folklore part is that the modern economics seminar format has its roots at @UChi_Economics. At least that's what people there say. They weren't seminars, they were workshops.
The difference is that in a workshop all participants were expected to have read the paper.
It wasn't a presentation, it was a discussion of a paper that everyone had read. Imagine everyone participating in a 90 min discussion, giving the author feedback on their paper and then clapping at the end. Clapping for whom?
With most outages over, wholesale prices back to normal, and some truly shocking bills coming due, the policy discussion regarding the #TexasBlackout is ramping up. Some thoughts:
"Texas has a deregulated electricity sector. They failed to keep the lights on. Therefore Texas must regulate electricity." We're going to hear this a lot. We hear the opposite argument whenever a regulatory agency is found to be asleep at the switch (ahem, @US_FDA).
This is a convenient diagnosis that results in ideologues taking turns driving us into a ditch. "You screwed up, so it's my turn to drive." There are likely to be specific causes of last week's breakdown that don't necessarily reflect systemic "markets versus regulation" issues.
This is comparing realized hourly load against predicted load based on the weather, time of day, day of week, etc. etc. ERCOT reports this for 8 different weather zones, and many markets break their total system load into separate areas, so about 100 are plotted here.
A couple of standouts: *YIKES* Permian basin!
You can see that wells (that use electricity for their pumps) in the Permian shut in when prices spiked over the weekend.
Some new results that highlight the stress that American households and businesses are currently under: Utility disconnections and fees in IL.
Thread:
This spring the Illinois Commerce Commission requested zip code-level statistics from utilities on a variety of metrics, including disconnections, disconnection notices, fees, and deferred-payment agreements.
I've compiled these numbers through November, 2020 for the two largest utilities, Commonwealth Edison and Ameren. Combined, they serve 4.9M residential and 600k commercial/industrial accounts. So this won't be a Chicago-centered story, it's about the whole state.
A thread on what electricity consumption in the US is foreshadowing for COVID-19's economic impact:
The data are still coming together, but the pictures are sufficiently clear and consistent across multiple sources that it's worth sharing these numbers with the caveat that revisions may change the picture for individual areas substantially. Apologies for hasty formatting.
You can find more background in this thread about the statistics from Europe: