Alex Imas: uncertainty

Transcript context

…I would like to pitch a rephrasing of that question. My view is that the individual forecasts economists like us would make, as individual forecasts, are not necessarily very useful. There was a blog post by Andrey Fradkin, Brian Jabarian, and Andrew Koh that came out yesterday looking at economists’ forecasts about the labor market. What they found is that there’s a ton of disagreement in every single direction. What they advocate for, and I’m in agreement here, is that rather than thinking about individual forecasts, we should be generating prediction markets where you get aggregate forecasts and wisdom-of-the-crowd effects. The reason I think this is because we have been famously terrible at forecasting. Let’s go all the way back to 1820. This debate we’ve been having is actually 200 years old. David Ricardo is one of the classical economists, not neoclassical. When the Industrial Revolution started happening, he wrote a bunch of stuff saying, “This is going to be great for everybody. Prices are going to come down.” But then he turned around and said, “Wait, I can see all these jobs that are creating value are going to be automated by these machines. This is going to be really bad. Everybody’s going to become unemployed, and there’s going to be political unrest.” And if you look at Ricardo’s predictions, they’re actually right. All those jobs that made money in Ricardo’s time got automated. If David Ricardo woke up and somebody told him all those jobs did get automated, and then asked him, “What do you think the prime-age employment rate is in 2026?”, I think he’d be surprised to be told it was the highest it’s ever been other than 2000. We have the highest number of employed people that could potentially be employed since 2000. That was the peak and now it’s the second peak basically. What David Ricardo ended up missing is that you have these economics of structural change, where everything that got automated became cheap. People had more money to spend, and then they started spending it on services. This is the lump-of-labor fallacy. David Ricardo didn’t consider that new jobs would be created. But it’s not obvious that money would go to services. Why wouldn’t it go to more automated goods and something like that? I’m not using this anecdote to say this is what’s going to happen now and that we’re going to have full employment. I’m using it to say it’s really hard to make predictions. What may be a really useful tool that economists have is to instead start with a premise. Maybe we start today: labor share is zero. Labor share has gone down. What could possibly explain this? Let’s write down an economic model of what happened. Phil will talk about this later today. Or you can write down a model that asks, “What if labor share just stays the same? What can make that happen?” If you don’t take anything else out of this conversation from me: We don’t have any data. I’ve been saying we need a Manhattan Project for data. share just stays the same? What can make that happen?” If you don’t take anything else out of this conversation from me: We don’t have any data. I’ve been saying we need a Manhattan Project for data. We don’t have data on consumer demand elasticities. We don’t know what they are. We’re not really tracking what jobs are getting created or destroyed. The O*NET database, with all of the tasks and different jobs, has been rarely updated and is super low quality. What is really useful is to think about the potential scenarios, map them out, and say what dimension of scarcity will generate each scenario. If there’s full employment, we can talk about the relational sector. If the labor share collapses, we can talk about other sorts of scenarios. That will tell us what data we should be collecting. It’s probably worth defining labor share and capital share real quick. The whole economy, the total sum of goods and services sold, is either paid out to people in wages or it’s paid out to capital, which is to say, there’s rent on buildings and shareholders of companies that get paid out. For many hundreds of years, ~60% of the economy basically gets paid out to humans in wages, and the other 30-40% gets paid out to people who own machines and land and claims on companies. The question is, if 60% is going to wages right now, does that shrink as AIs get smarter and better?…

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