How Reliable is Economic Data?

Today, I'm coming to you with a topic I've thought a lot about recently, which is economic data and how much we can trust that it's really painting a true picture of what going on in the economy. This is particularly on my mind right now because of a few things that have happened. We've seen major revisions in some important economic indicators, and president Trump recently fired the Bureau of Labor statistics commissioner who oversees the production of several important economic indicators. So are concerns about the quality of economic data as well as its vulnerability to political influences

So there's a number of different reasons why economic data quality has shifted into focus. The first and foremost is that we have seen fairly large revisions to The US payroll survey. That's brought concerns around how reliable is the data that we're collecting top of mind. There's also been a number of high profile issues with funding cuts, particularly around the CPI survey, and that's brought data quality issues top of mind. And then President Trump's decision in August to fire the BLS commissioner has brought data quality issues definitely to the forefront. There's also been a number of gl

So when you think about the actual drivers of these concerns about data reliability, what stands out the most?

So there's three things that I think are behind the overall deterioration in data quality. The first is that the pandemic led to wild swings in economic activity in ways we hadn't seen before. This led to unusual dynamics, which has made seasonal factors hard to pin down correctly. And so if we're looking at sequential data, whether it's activity or inflation, seasonal dynamics have added to data noise. Second, and more of a structural issue, statistical agency funding has been pretty stagnant over the last ten to twenty years. It's declined in real terms in The US. And so this has limited sta

I then asked former BLS commissioner Erica Groschen what she makes of the particularly large revisions to the recent employment figures and data quality concerns more broadly.

It's important to realize that revisions are not bugs. They are features. Right? It's very easy to produce statistics that you never revise. Right? You do it once, and you don't do it again, and that's that. So your choice is you can have it very early and have it be lousy quality, or you can have it very late, great quality, but after you want to need it. So programs that are designed to have revisions recognize that there is value in timeliness and there's value in accuracy, and this is a way of providing both that can be frustrating to users, but any user is free to ignore the first one or

In this recent episode, they didn't cancel out. So what do you make of that, and how unusual is that?

So there usually are some revisions, but the these were large, and the two revisions were on the same direction. So that suggests that there might be something going on. And if you look at when you're likely to get the biggest revisions and particularly the biggest negative revisions, those are often around economic turning points. So going into recession or or slowing dramatically suggests that the companies that have been hit first are more likely to delay their reporting. So it's not a flaw in the system. It is what happens when the economy does this. This is how you see it in the data.

Let me follow-up on a couple of things. I just kinda stuck in my mind. One is there has been a proposal that, for example, some of the monthly data goes to quarterly because it would potentially avoid revisions. Is tha