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In Conversation with Erica Groshen

Summary:
The promise of real-time economic measurement Clemens: I should say I was not an economic observer because during the Great Recession of 2007–2009 I was in graduate school, and I was taking political science, so I was not as keyed into this kind of thing. But it’s really striking to me how quickly economists responded to this one. We had working papers published on our website over the past 18 months where, in some cases, the paper was analyzing data that were 2 weeks old. For starters, is this a relatively new phenomenon in this recession? And if so, what’s contributing to this sudden demand and the sudden supply of all this high-frequency research? Groshen: I think there are at least three factors at play here. One is more availability of data

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The promise of real-time economic measurement

Clemens: I should say I was not an economic observer because during the Great Recession of 2007–2009 I was in graduate school, and I was taking political science, so I was not as keyed into this kind of thing. But it’s really striking to me how quickly economists responded to this one. We had working papers published on our website over the past 18 months where, in some cases, the paper was analyzing data that were 2 weeks old. For starters, is this a relatively new phenomenon in this recession? And if so, what’s contributing to this sudden demand and the sudden supply of all this high-frequency research?

Groshen: I think there are at least three factors at play here. One is more availability of data and computational power than we ever had before, with some from the private sector and some from the public sector. A second unusual factor is that we knew exactly when this recession started. And we had a good sense that it was going to be deep and profound. We didn’t know exactly what it was going to look like because it was unprecedented, but usually it takes 6 to 8 months for the National Bureau of Economic Research [the arbiters for dating U.S. recessions] to declare the beginning of a recession. And this time we didn’t need NBER to declare it, but NBER did it very fast because, again, we all knew it happened and that’s not typical. So, everybody was primed and on the same page. No one was disputing whether it was happening at all. That whole part of the discussion just was skipped entirely.

The third part is that even though policymakers always like to complain about how economists are always reinventing the wheel and don’t really learn anything, the truth is, we have learned things about recessions and how to think about them and how to measure them. Our recent research and the policy experiments that have taken place have taught us a lot of lessons. So, economists were primed intellectually in a way that we hadn’t been before. All the studies on previous recessions, conducted with modern data and modern theory fed directly into policymakers having the tools ready to address the coronavirus recession.

Clemens: And I would just add that because everyone immediately knew we were in a recession, there was so much policymaker demand for answers and solutions. Right?

Groshen: Yes. I was really impressed and heartened by how quickly the policy community reacted, even in this time of polarization, which seems to interfere with almost anything else getting done. Our leaders enacted some very strong and innovative policy responses, which worked remarkably well for how quickly they were implemented and how large they were. I found it really heartening that policymakers could and did do this. For me, it was like this wonderful moment of “yes, we can do much more than we think we can do.” It was awesome.

Clemens: And the policy responses were responsive to the data we were seeing, right?

Groshen: Yes.

Clemens: And responsive too, I think, to what economists were saying.

Groshen: Right.

Clemens: So, building on that, let’s just take it as a given that more timely estimates are better, and that we’d like more timely estimates going forward.

Groshen: Absolutely. More timely. More granular. More flexible.

Clemens: Right. That’s a significant demand to place on the federal statistical system. In a general sense, what do we need to get there? Is it primarily a question of using data we have in better ways? Or is it going to be primarily a question of finding new data, collecting new data? And what do you think are the big places in the federal data infrastructure that we need to work on?

Groshen: The statistical system really expanded its capacity to inform decision-making when survey methodology was developed [around the 1940s]—specifically, when the statistics and the cognitive work was done so that agencies figured out how to choose samples, process the information, and ask the questions that would elicit the information needed. And so that was a huge focus of the statistical system for many years.

Economists always used some administrative data because that was all they had to begin with. So, they started with administrative data, then they developed a huge survey capacity that has been really powerful and essential. Now, in some ways, we’re at a third iteration, where external administrative and private data have burgeoned. So, the statistical agencies have to get access to that information (and that’s not been trivial), and then find ways to use it to augment or substitute for survey data.

I think survey data will remain a very important part of what statistical agencies do because there are some things you cannot measure without asking people, such as why they are not working.

Yet this wealth of other new data out can be tapped to improve our official statistics. You can divide those other data into two parts: government administrative data and private-sector data. In order for statistical agencies to make better use of government administrative data, some practices and legislation will have to change to allow more access and, in some cases, improve the administrative data. Such changes are difficult, although inroads are being made.

Turning to private data, we’re still at the infancy of figuring out how to work with the private sector appropriately. Many holders of private data want to help improve official statistics, but they have important concerns—for example, about protecting confidentiality and intellectual property. Getting the right mechanisms in place is a real challenge because of the needs of the two parties. Even with some commonality of interests, there are some needs that seem to conflict, so the parties will have to work through them.

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