 Now, the concern I think that came up is that the appearance of political interference, who knows what actually happened, but the point is that if there is political pressure on the measurement, then that can substantially affect the aggregate. The language that has come out of the administration has, I think, not helped the cause of the career civil servants appropriately. When you're making decisions that are important to serve the citizens of the country as a whole and the population of the country as a whole, then you need good data to be able to allocate those resources. Now, if those data are biased in some way, people are not going to get counted. And if they're not counted, they're not going to get resources. People matter. It's government by the people of the people and for the people. If you don't know who the people are, you don't have a democratic system of government. And if you don't have high quality data, you can make lots of mistakes. So we didn't have high quality data on the opioid crisis. And so it kind of surprised everyone how bad it was because we had no way of measuring it. Certainly in the United States, I don't think that has been a major issue in dictatorships. Government data is influenced by politics because if you control the message of the data, you control an awful lot of messaging that's going on in the country. Anyone who's worked for the World Bank or in totalitarian countries will be able to tell you that government data is the first thing that goes. And what you're seeing happening here is people are going to multiple other sources. So they're going to Johns Hopkins, as you know, they're going to World Amita. They're going to 1.3 acres. People are getting their data from lots of different sources. And a lot, I don't want to cast dispersions on any of those data sets. But how does the data that they put out compare with some measure of ground truth? How does that effort persist over time? How do we standardize measures across countries? The private sector, you've got things, measures that are being popped up because people are trying to sell you things. Or because there's a marketing, there's a profit motive in it. And you can't distinguish that from multiple other sources. What I talk about in the book, which is called democratizing our data and manifesto. What I talk about in the book is reducing the monopoly power in the federal system. Because obviously, if you have a monopoly power, you've got a single point of failure. And then you're vulnerable to these political pressures that we're seeing. So what I talk about is a network system that pushes the development of measures and indicators down to the states, to the local areas, to the regions, who are closer to the data, who have maybe more sense of the way in which the data are generated. But combine that with the federal system so that you get consistency in kind of that quality focus that I was just talking about. The current system clearly isn't working. When I wrote the book, I did not expect the coronavirus pandemic to highlight all of the fragilities in our data collection system. I talk much more about GDP and unemployment, but all of the fragilities of our current system have been exposed, I think, with the pandemic.