The hidden backbone: The data behind the economy

In this episode of Econ To Go, Erika McEntarfer, the former commissioner of the U.S. Bureau of Labor Statistics, joins host Neale Mahoney to explore the public data system behind the numbers we hear every day, like inflation, unemployment and GDP. 

While politics played a role in her firing last August, “the production of economic data is not partisan,” she says.

McEntarfer, who is the Tad and Dianne Taube Policy Fellow at SIEPR, explains why America’s statistical system is widely considered the global gold standard, but also why it’s facing new pressures from rising survey costs, declining response rates, threats to independence, and limited budgets. 

Follow along as we explore several key themes, including:

• (01:14) Challenges and opportunities in the economic data system
• (05:30) Public vs. private data
• (09:14) Modernizing the data system
• (18:03) Implications of firing your chief statistician
• (23:53) Can we trust the data?

An economist who has served more than two decades in nonpolitical roles in the U.S. government, McEntarfer focuses on economic measurement, labor markets, and the public statistical infrastructure that underpins economic policymaking. To learn more about her work and the U.S. economic data system, explore these links:

• Until Trump Fired Her, She Was an Economist With Bipartisan Support
• The Value of US Government Data to US Business Decisions

• U.S. Bureau of Labor Statistics

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Econ To Go brings Stanford economics into your everyday life — served with a side of coffee. Hosted by Neale Mahoney, the Trione Director of SIEPR and TG Wijaya Professor of Economics, the video podcast takes you on a walk across campus with leading thinkers as they unpack the ideas shaping our economy, public policy, and daily lives. Smart, curious, and conversational — it’s Stanford economics, to go. 
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TRANSCRIPT 

Econ To Go: Episode 3 

Neale Mahoney: For most of us, economic data is something we read in the headlines, inflation, unemployment, GDP. But those numbers don't come from thin air. Behind them is a massive public system of data collection and analysis. Surveys are submitted, forms are filled out, models are run, all with the objective of giving us a clearer view of the country's economic health. That system is the backbone of economic decision making, but it's under strain. Response rates are down, budgets are tight, and in some cases, politics has begun to seep into what was once sacred ground.

Erika McEntarfer: I opened up this email, and by then I had a few from, I think it was an NBC reporter. And he was like, "Do you wanna comment on this tweet that the president, that he is going to fire you for the jobs numbers?"

Neale Mahoney: I'm Neale Mahoney, economist and director of the Stanford Institute for Economic Policy Research. In this episode of Econ To Go, I sit down with Erika McEntarfer, a labor economist and former head of the Bureau of Labor Statistics. We talk about what's working, what's not, and why the integrity of our public data system matters now more than ever. You know, you've said that there's sort of key challenges in the US statistical data system. How can we overcome those challenges and what are the consequences if we fail?

Erika McEntarfer: So the interesting thing about the US statistical system and particularly economic data is you have to keep two thoughts in your head simultaneously. And one is that US economic data is actually very, very good.

Neale Mahoney: It's the envy of the world.

Erika McEntarfer: It is the envy of the world. The richness of the data, the timeliness of the data, it's really hard to match. If you do international comparisons, you'll discover very quickly how advantaged we are. The other thing that you have to keep in your head is that the system is in a certain amount of danger in terms of its sustainability. And those dangers are fiscal. So the costs of fielding surveys are increasing, but the budgets are not keeping up with those costs. The others declining response rates. It's harder to reach respondents, that's true for households and businesses. We did start in the US, relatively advantaged. We had pretty high response rates. European statisticians are like, "I would kill for the response rates that you guys have in the US," but they are declining and they're declining everywhere.

Neale Mahoney: That's interesting. I knew they were declining and, you know, you can map that to declines in sort of civic participation across many dimensions. I didn't actually know that they started from a higher place in the United States. That's, in some sense, encouraging, even if there's still a lot of work to do. So, you know, fiscal challenges, declining response rates, what are the tools we have to combat those challenges?

Erika McEntarfer: In the fiscal space, this was a big thing that I was focused on when I was running the Bureau of Labor Statistics was to try and draw attention to the dangers of underfunding this great economic data system that we have here, and all the advantages that it incurs on the US economy. Very early in my tenure we realized that we were not gonna actually be able to field the entire sample of the current population survey, which is one of the most important pieces of economic data that we have. It underlies the unemployment rate. And we were gonna have to cut the sample because we couldn't afford to field the entire survey and cut the sample significantly. And so I decided to use this, you know, terrible thing that was coming to draw attention to the problem.

Neale Mahoney: So a teachable moment about the value of the data.

Erika McEntarfer: Exactly and it was true that people didn't understand that the fiscal situation with US economic data was that bad because the, I mean, people understand how important the current population survey is. Lots of people use this data and they were like, "Why can we not fund like one of the most critical pieces of our economic infrastructure?" And I'm like, "We literally do not have the money. We have shaken every sofa cushion and there's no more change to be had." And they, you know, we did actually get a lot of articles. You probably saw some of them.

Neale Mahoney: The analogy that springs to mind, it's like one of these small muscles in your back. Like, you don't think about it, you know, most of your life, and then suddenly you injure it and you can't think about anything else. And it's how do you convince people to, like, invest in something when they don't know how much they need it?

Erika McEntarfer: Exactly. Part of it's just you count on that muscle in your back until it's injured, you don't realize, like, how much you were counting on it because you took it for advantage.

Neale Mahoney: Spoken as a man in my mid 40s, who has strained my back.

Erika McEntarfer: Yes, you take it for granted until it doesn't work anymore. And then you're like, you know, what could I have done to have saved it?

Neale Mahoney: I think there are some people who think, look, we do have great private data, lots of firms, nonprofits, governments use that data. Is that data a substitute for government data? Is it only a compliment? Like, how do you think about the private sector data sources?

Erika McEntarfer: The short answer is private sector data is a great compliment, but cannot replace what the statistical agencies do. And again, the shutdown's kind of an illustrative example of what would happen if, you know, the Bureau of Labor Statistics went away, because it was shut down for six weeks. So we do have some private sector providers of payroll data. There are certain private sector producers of price statistics, but they're mostly goods. Goods is only 35% of US consumption. The other 65 is services. And there aren't that many private sector providers of service data. And, you know, you have these price statistics, but you also need the survey that tells you what individuals are actually spending money on so you can weigh the basket appropriately. And that's statistical infrastructure as well.

Neale Mahoney: And so, in some sense, the private data can be really, really great in its domain. Like, you can get incredible data on the prices at supermarkets. The problem is the private data is not comprehensive. And so, you know, grocery spending is like 8% of the consumption budget, and so all the granularity in the world on spending and grocery stores is not gonna teach you about the service sector, for example. And so if you're trying to balance inflation and unemployment if you're buying sovereign debt in the United States and you only have visibility into 8% or 25% or even 45% US economy, you're missing more than half the story.

Erika McEntarfer: Exactly. So yeah, we can get pieces. But you can't get the comprehensive picture. And a lot of those pieces also rely on public sector data to benchmark their own statistics. And, you know, ADP is a payroll company. They have churn in the businesses that use them. They don't know until they look at BLS data how much of that churn is representative or, like, their own sample and how it's changing its composition over time so.

Neale Mahoney: Private data can offer precision, but only in slices. Without a full picture, it's hard to guide the economy or earn public trust. Still, Erika sees reason for hope, not necessarily in the systems, but in the people still holding them up.

Erika McEntarfer: It was, one of the best parts of the job was just talking to the actual data experts, the people who had spent 25 years perfecting how we measure employment, how we measure unemployment, how we measure prices, and the depth of knowledge that they had. And even though we lost a lot of staff from the DOGE cuts, many of those people are still there. Most of them are still there. And so they give me enormous hope for the ability of the system to hang on.

Neale Mahoney: These are mission-driven career staff who understand the value that they're providing to users and have, you know, chosen to take a salary cut and still work long hours because they understand that this underpins our economic system. So you've spent years working to modernize statistical systems. What are the most promising approaches and how optimistic are you?

Erika McEntarfer: I worked in modernization for at least 15 years, and there's a lot of things that we can do to shore up the survey-based collection system that we have from the 20th century. The most promising avenue for modernizing statistics is, like, what we often refer to as a blended data approach, where you ask the respondents the things that are otherwise really hard to collect. So unemployment is probably a key one here. So unemployment, you really have to go to a household and find out what they were do, like were they working? If they weren't working, were they seeking work? There's no administrative solution to this problem because lots of people who are sitting at home not working are doing other things. They're taking care of small children. They're taking care of elderly relatives there in school. And so you don't really know why people are out of the labor market unless you ask them, and if they're trying to get in. On the other hand, there are domains where, like wage and salary income, where we have a lot of rich administrative data, and we know that this is something respondents really don't like providing themselves. And so you can use, like, IRS data, unemployment insurance, wage record data to help fill in and take response burden away from individuals. So you just, you have to go sort of item by item in terms of the potential for this other, like, alternative data where it can fill in.

Neale Mahoney: In some sense, you see this statistical data system sort of moving towards a quilt, where surveys are covering part of it, administrative data are covering part of it, maybe even private sector data, sort of another patch in the quilt, and that's how we can get coverage you know, at a reasonable budget and allowing for all of the strengths of the administrative and private data when we have it. Super interesting.

Erika McEntarfer: The low hanging fruit is really, like, so here is the case for price statistics. So some price statistics are easier to collect than others. Like if items sitting on the shelf is the easy case, but, like, cell phone service is really hard to collect.

Neale Mahoney: I don't know what I pay for cell phone services.

Erika McEntarfer: If I open up my cell phone plan, I am also surprised what I am paying for. So this is actually pretty difficult information to collect, and it turns out it's easier to collect it commercially, because the commercial data provider has very good data on what people are actually paying for. So that's kind of the ideal case where a commercial data provider can, you know, you can, you get higher quality data and, you know, hopefully very complete coverage.

Neale Mahoney: Got it, but that takes dedicated staff who understand the industry's making judgment calls about when we can rely on the survey, like when we can work with the commercial providers, when we can work with administrative data. So it's, there are challenges with doing that.

Erika McEntarfer: Absolutely. So there's this, it's called a holdup problem. So we were depending on a commercial data provider for egg prices. The data provider got bought up by private equity and they came back and they tripled their charge for what they were going to provide us. And, you know, it's a monthly indicator. We had two weeks, we didn't have the money and we were like, "What are we gonna do? We don't have egg prices."

Neale Mahoney: And like egg prices, maybe gasoline prices and egg prices were the two most important prices in the US economy.

Erika McEntarfer: Yes, at the time, eggs was not a good thing to be missing. We did find a solution, but that's sort of an example of like the dangers of these relationships is that the incentives of the companies engaging in them can change.

Neale Mahoney: So lot of talk about AI. Does AI offer some tools that we can use to solve some of these challenges?

Erika McEntarfer: So that was something that we were discussing very heavily throughout 2024. One of the most promising things we were hoping we could do with AI is turn unstructured business data into structured data to reduce response burden for businesses. So, like, if you ask, like, Amazon, like, just give me what you've got, you don't have to put it in specific format. If we can use AI to just take the company data and structure it in the way that the statistical agencies need, that would be super promising.

Neale Mahoney: Basically, take their data and have the AI then populate you know, your survey, and then you have the survey responses.

Erika McEntarfer: Exactly. So that was something we were investigating. Census has done some work here with some companies, and, you know, they were, they have a much bigger budget than the Bureau of Labor Statistics, so we were sort of letting them lead the way and following in their footsteps. So I do think there is some promise there. I actually had a whole basket of AI related projects when DOGE arrived early in the administration. The early word was they were gonna help us with AI. And I was like, "Great, we could use some more resources here." So, you know, I had this whole list of projects for them and instead I wound up sitting across the table from a member of DOGE and they were like, "So we want to fire all of these statisticians and replace them with the AI." And I was like, "I don't think that's actually possible, but if you can explain to me how it is possible, I am all ears." And then they would just stare at me blankly and tell me that I was not cooperating with their vision. I was like, "No, I don't actually understand how you replace a time series statistician with an AI model, but if you can explain it to me, I'm all ears."

Neale Mahoney: The history of technological diffusion is that it takes time. And so we can be optimistic about AI and still not think that, like, we can have massive shifts in how we collect and interpret data immediately.

Erika McEntarfer: Exactly, so, I mean, I think we're still in the businesses figuring out how to make this technology useful phase. I am very sympathetic to all of the companies trying to figure out how to use this technology now in a safe, privacy preserving way. So, you know, once you bring it into an enterprise where you have to protect things, it gets more complicated, it gets harder. Risk management, like, you can't have the AI, you know, deleting all of your data. Like, there are risks to manage here. And so it's gonna take companies a while to figure it out. It's gonna take government a while to figure it out.

Neale Mahoney: So we're still in the early stages of figuring out how AI fits in the world of economic data, and the stakes are high. At the cafe, Erika tells me that sometimes it's the most dedicated users who remind us what's really at risk.

Erika McEntarfer: So I have 9 to 10 weather apps. Many of them premium that you have to pay, like, $40 a year for, worth every penny because on the water, as a sailor, accurate weather data will save your life. And this is actually true for economic data too. So if you're just like a regular economic actor, you might need to just have the default, like, sense of where the economy is, but if you're like the Fed or a bond trader or you're very actively engaged in markets, you have, like, a data release calendar sitting on your desk and you pay attention to every feed that comes through because you need the most complete picture of what is happening in an economy to do your job.

Neale Mahoney: There's a set of super users which are, like, hugely reliant on this data and, like, wouldn't be able to make important decisions without it.

Erika McEntarfer: Absolutely, so when I ran the Bureau of Labor Statistics, I suddenly became instead of just a regular economist, somebody who could call, like, a regional Fed governor and be like, "Hey, can we talk about something in the data?" And they'd be like, "Absolutely." Because the Fed is like a sailor. Like, they are reading all of the tea leaves. They want to know everything that is going on in the economy so they can make monetary policy decisions. So they are super users, they literally cannot do their job without the Bureau of Labor Statistics, the Bureau of Economic Analysis, The Census Bureau. Those are the big three.

Neale Mahoney: So you led the Bureau of Labor Statistics until August of 2025. Your departure made headlines in a big way. When did you find out that you'd been sort of relieved of your post? And can you just talk through that experience?

Erika McEntarfer: So it was job stay for the July release. We had been you know, we'd spent the morning at the Labor Department briefing the Labor Secretary the day before. We had briefed the White House on the data and, you know, by afternoon on a jobs release, things are starting to wind down a bit. And we were having a social gathering in our office for the staff. And I looked down at my phone and I saw that I had missed an email from a reporter, who wanted to ask me about something Trump was tweeting, and I was like, "Oh, let me go check this in my office." And so I walked down the hall and I opened up this email, and by then I had a few from, I think it was an NBC reporter, and he was like, "Do you wanna comment on this tweet that the president, that he is going to fire you for the jobs numbers?" And I have to say, my first thought was, I thought he was just threatening. I assumed I hadn't actually been fired, so I started thinking together a calm strategy. I'm like, "Oh, it's Friday afternoon." I gotta assemble a team here to, like, deal with this, and I'm already, like, 10 minutes into this thought process. When I look and I realize, oh, I have missed some other emails during this gathering, and one of them was from the presidential personnel office, and it was a termination letter. And I was like, "Oh, I am actually fired." Okay, that's a whole different crisis than the one I thought we were entering. It was an unbelievably crazy moment because all, like, while what I just described to you was happening, my phone is completely blowing up, because this has hit the media, it's on television, people are texting me, people are emailing me, my family is calling me, like everyone is just reaching out all at once and my phone is just, all my phones are just buzzing and buzzing and buzzing and buzzing. And it was just wild.

Neale Mahoney: What was the response of, you know, the economic community?

Erika McEntarfer: So I should explain one reason I assumed this was just a threat and not an actual execution of a firing is because firing your chief statisticians is a shock to trust in your economic data that has real economic consequences. So it's not something you really want to do as a rule. So I assume somebody was gonna, you know, tell him actually you don't want to do that.

Neale Mahoney: Yeah walk it back once they realize sort of the-

Erika McEntarfer: Once you realize the consequences of such a move, the economic community immediately realized the consequences. Many, many people spoke out in the aftermath of my firing, both defending my work, but also just saying, "You do not want to do this." Like, this is countries where they have fired their chief statisticians, Argentina, Greece, it's not, it's just not a good list.

Neale Mahoney: And prominent people from the right, as well as from the left, from across the political spectrum, I think, were really ringing the warning bells as loudly as they could.

Erika McEntarfer: Absolutely.

Neale Mahoney: Which might be cold comfort for you, because you just lost a job and are in the middle of a sort of media storm. But on the other hand, it also must be reassuring to know that, you know, there's this huge coalition of people that spans roles in government, roles in the private sector and academia across the political spectrum, who are frankly outraged by what has just happened.

Erika McEntarfer: Yeah. No, absolutely. The anger was very bipartisan at what had happened. My predecessor leading the Bureau of Labor Statistics, a Trump appointee, he was very vocal, went right out in the press saying, "This is nonsense, this is dangerous." And that was, I think, very effective in communicating to the world that what happened here was not a partisan I mean, that was a partisan act, but the production of economic data is not partisan. This is something that really you want. Republicans, Democrats, everybody wants this data to be accurate and trustworthy. There's no incentive here to make it less trustworthy.

Neale Mahoney: Yeah and it doesn't make you feel any better that, like, maybe this is just us sticking our hand on the stove and we won't do it again when it's your job. But, you know, the strength of the reaction, I hope, sort of inoculates us against doing something like this again. I don't know but I try to be an optimist. It's what gets me through the day.

Erika McEntarfer: I think cautious optimism is definitely warranted here. So the number one question I get from journalists and just the general public is, can we still trust the data?.

Neale Mahoney: Can we trust the data?

Erika McEntarfer: Yes. So firing one person doesn't, is not enough, and they fired me, but I didn't actually touch the data. So in the aftermath of my firing, the BLS staff released a statement saying that you will know when the data is not trustworthy because there will be a walkout. We will quit. We will leave.

Neale Mahoney: In some sense, that's the strength of the system, right? That it's, you know, it's the, you can break, you know, like one stick, but you can't break 1000 sticks and there's 2,000 people at the BLS, and that's what gives that system its resilience and strength. Is that an okay analogy?

Erika McEntarfer: Yeah, that's a great analogy, actually. If there is a silver lining to having been fired, people are paying more attention to federal statistics than they have in a while, and there is broad bipartisan support for this work, but because it's like that little muscle in the back, you know, everybody's been focused on their biceps, their triceps, they forgot about the little muscle in their back.

Neale Mahoney: How debilitating, right, straining that muscle is. But no, this is, it's a wake-up call.

Erika McEntarfer: Yeah.

Neale Mahoney: And, you know, like, never let a crisis go to waste. There is an opportunity now to really think about what the next chapter is for these agencies.

Erika McEntarfer: Yes. And they don't need much. Federal statistics are really cheap when you consider the value that they bring, the economic value. Even there's a paper out there that, so the total amount of the cost of, like, not just BLS, but census economic data, BEA, economic data, it's about $2 billion.

Neale Mahoney: We're a $30 trillion economy, and we're making decisions about how to allocate resources, and we're spending two billion on visibility. Of course, the private sector is also spending a lot on their own visibility, but on the margin, right, there are still large returns from an additional investment.

Erika McEntarfer: Yeah there's enormous economic returns from additional investment in this data.

Neale Mahoney: What would you tell the next generation of economists and public servants about public data that our generation had to learn the hard way?

Erika McEntarfer: Don't take the small muscle in your back for granted. Almost every single data set we have in this country was forged in a crisis where we needed it. And what this rich data that we have now is forged out of all of this economic pain that mostly our grandparents and great-grandparents had to experience. We are enjoying the dividends.

Neale Mahoney: Right, inflation crises led to the Consumer Price Index and the measurement of price data.

Erika McEntarfer: Exactly.

Neale Mahoney: And we don't want to turn that off just because we haven't had a crisis that reminds us how important it is.

Erika McEntarfer: Yeah, when the next crisis comes, we won't have it if we let it die.

Neale Mahoney: On that note, thank you so much for coming on Econ To Go, and for your service.

Erika McEntarfer: Thank you for having me.

Neale Mahoney: Public data is one of our few shared foundations, a system built on trust, neutrality, and transparency. And when that foundation cracks, it's not just the numbers at risk, it's the decisions that depend on them. Erika's story reminds us that defending the integrity of public data is not just a bureaucratic concern, it's a democratic one. I wanna thank Erika again and all of you for listening today. I'm Neale Mahoney, and Econ To Go is where we bring Stanford Economics into your everyday lives. If you enjoyed this episode, subscribe or follow wherever you get your podcasts. We've got more smart, curious conversations coming your way from the Stanford University campus.