Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots Podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, I've said this a bunch of times, and we've
Speaker 2: sort of danced around this issue on the podcast before,
Speaker 2: but I've said this a bunch of times. I'm sort
Speaker 2: of fascinated by the degree to which we sort of
Speaker 2: take numbers on the screen. For granted, Like you know,
Speaker 2: we look at a price of a stock and it
Speaker 2: exists on the screen, it's like, where did that come from?
Speaker 4: How did that get there?
Speaker 2: Or like the first Friday of the month, the job's
Speaker 2: data flashes on the screen and we just start talking
Speaker 2: about what the data said, what the unemployment rate was,
Speaker 2: et cetera. But we don't really talk enough about like
Speaker 2: what had to happen behind the scenes to get that
Speaker 2: number to the screen, right.
Speaker 3: I find this really interesting as well, because obviously there's
Speaker 3: the data collection portion of this, Like, yeah, you have
Speaker 3: to go out and talk to people for certain surveys,
Speaker 3: certain data points, but also there are so many qualitative
Speaker 3: and subjective adjustments that you can make to that data. So,
Speaker 3: for instance, with CPI I didn't know that CPI waitings
Speaker 3: are like different depending on what city you're in. So,
Speaker 3: for instance, like food at home could matter a lot
Speaker 3: more in I don't know, Minneapolis compared to Chicago. It's
Speaker 3: really interesting. And there's also qualitative adjustments. Yeah, so if
Speaker 3: your fridge gets Wi Fi connected, then that gets incorporated
Speaker 3: into the price as well. So it's really interesting.
Speaker 2: It's super interesting, and like I have a you know,
Speaker 2: this is the issue. We don't talk about data collection,
Speaker 2: but it's obviously some of the most interesting stuff there
Speaker 2: is because of course, you know, things like food at
Speaker 2: home and one city should be different than another city
Speaker 2: if you really want to collect a basket, and so
Speaker 2: means that you have is some of the most interesting
Speaker 2: economics work being done anywhere in a realm that virtually
Speaker 2: gets no attention. And when we talk about data collection,
Speaker 2: you know, you hear it a lot in politics, right,
Speaker 2: surveys have gotten you know, no one answers the phone.
Speaker 2: It's you know, it's harder and harder to do high
Speaker 2: quality surveys. I think you've written about this, like response
Speaker 2: rates and just the actual cost of collection of all
Speaker 2: this data is on the rise.
Speaker 3: Yeah, so I have a bunch of thoughts on this.
Speaker 3: I'll just say talking about the economic data might be
Speaker 3: timely as well, because we know that Trump doesn't really
Speaker 3: respect I guess a lot of official economic data. And
Speaker 3: he's also trying to cut back on costs or funding
Speaker 3: of a bunch of different government programs. So it's clear
Speaker 3: that you know, entities like the Bureau of Labor Statistics
Speaker 3: could potentially be in the crosshairs here. But you're right,
Speaker 3: response rates lower, response rates have been happening for a
Speaker 3: long time, and it's really easy to look up like
Speaker 3: just how bad things have gotten. If you look at
Speaker 3: the BLS website, for instance, look at response rates on
Speaker 3: the housing portion of CPI that's gone from about seventy
Speaker 3: percent back in twenty fifteen to just fifty seven percent. Now, wow,
Speaker 3: response rates on jolts have gone from I think sixty
Speaker 3: seven percent to just thirty percent, So that's pretty amazing.
Speaker 3: And the other interesting thing here is it's not just
Speaker 3: a US problem, right, Like there's no US exceptionalism here.
Speaker 3: You see the same pattern in other economies like the
Speaker 3: UK and New Zealand, and actually in the UK just
Speaker 3: last month, the Office of National Statistics said it wasn't
Speaker 3: going to publish PPI because of a data quality issue.
Speaker 3: And also trade data had a problem because of errors
Speaker 3: in the data provided by HM Revenue and Customs. And
Speaker 3: now there's a task force to look at all of
Speaker 3: these mistakes and data problems at the ONS. So something
Speaker 3: that goes beyond the US here.
Speaker 2: This is a really big deal, especially when you just
Speaker 2: consider how much economic activity is based on being able
Speaker 2: to look at high quality data and make decisions from it,
Speaker 2: obviously in the market, but also in just sort of
Speaker 2: the quote real economy, et cetera. Anyway, without further ado,
Speaker 2: we really do have the perfect guest, someone who knows
Speaker 2: a lot about how the sausage is made and why
Speaker 2: making the sausage is getting more expensive. We are going
Speaker 2: to be speaking to Bill Beach. He was most recently
Speaker 2: the commissioner of the BLS, and he knows all about this.
Speaker 2: He was the fifteenth commissioner at the BLS, does research
Speaker 2: affiliated with the Economic Policy Innovation Center, and he's going
Speaker 2: to talk to us about all of these issues.
Speaker 4: Bill, thank you so much for coming on odd Lats.
Speaker 5: Oh Man, it's just it's great to be with you
Speaker 5: and Tracy. Thank you very much. It's a great topic.
Speaker 4: It's a great topic.
Speaker 2: I don't know like when the process begins, but it's like,
Speaker 2: I get this number. It says what the jobs report,
Speaker 2: it says how many jobs were created that month? How
Speaker 2: did that number get onto my screen or ontobls dot gov?
Speaker 5: And it gets there every month, Yeah.
Speaker 2: Every month, every month. It's never missed except maybe once
Speaker 2: send like a hurricane or so. Anyway, So to get.
Speaker 5: That first, yeah, the first Friday report, the Jobs Report,
Speaker 5: which comes out at eight thirty Eastern time on the
Speaker 5: first Friday. It consists of two surveys. So let me
Speaker 5: talk about the first one, which is the one you
Speaker 5: just mentioned that the number of jobs. That is a
Speaker 5: survey of about four hundred thousand firms out of eleven
Speaker 5: point three million firms in the United States, about four
Speaker 5: hundred thousand of them have agreed voluntarily to send in
Speaker 5: a pretty complex survey response. Every month, they send that
Speaker 5: survey to electronic collection centers. There's one in Chicago, there's
Speaker 5: one in Atlanta. And that survey has to contain the
Speaker 5: twelfth day of the month, if it is a work day,
Speaker 5: or the closest workday to that twelfth of November, or whatever,
Speaker 5: and the reason for that is we want to get
Speaker 5: at least one pay period in the report. Okay, So
Speaker 5: they submit that. That data continues to be collected through
Speaker 5: almost the end of the month, not quite, but almost
Speaker 5: the end of the month. It goes from regional offices,
Speaker 5: these regional collection centers to the national headquarters in Washington,
Speaker 5: where about forty people sometimes less, out of the two
Speaker 5: thousand people that work there massage the data, which means
Speaker 5: that they take the data which is in the survey.
Speaker 5: It's a sample, it's just a fragment of the total population,
Speaker 5: and they multiply the responses by what are called weights,
Speaker 5: and that gives us the national number. So that number
Speaker 5: is all done pretty much by Tuesday night, preceding the Friday,
Speaker 5: and on Wednesday, the Commissioner gets to hear the data.
Speaker 5: The Commissioner, by the way, plays no role whatsoever in
Speaker 5: massaging the data or multiplying the survey results times the weights.
Speaker 5: Then I would brief the White House. The White House
Speaker 5: would then brief the Federal Reserve Ward chairman, the Secretary
Speaker 5: of the Treasury on Thursday. That Thursday night, usually they're
Speaker 5: sworn to secrecy, and then at eight o'clock in the morning.
Speaker 5: I would walk over to the Department of Labor, because
Speaker 5: blas's headquarters is about a twenty minute walk from the
Speaker 5: Department of Labor. I would brief the Secretary of Labor
Speaker 5: and for an hour from eight thirty to nine thirty,
Speaker 5: the Secretary of Labor and his staff had to remain quiet,
Speaker 5: and then at nine thirty they could answer questions of
Speaker 5: the press. So that basically is the process for the
Speaker 5: jobs report. There's another survey there. It interrupt me any time,
Speaker 5: but that's where we get the unemployment raise, called the
Speaker 5: Household Survey, and that's a survey of sixty thousand households
Speaker 5: selected out of a sampling frame. We can talk about
Speaker 5: that term to be representative of the entire United States
Speaker 5: by demographic segments. You know how many people are in
Speaker 5: the certain age group, male, female, the racial and ethnic compositions,
Speaker 5: and geographic locations. So it's a complex sample. That sample
Speaker 5: is done usually by Monday of the week prior to
Speaker 5: the release on Friday, and then again I get to
Speaker 5: see the results, so the commissioner gets to see the
Speaker 5: results on Wednesday. So that's kind of the calendar. Both
Speaker 5: the household Survey and the Employment survey cover the same
Speaker 5: two week period in the month. Now there may be
Speaker 5: a day or two switch there. Sometimes the household survey
Speaker 5: has a few more days in it than the establishment survey,
Speaker 5: but they all have that twelfth of the month because
Speaker 5: we're trying to get the middle of the month. By
Speaker 5: the way, that's really important because if you have a
Speaker 5: natural disaster that happens prior to the twelfth or after
Speaker 5: the surveys are closed, that does not affect the results,
Speaker 5: even though you would think since it happened in the
Speaker 5: month prior that you know, the hurricane might have affected
Speaker 5: implument results.
Speaker 3: So one thing you mentioned is companies, you know, voluntarily
Speaker 3: responding to these surveys. And one thing I always wondered
Speaker 3: about is how how I guess onerous or resource intensive
Speaker 3: is responding to these surveys. So do I have to
Speaker 3: have like a person who's dedicated to doing this every month?
Speaker 3: Does it consume resources? How long is this going to
Speaker 3: take me? And then also does the BLS ever try
Speaker 3: to fact check some of these self reported data points?
Speaker 5: Those are great questions. So the survey is not just
Speaker 5: one or two questions. It takes about thirty minutes to
Speaker 5: fill it out completely. We do get responses that are incomplete,
Speaker 5: and we accept those incomplete responses. The survey is usually
Speaker 5: filled out by someone who can access the company's data
Speaker 5: on wages, on total employment, on employment by supervisory or
Speaker 5: non supervisory job functions. In other words, it's a person
Speaker 5: usually in the operations office of a company. Now we
Speaker 5: ask companies that are very large, you know, like the
Speaker 5: big box retailers, all the way down to really small
Speaker 5: corporations or small firms, and it becomes more and more
Speaker 5: of a difficulty for the smaller firms, and we're very
Speaker 5: cognizant of that. So BLS has reduced the scope of
Speaker 5: the survey to get it as small as possible and
Speaker 5: as free of burdens and compliance as possible. It's also
Speaker 5: an electronics survey so that they can fill it out
Speaker 5: on their computer screens and hit a submit button, which
Speaker 5: greatly greatly improved our response rate, by the way, so
Speaker 5: that survey is not so hard now to fill out,
Speaker 5: though I think it still takes about thirty minutes, maybe
Speaker 5: more if you have to dig out the data if
Speaker 5: you're not in possession of it. The household survey is
Speaker 5: a totally different beast. It is a long survey. It
Speaker 5: consists of over one hundred and twenty questions. It really
Speaker 5: is hard to fill out if you kind of don't
Speaker 5: know the terminology. Let me just give you one. We
Speaker 5: asked the question on employment. Are you working or have
Speaker 5: you looked for work in the past four weeks? That's
Speaker 5: the key question. We've been asking that question for since
Speaker 5: nineteen I think nineteen forty three. If they answer I'm working,
Speaker 5: then they are considered employed. We'll have follow up questions
Speaker 5: about that, about their industry, about their tasks and so forth.
Speaker 5: If they say no, I'm not working, then we say,
Speaker 5: well did you look for work in the past four weeks?
Speaker 5: And they'll say, well, you know, maybe I thought about it.
Speaker 5: Well thinking about it. It's not enough you can look
Speaker 5: for one day in the past four weeks and be
Speaker 5: considered in the labor force but unemployed. So that sometimes
Speaker 5: we have to have a verbal follow up rather than
Speaker 5: they're just you know, just filling out a form. There
Speaker 5: has to be a conversation. That's why we don't do
Speaker 5: more than sixty thousand households because it is it's burdensome
Speaker 5: for the person who's the questioner.
Speaker 2: This leads right into I think what I was about
Speaker 2: to ask you, which is, you know we're having this
Speaker 2: conversation April twenty twenty five. Your term of office at
Speaker 2: the BLS ended March twenty twenty three, so you're actually
Speaker 2: under both the two recent presidents, under the first Trump
Speaker 2: administration then Biden. But let's say, okay, so let's say
Speaker 2: it's March twenty twenty three. How much costlier is that
Speaker 2: process of commune unicating with the households than it was,
Speaker 2: say in twenty thirteen or two thousand and three.
Speaker 5: It was more and more costly, certainly, and that's because
Speaker 5: of inflation and how inflation affected the wage bill for
Speaker 5: the survey. Okay, the survey is taken by the Census
Speaker 5: Bureau under a contract by BLS, So we pay the
Speaker 5: Census Bureau to take this to go out in the
Speaker 5: field and conduct the survey. Here's an idea of how
Speaker 5: much more expensive it was. In twenty nineteen, which is
Speaker 5: the first year of my term, I didn't have to
Speaker 5: find any additional funding for the household survey. It might
Speaker 5: have been just a small amount, but nothing significant. By
Speaker 5: twenty twenty three, we were having to find almost four
Speaker 5: million five million dollars. More so, the costs of the
Speaker 5: survey is that ye per month, that would be for
Speaker 5: the entire year. The cost of the survey went up significantly.
Speaker 5: Now your listeners will be thinking about the federal budget,
Speaker 5: which is in the trillions of dollars. You'd say, oh,
Speaker 5: that's not very much money. But the Bureau's budget had
Speaker 5: has not really changed for almost a decade, and our
Speaker 5: costs have gone up, you know, progressively, especially during the
Speaker 5: period of significant inflation which we had starting in twenty
Speaker 5: twenty one, so that you know, finding additional money is
Speaker 5: really hard. During the pandemic, I found millions of dollars
Speaker 5: of couch money, unused conference fees, unused travel fees, so
Speaker 5: I could apply that couch money if it were, you know,
Speaker 5: to p purposes like buoying up the CPS or building
Speaker 5: a data We built a brand new data center out
Speaker 5: in the suburb of Washington. But once we were back
Speaker 5: in business and fully using our entire budget, it was
Speaker 5: very difficult to find those dollars to fill the hole.
Speaker 5: And I think you could safely say I've been arguing
Speaker 5: this now for over two years. Our surveys, but especially
Speaker 5: the current population survey, the one that gives us the
Speaker 5: unemployment rate, the labor force data are dying, They're decaying.
Speaker 5: They're in very serious trouble and we will have if
Speaker 5: unless we modernize that survey, we will see a time
Speaker 5: when we will be like the British right, unable to
Speaker 5: publish portions of it that just don't have sufficient sample
Speaker 5: for statistical release.
Speaker 3: Okay, I'm going to ask the obvious question here, but
Speaker 3: why have costs of conducting these surveys gathering this data
Speaker 3: actually gone up? And I understand, you know, building data
Speaker 3: centers is probably an expensive process. But on the other hand,
Speaker 3: you know, going electronic instead of mailing out thousands and
Speaker 3: thousands of surveys in theory, I would imagine maybe that
Speaker 3: saves you some money. So what exactly is causing the
Speaker 3: increase in expenses here?
Speaker 5: The problem is not with the electronic surveys. They will
Speaker 5: eventually be very costly and they're there. We're suffering from
Speaker 5: a different problem, which is just public support for the
Speaker 5: electronic surveys. So the jobs report, that is that portion
Speaker 5: of it that's electronically captured by these collection centers in
Speaker 5: Chicago and Atlanta. I think those are going along all right,
Speaker 5: for the time being, Our real problem is in the surveys,
Speaker 5: the data of which is collected by people survey survey,
Speaker 5: field survey people. They go out and they talk to
Speaker 5: the households and they're The cost is the obvious one.
Speaker 5: It is the wage bill. These people are highly skilled interviewers.
Speaker 5: They are not high school graduates, and I have nothing
Speaker 5: against high school graduates, but they are educated, very trained,
Speaker 5: oftentimes economists, who will go out and speak to people
Speaker 5: and to follow up interviews, oh my gosh, and they'll
Speaker 5: spend hours and hours do it this. Well, okay, fine,
Speaker 5: that's great, but it costs a lot to keep those
Speaker 5: people employed. They have other opportunities, so we have to
Speaker 5: have competitive wages. The wage bill is driving that. Collection
Speaker 5: costs are a little bit higher, that is the processing costs,
Speaker 5: but the big part of that is the wage build
Speaker 5: and the wage bill is not going to go away.
Speaker 5: That's just going to continue to go up. So we
Speaker 5: need to do with the household survey what we have
Speaker 5: done with the establishment survey, the firm survey, and that
Speaker 5: is modernize it so it is more electronically collected. And
Speaker 5: then we also need to integrate data which can be
Speaker 5: obtained through the Internet on households. Households maybe go on
Speaker 5: to a platform and with a tablet or something and
Speaker 5: supply Maybe we go to a million households and they
Speaker 5: supply five or six questions and we combine that or
Speaker 5: blended with a person to person collected data. That's that's
Speaker 5: what we call modernization. I mean, I mean that sounds
Speaker 5: so innovative to your listeners, but it is very innovative
Speaker 5: for the statistical system. Unless we do that, the cost
Speaker 5: will continue to rise. The response rates, by the way,
Speaker 5: are continuing to decline. So that's public support and we
Speaker 5: will be we just won't have these surveys in the future.
Speaker 5: There is too expensive.
Speaker 2: I want to get to the modernization in a second,
Speaker 2: but just talk to us about I mean, I think
Speaker 2: you said these the surveys dying, which is pretty dramatic.
Speaker 2: Given the centrality of this between the response rates, the
Speaker 2: increased wage bill, et cetera. How severe is this crisis?
Speaker 2: How much time are we talking about because I imagine
Speaker 2: it gets harder and harder to find that couch change
Speaker 2: to keep it going.
Speaker 5: Yeah, Actually, we're out of time. We have to cut
Speaker 5: down on the sample and I'll give you I'll give
Speaker 5: you the evidence, my successor, the current Commissioner of Labor Statistics,
Speaker 5: at the end of last year that we would have
Speaker 5: to reduce the sample in the CPS, that's the household
Speaker 5: survey by five thousand households in order to just publish
Speaker 5: the rest of the sample. Okay, by cutting back on
Speaker 5: the sample, you cut back on publishing details in the
Speaker 5: current report. You may not have enough to publish on
Speaker 5: teenagers anymore. On certain demographic groups, you may not be
Speaker 5: able to publish details on certain age intervals, like everyone
Speaker 5: between the ages of fifty five and sixty or sixty
Speaker 5: five and seventy five. Every time you reduce the sample
Speaker 5: or your response rate falls and it kind of reduces
Speaker 5: it for you. We can talk about that next. What
Speaker 5: suffers are the details. Let's go to the CPI, which
Speaker 5: BLS also does. The CPI is a very big survey,
Speaker 5: important survey, but it is oftentimes the case that we
Speaker 5: don't have details for certain parts of the basket of goods,
Speaker 5: and so we will average across some months and published
Speaker 5: for another month based on averages of prior months. But
Speaker 5: then we run out of the integrity of that average
Speaker 5: and we can't publish the bread price or something like that.
Speaker 5: That happens more and more because we're getting less and
Speaker 5: less cooperation by retailers to allow our surveyors into their
Speaker 5: stores and to go around grabbing the potato chip bag
Speaker 5: and counting the number of potatoes in the back. So
Speaker 5: we've had to innovate on the CPI, on the producer
Speaker 5: price Index, on the import export price indexes. I hope
Speaker 5: you haven't noticed the innovations because you're not supposed to,
Speaker 5: but those innovations have saved, absolutely saved the import and
Speaker 5: export price. We had response rates down in the twenty centiles,
Speaker 5: completely unpublishable detail. During the time I was commissioner. We
Speaker 5: went to using data from the Commerce Department that is
Speaker 5: collected by the customs officials and it's working out just great,
Speaker 5: so we completely left the surveys there. The PPI has
Speaker 5: problems just like the British are having. Now not as bad,
Speaker 5: and we're using more and more data from private companies
Speaker 5: who will give us their data from the Internet, where
Speaker 5: we collect that data and we combine that with the
Speaker 5: survey data to keep the things alive on the CPI.
Speaker 5: It's very interesting. We get all of our retail gas
Speaker 5: prices now from a private company that aggregates gas prices
Speaker 5: for the retail gasoline industry, and it's a very good source.
Speaker 5: A lot of our housing prices now you mentioned housing,
Speaker 5: We're going to more and more to aggregators, private aggregators.
Speaker 5: So there are strategies for saving the survey by blending
Speaker 5: in private data with the survey data. That is not
Speaker 5: the case with the household survey of the labor force.
Speaker 5: Nobody really collects that data except BLS. And that's why
Speaker 5: these demographic surveys, Census has a bunch of them. Health
Speaker 5: the health department, the Department of Health has a lot
Speaker 5: very in very serious trouble.
Speaker 3: So I take the point about innovations. But one of
Speaker 3: the things that's been happening recently is we have been
Speaker 3: seeing bigger and bigger revisions in a lot of the data.
Speaker 3: Is that because of the response issues, So more late
Speaker 3: responses mean that you know you're going to get revisions
Speaker 3: later on as those responses are incorporated. And the reason
Speaker 3: I ask is I remember speaking to someone at the BLS,
Speaker 3: and here shout out to the people working at the BLS,
Speaker 3: because you can actually just call them and ask questions.
Speaker 3: They're really really responsive, amazingly, like the only government department
Speaker 3: that I really know where they'll just pick up the
Speaker 3: phone and talk to you about methodology. Anyway, I was
Speaker 3: asking about revisions to PPI for mayonnaise, of all things,
Speaker 3: and the data there had been revised from like five
Speaker 3: percent to ten percent, which is a pretty big change
Speaker 3: in the space of a couple months. And he was
Speaker 3: talking about how the PPI is subject to revision up
Speaker 3: to four months after it's published, and it gets updated
Speaker 3: as new replies come in. So I imagine if response
Speaker 3: rates are going lower, then maybe that's contributing to some
Speaker 3: of the revision issue.
Speaker 5: By and large, our issues have to do with response rate.
Speaker 5: You're absolutely right, and I'm before I go further, I'm
Speaker 5: very very happy you said that about BLS because BLS
Speaker 5: prize itself and being responsive and transparent by the way,
Speaker 5: so that's a good thing. So I think there are
Speaker 5: two sources of this problem. The first is really hidden
Speaker 5: and no one talks about it. So let me just
Speaker 5: mention when we refresh survey, and you have to do
Speaker 5: this because people are asked to give their survey responses
Speaker 5: only for a few months, right, and then they drop
Speaker 5: out and you have to find new people. The regional offices,
Speaker 5: there are six of them in the United States for
Speaker 5: the BLS, they have people who go out and do
Speaker 5: what's called initiate a new survey respondent. It's getting more
Speaker 5: and more difficult now to initiate that respondent. Well, people
Speaker 5: are saying, we just don't have time, you know, Oh
Speaker 5: my gosh, there's so many important things to do. We
Speaker 5: don't want to give you our data because if we
Speaker 5: give you our data, then the IRS is going to
Speaker 5: be after us, or we're going to have some kind
Speaker 5: of osha of you know, we don't. BLS's data is
Speaker 5: completely protected from the law enforcement aspects of the federal government,
Speaker 5: absolutely only for statistical purposes. That does not prevent help
Speaker 5: or people saying, oh, it's going to happen, you know.
Speaker 5: So if your initiations are dropping and it's harder and harder,
Speaker 5: no wonder the response rates are dropping because people are
Speaker 5: less enthusiastic. Even if they say yeah, I'd love to
Speaker 5: be a part of it, they're not as enthusiastic as
Speaker 5: they were a generation ago. So the real problem starts
Speaker 5: at the initiation level, and we're seeing that not only
Speaker 5: in the household survey and in the price area, but
Speaker 5: we're also seeing that in another survey we haven't talked about,
Speaker 5: the National Compensation Survey, which is a wonderful survey on
Speaker 5: how much people are getting in union houses, union households
Speaker 5: and non union households, and Northeast and the Southwest is
Speaker 5: terrific about that. The Joelts report the job opening is
Speaker 5: in labor turnover survey that you mentioned at the opening.
Speaker 5: Its response rate has fallen dramatically, and it's largely because
Speaker 5: people are less enthusiastic generally about participating in government surveys.
Speaker 5: It's going to be hard to stem that tide, and
Speaker 5: I think the only way we can do it is
Speaker 5: by going to these platformed surveys, that is, surveys that
Speaker 5: use the Internet as the main platform, integrating a lot
Speaker 5: of private data, and being highly original in the way
Speaker 5: we think about preparing a statistic for public use. We're
Speaker 5: just not going to be facing a different world overnight
Speaker 5: of really happy people wanting to give all their data
Speaker 5: to the government.
Speaker 2: It's really a fallen world. Sometimes I'll say that, you know,
Speaker 2: we won't let you in the store to you know,
Speaker 2: look at the price of bread anyway? What would it
Speaker 2: take to get there? Can this be done unilaterally within
Speaker 2: the BLS, this sort of big upgrade. Would it need
Speaker 2: to be something involved with Congress? Would it need a
Speaker 2: budget allocation that would come from Congress? What would need
Speaker 2: to happen to get the sort of modernized statistical collection
Speaker 2: system that you would like actually in place?
Speaker 5: I think we have to approach this like we might
Speaker 5: approach roads and bridges right. Once Congress becomes aware that
Speaker 5: there's political liability and ignoring a problem, they generally focus
Speaker 5: on it until it's fixed. And that was the case
Speaker 5: with our national highway system still is a case with
Speaker 5: our national highway system. We still have a lot of problems.
Speaker 5: When I go to Congress and I talk about the
Speaker 5: CPS and response rate, how we're going to lose the
Speaker 5: unemployment rate, I get immediate response, and nobody, the Communists,
Speaker 5: no one's ever said that to me, Oh, let's fix it.
Speaker 5: So in the last Congressional Continuing Resolution, which is passed
Speaker 5: a few weeks ago, BLS got six million more in
Speaker 5: funding just to fill that hole that we've been talking
Speaker 5: about on the wage bill. And that was an amazing
Speaker 5: thing to get in a continuing resolution and increase in funding.
Speaker 5: So I think Congress, presented with a plan, or the
Speaker 5: administration of President Trump, is wide open to disruption and change.
Speaker 5: I think if we develop an aggressive, bold, comprehensive plan
Speaker 5: about how to rebuild the statistical system so that we're
Speaker 5: using our resources much more efficiently, perhaps combining some agencies
Speaker 5: together instead of having the twenty four separate statistical agencies,
Speaker 5: maybe we ought to have just a handful, and then
Speaker 5: going from there to a highly innovative different way of
Speaker 5: collecting and disseminating data. Then our roads and bridges, statistically speaking,
Speaker 5: won't be disintegrating or decaying. We will have new concrete,
Speaker 5: will have new structures, and you can see a future
Speaker 5: for the statistical system. I think right now, speaking as
Speaker 5: a former director of an agency one of the most
Speaker 5: important statistical agencies and not the most important statistical agency
Speaker 5: in the world, that future looks grim to me, and
Speaker 5: so change is required. It has to happen, and I
Speaker 5: think that's what we have to do. Presented with the plan,
Speaker 5: let Congress see what we're going to do and have
Speaker 5: them fund modernization, not continuation of the current system.
Speaker 3: This is a little out there, but could you if
Speaker 3: the problem is the response rates and incentivizing people to
Speaker 3: actually answer these surveys, could you potentially pay them to
Speaker 3: do it.
Speaker 5: We've tried that. It's been tried a lot. Good suggestion. Yeah,
Speaker 5: you know, pay them dollars. Okay, tried that, didn't make
Speaker 5: much of a difference. Then we thought, well, maybe they'd
Speaker 5: like to have these cards you can take to retailers
Speaker 5: and buy anything you want, right, gift cards. We tried, Yeah,
Speaker 5: gift cards. So we tried that. Now that's not the issue.
Speaker 5: I think you could pay people a lot of money,
Speaker 5: say one thousand dollars a month, and they might participate
Speaker 5: because that's a lot of money, But we can't afford that.
Speaker 5: That's not out there for the statistical system. So inducements
Speaker 5: may help with the margin, but they don't change the
Speaker 5: trend line, which is going negative on the response rate.
Speaker 5: I think we're going to live at that response rate
Speaker 5: for a while. I do believe it's generational. I think
Speaker 5: you can see in the really young kids now, not
Speaker 5: the ones that are under five, but the ones that
Speaker 5: are in their teams, kind of a return to doing
Speaker 5: things together, having more social events. Maybe I would say
Speaker 5: the bowling leagues are coming back, and maybe that's a
Speaker 5: cultural change that leads to a more a greater sense
Speaker 5: of participation and support of public institutions, one could hope.
Speaker 3: I like the idea of all the kids coming together
Speaker 3: to answer surveys from the BLS. That's great.
Speaker 5: I think you get some really interesting answers there. But yes,
Speaker 5: short of that, we have to be innovative. We have
Speaker 5: to change, We have to think outside the box. Otherwise
Speaker 5: this infrastructure which we all need, you know, it runs
Speaker 5: our country. There's no economy without the Bureau of Economic
Speaker 5: Analysis and the GDP numbers. They don't grow on trees.
Speaker 5: That's going to be either going to go away or
Speaker 5: become less reliable.
Speaker 2: You know something that strikes me while listening to you talk,
Speaker 2: and like, oh, you found a way to allocate you know,
Speaker 2: unused travel spending so that you could keep the surveys
Speaker 2: going after the wage bilt went up, et cetera. I
Speaker 2: keep rustling the couch change and I think about this
Speaker 2: in you know, you mentioned Okay, well maybe the new
Speaker 2: president is you know, theoretically open to shaking things up
Speaker 2: and doing things a different way. Would you argue, pretty persuasively,
Speaker 2: isn't necessary on the other hand, you have this sort
Speaker 2: of dose kick which is sort of premised on this
Speaker 2: idea that every agency in the government somehow is just
Speaker 2: egregiously wasteful. That all of these agencies must be so
Speaker 2: wasteful that you could cut aggressively and you almost certainly
Speaker 2: aren't actually going to hit any bone. Seems to be
Speaker 2: a sort of premise of some of the cutting. It
Speaker 2: doesn't sound like to me when you described to be
Speaker 2: a less in twenty nineteen through twenty twenty three, that
Speaker 2: this is an agency that was just you know, larded up,
Speaker 2: but had plenty of plenty of fat that can be
Speaker 2: trimmed off.
Speaker 5: No fat, no pad at all. But you know, this
Speaker 5: is the age old conflict right between the entrepreneur and
Speaker 5: the accountant. The entrepreneur always looking for innovation and changed
Speaker 5: and higher return on investment, and the accountant is always
Speaker 5: looking for waste and abuse. Are we using our pencils
Speaker 5: until they are only three inches long? I think that's
Speaker 5: a fruitful thing. I think you have to have both
Speaker 5: of those forces working all the time and overtime. Right now.
Speaker 5: I don't doubt for one second that a lot of
Speaker 5: the federal government could use a thorough scrubbing on the
Speaker 5: things that Doge is looking at. The statistical system has unfortunately,
Speaker 5: in my opinion, but fortunately now been through that scrubbing
Speaker 5: over the past fifteen years, no real budgets, and yet
Speaker 5: an increase in responsibility. So efficiencies have been gained there
Speaker 5: just from the brutality of living year after year after
Speaker 5: year with the same dollar amount while your costs are
Speaker 5: going up while inflation is changing. Don't mind that because
Speaker 5: efficiencies can occur, we can do more with fewer dollars.
Speaker 5: That's okay. Innovation, on the other hand, has to be
Speaker 5: kind of funded by your retained earnings, and we don't
Speaker 5: have that in The statistical Congress has that, So we're
Speaker 5: not getting the dollars necessary to innovate and secure the future.
Speaker 5: That's the problem I want everybody kind of focus on.
Speaker 3: So you mentioned earlier that you had used internet data
Speaker 3: to try to make up for the lack of a
Speaker 3: certain data point or a certain response from the surveys,
Speaker 3: and I'm curious, there is the sense nowadays that everything
Speaker 3: we do is tracked and recorded somewhere. Could you potentially
Speaker 3: use some of that type of data instead of voluntarily
Speaker 3: reported responses.
Speaker 5: So to an extent you can use that. If you
Speaker 5: have an unambiguous signal, and you can capture that unambiguous
Speaker 5: signal month after month, why not capture it? Why not capture,
Speaker 5: for example, certain pay bands or other things that are
Speaker 5: happening in the labor force, or with wages, or with
Speaker 5: working conditions. The restraint there is that not everything we
Speaker 5: want to know about the world is unambiguously signaled every month.
Speaker 5: And I go back to this seemingly easy question, are
Speaker 5: you working or looking for work? You would be surprised
Speaker 5: at the number of people who say I'm working, But
Speaker 5: then we make that query, did you work for pay? No? No, okay,
Speaker 5: I did the dishes? Okay, fine, Well that doesn't see
Speaker 5: in the mind of the respondent, work is defined differently
Speaker 5: than it needs to be defined in the statistics. Are
Speaker 5: you looking for work, Well, yeah, I'm looking for work.
Speaker 5: When did you look for work last year? Okay? See,
Speaker 5: that doesn't count as the key determinant of whether or
Speaker 5: not you're in the labor force. You're in the labor
Speaker 5: force if you're working or looking for work in the
Speaker 5: past four weeks. So that seemingly simple question is not
Speaker 5: going to be unambiguously signaled by somebody answering an Internet question.
Speaker 5: Because of the many different ways we think about our
Speaker 5: lives and about what work is. We find this particularly
Speaker 5: true as our country becomes I think it's a good thing.
Speaker 5: More and more culturally diverse. People come in with very
Speaker 5: different views of what is work of, what is pay of,
Speaker 5: what is a family of, what is a household? And
Speaker 5: we have to work harder and harder and harder to
Speaker 5: make sure that their responses fit this continuous since nineteen
Speaker 5: forty three, continuous stream of data that allows us to
Speaker 5: do these wonderful time series analyzes. That's the issue tracy
Speaker 5: with using Internet data. Some of it can be used
Speaker 5: and blended in, some of it from the private sector
Speaker 5: can be used and blended in, but some cannot. You
Speaker 5: still have to have that survey instrument out there, asking
Speaker 5: the hard questions, doing the follow ups.
Speaker 2: You know, there are some people who simply do not
Speaker 2: believe these statistical agencies are honest, or they believe they
Speaker 2: you know, they do not trust when academic economists explain
Speaker 2: why data gets revised a year later or something like that,
Speaker 2: and they assume that there is political influence of some sort,
Speaker 2: or they believe it and you know, it goes back
Speaker 2: years and I remember two thousand and twelve years ago,
Speaker 2: Jack Welch, the Chicago guys will do anything, can't believe
Speaker 2: these jobs numbers, et cetera. You were appointed in twenty
Speaker 2: nineteen by President Trump and you crossed over to Biden.
Speaker 2: What do you say to people who do not believe
Speaker 2: that they can trust these numbers?
Speaker 5: Well, I can't dislaunch their suspicions without going to some
Speaker 5: factual basis. So I take them through the simplest revision process,
Speaker 5: which is the jobs revision that you know. We had
Speaker 5: an eight hundred and eighteen thousand preliminary estimate of a
Speaker 5: decrease in employment that was announced last August, and everybody
Speaker 5: President Trump was running for at the time, and he
Speaker 5: just said, look at this, it is totally dishonest. Bls okay.
Speaker 5: So this happens every year. We compare our numbers to
Speaker 5: a sampling frame with all the people who work in
Speaker 5: the entire country, and we say, is our estimate of
Speaker 5: total employment based on a much smaller sample than all
Speaker 5: the firms. Is it accurate? And when we go up
Speaker 5: there and we compare it every March to this total universe,
Speaker 5: we sometimes find we're spot on, you know, less than
Speaker 5: one tenth of a percent off, and sometimes we find
Speaker 5: we're as much as two percent or three percent off.
Speaker 5: That we're off more often when the economy is either
Speaker 5: diving into recession or growing rapidly out of a recession,
Speaker 5: or has a period of really bad time happening. So
Speaker 5: when I take them through the revision process, they usually
Speaker 5: come out and say, you know, I never knew that,
Speaker 5: And so the next time they think about, oh, the
Speaker 5: BLS is lying, they'll have that in their mind that really,
Speaker 5: we did this every year it is the same way,
Speaker 5: and we're pretty good with those numbers.
Speaker 3: So I want to ask you about qualitative adjustments because
Speaker 3: I find these so interesting. And the example I used
Speaker 3: was fridges that now have I don't know, new and
Speaker 3: interesting features. And my understanding is that if a company
Speaker 3: is selling a basic refrigerator for like one thousand dollars,
Speaker 3: and then the next year it sells this new advanced
Speaker 3: refrigerator for one thousand, one hundred and fifty, and then
Speaker 3: it's also spending additional money, so like an extra hundred
Speaker 3: or whatever to make the more advanced fridge. In cases
Speaker 3: like that, the BLS would use a qualitative adjustment and
Speaker 3: then the year on year PPI would be something like,
Speaker 3: I don't know, it would be five percent instead of
Speaker 3: the fifteen percent change in the actual price. How do
Speaker 3: you actually go about making those qualitative adjustments? And I
Speaker 3: imagine they must have been getting harder as things become
Speaker 3: more technologically complex.
Speaker 5: Well, you're absolutely right, it is very difficult to do that,
Speaker 5: but we do a lot of training. A lot of
Speaker 5: people don't know that. At the National Headquarters there is
Speaker 5: a training suite. I think it's on the second second floor,
Speaker 5: at least be before we moved out of that building.
Speaker 5: And so people from the regions who are the CPI
Speaker 5: and PPI field teams that go out and do the surveying,
Speaker 5: they come to the bl AS for training. And in
Speaker 5: these training rooms our kitchens, there are grocery store aisles.
Speaker 5: We've recreated kind of the inventory you might find in
Speaker 5: a grocery store or a warehouse, and we will train
Speaker 5: people from time to time on the changes in the
Speaker 5: items in the CPI, the two hundred plus items the
Speaker 5: CPI or the items that we're surveying at the producer
Speaker 5: price level. So if there is a change all the
Speaker 5: way from the number of potato chips in a bag,
Speaker 5: which is a very important thing. And when we do
Speaker 5: the we will look at fast foods and potato chips
Speaker 5: and so forth, all the way to the technology involved
Speaker 5: in diamond cutting. We will be training people to observe
Speaker 5: the change and work that into their evaluation of the
Speaker 5: product when they're in the store, when they're in the warehouse.
Speaker 5: So the field person will literally pick up a jar
Speaker 5: of if I could say pringles, right, and they'll say, well,
Speaker 5: last month I had thirty six springles in here, and
Speaker 5: it's this month it's the same price, but we only
Speaker 5: have thirty two pringles here.
Speaker 3: Wow. True regularity.
Speaker 5: Yeah, so that that fits into.
Speaker 4: Now price perles keep going exactly.
Speaker 5: But it has to be that way because what happened
Speaker 5: is the retailer will keep the or the producer will
Speaker 5: keep the price at the same level, but decrease the
Speaker 5: item count in the in the product bag, and that
Speaker 5: means that the product has actually gone up in price,
Speaker 5: not have the same price. Let me just say one
Speaker 5: more thing, great question for Tracy. We have. That's the
Speaker 5: reason why we can't go to the internet and get
Speaker 5: all of our prices because when you go to the internet,
Speaker 5: you can't sometimes see the quality adjustments that are there.
Speaker 5: You can't see that the fact that the hot dogs
Speaker 5: are just a little shorter than they used to be,
Speaker 5: or the apples a little smaller than they used to be.
Speaker 5: I don't know about the apple thing, but the shorter
Speaker 5: hot dogs is definite the case. So you have to
Speaker 5: we train these people. They're highly trained. That's they're also
Speaker 5: very expensive because they're highly trained people and they can
Speaker 5: see and know when to look and when to check
Speaker 5: for quality improvements. Electronics definitely, you know, but a lot
Speaker 5: of times the producers of the electronics will heavily advertise
Speaker 5: the changes, make it known to everybody, because that's what
Speaker 5: you're selling new and improved. It's all of these other
Speaker 5: items that it's more subtle. And you know, housing, Housing
Speaker 5: is a big deal because the house is more valuable
Speaker 5: if it has improvements in it, and some of those
Speaker 5: improvements are completely invisible. So we're we're very conscious of
Speaker 5: the fact that quality governs the price structure.
Speaker 2: Bill Beach, that was a fascinating conversation. I feel like
Speaker 2: some of these areas like even just like I'm talking
Speaker 2: about the art of actually conducting an interview you see
Speaker 2: if someone's in the labor force, really fascinating stuff.
Speaker 4: Really appreciate you coming on. There's something we want.
Speaker 2: We wanted to talk to you, to talk to someone
Speaker 2: long time as so appreciate you joining outlines.
Speaker 5: It's been it's been a pleasure. Thank you very much.
Speaker 3: Thank you so much.
Speaker 4: That was great, Tracy. I thought that was great.
Speaker 3: I'm so glad we finally did that one. Yeah, a
Speaker 3: couple points. So, first of all, I did not know
Speaker 3: that hot dogs have been getting shorter.
Speaker 4: Me neither.
Speaker 3: And it's being incorporated into the inflation data, So all
Speaker 3: the people complaining about shrink flation, I guess you know
Speaker 3: it is mitigated. Yeah, exactly. And then the other thing
Speaker 3: I would say is I thought the point Bill was
Speaker 3: making about the loss of granularity in the data was
Speaker 3: really important. So the idea of getting to like the
Speaker 3: tails of the distribution or certain minorities. And the reason
Speaker 3: I say that is because we've been seeing a lot
Speaker 3: of regional and social variation in a lot of these
Speaker 3: consumer surveys. Right, So obviously people who are poorer have
Speaker 3: been feeling terrible during the days of high inflation, and
Speaker 3: people who are rich feel, you know, pretty good, good
Speaker 3: but also inflation in Florida has been higher than elsewhere
Speaker 3: in the country. So I think it does become more
Speaker 3: important to get really really specific with some of these
Speaker 3: statistics as we see those differences increase.
Speaker 2: It's really interesting to me to think about government high
Speaker 2: quality government data as like this public good and try
Speaker 2: to imagine infrastructure, right infrastructure, and try to imagine the
Speaker 2: amount of economic activity that exists because this thing is
Speaker 2: offered for free that we don't that people don't have
Speaker 2: to pay for. And obviously in the investing world there's
Speaker 2: a tremendous amount of interest in all this, but it's
Speaker 2: obviously not just you know, the investing world, and so
Speaker 2: all of these questions, whether you're starting a business or
Speaker 2: whatever it is, you know, on some level you can
Speaker 2: do because there is consistent, trustworthy data. And the thought
Speaker 2: of like that going away, and what I imagine what happened is, yeah,
Speaker 2: sure you'd have like private versions of varying quality that
Speaker 2: try to replace it, and that exists today. You know,
Speaker 2: there's private measures of inflation, et cetera. But like that
Speaker 2: would like start to deteriorate the idea of like there
Speaker 2: being a gold standard, and so then you hear it's like, oh,
Speaker 2: here's like a oh, we needed to find a few
Speaker 2: more million dollars to pay the budgets of the survey collectors.
Speaker 2: Like how many billions are riding on that few extra millions?
Speaker 2: Oh yeah, right, like how many hundreds of billions in activity?
Speaker 2: So it's just really interesting. And then his thing explanation
Speaker 2: at the end, why like survey collection for something like
Speaker 2: this is it makes sense to do as a trained job, right,
Speaker 2: and even like the subjectivity of like are you working
Speaker 2: and what does that mean and et cetera. It's really
Speaker 2: interesting to think about why it's not trivial to just
Speaker 2: send out a survey or send out a high school
Speaker 2: or send out a volunteer or something like that.
Speaker 3: Absolutely. Also, I just love the idea of someone at
Speaker 3: the BLS counting how many potato chips are in a bag?
Speaker 2: I know, you know what I'm imagining, like I don't know,
Speaker 2: like someone with like a monocle, yeah, or something like.
Speaker 3: The magnifying class examining shifts seeing how big they are.
Speaker 2: We have we're having the same image in our minds,
Speaker 2: that's sure right now?
Speaker 3: Yeah, all right, shall we leave it there?
Speaker 4: Yeah, let's leave it there.
Speaker 3: This has been another episode of the Authoughts podcast. I'm
Speaker 3: Tracy Alloway. You can follow me at Tracy.
Speaker 2: Alloway and I'm Jill Wisenthal. You can follow me at
Speaker 2: the Stalwart. Follow our producers Kerman Rodriguez at Kerman armand
Speaker 2: Dashill Bennett at Dashbot and kil Brooks at Kale Brooks.
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