Transcript of the talk, from the video's captions. Auto-generated: speaker names in particular are unreliable. ======================================================================== # Bias in the Level, Trend, and Cyclicality of U.S. Unemployment Authors: Bruce D. Meyer, Derek Wu, Matthew Stadnicki, Patrick Langetieg, Mark Payne Discussant: Thomas F. Crossley Video: https://www.youtube.com/watch?v=aXOe_NS3iVo&t=18660s ## Talk (05:11:00 – 05:34:49) [05:11:18] >> So thank you for including the paper. I really appreciate it. Um, like David Wilcox, I asked my wife for advice. [05:11:32] Um, and I told her that I was trying to get used to the idea that I was not the last paper but the finale. And she said, "Good luck with that." Um [05:11:46] and uh so this is um joint work with Derek Woo and Matt Stad Nikki who's promising graduate student [05:12:00] on my left the real young guy there and uh also Pat Langatig and Mark Payne. All the results have been through census [05:12:12] disclosure review. Some of them done this week and so we have Emily Wiznooki and a unknown uh DAO to thank for that. [05:12:25] Um so got it. [05:12:37] Thank you. Um so the motivation for this paper is that unemployment is arguably the single most important and frequently referenced economic statistic [05:12:51] used in business decisions to set monetary policy. It's in fundamental economic relationships like the Phillips curve, Vulan's law, etc. and is a [05:13:04] dependent variable, explanatory variable in many studies. [05:13:10] Uh there's a long literature looking at the measurement of unemployment. [05:13:15] It tends to focus on the inconsistencies that you can find in the data. So, differences between rotation groups or [05:13:27] differences between responses that you get when you ask about labor force status multiple times. Um, there's the fundamental problem that it's [05:13:42] difficult to obtain validation data that aligns in concept and timing with survey reports of unemployment in the US. There [05:13:53] is some work in Sweden using such data. What we do is provide the first assessment of errors in unemployment using validation data with true status [05:14:07] in the US but only for a subset of the unemployed unemployment insurance recipients. We're going to link data from the CPSASC [05:14:21] over 14 years to IRS form 1099G to measure non-reporting of annual unemployment. [05:14:31] Uh we'll verify that the 1099Gs are a accurate measure of unemployment. [05:14:39] will examine uh determinance of misreporting in two years. [05:14:45] We'll show the close relationship between annual unemployment and the official measure. So that bias in one implies bias in the other and that will be crucial. [05:15:00] Um, we'll use the link data to estimate corrected annual and monthly unemployment rates and then document the exter the internal survey discrepancies [05:15:14] between reports of unemployment insurance receipt and unemployment over a longer time period. and use those internal discrepancies along with other survey variables [05:15:28] uh that are combined with guidance from the linked data to estimate public use corrections over a longer time period. [05:15:38] So what do we find? uh we find that a very large share of the unemployed report no unemployment in the year that they receive um unemployment insurance. [05:15:51] 48% in 2010, 64% in 2023. [clears throat] I'm used to seeing false negative rates like this given that I've been staring [05:16:03] at validation data for 15 years. um just on but um and others who have done this have also seen high rates like this. [05:16:15] John Rothbomb at Census on Wednesday was saying that getting a 60% reporting rate on an income source besides earnings and [05:16:29] social security was unusual. Um, so this misreporting rate that I'm showing you is uh striking given that [05:16:42] unemployment insurance recipients must have applied for unemployment insurance, must certify search, they're receiving benefits every week or two. Um, [05:16:55] unemployment is more likely to be salient for them than for other unemployed individuals. Yet you get this high rate of not reporting unemployment. [05:17:06] Um misreporting is predicted by the characteristics of the unemployed. [05:17:12] It's more common when few unemployment benefits are received. [05:17:18] Most errors result in people being classified as employment as employed. So that's uh a reason it fits in with this conference. [05:17:30] Um errors higher are higher for black individuals by a lot but for proxy respondents and other groups. The corrected annual and monthly [05:17:43] unemployment rates are higher. Um black white gaps are higher. uh corrections have risen over time and the resulting cyclicality of corrected [05:17:57] unemployment is somewhat different. So let me tell you about the data and linkages. [05:18:04] So our primary source of data is the CPS asex since 1988 that gives us data on reference years 87 forward. Um, we're [05:18:19] going to focus on estimates of misreporting of unemployment for the years 2010 to 23 over which we have the [05:18:31] tax records. Um, our focus is mostly going to be on the first and last years because those are the years for which we've disclosed the the most information. [05:18:46] Um, the CPSASC is the source of official income statistics and respondents are asked in the survey if they received unemployment insurance during the prior [05:19:00] calendar year and if so, how much. They're also asked about the number of weeks spent in uh the various labor force states um employed, unemployed, [05:19:13] not in the labor force. While the ASC is not the official source of unemployment statistics, it's a supplement to the monthly CPS, which is um and we're going to also bring in some [05:19:27] data from 2010 for the SIP to support some of our findings. [05:19:35] Um the administrative data are 1099gs, the universe for uh 14 years. [05:19:44] uh they are information submitted to the IRS typically by states about government payments that were made. Box one has unemployment compensation. [05:19:57] It can also include payments for some small programs that certain states have paid family leave, short-term disability. Because of that, we're going to exclude California from our analyses [05:20:10] because they have a pretty large STDA program. Um, we also examined partial UI workshare programs and fraud and [05:20:24] we concluded that they're part of the story here but small very small and we in the end ignored them in the analyses that I'm going to [05:20:36] show you. Um, we also use W2 data to verify that the earnings declines that we see among unemployment insurance recipients who don't report any [05:20:50] unemployment are similar to the nonreipients who do report unemployment. [05:20:56] Again, suggesting that it's real unemployment and that the durations of of unemployment are aren't that different. [05:21:08] Um so we link data using protected identification keys. Link rates are quite high. Um we exclude those who are [05:21:21] whole item imputes. Uh and then we rewe account for that. Um let me get to our our results. Um the [05:21:35] errors in survey reported um unemployment as indicated by administrative unemployment insurance records um are providing us a an estimate of the [05:21:50] false negative rate of unemployment reporting for unemployment insurance recipients. [05:21:58] Um the 2010 CPS rate was 48% for SIP which has a much shorter recall period [05:22:09] it was 43% but the SIP also um does a lot of other things to improve reporting. It the responses are based on three or four interviews over the year [05:22:24] rather than one. And uh the survey tries to avoid proxy response by asking for all household members that are available [05:22:35] to report. Um in the CPS of the 48% never unemployed, uh 69% worked during the year and 53% [05:22:48] said they worked full year. Um so most of them uh were saying they were working rather than unemployed. [05:23:00] Um this figure in the um dark closed circles uh shows um the UI [05:23:12] or I'm sorry the um unemployment uh misreporting rate by ventile of unemployment insurance receipt. uh [05:23:23] ventiles being by uh dividing the uh dollars into 20 bins here. So what this says is that the um lowest ventile [05:23:37] of benefit receipt where people on average received about $500 about 80% of those individuals said they weren't unemployed at all. going down to the um [05:23:50] highest ventile where people were receiving 25,000 uh about 30% said that they um had had no unemployment during the [05:24:03] year. Besides those with few UI benefits, misreporting is higher among black individuals, proxy respondents, [clears throat] and some other groups. [05:24:13] Um we assess inconsistencies in survey data for calendar years 87 forward for a longer period because we [05:24:25] can't get the validation data the tax data uh for the entire period. Um and these are individuals who report receiving UI in the survey but who [05:24:39] report no unemployment. Again, we exclude California. We drop those who are item imputes. And here is this inconsistency rate as what we think of as a survey equivalent of the [05:24:54] misreporting rate. And it's in the dark circles. Um you'll see that the share of individuals who report UI receipt but [05:25:06] fail to report unemployment um goes up and down quite a bit but it's uh tends to be um uh lower in recessions [05:25:19] here. Um the unemployment rate, the official unemployment rate is marked with the dashed lines here. And you can see that the inconsistency rate is much lower around the recessions [05:25:34] that are marked with the uh shaded areas. [05:25:39] Um here we've added in the um misreporting rate in the open diamonds and you'll see that they are [05:25:53] almost a constant difference higher than the inconsistency rate. Um and that's something that we'll use to try and estimate errors in unemployment in years [05:26:06] for which we don't have the link data. So let me tell you about the monthly and annual unemployment rates. the basic [05:26:19] monthly the official rate um takes labor force status in the previous week on a monthly basis to calculate a monthly [05:26:30] rate. The ASC asks people to report weeks unemployed and weeks worked in the entire calendar year and from it you can calculate an annual unemployment rate. [05:26:43] The two should be related. The expected value of the annual average of the monthly rate should equal the annual rate though they could differ because of [05:26:54] sampling and measurement error. And the errors we present um in the CPSASC are also likely to be present in the monthly because the ASC is administered [05:27:09] to those in the monthly. um the shorter recall period is unlikely to have a large effect. SIP um suffers [05:27:20] from similar error but has a shorter um recall period and has does other things to reduce error. Um other work finds small precisely [05:27:34] estimated recall bias in the CPS for other variables. But what is probably most compelling is just look at the two [05:27:43] rates and you'll see here in uh uh black is the um the monthly rate averaged over a year. [05:27:56] In red it's the annual rate. They're correlated 0.99 almost have a constant difference between them and there's no trend in the difference. [05:28:07] Um so to construct a corrected unemployment rate, we divide the unemployed into those receiving unemployment insurance and those who don't. And then within each group, we [05:28:21] split unemployed unemployment unemployed weeks into incidents and conditional durations. [05:28:29] Um and we only um correct the uh incidents. We don't correct um labor force here um because it's big relative [05:28:42] to the uh to the numerator. Um and the changes in uh U that we're going to make are mostly [05:28:57] um changing people from E. So that leaves out labor force the same. Um what we do is we just assume that all unemployment insurance recipients are [05:29:11] unemployed. Um and that then allows us to just rescale the incidence of unemployment. [05:29:22] um that is um I think being a little bit aggressive but on the other hand we apply that same factor to [05:29:34] um nonreipients for whom it appears that unemployment insurance unemployment is less salient. Um we [05:29:46] uh there individuals who didn't file, didn't have to certify search, didn't receive benefits. Um and um [05:29:59] we consider an alternative that the um misreporting for non-reients is more like those who were not UI recipients. [05:30:14] And that um I think um is a an alternative that I'm not going to actually show you any of those results, but they would go and [05:30:24] make uh uh the corrections much bigger. Um so our correction also accounts for false positives. That's in this uh [05:30:36] equation here. And our correction then looks like uh this in the figure where it's um shows the official rate in black [05:30:49] circles and then in the open diamonds are our corrected uh rates. In the last two years there are the largest proportional corrections. Um but with the short time [05:31:03] period it's difficult to separate a a trend from cyclicality I think is fair. [05:31:10] Um, we can use the public use data to come up with uh public use corrections using the um [05:31:24] um the uh uh uh various survey equivalents of the things that go into our restricted data correction. Um there are five inputs that we need. The most [05:31:38] important one is the um the misreporting rate which we estimate as a fixed [snorts] amount greater than the [05:31:50] inconsistency rate. Um and if you plug the survey equivalence and that inconsistency rate into our equation, you get this correction. And here's what [05:32:05] it looks like. Um, so I've just layered our public use correction on top of that earlier graph that showed the restricted data [05:32:18] correction. And you'll see that over the years where we have the uh two corrections, they line up very closely. [05:32:27] You'll also see that the correction is larger in percentage points in recessions but smaller in proportions. [05:32:38] Um and that uh the corrections as a proportion have have tended to uh go up a bit over time. So let me just hit the [05:32:51] last two slides quickly. Um public use and link data corrections are very close to each other though the public use approximation average is [05:33:03] slightly lower. Uh we estimate uh that corrections average 2.7 percentage points a 48% increase over [05:33:14] the published rate. They range from 1.4 percentage points to five. Um the corrections appear to be higher in recent years relative to published [05:33:27] rates. The corrections in the last two years are the largest in the series and four of the six largest proportional corrections occur in 2018 or later. Um [05:33:41] cyclicality is altered. uh the um bias is higher for certain groups, particularly black individuals. [05:33:52] Um there's a stronger strong suggestion that the under reporting and bias has risen. Um, I should emphasize, and I'm sure Tom will, that the corrections rely [05:34:05] on uh accurate recording of unemployment by the 1099GS, that under reporting for nonreipients being is the same as UI recipients, and [05:34:20] then lastly, that reported durations are unbiased and we can extrapolate from those who are reporting. Um, this is early work. Uh, we just finished our [05:34:33] first draft a week ago. So, we would welcome comments and uh we'll be incorporating um your your thoughts into a new version. Thank you. ## Discussion — Thomas F. Crossley (05:34:49 – 05:47:14) *Shared discussion for Session 6: The CPS and Employment Statistics; the discussant covers every paper in the session.* [05:34:49] [applause] Okay. Thank you, Bruce. And Tom Crossley is the discussant for this session. [05:35:03] So uh let me start by saying well I enjoy reading these two papers very much and so thank you to the organizers for inviting me to the conference and giving opportunity to comment. [05:35:23] Okay. I'm sorry. [05:35:36] So, I'm going to start with John's paper. Um, let me say it's a very nice paper. Um, I think the methods, as far as I can tell, are sound. Um, there's not just a nice paper here, but there's also a a data product for the research [05:35:50] community. So I think they're very much to be commended for both writing the paper and sharing uh that product the the new weights. Um so I think that's great. Um I'm going to just make a couple of comments. I'm going to say something about uh you know [05:36:05] international comparison. Um I want to say something conceptually about you know why they're doing this and maybe why the BLS shouldn't do this. So how we think about revision or not revision uh official statistics. And then I just because I'm the discussant I have to [05:36:19] maybe think about whether there are any downsides to what they're doing. [05:36:25] Okay. I'm not getting this to move. [05:36:40] >> I need to get the cursor on the slide. >> Okay. [05:36:51] Okay, hold on. Oh, I see. Maybe this way. [05:36:55] >> I've got it. >> Yeah, sorry. Okay, so um you know, so I just wanted to offer a comparison of what the BLS or my understanding of what the BLS does uh with a couple of other jurisdictions and this paper. So we have [05:37:10] these uh population data which are being updated in the in the US annually uh every January and then when and then you know we reerate to the new population totals but we don't go backwards so we only rewe going forward so there's no [05:37:24] you know even though we have new updated population information about you know month prior months we don't update and this leads as John described to a sharp uh December January step which could be up or down every year but this is not [05:37:38] unique to the BLS. So, you know, this h exactly the same thing goes on in the UK um where the population well in fact actually it's a little bit worse. Um so there are mid-year [05:37:52] population estimates um and up until the pandemic what would happen is roughly annually those would come up and there'd be a forward for forward only revision to the labor force uh to the population numbers to which the labor force [05:38:06] statistics were related. So exactly like here with a sharp step at each rebasing. [05:38:11] Um the difference was you know in the US the my the um the revisions are you know lock step Janu January um always the same whereas in the UK they moved around a little bit. So here you know there's this everyone knew what was going to [05:38:25] happen when it was going to happen. That wasn't quite the case in the UK. And then um since the pandemic uh there's been a whole bunch of revisions to LFS weights to those of you who follow the UK case and things happened like [05:38:39] Scotland had a brave heart moment and did their census in a different year and so it's been a bit of a mess. Um so things here were already maybe a little bit better than some jurisdictions. Then there's these pesky people who live up [05:38:52] north, right? So, Statistics Canada, interestingly, does something quite like what I think this paper pro proposes. [05:38:59] So, there the population numbers are rolled forward from each census using administrative data, vital statistics and administrative data on immigration um until they get to the next census in which they're revised and then they do [05:39:13] revise backwards unlike the BLS or the ONS, but only back to the last census. [05:39:19] Um, and in Canada, censuses are every 5 years. Um, what John and his co-authors are proposing is they're going to roll backwards at each update, which is going to be more frequent than the Canadian case, and they're going to go back as far as they need to. So, 10 years if [05:39:34] there's only changes back to the last sentences, but farther, if I understand correctly, when necessary. U So, there was something at least in Canada that looked a little bit like this already, which I thought was an interesting thing. [05:39:47] The other thing I wanted to um think about a little bit is you know when you think about economic statistics broadly sometimes we revise and sometimes we don't and and the way that I think about that is sort of this 2x2 table you know there are cases and and you know [05:40:01] national output or GDP is an obvious case where we need to publish before we have most of the data so there's sort of a strong incentive to revise and there's not much cost to revision because there aren't things you know actions or legal ties to the data the contrasting case in [05:40:16] the upper left hand corner is the CPI right where there are tips and colas and if we revise the data that would cause big problems but at least the way the CPI is currently done most of the data is there in time we have the prices [05:40:29] pretty quickly we're using lagged quantities um of course we could change that and we could move the inflation measure into the right top right corner if we use tornquist indices or we could move GDP to the top right corner if we [05:40:43] had GDP linked bonds like I guess the Greeks do, but we don't at the moment. [05:40:47] Um, and so the point I wanted to make is the case that's in the top right hand corner where you both have a strong incentive to revise and a strong incentive not to revise is labor force statistics because they are tied to some things like triggering extended UI [05:41:02] benefits. Um, and so you want to keep them frozen. But on the other hand, as John described, you do get the updates to the population information, right? [05:41:10] And so the way I think about this paper is, you know, and the Canadian example shows that different statistical agencies might make different choices in this quadrant. Um, and so what I think about John's paper is doing and his co-author's paper is doing is res [05:41:24] resolving this tension for us by giving us two separate series, a research series and an official statistics series. [05:41:30] Um, so are there any downsides to having a par parallel harmonized series? I don't think there are very big ones. [05:41:38] There's obviously a resource cost. I guess uh they've decided to bear that cost. Um you know we no longer have a single canonical record of what the employment rate was in June 1990 for [05:41:51] example. Um we have we'll have multiple unemployment rates and you know there is a sort of proliferating series thing here which is um you know they'll there'll be every time they update there's a new backward series. And so we [05:42:05] sort of ch tra traded jumps for a need to really do do version control and and release stamping. Um you know I served for a long time on Canadian and and UK advisory groups to national statistical [05:42:18] agencies and and my colleagues in the agencies really dislike proliferating series because they worry that it'll undermine confidence in the uh [snorts] official statistics. But I you know I was never fully persuaded by that argument. So the bottom line for me is [05:42:33] these issues are manageable. the benefits outweigh the costs and this is a really welcome contribution. Okay, so now let me talk about uh Bruce's paper with his co-authors. So this is another really important paper uh from Bruce and his co-authors. I find it very [05:42:48] convincing. I I'll try to make a few caveats because it's my job is to discuss it. It's a worrying finding for sure, but what I like about the paper is it's constructive, right? They're not just try documenting a problem, but trying to show how to do a correction [05:43:01] and how to overcome the problem. Okay, so here's the things that you might want to think about. [05:43:08] Is it possible to legitimately not be unemployed and have you unemployment compensation on your U 1099G? Okay, so I spent the last few days trying to learn about 1099gs because I didn't know anything about it. And it turns out [05:43:22] there are a lot of reasons that could happen. All right, for one thing, they're on a cash basis. So, you know, you could be unemployed at the end of the year, um, file, not get paid until the next year. You might be back at work [05:43:35] and you would have income in the second year even though you're never unemployed in the second year. So, that sort of cash versus acrruel basis could cause some problems. Um, there some states have short time compensation. So, they play unemployment benefits people who [05:43:50] have lost hours rather than employment entirely. Um, as as the paper notes, there could be other payments in this box. You know, there could be um you you know, disability as UI substitute and so on. And for exactly that reason, Bruce [05:44:04] and his co-authors dropped California from the analysis. There could be claimment fraud um so people could receive benefits when they're not actually unemployed. There could be identity theft. So apparently there was a surge in identity theft targeting [05:44:18] unemployment insurance in the United States in uh 20 and 21 um where you know people were never unemployed never filed and then got a 1099 saying they'd received uh um unemployment insurance compensation. [05:44:32] And another thing is that when um when when unemployment benefits are repaid if they were inappropriate if there's an overpayment um that's often not corrected in the 1099. So that would be another reason where somebody could have [05:44:46] a 1099 that said that they had payments when they in fact were not did not and they weren't unemployed. All of these are small uh Bruce uh and his co-authors look at a number of them and try to do something about it. I think the question [05:44:59] I would ask is do a lot of small things add up to something that's not quite as small and in particular many of these things change over time like the you know there was a spike in identity theft [05:45:10] fraud was moves over time um the the cash basis problem was particularly acute during the pandemic because states got really behind on processing so for [snorts] all of these reasons I don't think none of these thing even in total these things won't made the main result [05:45:25] go away but they could change the sort of cyclical pattern And I think what we'll see over time is that Bruce and his co-authors will you know refine their estimates as they as they document these things. Um [snorts] a second thing you might be concerned [05:45:39] with is you know this um extrapolation from the ASC to the monthly CPS. And you know this sort of rests on two arguments. One is that the SIP which collects unemployment data in a very [05:45:52] different way from ASC has a very similar bias and that the two series are close together and I understand the argument. Nevertheless, the ASC and the monthly CPS are so different in the way that they collect unemployment that it's [05:46:05] just I'm still getting my head around this. um you know the the ASAC in particular has these funny gate questions where you you know if you you sort of say um did you do any full-time work at all during the year and if you [05:46:20] say no and then you say um you spend any time trying to find work or find work or on a layoff. So if you say no to if you get that gate wrong for the last year then you never get the how many weeks question right and so I think there's [05:46:34] work in the past not looking so at the ASC but it's at its predecessor the work experience supplement showing that these rates are pretty different um so that's a worry if I can say one more thing um there the other I think big assumption [05:46:47] here is that the um the UI recipients and non-reients uh were imputing from one to the other but of course that goes exactly the other way Right. Um probably that means that Bruce's and his co-author's numbers [05:47:01] are too low. Um so I you know I think there's convincing evidence of of an important problem. I would and this is a brand new paper as as Bruce said and I would imagine that the magnitudes will continue to be refined. Uh but I really ## Q&A (05:47:14 – 05:57:05) *Shared floor discussion for Session 6: The CPS and Employment Statistics.* [05:47:14] enjoyed both papers. Thanks very much. [applause] >> Okay. Thank you Tom. We have a few minutes for audience questions and [05:47:26] comments. We have one Katherine. >> Um just a a a couple of quick comments. [05:47:32] One on the Tom's comment about the the first paper and the revisions and the the CPI being in the corner where it's you high significance and not revised. [05:47:45] There is actually a revised CPI that's now used for important purposes specifically for indexing the tax code and we we found a way to do it. I remember when that measure was first introduced having back and forth with [05:47:59] Janet Yellen who at the time was at the CEA about um how you could use a an a revisable CPI for all of these purposes. [05:48:10] And we were convinced then that it was doable. And I I think it it is, but um it it makes life a little more complicated. The the main thing I wanted to say is that you I I'm a huge admirer [05:48:23] of all the work that Bruce has done on the you know issues with reporting in in surveys and I think it's very clear that the annual ASEC numbers you under miss a lot of people who were unemployed at [05:48:38] some point during the year. But where I really have trouble making the leap is from that to saying that the monthly unemployment rate is similarly biased. [05:48:47] And Tom pointed out, you know, one of the the the re the the sequence of questions is completely different for identifying whether you're un whether you were unemployed at any point during the last year and whether you were [05:49:00] unemployed this month. asking people whether they were unemployed at any point during the last year. It's a it's a very different memory exercise than asking about what [05:49:12] you did last week. The evidence in support of the idea that these problems carry over is that the you know the unemployment the annual unemployment rate and the monthly unemployment rate [05:49:25] kind of track each other. But they that sort of assumes that the reported durations are right. [05:49:36] We know what happens when pe you monthtomonth in the CPS when people go from being out of the labor force say to being unemployed. [05:49:45] They should report zero weeks of unemployment or you know zero to four weeks of unemployment when they come in. [05:49:51] But they often say things like, "Oh, I was unemployed 24 weeks or whatever." And so if you if you have understatement of the incidents and overstatement of the duration, you could have [05:50:06] unemployment rate based on the annual data that looked a lot like the unemployment rate based on the monthly data. But that could be because the monthly unemployment rate is right and the annual unemployment rate is affect [05:50:20] reflecting two offsetting errors. So I I I could be convinced but it at this point I I am not. [05:50:35] Um uh this is for you Bruce. There's two things I want to think about this are related and related to what Katherine was just saying. One is I wonder whether or not you can use the longitudinal sorry my name is Seth Murray from the Federal Reserve. Sorry. Um whether or not you can use the longitudinal nature [05:50:48] of the CPS to look at individuals who are in the fifth to eighth wave when they're reporting in the ASAC and to look at what they were responding in the previous year in terms of their unemployment status and see whether or not those for those individuals who reported as being unemployed whether or [05:51:02] not you're seeing uh a failure to report being unemployed along the lines of what you're what you're expecting. Another is is you could then also couple that with my second comment which is uh I wonder how much this might be driven by temp layoffs where people might think of themselves as being employed [05:51:17] continuously last year but actually they were on temporary layoff and so they're going to show up as unemployed in the basic um but then not be reporting as as unemployed when you're asking them uh about their status a year prior and in particular it could actually be accounting for the very large magnitude [05:51:32] you're finding because I'd expect that people who are on temp layoff probably have shorter durations of temp layoff And so that's going to be even though there in any given month they're only 12% of the unemployed in that month, they're probably actually pretty large number of individuals when you look over [05:51:46] the course of the year. Um, and so that might be more than 12% of of individuals when you look at who is unemployed over the last year. Um, and so those are my two suggestions. [05:52:06] Uh I'm Hijan from the Federal Reserve Board and uh so I have one question to Bruce and another question to John. So I think Bruce research makes sense to me and is largely consistent with the research that I've been doing. Um but I [05:52:21] have one questions about the causes of the understatement of the unemployment rate. So it seems like your presentation suggests that you know the causes of the mclassification um I mean the cause of the understatement of the unemployment rate [05:52:35] is likely to be due to mclassification and I always wonder whether the mclassification is between employment unemployment or unemployment nonparticipation and also you know then the the second question maybe more important one is [05:52:50] that what are the causes of the mclassification is it because of the shame of reporting active job search but you know turned out to be unsuccessful or is it because of the ambiguity of the concept of activity job search active [05:53:03] job search the reason I'm asking this is you know depending on the cause you know the solution can be different and the question to John is that you know I really think that you know your research is very useful um and this can be an aggressive statement but the [05:53:18] overstatement of the labor force participation rate you know seems to be you know mclassification due to population you know weights Um but I was wondering whether the workers in the gray area you know the level of those who are in the part-time for economic [05:53:32] reasons or marginal attached workers you know how they are affected by this you know population rates. So I'm asking this because you know if the main source you know of the bias is you know heavily affected by the people who are in the [05:53:45] gray area then the implications for you know assessing the economic slack can be different. [05:53:52] >> Okay great. Uh so I think we'll give one minute each if we can to we'll do first response for for John. [05:54:03] >> Thanks. Um I'll just thank Tom for great comments. Uh really appreciated that. Um uh Hij on your question. I think uh a lot of what we find is the differences [05:54:15] uh in the LFPR is driven by uh people at older ages versus population at younger ages. sort of very broad differences. [05:54:24] And so I would be a little less worried about the the gray area driving some of that. But that's something we can have an exact answer to um uh to speak to. [05:54:35] Did you want to say anything? >> I had one other followup. Thanks again, Tom. Uh you mentioned the real time the sort of why might we not want to do this? uh sort of effects on real-time indicators and one of the things we showed is the effect on the unemployment [05:54:50] rate which is where most of those are based on was was dimminimous essentially. So I think you'd be unlikely to draw different conclusions in real time. Um [clears throat] and then the proliferation of series problem [05:55:03] is is a real one and we don't have a great solution to that other than by noting there still is an official series and we would point you to that for official statistics. [05:55:15] And Bruce, I see you have a microphone over by you. It's it's it's right there. [05:55:20] Oh, okay. Uh, I'd like to thank Tom Crossley for his comments and I'd like to thank Abra Katherine Abraham for her kind words and [05:55:34] her engagement in uh my work. I want to respond to two of her comments. Um, first it's a tricky game to try and infer [05:55:48] what's going to happen from differences in wording and and survey design. I can remember the 93 CPS redesign and how everyone thought it would make the [05:56:01] unemployment rate go up, but unemployment rate went down. Um and um so I think really the proof is in the two measures lining up having a [05:56:15] correlation of.99 and and almost a fixed difference over time. And uh Katherine's comment was that assumes that the durations are right. No, it's a [05:56:30] check on them being right or at least the bias in them being constant over time because the fact that they line up says that [05:56:43] either the durations were right or differed by a constant such that it it uh doesn't uh uh it leads to a constant [05:56:54] difference in them. So, Okay, let's thank our presenters and discuss them.