Transcript of the talk, from the video's captions. Auto-generated: speaker names in particular are unreliable. ======================================================================== # Expanding the Frontier of Economic Measurement: Weekly Regional Employment Statistics Authors: Matthew Bell, Abe Dunn, Eric A. English, Kyle K. Hood, Steven Mance, Lowell Mason, Brian Quistorff, Michael Stepner Discussant: Jason Faberman Video: https://www.youtube.com/watch?v=HSCV6-Vfc4U&t=7084s ## Talk (01:58:04 – 02:18:40) [01:58:04] Terrific. Thank you. Um the the next presenter presenting on behalf of a large team of people that's listed in the program. It's childhood from PCA. [01:58:47] Okay. Um, I'd like to thank the organizers, Katherine Abraham and Matthew Shapiro for having us here um to talk about our paper on expanding the frontier of economic measurement. [01:59:00] I'm going to stay for a minute on the initial slide. I think some people might look at this and say, well, aren't you overclaiming a little bit? I will say that this is sort of a continuation of uh of some previous work that we [01:59:15] published um earlier this year. And the title kind of is parallel to that. So what we're going to talk about right now is a weekly regional employment statistics series uh that is hot off the press that was based on some earlier [01:59:29] work uh initially that we had done. Um this is a a huge project in terms of the number of individuals involved. Um it's across several agencies including the Bureau of Economic Analysis which uh I [01:59:43] am and so I got to design the slides. Um also the University of Toronto and Opportunity Insights. Opportunity Insights uh of course was the one who came to us with with the the the agreements in place such that we could [01:59:58] use these data. Um and then there are also researchers from the Bureau of Labor Statistics and uh the Census Bureau who are involved including Steve Mance that you saw earlier. Um Lel Mason is no longer with the BLS but did a ton [02:00:11] of work on this project while he was there and so um and so BLS gets the credit. I will also say that uh that the views expressed in this paper [02:00:26] uh well this is a really long list they may not be well they may or may not be the views expressed by these agencies. [02:00:33] Um but the key is we don't know so don't assume they are. All right. So let's just go. Now this paper is not going to go into a lot of sort of [02:00:46] serious mechanical uh ways that we've adjusted these these statistics. Um like I said we have another paper done at all in 2026 that that kind of goes through a lot of the experiments and the and the different [02:00:59] things that we did. Um the overall goal is to think about what we can do with the data that we have. Um so economic shocks [02:01:14] they don't really wait for statistical releases. You know you can see um somebody talked about a hurricane uh earlier today and um you know we can see major employers expand or contract. [02:01:28] uh you might look at seasonal activity in a in a summer vacation location that that you know we know the peak is in August but perhaps um there's some more interesting stuff going on. [02:01:43] The the problem is, of course, that reliable official statistics that are published at a monthly or a quarterly um frequency and arrive at substantial lags are probably there. There's a lot [02:01:57] of interesting stuff going on that we can't really see. And I know that, you know, we we've heard other work today about stuff that's happening at a weekly basis. um this is going to be a bit [02:02:10] different but uh but is a similar motivation. [02:02:17] Okay. So the first thing I'm going to talk about is the weekly employment index um that we're putting together. [02:02:24] It's going to be we're going to put together weekly indices of private sector employment. It's going to be based on surprise surprise payroll micro data. um the payroll micro data that we use we we [02:02:37] we use the term payroll micro data but the website that it's being published on tells you who it is so I think I can no longer have to uh be quite so koi about it um we have we're going to have [02:02:52] coverage the key the the interesting thing I think about this uh data that people will will find useful is we're going to have a full coverage of the US uh we're going to have counties and so therefore or if you have counties, you [02:03:05] have metro areas and states. Um, this payroll provider covers more than 5% of US private sector employment. It's it's [02:03:16] not uh unlike ADP. It's it's it's it's not dominated by large multi-establishment firms. It's more smallish firms. Um and then the growth rates that we measure or [02:03:30] the growth that we measure will be a matched continuing employer chained growth rate. Um and so we will not capture firm births and deaths in this series at all. Um and so that makes it [02:03:43] easy for us. Uh but of course all the caveats apply to to to that. [02:03:52] Um so I we can sort of split three axes of we can sort of split the different granularities that that we see in employment data along three axes [02:04:05] frequency uh geography and latency. I mean there's there's also I guess a additional axes you could uh in terms of characteristics of the firm or characteristics of the employee uh that aren't mentioned here like industry or [02:04:20] or maybe income level. Um but we'll just mention here that you know CES we see a monthly frequency relatively low latency um but we don't have county information versus QCW we see a monthly frequency [02:04:34] and and down to the county geography um but a relatively higher latency. So what we get with the weekly payroll series is a high frequency high geographic [02:04:46] granularity and very low latency series. Um interest. Oh, sorry. [02:04:56] Yeah. So, so then um I'm going to talk a little bit about the method. [02:05:06] Uh some just a few little details about the method. [02:05:10] Just similarly to what the CES does, we'll define weekly employment using payrolls for the pay period that contains a particular day of the week. [02:05:22] In this case, Saturday, just like um the 12th of the month, I think is what CES does. U we're going to match continuing employers. So that's an employer that's observed with employment in the current week, the prior week, and four weeks [02:05:36] earlier. And we decided on this based on on some various testing. And then we're just going to chain the week- toeek growth rates to get a county uh employment index. [02:05:51] Um now I'm just going to go straight on to show you uh what the results look like. We're going to talk about some national benchmark examples. Move on to a couple of event studies. Uh both of these event studies are interesting because they are the same hurricane [02:06:05] Helen or Helena I don't know Helen but they have they were very different events. Um one of them was a storm surge event and one of them was a [02:06:18] was sort of a rain uh related flooding event. Okay. Then we're going to look at some paired error histograms to try to see how accurate um and bi and unbiased [02:06:30] the numbers are. Um and then I'll just conclude. [02:06:36] So just sort of the national plots if you if you create a national series with all the payroll data. Uh this is the not seasonally adjusted one. Um I think in 2023 January is when the indices are [02:06:49] equal. The orange line is QCW and the payroll processor line is this sort of greenish one. [02:07:00] Um, and I think from that you can see the seasonality makes it a little messy, but they look like they're they're sort of tracking each other fairly well during that period. Um, once you [02:07:10] seasonally adjust, we use a a weekly seasonal adjustment. um that's sort of a moving average based robust [02:07:21] adjustment. Then what you see here is uh that that they're that they are fairly close between the 2021 say and um and 2024. There's a slight divergence after [02:07:35] this and we don't know exactly what's causing the divergence. could be the the chaining uh of the within firm employment or it could be uh something to do with the sampling differences. [02:07:50] Okay, so now here's the event studies. We're going to start with Asheville. [02:07:55] What happened in Asheville? I think in se morning of September uh 27th or so of 2024, there was a lot of flooding. This is [02:08:07] sort of the pre-event situation. The um yellow line is the QCW that was sort that's sort of expost. Uh the red line is is um the CES and this this uh [02:08:21] greenish one is the payroll processor. As we go in time toward the event, you see this vertical line. Um we see that here on the 26th there we immediately Is [02:08:34] this the 26th? Oh, this is Oh, this is in this is the last week of October. We were able to identify a a significant reduction using the the weekly data where um we don't see anything yet from [02:08:48] from the BLS. Um that goes on for a couple of weeks. [02:08:54] um we start to see a recovery and then we've we you know and then with the monthly data we can finally see uh this the CES showing a similar drop just based on the the dates uh the the month [02:09:07] that includes the 12th or the uh the payrolls on the 12th and then we can kind of continue to see this recovery and so there is um you know it's it's definitely giving us a a sort [02:09:21] of magnifying glass into what's going on in this um in this particular county for the leisure and hospitality industry. [02:09:32] Okay. Um similarly there was this uh hurricane Helen similar time period was uh but in Tampa it was a storm surge event um causing uh causing a reduction [02:09:45] in employment. You can see uh that the reduction employment was was a little bit different here because the recovery uh was a was a little bit slower in the in the QCW. Um again [02:09:59] the the weekly series shows an immediate drop in this in the second to last week of October. Um and then we see three periods of drop and then things start to recover before uh that you know that the [02:10:13] CES is able to reveal anything for us. Um and then over time we see this recovery. Now here it's uh if you look at what happens [02:10:24] to the recovery it does look like um the the green series is showing maybe a more similar type incomplete recovery than the red series is showing uh before the [02:10:39] benchmark. Okay. So those are the basically the two examples that uh that we have to to show. uh we can look at some error distributions to see [02:10:53] sort of how how the the payroll data compare to the other the the CES data. [02:11:01] I'm going to show you eight error distributions. I can flip through them a little bit quickly because uh they're all kind of similar and I I think we all know how to interpret um [02:11:14] histograms. So these are positive and negative errors which is just the the the difference between the two. You can see that in most cases this is for all industries. [02:11:29] The first print CES is more tightly concentrated around zero than the payroll provider data. [02:11:40] the um correlations are slightly higher. The when we look at trade, transportation and utilities again you see a more [02:11:52] spread out distribution. When we look at um this one is professional and business services and this one is education and health services. These are [02:12:06] the uh the the industry groupings that we're that we're publishing. [02:12:11] Um and then leisure and hospitality is the last one. Oh, okay. Um and this one looks a little bit biased, but that's the one I was showing you from the hurricane Helen graphs. And so [02:12:26] um we'll just have to potentially think about what's going on there. Okay. So I can show the error distributions also for [02:12:40] um including COVID. Again, it's a pretty similar picture for the for this approximately six-year period and um with the payroll provider having [02:12:53] a slightly more spread out error distribution. [02:13:00] Okay, so sort of to review what we've talked about today, the payroll series are not really uniformly more accurate than the [02:13:10] uh you know the existing CES. Um and the first published CES estimates are generally closer to the final QCW. [02:13:20] there there's really the most information in these weekly uh payroll provider series when there's like a big economic movement that's large relative to the sampling noise [02:13:34] and so it's really the comparative advantage I think of using this is timing and and sort of the geographic resolution we can get county level information we can identify the onset of [02:13:47] large local disruptions trace short run adjustment and recovery and then you know look this allows us to kind of look inside these monthly uh and quarterly reference [02:14:00] periods. So the payroll provider excuse me payroll provider data can provide us with employment measures at availability that's not that doesn't [02:14:12] exist in the weekly statistics. They track what we look you know from these examples they track these these employment movements well um and its value is sort of a magnifying glass [02:14:26] where we can identify these local changes. [02:14:30] Of course we we still need the official statistics like QCW and CES u for representativeness and value validation. [02:14:44] So in addition and I have uh about five minutes left. I can so I got an email today at 12:07 [02:14:56] p.m. after I arrived here that uh in fact these data had been published on the opportunity insights um tracker. It's going to well this says economic tracker. I think normally I go [02:15:11] to track there.org. >> Same. Okay. So, they're they're both the same. I think the one we put in the paper was track the recovery and we can [02:15:21] look at Oh. Oh. Oh, sorry. I I got it. So, we can live stream. [02:15:28] >> Oh, hit this. Uh, >> I know you taught me how to do this and then I forgot. Okay. [02:15:35] Um, so that's so that the people on um YouTube can see. And we're uh let's see what county is this. [02:15:43] Okay. Where's Northern? Okay. So, here's Fairfax County, for example. Oh, I don't like that one. Uh, okay. You know what? Let's look at Maryland. I already looked at Maryland. [02:15:55] I don't want to surprise myself. Um, okay. So, Montgomery County is here and we can look at for example professional and business services and you know we can [02:16:08] see this um or we can look at total employment and this is relative to January 2023 um and so yeah [02:16:21] if if you're from Montgomery County I don't know what happened in late 2024 early 2025 but it looks like a lot of business services were decreasing during that period. Um and then um but the [02:16:33] overall uh number is pretty is pretty flat. Okay. And then we can you know you can see similar patterns in the different counties. So Prince George County um in these in these particular [02:16:46] two indices has a has a somewhat different pattern um which allows you to kind of zoom in there. [02:16:53] Um okay. So you can go here to get there. You just go to uh the employment and then you can look at counties, you can look at states um which is a [02:17:04] slightly different source. Oh no, same source. Um or you can look at metro areas. Anything else you want to Okay. [02:17:15] Okay. Well, that's everything. Thank you very much. [02:17:17] >> Thank you. Are you able to say who the provider is? [02:17:27] >> Well, it says right here. >> So, this is the the provider. [02:17:33] >> Okay. Paychecks. [02:17:38] >> So, thank you very much and I we'll go on to the next presentation and and then we'll discuss it and then have some time for general questions at the end. So our our final paper in this session is will [02:17:52] be presented by John again on behalf of a team of collaborators. ## Discussion — Jason Faberman (02:43:45 – 02:54:29) *Shared discussion for Session 2: Utilizing Payroll Data; the discussant covers every paper in the session.* [02:43:45] All right. Thank you and uh thank you for the opportunity to uh discuss these papers. Uh I will go through all three of them in as much detail as possible in 10 minutes. [02:43:56] All right. So as you might guess, we're going to talk about more kind of the highle's comments and high level thoughts about these three papers. [02:44:02] Luckily, they all share uh some common themes in terms of what they're trying to do and for me at least my interpretation uh of the three also in in kind of the takeaways uh to varying degrees. Okay. So the key theme here or [02:44:16] key theme here is these papers are focused on using new data to improve on at least on the employer side uh labor market statistics in one way or another. [02:44:24] Uh the Kerman LA paper is looking at private sector business data and how that combined with a specific methodology that focuses on on getting the openings and closings um estimated correctly. uh how that can improve our [02:44:39] our data and and do so also in kind of a a fairly important niche among smaller high turnover businesses which our standard business surveys um tend to either not capture well enough or mismeasure in one way or another. Uh the [02:44:52] Belladall paper is looking at how much we can push um how much we can push private payroll data uh to get much better regional statistics on employment and earnings at uh at a much higher uh geographic detail and also a much higher [02:45:07] frequency. And then the most recent paper with UK data is looking at how we can use not just private sector data but internal administrative data uh to improve what what the UK and and and statistical agencies more broadly are getting from their surveys on the [02:45:22] employer side. Okay. So, I'm going to go through each paper um and contribution and kind of the the main takeaways I I hope we all take away from each one in terms of what they're trying to do and and what the successes are and then I'm going to have [02:45:35] some comments on what the limitations that come about from each one. Okay. So first with the Kerman LA paper um like I said this is using private sector data from three ser three sources the homebased data in addition to Advan and Safecraft to to in partly do a data [02:45:50] validation exercise and in some sense see how well we can capture um movements of employment and earnings um but also to apply this methodology Andre was talking about at getting a better estimate of of openings and closings and [02:46:04] you know it's important to understand as Andre pointed out and I think a lot of people that work with these data know the the private sector data suffer from this problem of being an opportunity sample convenience sample. In other words, um firms are entering and and exiting their their surveys uh or their [02:46:19] data for for no other reason than business related reasons, not being a true opening or closing. The real uh the real contribution here from this paper is putting together a methodology that that better identifies true openings and closings from these data. And andre [02:46:34] didn't show you all the all the results. um he said is a pretty sobering exercise. It they don't do that badly in in some sense. Is it is it a perfect match? Is kind of does it meet the bar for statistical samples? No. But the the the methodology certainly makes some [02:46:47] some progress here. It performs relatively well. Um and and as Andre mentioned there there's this drop off that the data seem to be showing in terms of uh in terms of the creation of new businesses over the last year or so. [02:46:59] Um that hasn't showed up in the in the published uh government statistics, at least not yet. So it' be interesting to see how that looks like postbenchmarking um a little less than a year from now. [02:47:10] The regional employment statistics are are taking data from a from a private payroll survey uh like I said to get these more detailed estimates at the regional and um regional regional level and do it at a weekly frequency. So very high frequency data. That's one of the [02:47:25] benefits they have from from the payroll surveys. You people get paid every week. [02:47:28] So they have they have these uh they have the higher frequency data and potentially have it at a higher level here. um they they they show that um I'll argue how how well it it tracks but the national level um we get similar [02:47:42] estimates in in terms of employment um and and the real uh the way in which the authors are trying to show here the real power of these data is to look at these natural disaster events where where published data statistics or or surveys [02:47:56] aren't necessarily going to get the magnitude of the the true impact of a natural disaster like an earthquake, hurricane, flood and so on. [02:48:05] Okay. And then finally, the most recent paper presented um John and Johannes showed that that that essentially the UK equivalent of the United uh United States LEHD program um can be used to generate labor force statistics on [02:48:18] employment and earnings. Um and and really, you know, if if you've worked with the LHD or worked with any any of the administrative data like the QCW, maybe you found yourself a little bit jealous of of how the data is collected [02:48:31] there. Um, and you should because it's kind of ridiculous how much nicer they have it relative to us. Um, the the the the tax records are essentially a real time ser administrative set data set of [02:48:45] uh of wage records um continuously updated and updated at the national level. So in other words, unlike in the US where the federal government has to essentially herd 51 sheep of labor market uh labor market uh departments at [02:48:59] at the state level to get all the data. um and not only wait for all of it to come in but then process all those data which are collected 51 different ways. [02:49:07] Uh in the UK it's all done at the national level and it's all um all done with a uniform um reporting um reporting paradigm. So they they don't have to worry about any uh any of those issues and as a result they're able to produce [02:49:21] um not only consistent estimates that match up with the survey data very well. [02:49:25] Um but they're also to do it a much timelier way than we would be able to do with either the QCW or LHD here in the US. [02:49:33] Okay. So each of these papers are showing varying degrees of promise. Um all three have limitations one way or the the other. So I'll talk about those for a couple minutes now as I finish up. [02:49:43] So with the criminal LA paper, the usual caveats about using private data sources apply. Um though this methodology really goes to the heart of one of the bigger issues we worry about um with these private sector data sources is that that's that they're a convenience [02:49:58] sample, not necessarily a probability sample. Um and that's that's really kind of the one thing I think to take away here. um they they focus on this this niche of of smaller businesses within particular industries, these higher turnover businesses. But these are also [02:50:11] particular, you know, precisely the ones that we that we tend to care about for measuring um or updating birth death models and and and and the like. It's it's the small business that give us the issues. So there's some promise here. um [02:50:26] you know there's some data measure measurement issues I won't get into but there's there there are the you know there there's real promise here in terms of the methodology if not the particular data set itself now the the Belladol paper with uh the [02:50:39] payroll survey data um I think it's a great exercise I think it's a very useful exercise but for me at least when you dig a bit deeper into the data I'm wondering whether it's up to the task of these weekly regional uh estimates that [02:50:52] that the authors are after um for on we saw in the national level data there in terms of matching up to QCEW um generally it looks similar but in terms of the quantitative differences um for for large period it's on the order of several percentage points in in terms of [02:51:07] the level of of of employment over certain periods and and Kyle didn't show the the numbers but in the paper they also look at these uh correlations over time across states of how well the the series match up um QCW versus the [02:51:21] payroll survey and and the correlations aren't that strong across a large swath of states and even some states negative, right? So, so the the the matching of the data at the at the regional level isn't all that great. Um the other issue [02:51:35] is that the the the nature of the data or or at least the the methodology they use, they focus on continuing establishments and they do that to to get consistency of the growth rates which you need for for consistent growth rates. One of the issues though is on on [02:51:50] the plus side, these data allow them to, as Kyle showed, allows them to evaluate major natural disasters or other major shocks to a local economy in ways that the survey data do not. Um, but at the [02:52:03] same time, they may be missing things. So, so one of the things that came out uh at least when you compare the the payroll survey estimates uh of the two counties following the hurricane uh the payroll survey estimates compared to the QCW is they show a much bigger drop on [02:52:17] impact, but they also show a much stronger recovery. My read of that is that's probably because we're missing all of the exits that may have happened during that period by focusing on the continuous establishment. So unless there's a way to to for example apply [02:52:30] the methodology from the first paper to the payroll survey data, we may be limited in what we can say even at a local level um from from these payroll survey data. And then finally, the the the Bryson and all paper. You know, like [02:52:44] I said, it's hard to not be a little jealous of of the UK uh matched employer employee data because it really does seem they're they're able to do quite a bit um given the nature of how the data is collected um and how well it matches to the surveys. in that, you know, of [02:52:58] all these surveys I've seen across several of these conferences now, this is probably the most promising in terms of something that could either complement or if not completely replace an existing um existing major uh labor [02:53:10] statistics survey. Um you know, obvious the the obvious kind of cheap shot caveat is it's not on the household side. We know in the UK their their household survey has some issues. You're not going to get any information about the non-employed um from this survey. Um [02:53:25] but on the plus side, I think there's more information they can get um more than just the the the uh employment and earnings. Um particularly they can follow in the steps of the Qi uh QWI estimates and and produce some estimates on on worker turnover and the like. Um [02:53:39] just as as we have in the US um because you know the data is given how good the data is for the the basic accurate statistics, it's likely be just as good for those statistics as well. So to summarize, these are three really nice papers that tackle, you know, the the [02:53:54] general theme of how much can we use the these external data sources to improve our at least employer side labor market statistics. Um they all do fairly well and provide provide some useful evidence and useful information and and in some cases useful methodologies for tackling [02:54:08] that. Um but just as you know a lot of other papers in in this um this line of literature has found there's some implementations as well. Thank you. ## Q&A (02:54:29 – 03:10:19) *Shared floor discussion for Session 2: Utilizing Payroll Data.* [02:54:29] So we have we have some time now for uh questions from the audience questions or comments on any of these three papers. [02:54:40] >> Okay. So great great papers. I'd like to ask a question from the UK at least to my eye going all the way down to the bottom of your paper and say why not just do the [02:54:53] full this is very convincing that that you can get these data this way and then if there data which are missing uh there are various ways that these that get get other data that will fill the [02:55:08] gaps and there are basically two routes. one would be enhancing the the wage equity that the data going to the uh to the Treasury and that's that's what they did in Australia. Uh and I hast [02:55:21] if you're going to ask askers for more responses like parttime versus full time or other things that tax administration you have to say quit quote pro we're not going to ask you on on survey and if you [02:55:35] need more from the household side you could you could other say what we say picking the getting the getting the household data say demographics from across the census [02:55:47] data. So I would this is great and it seems like an infrastructure to sort of that could be enhanced to fill fill in a lot of the gaps fairly in a fairly straightforward way. [02:56:00] So may I John or do you want to res respond to Matthew's proposal for a revolution? [02:56:11] >> Thank you. Yeah. So um we absolutely recognize the opportunities there but we also recognize the the responsibility that we have to um not uh upturn the apple cart. I don't know this is a [02:56:25] phrase we have in the UK. So um I think the wholesale replacement is challenging because these as as I briefly mentioned the surveys do provide additional information which is very hard to get [02:56:37] from uh the firms. Now Matthew's response to that was well why not get the tax office just to ask more of the firms and I think you know the pressure for that will will come but the tax [02:56:51] office's job is to administer tax not to generate statistics uh it like any organization has resource constraints and it has to fulfill its main mission. [02:57:04] So that's a challenging conversation to have. Um it's a reasonable ambition I think but it's it's um very difficult in practice. Uh there have been conversations about collecting more detailed hours data, occupation and so [02:57:18] on but um they they usually found on the practicality. Uh there are opportunities however to link the data that we have to other sources. So we can potentially [02:57:31] link to information on a benefit receipt which will allow us to identify when somebody disappears from the employee records is that because they're inactive or unemployed and so on. So that's a [02:57:44] potential for the future. And one of the good advantages of the data that we have is it has uh the ONS has developed some um indexes which allow us to link across [02:57:58] from our data set to quite a wide range of other administrative data set. So I think that will come uh in time. And just to point to Jason's uh prompt about [02:58:10] generating QWI type indicators, job to job flows, that's in our near-term horizon. So we're expecting to publish something out of that in the next six months and you'll see that soon. [02:58:23] >> Great. Thank you. Um >> did you want to add? [02:58:28] >> So I I have uh Bruce and and David. So Bruce, since the mic's over by you. [02:58:34] >> Um, this is actually that same question. So if you link to um surveys, you can possibly take a lot of [02:58:48] the burden away from surveys by providing answers to some of the questions that were previously obtained from surveys and make it easier on the [02:59:02] So that seems like another route that you want to go with this wonderful that >> Yeah. [02:59:17] >> So it seems we lost something in 1776 when we separated. [02:59:24] I admit to having a severe case of Jason Paperman's uh uh what was the word you used? Uh envy. [02:59:33] >> Yeah. >> Um what So what I'd like to hear from our side of the pond actually is what would it take to create what you've built in the United States? [02:59:47] Can somebody talk me out of the delusional notion that we could actually overcome 51 different collection agencies and so forth and so on? So what would be what what would be involved in [02:59:58] getting what you have? >> I think why don't we why don't we let John Haltwanger try to answer that question. [03:00:13] >> It's called the Internal Revenue Service. I think what we do I I I think the by the way we have tremendous data in that this idea of this reporting in real time at such a high frequency we're not going to get there immediately but it is [03:00:27] true payroll tax reports come in on on a on a flow basis and uh takes a very long time for the census bureau to process them to build all of its products and so on but they are coming [03:00:40] through maybe put put on the spot it may that we can improve with working with IRS the processing of that because because the data is actually much more timely in principle uh to be able to start doing things like [03:00:55] this. >> Um Julie >> and I I should have said if people would just introduce themselves when they get the mic. [03:01:09] >> Hi Julie Hunt with Bureau Statistics. Um, so the US, yes, we have two forms. [03:01:15] Obviously, one's the IRS. Um, but any of you who are from around the RS would not like to share. Uh, and once things go to Census Bureau, they never leave, which is great for census, but what the rest of us who want to work with statistics [03:01:28] isn't so great. The other one is through where we're getting the QCW through our state partners. Um, and we've been working a lot with our partners at the Employment Training Administration, having serious conversations with them. [03:01:41] that I was going to talk about a little bit tomorrow too. Um, but there is a movement on that side where a lot of um the the lack of utility or or could be utilized a lot [03:01:54] more is the lack of consistency and a lot of states are adding the enhancement to the wage records and there has been a formalized um methodology or definitions. Uh so they're having serious conversations at the national [03:02:06] side uh that I hope that much sooner than I ever see changing in the tax records. I'm I'm more hopeful on that side. So um yeah, which which mean which [03:02:20] is promising for everyone improving the whole statistical system because historically uh the states share with BLS and then BLS can then share uh with our partners and then we can share back with the states. certain improves the whole infrastructure uh which lifts all [03:02:35] of us up. >> I'm going to add one thing Andrew Foot from Census ID team. Um, one thing that we do have [03:02:47] at um, inside of the US, but the statistical agencies currently do not have access to is the national director of new hires, which is we see flow basics of every hire, like as soon as they fill out an IN9, it goes to the [03:03:01] national diretory of new hires and there has to be legislative changes to make it insurable to census and BLS that that would provide real time statistics on hires. provide very timely UI claims uh [03:03:16] information at the individual level and the UI wages the lag will be less than what we get from the states currently it wouldn't solve the GCW problem but there exists a lot of the information there but it requires a legislative change [03:03:30] because of where it lives within the executive bridge >> yes amen and >> so Eric or BS commissioner um and so you'll be [03:03:47] hearing a little bit more tomorrow about some of the steps that could help close all of these different efforts and uh the two I'm going to be talking about is [03:04:00] an effort to create standards for employers to use at the very And that opens up a number of opportunities for ways to collect the [03:04:12] data, gather them. We talked about that. So enhancing, standardizing the technology of the data and then also what are the changes that have to be made to the statistical system so that they can actually use these data to the [03:04:26] best effect. Just bit of foreshadow. [03:04:33] Any other quick comments before we take a break? [03:04:40] Okay. >> What? [03:04:44] So I thank you. I I I as Erica noted, we'll have a chance to pursue some of these questions a bit more tomorrow. Um why don't we stop here and thank our presenters again. [03:05:00] Uh we we will resume in about 25 minutes at 4:30. [03:06:09] That's kind of I [03:06:22] Get back. I can tell you [03:07:17] right Nice. [03:07:55] recognizer. It might be possible. [03:08:53] like our what we get from my head down. [03:09:23] That's actually angel. [03:09:44] There's more than one. I don't even know.