Transcript of the talk, from the video's captions. Auto-generated: speaker names in particular are unreliable. ======================================================================== # Using Administrative Payroll Data to Improve Official Labour Market Statistics in the UK Authors: Alex Bryson, John A. Forth, Kyle Jones, Yohannes Lemma Discussant: Jason Faberman Video: https://www.youtube.com/watch?v=HSCV6-Vfc4U&t=8320s ## Talk (02:18:40 – 02:43:45) [02:18:40] Thank you. Thank you very much. Well, thank you for the invitation to come and speak. As you'll recognize from my accent, I'm not from the US. Um, this is the delegation [02:18:54] from the UK. Very pleased to be here. Thank you. My name is John Forth. I work at City St. George's uh university, joined here by Johannes Lemma, who works at the Office for National Statistics at [02:19:06] the UK's National Statistical Institute. And what we're going to present here is the product of a collaboration between the ONS and an academic team. So uh the [02:19:19] other part of the academic team is Alex Bryson who's from University College London and then on the uh ONS side also uh Kyle Jones. And so um the sort of [02:19:33] backstory here is that in the UK we have historically had no full population linked employer employee data set. We don't have an LEHD or at least we didn't [02:19:45] until last November. So the the product of our collaboration is to build such a data set which covers all employees in the UK and allows us to track them uh as they move across firms uh over time. [02:20:00] And one of the uses that we're putting that data set to is to try and understand how we can integrate it into the process for producing official statistics. Uh and so we've heard [02:20:13] already from a number of teams who are working with payroll data. Our data that forms our data set is administrative payroll data. The difference with some of the sources we've heard about is that [02:20:28] we cover everybody. Okay. So we have full coverage of employees in uh the UK. [02:20:34] So let me give you the overview of what um I'm going to talk about and Johannes will come and int and and contribute in a moment. [02:20:44] Uh the ONS is engaged in a a very wide ranging program to try and modernize its labor market statistics and one part of that program is to find ways of integrating admin data into that process [02:20:57] and in particular administrative payroll data that comes from the tax office. [02:21:03] As I said, we use those data, those payroll data to construct the longitudinal uh linked employer employee data set. It covers all UK employee jobs at monthly frequency uh starting in 2014 [02:21:16] and going up to the present day. Uh similar to the data that you would use in the US to uh generate the quarterly workforce indicators and job to job flows estimates. [02:21:28] What we're doing in this paper is linking those administrative data to the survey returns that firms have contributed uh to the ONS in some of the survey measures that form the headline [02:21:42] estimates for employment and earnings in the UK. So we're trying to assess the coherence between the survey estimates and the adminbased measures both at the aggregate level and at the firm level. [02:21:54] The takeaway is at the aggregate level the survey responses on employment and earnings are pretty consistent with what we see in the admin data. So that gives us some confidence in existing surveybased measures. That's very important in the UK right now. Let me [02:22:08] say um at the firm level we do see discrepancies. Uh we're trying to understand some of those discrepancies. [02:22:15] But what we're able to do is to feed the information on those discrepancies to the survey production teams, the teams producing the headline estimates to try and uh identify possible reporting er errors. And what we'll do at the end of [02:22:29] the presentation is build on that exercise to try and step back and think about the different ways in which we can use admin data of this type to feed into the process of producing labor market statistics. So that's where we're [02:22:43] headed. So motivation downward trend in response rates to both household and business surveys. We've seen that across many countries of course but it's been particularly pronounced in the UK um and [02:22:56] in the UK context exacerbated by I think a long period of disinvestment in core survey operations within the ONS led to widespread doubts about the quality of surveybased labor market statistics. [02:23:09] Even at one point um those responsible for feeding information to the monetary policy committee questioning whether they should really be taking any signal from our household labor force survey at [02:23:21] all. Administrative payroll data has the potential to address uh potentially a number of the measurement challenges in surveys. So of course if it's comprehensive we avoid non-response [02:23:35] bias. Um, we observe our data with pretty high frequency. So, we're able to maybe capture short-term jobs and movements which are somewhat missing in quarterly data. Uh, we get the earnings [02:23:49] data direct from payrolls. So, that avoids the recall error that you might see again in a household survey. We have the ability to link workers to firms. [02:23:57] So, we can then measure labor labor market dynamics in the way that you're using uh the LEHD. And of course we have the integrated perspective of seeing workers, jobs and firms all in the same data set. [02:24:10] We started with a data sharing agreement in November 2025. So I mean actually the administrative payroll data have been inside the office for national statistics for a number of years. The innovation that we've brought is to link [02:24:24] the payroll data to firm level data so that we can make the connections with business uh surveys. So that's what we've been working on over the last year or so. A word about our data. So I want [02:24:37] to start on the right here with the administrative payroll data. In the UK, we have effectively a pay as you go tax system for employee earnings. So every time my university pays me, the employer [02:24:50] makes a deduction of my tax at source and pays me the net payment. The employer then has to send a record of both my pay and the deductions to the [02:25:03] tax office and they have to do that in real time. So if I'm paid monthly then they'll send a monthly submission. If I'm paid weekly, every week they send a submission to the tax office. [02:25:17] And so what the tax office is receiving is information about my earnings and whether I'm employed in certain firms. [02:25:25] It's like transaction data. What we do is we um turn that transaction data into a monthly record of employment and earnings for an [02:25:38] individual uh employee. The problem of those data is that they don't have a very usable employer ID because each individual employer might [02:25:53] have multiple payment schemes and all we observe on the transaction data is the payment scheme reference. So my university might have different payment schemes for monthly paid and weekly paid employees or it might even pay me under [02:26:07] different payment schemes. So maybe my regular pay might come out of one payment scheme, my bonus payments come from another payment scheme. So you don't have a clear identif identity of the job and the connection between the [02:26:20] job and the firm. So what we've done is to use a lookup file where we can link the PA schemes with enterprises understand which schemes belong to which enterprises and then allocate employees [02:26:35] to firms uh and do that in a regular basis. So as I said the data starts in 2014 it's monthly goes up more or less to the present day and we have worker and firm IDs. So now we have something [02:26:50] that looks a little bit more like uh your LEHD. [02:26:55] One of the work streams we're involved in, the one we're talking about here today is around the coherence between the admin and the survey data. So we're looking at two survey sources in particular. So one is the short-term [02:27:09] employment surveys and that's the business survey that the ONS uses as its main input to understand movements in headline employment. So it generates in this case a series called private sector [02:27:22] employee jobs. To operate that survey, the office sends questionnaires to 37,000 businesses each quarter and the business will report back to the office the total number of employees that it has on a specified [02:27:37] date in uh in that quarter. The second survey we're looking at is a survey of earnings and this informs the ONS's headline measure of earnings [02:27:51] growth, average weekly earnings. So to generate that measure, they're sending questionnaires to 9,000 businesses a month. Note only those with 20 or more employees. So it excludes small firms and the business must report the total [02:28:06] gross pay bill in uh their response. So what we're going what we're doing in our coherence work stream is essentially linking the firms that are in the survey. [02:28:20] We're finding those firms in our admin data, looking to see what the administrative payroll data tells us about how many firms, how many employees, sorry, that firm might have and what their earnings might be and [02:28:35] looking at the coherence between the two. [02:28:38] One challenge we have in doing that is that these surveys don't all operate at a firm level. Sometimes firms will have different reporting units, multiple [02:28:50] reporting units for different divisions and those reporting units don't align with the pay schemes. So just to show that visually, a simple business structure gives us no problems because the blue box here is [02:29:05] the pay scheme. This firm has one pay scheme. So we can identify all the employees together that work for that firm. And when the firm responds to the business surveys, it sends a single [02:29:17] response for it has one reporting unit. So this is no problem. We can uh identify or look at the coherence between the survey response and the [02:29:28] admin data for one firm as a whole. A complex business gives us some issues. [02:29:35] So if we look here in particular, enterprise B, you'll see enterprise B here has a couple of different pay schemes. maybe for the reasons I outlined earlier, monthly or weekly paid employees separated out and it has two [02:29:49] reporting units. So when it's responding on employment and earnings to the surveys, it might do separately for its manufacturing division and its retail division. So our short-term fix to resolve this problem is that we use the [02:30:04] business register to identify the employment shares across these two reporting units B1 and B2 and then we collapse the data across the pay schemes and divide them out in terms of shares [02:30:19] across the uh reporting units. We're working on a more sophisticated uh approach which a little bit like your um establishment level allocation in the LEHD allocates employees in the pay [02:30:32] schemes to different work sites or local units and then aggregates up uh from there. So that's the kind of setup of what we're trying to do. Johannes is now going to come and say a word about uh a [02:30:46] very high level overview of some of the early outcomes. It's very much work in progress. So we won't be presenting a lot of detail and then I'll come back at the end and just summarize where we're getting to. [02:30:58] >> Thanks John. So I'll start off with by looking at the uh employment. So the slide here presents uh our first set of coherence results comparing the employment estimates we get from STE [02:31:12] with that of the derived estimates from the linked employer employee data. So the lead-based estimates are derived for the survey observations which are then weighted up or grossed up by the survey weight to obtain an aggregate estimates [02:31:27] from the link data set. As you can see from the chart here, the two series track each other well, which gives us confidence that the the lead data captures the same employment trends as [02:31:40] the survey does. Initially you see the lead based estimates which is the dark blue line sits above the survey measure which is the dotted green line but that [02:31:52] is largely due to conceptual difference. So in the lead a job is counted whenever an individual receives any pay during the month whereas employment measures [02:32:05] are done on a specific reference date. So if a an employment begins after the reference date or it ends after it then they're treated differently across the [02:32:19] two systems. When we align the leadbased estimate to the STS uh reference data concept using start and end date the dark blue line becomes the light blue line which is pretty much in line with [02:32:33] the survey estimates. So this provides a strong evidence that our lead data can reliably reproduce headline employment trends supporting its use as a quality assurance tool. [02:32:46] While the admissive data cannot replicate all of the workforce breakdowns such as the the full-time part-time split, it does however provide a strong, robust and timely tool for [02:33:00] monitoring overall employment both in levels and trends as well. [02:33:08] So we've seen quite an encouraging alignment at the aggregate level but for operational use we need to see the coherence at the individual business level. So what you see here is the the [02:33:21] distribution of the differences between employment reported by businesses in STE to that of the employment derived from lead. As you can see, around 1/5if of firms report the same amount of the same [02:33:36] values for their employment as the lead derived estimates and around 42% of them are within 5% of the administrative data estimates. [02:33:48] Importantly, the distribution is symmetely symmetric and is centered around zero suggesting that there is no systematic bias to either source. So [02:34:01] this type of this is a clear example of you know how the admin data can support the statistical production while preserving the survey as a primary [02:34:12] source cuz we can use the admin data to potentially identify reporting errors and usual responses or businesses that may require further investigation. [02:34:26] We then repeat the same exercise looking at the average weekly earnings and we see a similar pattern. The figure you see here compares average weekly earning that is derived from lead with [02:34:39] the estimated collected through the uh monthly wages and salary survey. It's worth mentioning that this is based on total pay basis. [02:34:48] So as I as I've mentioned the the two series struck each other quite well both in levels and also in movements which indicates strong coherence. [02:34:59] There are however some important conceptual difference between the sources. So the MWSS collects detailed information on pay components such as you know bonuses, holiday pay or um [02:35:14] areas whereas the P pee data we've got only reports on a gross or total pay basis. [02:35:22] Even though we have this conceptual difference, we still are able to use this to quality assure the earning statistics at the aggregate level. Again when we see the distribution at the firm [02:35:38] level we see a similar pattern. You see a balanced symmetric distribution which is centered with no systematic bias towards either sources. [02:35:52] So, so essentially we can use the uh the PRTI data to identify unusual surveys responses and [02:36:08] returns also support the production team in investigating misreporting or errors. [02:36:17] So we we've seen strong coherence both at the aggregate level as well as the the firm level. But that's not enough for the administrative data to be useful [02:36:30] operationally. It needs to be processed and delivered quickly enough to support the statistical production cycle. So what we see here is a timetable for the stairs production timetable and the [02:36:44] delivery of the lead data set. So the box you see at the bottom is the time table for lead where is if we take the March as an illustrative reference [02:36:56] period. We see the pay RTI data received shortly after the end of the reference period following processing through the lead pipeline. An initial extract is available approxim approximately two [02:37:10] weeks later and that is followed by a revised extract where imputed values are replaced by with actuals uh a month after that. So in 6 weeks effectively we have a a very strong data [02:37:24] set available for STE which is the top box which supports a quarterly indicator. There is as you can see sufficient time for us to use both the initial and the revised administrative [02:37:37] data extract to conduct a thorough validation and quality assurance process. The timetable will be a bit tighter when we look at the monthly earning statistics but we're working [02:37:50] towards making some gains to shorten the period so that the PRTI will be available uh a bit quicker. So the remaining challenge is therefore more statistical and methodological [02:38:03] rather than operational. So uh invite John back to say a bit about that. [02:38:11] >> Thank you Hannah. Yeah. So I think we recognize that we've got a a strategic opportunity here because we have relatively high frequency data. It's monthly. It covers everybody. uh as long as we can really work out some of these issues between [02:38:25] the coherence of the admin data and the survey data, we have the opportunity to contribute to the real time production of employment statistics uh in an ongoing basis. But that's causing us of [02:38:39] course to pull back and say well what are really the range of options that we might have available to us? uh and they have different levels of risk but also different levels of return. So right at the top of course we have the [02:38:53] opportunity to do nothing to leave the survey series as they are and not really to take any advantage of the admin data that of course does the most to preserve the integrity of the survey series but misses out on the opportunities for [02:39:08] improvement. So maybe a second level option is parallel production. So maybe we have uh parallel estimates produced from the surveys and the admin data and any discrepancies are used to inform the commentary around the main series [02:39:23] perhaps urging caution over certain movements in the survey series because the admin data is saying something different. The next level might be to um perhaps produce complimentary [02:39:36] statistics. So maybe using this admin data, full population data to go into finer detail than the survey samples allow. Maybe fine industry detail would be one uh example. Of course relies on a [02:39:50] certain level of coherence between the survey and the admin source. But as Johannes indicated, we think we also have the potential to push into operational quality assurance. So using the admin data to identify potential [02:40:04] reporting errors in the survey and allowing production teams to follow up on those. Of course we could also be using the admin full population data to help with waiting for the surveys. We've heard a couple of papers which have [02:40:17] pointed in that direction uh uh already although in their case using the uh survey data to sort of inform um their how they wait their payrolls. We would be thinking of going in the other [02:40:29] direction. We could of course uh use the admin data for imputation of survey level estimates but in order for that to be possible we really have to be very confident about the coherence of the way in which we're measuring employment and [02:40:43] earnings in the admin data and that it's a perfect substitute for what's in the surveys. We could then move on to targeted replacement. So I mentioned the earnings survey doesn't cover small firms at all at the moment. earnings [02:40:56] growth in small firms is estimated, but we could be um using the admin data to give a real-time indication of that. And then ultimately, one could even think about replacing the surveys with admin data. That's not something we're [02:41:09] considering because as Johannes indicated, the surveys capture uh many more components of earnings for instance that are not observed in the admin data. [02:41:19] So, we don't think that that is likely to be the end goal, but there is a real menu of options that has to be thought through. So let me just summarize because I know we're out of time. The creation of the new uh lead in the UK from admin payroll data I think does [02:41:32] give us the potential to improve the UK's existing labor market statistics addressing various challenges. We've been exploring that potential uh over the past few months through some coherence work streams analyzing linked [02:41:46] samples which connect the admin data in our lead data set with the short-term employment surveys and the monthly wages and salary survey. You've heard some of the results. High level of coherence at the aggregate level. Some discrepancies at the firm level which may have the [02:42:00] potential to help us identify reporting errors and so on. One advantage we have is the speed of processing which really enables the the lead to be integrated into the production timeline within the office. Uh and we would hope that [02:42:14] eventually uh we see a role for lead in maybe extending some of those uh series. [02:42:21] uh that's informing some wider discussions in the office about the effective use of admin data in the production of economic statistics and I think on the previous slide I rehearsed some of the considerations in that respect and I think hopefully um our experience provides maybe hopefully an [02:42:36] interesting alternative perspective to some of the debates that you're having about how payroll data might feed in and be used for statistical purposes. So thank you for your time. [02:43:00] And the um discussive for these three very nice papers will be Jason Ver. ## 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.