Transcript of the talk, from the video's captions. Auto-generated: speaker names in particular are unreliable. ======================================================================== # Overview of the U.S. Census Bureau's Frames Program Authors: Deirdre Bishop Discussant: None Video: https://www.youtube.com/watch?v=aXOe_NS3iVo&t=6424s ## Talk (01:47:04 – 02:05:10) [01:47:04] That's funny. We're um ready to get started with our [01:47:27] next session and our first speaker will be Deerra Bishop from the Census Bureau talking about the Census Bureau Frames Project. [01:47:39] >> Thank you, Katherine, and good morning everyone. Thank you for the opportunity, the invitation to be with you this morning and speak a little bit about the Census Bureau's Frames program. Before I do that, I would just like to take a [01:47:53] minute to introduce myself. I've met many of you casually, but not formally yet. Uh in January I was asked to serve as the Census Bureau's associate director for economic programs which has been a true honor leading this [01:48:08] directorate over the past uh almost nine months now. Uh before I speak about the frames program I did just want to say a little bit about the economic directorate. You know FY26 as we heard earlier this morning was a tremendous [01:48:22] year of change but with that we were able to continue a lot of great work. We continued planning for the 2027 economic census and the census of governments and we released, this was a surprise to me, [01:48:36] 299 economic products in FY26. [01:48:42] 120 of those were our principal federal economic indicators. And even though we had a lapse in appropriations, when we returned, we were able to get things rescheduled, replplanned. We were able to get all that data out the door and [01:48:55] maintain the time series, which I know will make many of you very happy to hear that without changing the methodology. [01:49:02] Uh also interesting within the economic directorate, we're responsible for the management of administrative records. We currently have 222 data sharing agreements, memorandums of understanding with our partners. And I [01:49:16] think that's important when we think about the way the way forward. Uh this year we ingested 20 billion records alone from our tribal, federal, state, and local government partners along with commercial industry. Uh as we look to [01:49:31] fiscal year 2027, I'm happy to report we're going to continue moving things forward. When I started in January, I conducted a directoratewide town hall and then five listening tours because I [01:49:45] wanted to hear what's working well, where do we have opportunities to innovate, to change. We use that information to draft a new strategic plan which we're going to publish by the end of September. That will cover years [01:49:57] 2027 through 2031. uh that will include our work associated with the 2027 economic census, our benchmark census. Happy to report that we are hiring. When I came on board, [01:50:11] only two of eight senior executives were in place. I made the third. Over the past few months, we've interviewed and selected four more. So, we'll have seven of our eight senior executives on board [01:50:24] as we start the next fiscal year, which is great news. Additionally, 129 uh staff will be hired. 49 of those are on board. And so I hope this instills a little bit of happiness and confidence [01:50:38] in the room and makes us excited about how we move program new programs forward such as the frames program. And so let's talk about that a little bit. For the past six years, we at the Census Bureau [01:50:53] have been really re-engineering the way we do business, thinking about how best to operate moving forward. We brought a lot of experts together who focused on different areas across the Census Bureau. And you can see those areas [01:51:07] here, uh, our geospatial area, uh, business area, jobs, and our demographic area. And let's talk a little bit about what that means at the Census Bureau. [01:51:20] As you know, our mission at the Census Bureau is to serve as the nation's leading provider of quality data about its people and economy. And this means ensuring that we count over 331 million [01:51:33] people living in 146 million housing units and also that we count approximately 8 million business establishments along with over 14 billion records of [01:51:46] jobs. We're accounting for our frames of information about location, businesses, jobs, and people allow us to capture and publish these quality statistics. [01:51:58] For decades, though, these frames of information have been maintained in silos by separate entities, separate directorates at the Census Bureau with really little collaboration or [01:52:10] communication across directorate. I'm happy to report that the Frames program is changing that we're enhancing the way we communicate with one another. We're modernizing our infrastructure and the way we think about census and survey [01:52:24] taking. You can imagine this is not an easy task because there are a lot of people who think business the old way is business the best way and we're having to twist some arms when we think about change. [01:52:39] That's working though. Additionally, we're also operating on really heavy legacy systems, especially when you think about our geospatial and business side of the house. We're now shifting from commercial software to open- source [01:52:53] software. We're moving applications from on premises servers to servers to cloud infrastructure and we're incorporating the use of artificial intelligence and machine learning. This is very exciting. [01:53:07] With all that said, we have had to rely on the important work that we did together over the past 30 years that's been serving as a foundation for our way forward. [01:53:18] As illustrated on this slide, we had over the past created various framelike data sets containing information about places, locations, that's the geography, about businesses, about jobs, and about [01:53:31] people. Let's start here with the master address file. This is a national inventory of addresses historically developed to establish where to count people during the dennial census of [01:53:44] population and housing every 10 years. Since the 2000 census, this list has been continually updated and currently contains over 146 million housing units. [01:53:57] These addresses are assigned a coordinate of latitude and longitude in our geospatial database. That's what we call tiger. When you hear the census bureau say, "Our goal is to count everyone once, only once, and in the [01:54:11] right place," the math tiger system is what makes that right place possible. [01:54:18] Similar similarly, our business register is a master list of all the US business establishments. It tells us where the businesses are located, the type of industry that they're associated with, and how big they are. So, think [01:54:32] employment and payroll. and you know this very well to link business entities to each other and over time a lot of people including people in this room work to develop the longitudinal business database the LBD using the [01:54:45] business register related to jobs. The longitudinal household employer dynamics program, the LEHD program, grew to capture a large share of wage and salary employment [01:54:59] using state unemployment insurance wage records and by linking people with businesses. We've heard a lot about the LBD, the LEHD over the past two days. [01:55:09] And to enhance our ability to link people over different sources over the past decade or so while maintaining confidentiality, we've been enhancing the person identification validation system or PVS. [01:55:24] While these infrastructure projects supported the work at hand, what lacked was a comprehensive nature to the strategy and to the coverage. and it led to a lot of duplication across the Census Bureau and in some instances [01:55:39] differences in statistical measurement. We recognized that we needed to find a way to break down the silos across the agency. The Frames program brought the people together. These experts, these professionals are now sitting at the [01:55:54] same table. They're thinking about commonalities in their work and they're identifying opportunities to enhance existing new new existing products and create new products. They are looking [01:56:07] forward. On the last slide, we spoke about geography and the math tiger system. [01:56:15] This is the equivalent of what we call the geospatial frame. I mentioned that this was historically developed to establish where to count the people during the dennial census every 10 years. But now the geospatial frame is [01:56:29] being used to support nearly every census and survey at the census bureau. [01:56:34] What the frames program helped us realize though is that the geospatial frame could very effectively be used to track both residential and non-residential addresses. Importantly, those addresses that reflect the [01:56:48] businesses in our nation. We never focused on the businesses on the geospatial side before. [01:56:55] We learned this by matching the master address file to the business register. [01:57:00] And we were pleased to discover that we already listed 93% of the addresses in the business register within the master address file. For that reason, moving forward, we're going to create an [01:57:13] enhanced version of the MAF Tiger system to include both residential and non-residential addresses along with designations for mi mixeduse structures. [01:57:25] We've also put a lot of effort into developing standardization and matching tools so that internal stakeholders can use the same methodology to provide unique identification codes for [01:57:36] structures. In the past, users developed their own methodology in a vacuum. [01:57:43] But to me, arguably one of the coolest advancements being developed is what we call select your own geography. This platform will allow internal users at least at least to start with the hope that we can take this to external users [01:57:57] an opportunity to identify geographic areas of interest to their work unconstrained from our standard geographic areas like census tracks and block groups. They'll be able to query and produce statistics using their own geographies. [01:58:12] A very critical note to share here. The geospatial frame again supports all the other frames serving as a foundation in one way or another. [01:58:24] As I mentioned earlier, the business register has served as a source of information over the years, including development of the LBD and the business dynamic statistics products. [01:58:35] Now, we're researching ways to enhance the business register by incorporating new data sources, including administrative records and third-party data to increase the power of our business data. With the expansion of [01:58:48] this information, we're calling this the business frame. [01:58:53] The business frame leverages matching methodology to link these various third-party sources. Think point of sale data and the administrative records, think IRS, Social Security Administration, processed in the current [01:59:06] business register. The inclusion of new source data may help identify company births and deaths. Happy. I know many people in the room are thinking about that in a timelier manner and facilitate a more comprehensive sampling operation [01:59:21] for our economic surveys and products. For the past 10 years, 20 years, we've been continually updating the geospatial frame. From my perspective, we need to move forward and we need to be [01:59:34] continually updating the business frame to provide you with the most up-to-date information that we possibly can. [01:59:44] The next slide will be of particular interest to those in the audience if you're here because of this conference title, innovation and measuring employment. [01:59:53] The job frame expands upon the LEHD. Remember that's the longitudinal employer household dynamics program. [02:00:00] It's very well known for capturing a large share of wage and salary employment using unemployment insurance wage records from the states and by linking people with businesses. [02:00:12] The job frame is an inventory, as I mentioned, of over 14 billion job records containing a relationship between a person and a business in which at least $1 in wages was paid in the in a given quarter. [02:00:26] The job frame will now use IRS earning records related to wage and salary jobs and self-employment jobs to provide the most comprehensive annual snapshot of employment in the US economy. The job [02:00:40] frame can then be used to measure earnings from all sources of work-based income. [02:00:46] Enabling measurement of these dynamic patterns of the entire workforce allows for the creation of new and innovative products. Be on the lookout in December at the AEA stat conference to learn more about self-employment dynamic [02:01:00] statistics. Or you could speak with Andrew Foot, who's in the back of the room, who's our subject matter expert at the Census Bureau on this topic. Andrew, could you give a hand raise, please? [02:01:11] Okay, thank you. Now, looking forward, let's speak about the demographic frame. The frames program again builds on the last 30 years of infrastructure development to [02:01:25] harmonize efforts and integrate all these available data sources on a topic area. the work of the frames program. As we were thinking about this, we said we have geospatial, we have businesses, we [02:01:38] have jobs, but we don't really have people. [02:01:42] This work sparked the creation of the demographic frame, the newest of these four frames. It provides a comprehensive database of person level data as well as residents location. [02:01:55] This frame enables more timely, cost-effective and flexible statistical products. It reduces duplication across the agency and it creates a platform for incorporating new data sources as they become available. [clears throat] [02:02:09] Much of the contribution of the demographic frame is in leveraging, harmonizing, integrating a massive amount of administrative data and third-party records and this incor includes incorporating new records. [02:02:23] This is being used to predict the most likely residential address of a person on a nearly ondemand basis. The demographic frame this year alone we produced 18 extracts. The information is [02:02:37] being used to support 2030 census planning, the American community survey, htops, the household uh pulse survey that we heard about yesterday and population estimates just to name a few. [02:02:49] And as a reminder, the demographic frame would not be successful without the foundation of a comprehensive geospatial frame. Accurate data about places and location has allowed the demographic frame to leverage address information to [02:03:04] associate a person in a place that would not be feasible if it had started from raw address data alone. [02:03:14] Now, what are the broader benefits of the frame frames program? We've talked about this a little bit and on the slide you can see the frames program expands the Census Bureau's ability to report on changes and trends affect affecting our [02:03:28] nation's population, economy and communities. It reduces burden on our respondents, individuals and businesses by reusing existing Census Bureau data and allowing survey methodologists to [02:03:41] select more targeted samples. It reduces redundancies, improves the quality of our products, and enables more effective use of our resources. [02:03:51] And as the central source for the data, it ensures consistency in measurement and sources. I hope that's music to your ears. Linkable frames enable the Census Bureau to measure new economic and [02:04:04] demographic phenomena without the large startup cost of a new program. [02:04:10] Again, I really appreciate the opportunity to be here with you and provide a sneak peek of the Frames program. Happy to talk more about this on the break or in the future. The next two presentations that we will hear this morning are [02:04:25] closely related to the work of the Frames program in terms of integrating data sources and investigating discrepancies. [02:04:32] Henry Hyatt is here with us to discuss the implications of how differences in the creation and maintenance of a foundational frame data impact measures of business dynamism. And Martha Stinson is here with us to showcase work that [02:04:45] relies on integrating a number of frames objects because many important economic phenomena straddle two or more frames. [02:04:54] This is an example of overlap between frames and why it's so important that we work together across the agency. [02:05:02] We want to be able to describe relationships in multiple ways. Thank you again for the opportunity. ## Discussion — Benjamin Pugsley (02:27:03 – 02:37:48) *Shared discussion for Session 5: Measuring Employment Using Tax Administration Data; the discussant covers every paper in the session.* [02:27:03] >> [applause] >> Ben Pugsley will be the discusser. [02:27:50] Terrific. Thank you very much to the organizers for inviting me to uh uh to read these papers and uh and participate in in this discussion. So right off the bat, these are two excellent [02:28:04] presentations. Um I and it may the second one in particular it it made me think back because I I like Henry many years ago I had looked at the establishment um entry rate of [02:28:17] establishments in in the BLS data and entry rate of the establishments in in the the census data and noticed some differences and I couldn't really and I' I've talked to many of you in the in the room about some of those differences years ago and I couldn't really get to the bottom of it but the the census data [02:28:31] were a bit easier to use and it was a a little bit harder to get to get to the to the data on the on the BLS side and I and I kind of stuck with the the census data and of course over time these these differences got much bigger and and so I was really excited to have the chance to [02:28:46] read the paper by Henry and his co-authors to to learn uh learn more about the source of those divergences. [02:28:52] Um Henry concluded with the discussion about the Hopenheim and Rogerson model. [02:28:56] I love the Hopenhe and Roger model. I I don't have a I don't have a lot to say about the Hope and Hiden Rogers model in this particular paper because I think it's just an instantiation of uh a something that matters what that depends on the uh that depends on the [02:29:10] establishment size distribution. So we got to get the establishment size distribution right. Okay. So in the spirit of in the spirit of the conference and thinking about this frames program and thinking about uh integrating the the BLS and and the census data, I want to talk a little bit [02:29:24] about the challenges and the payoffs to integration. Um we have learned a lot and many of us already know about the two different sort of data streams that are going into the QCW program and going into the um and going into the census business register. Right? So the QCW is [02:29:39] built up from state level UI reports. Um Jason made an a joke yesterday about hurting 51 sheep. It's actually I mean in some ways that ends up being a little bit easier than than hurting one big [02:29:53] sheep in particular. uh but it's an extraordinary example of of uh federal state cooperation and coordination. [02:29:59] Okay, the census business register is coming from different streams. So primarily from federal tax reports and supplemented by the economic census company organization survey and other data streams. What I didn't appreciate until I read Henry and his co-author's paper is that the census led of which [02:30:14] I'm a more recent user uh links the QCW and the business register in an incredibly helpful way. Uh the problem is is that the QCW frame and the business register frame can disagree in really meaningful ways. And so my comments are uh about the paper and the [02:30:28] frames project and and the source of this disagreement. So I'm going to I'm going to start to by by saying yes, there's some differences in coverage. Um if you looked at the lines that that Henry presented, I would say that the [02:30:41] measures of total total in in quotation marks private payroll employment largely agree. Okay, so there yes, there's some differences, but they're not diverging. [02:30:49] the what's diverging is the number of establishments. So what does that mean? [02:30:52] That QCW shows far more establishments in the business register. That means that the um they disagree over the allocation of of this employment across places and and that's really really important. It's important for the Openheim Rogers model and it's important [02:31:05] for a lot of questions. So the QCW feeds into the business employment dynamics, the business register feeds into the BIS county business patterns and and and the BDS. um those narrow the gaps a little bit because some things about zero employment observations and and household employees and so on, but [02:31:20] there's still a really big large gap. Okay. Um so which one's right? I hate it when students do this. Yes, there there there's there there's both uh they're both right to some extent. Okay. So I want to narrow things down a little bit [02:31:34] and I want to say that if you're looking at larger establishments, I wouldn't sweat it. Okay. the the uh on the orange line here. So this is just the uh splitting the the uh the BDS and splitting the business employment [02:31:47] dynamics by by size. Um and this is so this is much smaller than than Henry did. So this is one to f one to four employees and and five plus employees. [02:31:57] And so the uh the orange and lines the solid one is the BDS and the broken one is is the business employment dynamics. [02:32:04] And they're they're pretty close. Of course, those are big establishments and so that that is a difference. But even in logs, it's it's it's relatively uh small compared to looking at the BDS, which is the solid green line and looking at the BED, which is the broken [02:32:17] green line. And that's there's a huge difference. Um and so small, very small establishments are growing really quickly in the the QCW frame and they're not growing uh as quickly in the the um [02:32:31] on the census side. So there's some differences in coverage. Uh the home health aids are on the on the BLS side. [02:32:37] You can quibble about whether they should be counted or not. But there also is some differences in conceptual differences in in what counts as an establishment. Um for example, the different ways of feeding into um different different ways of of taking [02:32:51] reporting units and converting it into uh converting it into uh work sites or or uh locations of of business activity. [02:32:59] Okay. So the surging and stalling paper I view it primarily as understanding the source of divergence especially for these really small establishments and I would say kind of despite the title the title's a little bit of a red herring. [02:33:12] It it doesn't read as a critique on on declining dynamism. What it tells us is there's more employment in these small establishments potentially and that could be good or bad for for efficiency. [02:33:23] The problem is is that there's real discrepancies in these publicly facing tabulations that are designed to measure similar things. And so the rest of my presentation is is a is a comment. It's kind of an easy one to make. Uh but it's a comment and a wish. Okay. And both of [02:33:37] these things apply to the frames project as well. So what's the comment? Uh the comment is we have to pay attention to the rising incident of remote work. And the QCW frame is particularly uh sensitive to this. You already heard about this this morning, but as you [02:33:49] know, UI tax is collected typically by the state where the work is performed. [02:33:54] And so if you're an employer where you have fully remote or even hybrid employees, they're going to pay their state unemployment tax to the state in which the employee performs the work. [02:34:02] Now, sometimes this can get quite complicated. Um but there's a whole uh framework for localization of work rules that provide guidance for these complex cases. And at the end of the day, that um employee and that payroll goes to a particular state and that state might [02:34:16] not be where the where the headquarters or where the actual business itself is located. So multiple work site reporting actually does try to take this into account. So they try to not count the the remote employees. But the problem is is when those remote employees are in [02:34:30] another state, it generates another reporting unit. It can be hard to sync these things back up to realize that this is not an actual establishment in another state. It's just somebody who is a coder and and working in Michigan and [02:34:42] and um or let's say working in um New Jersey, but actually living in uh living in Michigan. Okay. And we know remote work is important. So this is taken from a a census paper and you can see that [02:34:55] remote work triples uh since since 2020. Okay. [02:35:01] Okay. So this is rising incident of remote work. Um the Henry and his co-authors showed that the 54 and 56 the professional business services that's a large part of this this gap. Now their data only goes back to 2016 where they have the the joint distribution but even [02:35:16] then if we look at those those industries these are these are ones that were early adopters of of uh of remote work. Okay. So the census and the the business register and the county business patterns the the way in which they ingest that information on on [02:35:30] locations wouldn't be contaminated if if we think it's actually contamination by these employee work arrangements. And so one big question for for this paper is, you know, how much of this divergence of small establishments is is explained by [02:35:43] the changes over time in in work arrangements. Okay. Um I got about a minute left. Uh I that was my my comment. Oh, two minutes left. Okay. Uh here here's here's my wish and and this [02:35:56] is this is aspirational. Um, I wanted to say that the so I can just use the BDS, I can just use the LBD because it's not necessarily sensitive to to these problems. But the as we know on the the census side there's there's [02:36:10] shortcomings as well. Uh, timeliness is an issue and and timing of an establishment birth and death is is a is a really significant issue. Even if we have it um even at years that end in two and seven, we we get a fairly precise [02:36:23] measure. So there are uh shortcomings to the the the census side and there are many advantages to to the QCW frame. So for the surging and the the the stalling paper um this is sort of a a challenge [02:36:37] to the the authors. Um I think everybody in this audience is probably already convinced. Um, I'm I'm less interested in the the economic uh sort of the outcome of a Hopenheind and Rogerson model that's matched to a particular [02:36:51] establishment size distribution than I am in in a user guide for how to harmonize the the data that we have these two we have these two data sources that are both providing signal uh not not just not just one adding noise and [02:37:03] and so I'm more interested in in how how do I use the QCW data to to enhance the um the business dynamic statistics and for the frames project. Uh Henry alluded [02:37:15] to this as well. Um, the QCW obviously feeds into the jobs frame, but I think what we're what we're learning and and this is a great example is that the QCW has great potential uh not just for for [02:37:28] jobs obviously, but but for the higher frequency flow of UI data to help us more precisely measure business locations in the geospatial frame uh as as well as the boundaries of of the business in the the um what is referred [02:37:42] to as the businesses frame. So I will uh I I will end on on on that. Thank you. ## Q&A (02:37:48 – 02:50:51) *Shared floor discussion for Session 5: Measuring Employment Using Tax Administration Data.* [02:37:48] [applause] >> So we we have a little time for uh questions and comments and I will take advantage of having the mic in my hand [02:38:02] to ask a question. um which is I I mean I make a comment and and ask a question which is I I'm not terribly surprised that there's more establishments in the [02:38:13] BLS frame. um the it it was described as being based primarily on unemployment insurance reports, but that's not exactly right because for a long time the BLS has also done uh uh created the [02:38:27] multiple work site report, which is an effort to go out to businesses that have a single unemployment insurance account number to ask them about where the locations are of all the people who are working from them and identify the [02:38:41] individual establishments. So, it's not really surprising to me that there's more establishments in the BLS frame. [02:38:47] Um, but I I I guess there's also a question about when you really care about these establishments and when you care about the firm, if you will. So if you're if you're concerned about, you [02:39:01] know, productivity and um the responses to shocks to the the the e economic environment, it may be that what you really care about is the decision-making unit, which would be [02:39:15] the firm rather than the establishment. If you're a local, you know, community development person, you may care about the, you know, where the people are actually working because, you know, that's going to affect, you know, who's paying property taxes and and all of [02:39:30] that kind of thing. So, I this is maybe more a comment than a question, but I I don't know. I'd be interested in Henry's thoughts about how how I should think about that and which is relevant when and may John may have a comment that's [02:39:46] related to to this. >> Why why don't we actually why don't we collect a few comments and then >> so I've been watching both of these projects which are are great. It's it's [02:40:00] the the frames project's fantastic. I kind of question for Dedra that might be hard to answer which is you know this is amazing breaking down of the silos and integrating all this data. I don't know whether you can give us a timeline of of [02:40:13] when we're going to start seeing uh of I'll say new products. You might say these two papers we just saw now and and whatever are are the new products but but what are the timelines? and and I I guess I'm uh particularly interested in [02:40:27] will this speed up the timing of the business register and then various data products like the CBP and uh and the BDS. So for for Henry I I I've been talking to that team for the last couple [02:40:40] years about this paper. uh ju just so these authors already know this this comment but I think there's another puzzle out there that's kind of interesting even within the BLS data which is if you if you take the beed [02:40:54] data and you compute the number of establishments using a longitudinally consistent method you start in a particular year and then you take establishments and you add add all the openings and you subtract all the closings and you and you track that [02:41:08] measure it's actually much closer to the BDS than then than than uh actually the total number of establishments who reported the QCW and BED. So it doesn't look like this is coming from at least [02:41:21] measured openings and closings. And so that's I think a puzzle that we need to figure out. Um the other comment I really echoing what uh uh Katherine said it when actually look at the firm level [02:41:36] data, the data sets aren't so different. The firm counts are actually pretty similar. And I agree with Katherine. I mean, one one for those of us working in firm dynamics and establishment dynamics, we always kind of confront the problem that Katherine's already identified. [02:41:54] >> There's a mic. >> Um Henry, um if you wouldn't mind identifying yourself. [02:42:01] >> Oh, Andrew Reamer, George Washington University. Um great work. uh within sectors to what extent are you uh do you think that there's differences in classification by census and BLS of the same [02:42:16] establishment so that some of the differences within uh nakes codes particularly at the four and five six digit level is that the establishment appears in another another nakes code and context for that is in in [02:42:30] the development of the AIES annual integrated economic survey census told OM I'm quoting nakes classifications can be unnatural or challenging for some businesses. So, uh there's a challenge for census to get the NAKES code right [02:42:44] because businesses are self-reporting. Okay. Uh Ryan Decker from Federal Reserve Board. This is such awesome stuff. Um but I have a comment sort of picking up on Katherine's comment about [02:42:58] uh like the boundary of the firm. Uh if you drill into these 624 establishments where I think about half the excess is uh and if you go look at the QCW what you find is that in 2013 [02:43:11] um they were they became 624 and had previously been 814 private households as Henry was was talking about. So that was a decision that was made for the QCW. Um it wasn't sent back into history. QCW is not a time series. They [02:43:24] will tell you if you ask uh them about that. Um but my first sort of idea is uh should we have more scope for coordination across the agencies for deciding where to put some of these different establishments is kind of related to Andrew's comment. Um but then [02:43:38] the broader thing is these look to be like basically individuals who have some sort of uh employer uh business records but in some ways are acting a little bit more like non-emp employers. And so I think the bigger P and and indeed if you go drill down it looks like they [02:43:52] probably have one employee per establishment and these are some very small number kind of like what Ben showed. So what I'm wondering is so we're treating these folks as establishments employer establishments. [02:44:03] Are we consistent across all the other sort of quasi employer uh uh arrangements that could be out there like temp help workers construction dispersed location workers. There's some documentation on that in the BLS uh stuff. contractors, the remote work [02:44:17] thing has come up. Um, when we're going to go calibrate these models to the firm size distribution, you know, saying, okay, well, this one decision QCW made, we should probably let that count, but what about all these other, you know, uh, decisions? What should we do about [02:44:31] them? And then the real elephant in the room, which is the non-employer universe and this very clear line between the employer and non-employer universe that exists in all of the data that we're going to hear more about later today. [02:44:41] So, I'm it's not a question, it's a comment that um I think we have a real challenge here and I'd be very hesitant to say, "Okay, let's go with QCW for this firm size stuff or or establishment size." I think we have a much bigger mess on our hands. Uh as non-employer [02:44:56] universe has grown tremendously, uh as folks here have shown, um I I I think we've got a lot of work to do here. [02:45:05] respond. >> Um, let me just go in the order of the papers and ask Dear Dre to respond and then and then you thanks John. Thank you for your [02:45:23] question. I think it's uh important to remind everyone that the work that's happening within the frames program is primarily concentrated on the foundational elements that are used to [02:45:36] create these new products. And so when we talk about enhancing the geospatial frame to include both residential and non-residential addresses and then also [02:45:49] attribute mixeduse structures and ensure that those flags are represented to show where we have one of those addresses in the geospatial frame, the business frame, the demographic frame. Those are all foundational pieces that are [02:46:04] strengthening what we release next. Okay. [02:46:08] A good example of a new product coming out the door is the self-employment statistics that we talked about that's going to come out in December. And then another piece of what's making the timing better is that we are moving from [02:46:23] off on prem servers to the cloud and we're moving away from SAS and Access DB and SQL code to Python and R. And John and I were speaking a week ago or so and [02:46:37] I talked to him about a briefing that I had just received at the Census Bureau about two products related to justice and criminal justice. And I said it was amazing to hear these two products that [02:46:50] had moved off of Access DB to um R or Python and are now maintaining all of their documentation and code in GitLab repositories. The one product was able [02:47:04] to go from and I wrote this down three weeks in production time to four days. [02:47:10] The other was able to go from one month to one week. So you see how this is kind of all coming together. The advancement of technology, the strengthening of the foundation, it kind of fosters creativity, innovation along the way. I hope that [02:47:25] helps. And then to the other commenter about having more conversations across agencies, I definitely agree, especially after reading Henry's paper and sitting here with all of you. We need to do [02:47:39] better at that and I think the leadership is starting to be in place to enable that. Um, but we also need to talk more within agency. So, we'll work on that too. Thank you. [02:47:52] So, so I I'll try to go through um the comments in roughly roughly reverse order. So, uh I'd like to I'd like to take issue with some of the numbers that we've been throwing around, including by myself. I was saying like census [02:48:05] publishes 7 12 million employer establishments. Dearra said 8 million and if you go to the the county business patterns there there are some some definitional differences and you can get a 8 million number. I am going to say the Census Bureau doesn't publish 7.5 [02:48:18] million establishments. It doesn't publish 8 million. It publishes 32 million establishments uh operating in in 2023 because if you add the number of employer and non-employer establishments and when it comes to non-employers, Census Bureau does call them [02:48:33] establishments. So, so yeah, it it's not it's not 7 12, it's not 8, it's 32 million. So, uh it it's true like uh how do we define an establishment? [02:48:42] Non-employer establishments, it's generally just a sole proprietor. It's one person uh and we count that as an establishment. uh for home health aids, it's one person going to one like location. They're employed by a particular household who's reporting the the services of the person they employ, [02:48:56] right? It looks very similar to a non-employer. So, I'm I'm I'm very uh similar uh to Andrew Reamer's comment. [02:49:01] Uh so, uh I am certainly uh sympathetic to the idea that we really need to be releasing uh data that's relevant for policy analysis. Uh I can't tell you how many times when I present this, I'm asked about uh particular associate application to the chips act. uh and uh [02:49:16] really in order to do these sort of industry level classifications comparisons uh we need to be working on uh not just looking at things at the firm level which is what I'm doing uh but more like uh linking the establishments to each other which is which is a challenging exercise uh [02:49:31] Martha Stinson and her team uh have been looking into this some of my colleagues in LHD have been looking into this uh but you know we we still really need to to be uh pushing on that so in my opinion uh industry largely agrees and [02:49:43] it agrees except when it when uh doesn't uh and then um uh John you have your your your comment is you know it's going to be a paper so the um the the reason that you can't recover the total number of establishments and the business [02:49:57] employment dynamics uh is because of a very straightforward reason when um a business previously existed and it you know it responds to the the um multiple work site report for the first time or [02:50:12] the annual refiling survey. uh it that those uh transitions are suppressed, right? It's it's really that uh and the states actually provide the data that we need uh in LEHD to to to quantify that. [02:50:23] But that that's really that's where the bodies are buried. Uh and then uh yes, Katherine, I I am really uh uh sympathetic to the the question that economists have been asking for centuries or or uh what are the boundaries of a firm? Why do farms matter? Uh what are the boundaries of an [02:50:36] establishment? And uh yeah uh having those sort of uh conceptual issues really pinned down uh will really help us make progress in in measuring things in a in the most relevant way. Thanks.