Transcript of the talk, from the video's captions. Auto-generated: speaker names in particular are unreliable. ======================================================================== # The Nonemployee Workforce Authors: Christopher Goetz, Henry R. Hyatt, Nick LaBerge, Adela Luque, Vitaliy Novik, Kristin Sandusky, Martha Stinson, Stephen Tibbets Discussant: Susan N. Houseman Video: https://www.youtube.com/watch?v=aXOe_NS3iVo&t=10251s ## Talk (02:50:51 – 03:11:44) [02:50:51] >> Thank thank you to all of our presenters. We are not going to take a break now. We are going to turn to the next uh paper um which will be presented by Martha Stinson on behalf of a large [02:51:04] team of collaborators. reasonable stretch. [02:51:12] Feels great. So, my name is Martha Stinson. I'm here [02:51:50] today to talk about the non-employee workforce. This is joint work with my many awesome colleagues at the Census Bureau, most of whom are are here today. [02:51:58] These views are our own and all of our results have been reviewed to make sure we don't disclose any confidential data. [02:52:04] So this work is part of a larger project to enhance the data on self-employment that the Census Bureau publishes. We have from a long time published point in time estimates of the number of businesses that operate without [02:52:17] employees uh called non-employer businesses as Henry alluded to. [02:52:23] And this time series goes on for a long time. It does not provide us any information about any kind of transitions of people in and out of this type of work. [02:52:32] There has been over the past 15 years a lot of research done about self-employment at the Census Bureau. Um my team member Kristen Xanduski has worked with Katherine and John uh to compare self-employment reports to the C from the CPS to those in the tax forms [02:52:45] and spent a long time thinking about uh how these reports differ and the advantages and disadvantages of administrative data. Our other team member, Adelauca, has been involved now for quite a while in matching the people who run these non-employer businesses to [02:53:00] their demographic information and publishing an augmented series called non-employer statistics with demographics or NESD. [02:53:07] But in spite of or as this work has developed, we have wanted to build on it and to build true dynamic products for self-employment. In particular, what we feel like we're missing right now is the opening and closings of these [02:53:20] non-employer businesses. We're missing movement of people in and out of self-employment jobs. And as the debate has grown over what these administrative data mean and are is self-employment really increasing over time, uh the lack [02:53:35] of these dynamics hinders our knowledge in this area. So, this team has been set up to produce new work to build a comprehensive infrastructure for all types of self-employment work. And our goal is to produce products that are similar to the quarterly workforce [02:53:49] indicators or the business dynamic statistics but for self-employment uh jobs and for these non-employer businesses. And then we'd like to produce micro data that can go in the Census Bureau's FSRDC centers and be [02:54:02] used by people to study self-employment and business ownership. I will say that everyone in this room agrees about the need for very timely data for policy makers. And I will say that I also very strongly believe that the Census Bureau [02:54:16] has a role to archive historical data and to produce these time series so that policymakers can see a spike if there is no longitudinal data. You can't tell when something has changed. [02:54:28] So what are we going to do? We're going to use new data that over the past 5 years or so has become available at the Census Bureau, shared by the IRS. in particular, payments that link contractors and gig workers to the [02:54:41] people paying them, payments to business owners in partnerships and S corporations, and then IRS forms that link people to the businesses they own through EIN applications. [02:54:54] And we're going to link this to data that's existed for a long time at the Census Bureau, income tax filings of people and businesses and payroll tax filings of businesses. [02:55:03] And we're going to build this database by identifying and linking all the business owners either to employer businesses or non-employer. We're going to longitudinally track the non-employer businesses in a new product called the longitudinal business database for [02:55:17] non-employers which will complement the employer version of the LBD. This will allow us to measure entry and exit and reorganization. And then we're going to have a master database that will be part of the frame the the job frame and it will be the self-employment job. So it [02:55:31] will be sort of a subset and it will merge together with the wage and salary job frame so that we can see movement in and out of these different types of jobs. All right. So where does self-employment actually fit as we're trying to measure all of these dynamic [02:55:46] things? And one of the first things our team ran across was this question. What is self-employment? Is it a job or is it a business? And our first work on this which we presented last year at the MBR changing nature of work conference. We [02:56:00] thought of everyone who got a who filed a business tax return as a business owner. And we have this title 33 million in counting as Henry is alluding to. But we got a lot of push back on is it really correct to think of some of these [02:56:13] self-employed people as business owners. Would we um need to think about them in a different gradation? So we've started to think about the self-employed in this spectrum. On the left end of the spectrum are people who are not [02:56:27] self-employed. These are people who receive a W to work specifically for an employer and are not an owner of the business. Uh those are easy to identify and rule out as they are not self-employed. Uh on the far right end [02:56:42] of the spectrum are people who it's easy to say they own a business because they employ other people. We see them tied to a business that issues W2s and has employees. So those are clearly business owners and they have a self-employment [02:56:54] job. The hard part are the blue uh boxes in the middle. These are people where it's much less clear. On the right end you have or sorry on the left end you have the routine contract workers often [02:57:07] called non-employees. And these workers have a relationship with a business. It may even be quite a stable relationship. They may even go and sit in the office next to the employees but they don't get a W2. In the middle are freelancers or gig [02:57:21] workers. Uh here the the defining characteristics of these workers are that their relationships with their customers are transitory. They may be facilitated by an online app or they may be a less formal method but these [02:57:35] relationships are constantly changing and don't last very long. Finally, we have owners of independent businesses who simply have no employees but directly interact with customers. And so identifying all of these is is our challenge [02:57:48] spec to be specific. What the Census Bureau has has received for many years is this orange triangle of sole proprietorship business tax entities. [02:57:58] These are people who file a schedule C with their 1040 to report some kind of business income. [02:58:04] And all the types of workers that I've discussed in these three blue boxes, contractors, uh, ride share drivers and piano teachers teaching piano lessons at their house can all be filing this sort same [02:58:17] type of of schedule C. And so it makes it hard to distinguish them and we need more data to do this. So the 1099s have become our method of disentangling what types of self-employed workers uh, do we [02:58:31] have? The 1099 NEC or non-employee compensation will help us identify contractors. The 1099K is issued by platforms if you earn a certain amount of money uh to will [02:58:46] identify some of these gig workers. 1099Ks are also issued again if you meet a threshold to independent business owners who receive credit card payments. [02:58:55] And so this might help us identify some of those as well. [02:58:58] uh and so merging these two things together has has become uh the central focus of our most recent work. Today I'm going to talk in particular about our 1099 NEC uh match and focus on [02:59:12] statistics from that. Now it's important that we uh distinguish these because if we're going to produce public use statistics we want to be able to differentiate these groups because we expect that their time trends and patterns will look and their earnings will look very different. Okay. So what [02:59:27] are the complications of matching these ad risks? So I'll just say there are reporting thresholds. The next paper I think is going to talk about them in depth and and you'll you'll see the challenge that comes. Not everybody is going to get a 1099 because they don't [02:59:40] earn enough and this changes over time making it very complicated for us. Uh so there will be we will have to think about our time series and how to accommodate these thresholds. It's also the case that matching the 1099 CC's to [02:59:54] the schedule C's is hard some and there are many reasons for this and we are currently working on all of these trying to improve them match uh an employing firm can issue to an EIN instead of to a PIC a PIC being the [03:00:07] Census Bureau's person identifier uh and this is totally legal and fine the person decides to use an EIN for their business instead of a PIC and so then we've got to go get another piece of information so that this this information that connects the EIN and to [03:00:21] the pick in order to see who's actually getting this 1099. It's also the case that families file 1040 tax returns and the person who lists themsel as the business owner on the schedule C is not [03:00:33] always the recipient of the 1099. This is potentially just accounting uh irregularities at the family level, but sometimes the the person who gets the NEC is actually the partner or even a [03:00:47] dependent. Even more troubling is sometimes there just simply no match of any kind. The family may file a 1040 but not any schedule C. We think potentially they may list the 1099 information on the [03:01:00] other income line. Maybe they don't understand they're supposed to file a schedule C. Sometimes the family doesn't file a 1040 at all. This might happen if they have no income tax obligations. [03:01:10] they're not eligible for the EITC and they either don't know or are choosing not to know that they owe self-employment income tax on their on their self-employment earnings uh or self-employment payroll tax. [03:01:24] So all of these matching problems lead us to two things. First of all, it makes it hard for us to correctly classify the schedule C filers. Um, but even more importantly, these 1099s who are not [03:01:37] filing any 1040 or any schedule C, uh, they lead us to a miscount of the self-employed. Okay. So, I'm going to show some statistics today. These were cleared through census and released this spring. They're not our final numbers. [03:01:50] They these numbers are very much in flux, but they're going to give you a flavor of of what's going on. So, I'm going to just skim right forward here to the interest of time. The first thing I'm going to show you is how many schedule C filers receive a form 1099 NEC using just a straight simple pickto [03:02:05] pick match. And the overlap is just over 13 million workers for almost 15 million schedule C's and about 22 million 1099 NEC's. Then there are about 12 million [03:02:18] people on each side who don't match. We hope that number will go down as our match improves. But we expect there to be a significant number of people who receive an NEC but who have not told us anywhere else in the tax system that [03:02:32] they get self-employment income. So this is an important augmentation of our understanding of who are the self-employed. [03:02:39] What type of people do get these 1099 NEC's? Uh here I'm showing the demographics and I'm splitting the NEC recipients into two types. The blue are these what I'm going to call unstructured self-employment. They did [03:02:52] not file a schedule C, at least not that we've been able to link yet. And the orange are the ones who are more structured who received the NEC and filed a schedule C. Uh, and you'll see that the demographics are very interesting. For the blue lines, you will see that they are [03:03:07] disproportionately not female, so male. They are disproportionately black and not white. and they're disproportionately Hispanic, US-born, and very young under the age of 35. [03:03:19] Almost 40% of this type of NEC recipient is falls in the under 35 age category. [03:03:26] Uh the the ones who are filing a schedule C are older and more likely to be white uh and a little bit more likely to be female. [03:03:37] Now, we're going to talk about the a little more specifics about the ACE distribution, but here split by sector. [03:03:42] Here I'm showing you within a sector, what is the ACE distribution look like of a 1099 NEC recipient for industries like arts, entertainment, and recreation and transportation and warehousing. [03:03:53] Again, we see over 40% of the recipients are very young under the age of 35. But for other sectors like mining and utilities, almost 60% of the recipients are quite old, age 55 plus. And then the remaining construction, educational [03:04:08] services, and uh what's my last one? Wholesale trade. Uh they are the the lion share of these 1099s are paid to prime age workers. [03:04:19] Okay. On this chart, I'm going to ask what else do these 1099 workers do? Do any of them also have wage and salary jobs? And here I'm going to split in a slightly different way. On the left, I'm going to show all the NEC workers. And [03:04:33] on the right, I'm going to show workers who they filed a schedule C, but they did not get a 1099 NEC. At least not we've been able to link yet. So, these are people who we would like to think of as the small business owners who just [03:04:45] don't have employees. Whereas on the left, I'm showing you people who could be more contractor types. Okay? So on the for the contractor types, the NEC workers, the blue portion of this pie chart shows how many of them [03:05:00] self-employment is their only job and it's 41%. [03:05:04] You can see 48% of them have both a wage and salary job and a self-employment job. So W and S and W is greater than S, meaning they made more their self their wage and salary job than their [03:05:16] self-employment job. So for almost half of these N9 1099 NEC recipients, they have something else going on that earns them more money. And then there's 12% that do both. When you look down at the [03:05:28] total NEC pay for these NEC workers, you can see that it's quite low. For those where it's their only job, it's just about a little over $30,000 in a year. [03:05:40] If they have a wage and saw job two, they make a little more closer to 40,000 in their NEC job. uh and if and if their wage and salary is the largest then they're making very little then they're this contract gig is something very much [03:05:53] on the side when we turn to the sea only workers now these people do look more like business owners here when you look at their receipts so this is pre them this is not profit this is pre-aking out [03:06:05] uh expenses uh you'll see that the ones who are just self-employment that's all they do they have all their money earned on a schedule C they make almost $100,000 a year on coverage. And if they have both types, their C income is even [03:06:19] higher. Uh, and they don't file very many schedule C's per worker, meaning they have generally one business they're filing the schedule C for. And you can see that the number that are only self-employed, that's all they do, is almost half. So, it's a different set. [03:06:33] So, we think that this distinction is capturing some real economic differences between these types of workers. Okay. [03:06:39] So, what types of businesses use contractors and issue 1099's? So this is an interesting question as we think about business dynamics. Uh if we think that companies are loathed to lay off their triedand-true employees, they [03:06:54] might move their margins of employment using contractors instead. So we're interested to know who are these businesses. So amongst multi-unit employers, over half of them issued at least one 1099 NEC in the year 2022. So [03:07:08] this is a very common thing for large companies to do have these contractors. [03:07:13] Single unit employers over 30% non-emp employers who have an EIN who could potentially issue these don't do it very often. Only 9% of the time uh do these non-emp employers use contractors. [03:07:27] Here I'm showing you the industry classification of employers who issued 1099 NEC's. On the right I'm just showing you the straightup share how many of issued 1099 NEC's landed in which industry and I'm showing you the [03:07:41] top five. So, professional scientific and technical services issues the highest share of 1099 NEC's but they are only about 14% of the total. So, this is quite dispersed. Lots of different industries are issuing these. [03:07:55] Construction, other services, healthcare and retail make up the rest of the top five. On the other side, I'm showing you within an industry what share of firms are issuing these. So, the government is the top issuer uh over 50%. Uh but [03:08:10] utilities while they make up actually a very small small share of 1099s it's not a big industry they heavily heavily use contractors we know from the earlier graph these are older workers uh education services information and [03:08:23] mining also issue all of them over 40% issue issue 1099s to contractors here I'm going to show you uh what share of how does this money that they're paying to their 1099 contractors stack [03:08:36] up relative to the W2s uh And here we see that the majority of them, 70% of them, uh the NEC payments is about 25% of total compensation when it's added together with uh W2s. All right. And now [03:08:51] let me talk about how dynamic these relationships are. And here's where we think we have the most to contribute. So these two bars show the total number of 1099 NEC's issued in first in 2021 and [03:09:04] then in 2022. 35 million in the first year and just a little over 37 million or so in the second. But underneath this uh these two big bars, large large numbers of these NEC contractors, there [03:09:18] is a lot of change. The black portion of these bars represent continuing filings of continuing workers. So I want you to think of a filing for a minute in your head as a job. And I want you to think of the black bars as the job continuers. [03:09:32] These are people who have the same relationship with their contractor employer over these two years. Now the hashlines show us job switchers. These are 1099 NEC's uh where the person continues to be employed in this manner [03:09:47] but they have a new business relationship. A different EIN issues them. And so the blues are the exiting ending jobs and the yellow is the beginning or starting jobs for these people who continue to work in this area. And then the solid bars represent [03:10:02] our our enters and exits. So the blue are people who quit 1099 employment outright. Their job ends and they don't go back to it. And the yellow are people who come in who enter this employment and it's a new business relationship, a [03:10:15] new job. And so what the point here is that there's all this churn in the 1099 world and in fact maybe much more than in the employment world. Okay. So challenges and next steps. First, I want to acknowledge because you're going to [03:10:29] all tell me this. Making a longitudinal time series with tax data is not for the faint of heart. We're well aware there have been lots of changes in thresholds. [03:10:37] The filing incentives are are very interesting and are going to influence. [03:10:42] So, this is a hard thing and we and we aware of that. Uh we also also want to highlight that the relationship between tax form business structure and operational business structure is not always straightforward. A business can span multiple tax forms and seem like [03:10:57] all these pieces are unrelated and they're not. And if we're going to truly measure economic activity, it's important that we put these pieces together, that we think about the employer and the non-employer firms that may in fact be related. And then finally, we have to think hard about [03:11:10] does an information return like a 1099 constitute a job? Do we want to measure that? Our goal uh when we present at our AA stat session that we're going to do in January is to have some public use tables, some preliminary versions. We're [03:11:24] going to have job transitions for the self-employed. We're going to have complete demographic characteristics by type of self-employment. We're going to have business transitions for non-employers. And we're hoping to have some employment dynamics for contractors at these employer firms. [03:11:39] All right. Thank you very much for listening to my talk and look forward to your comments. ## Discussion — Susan N. Houseman (03:33:29 – 03:46:34) *Shared discussion for Session 5: The Nonemployee Workforce; the discussant covers every paper in the session.* [03:33:29] [applause] The discussant will be Susan Houseman. [03:33:44] >> Great. Thank you. Well, while we are um setting up, I I just want to uh say I'm really excited to be discussing some papers on uh self-employment or non-employee employment. It's a really [03:33:57] undeveloped area in statistics and I think uh you know needs a lot of work and I'm very excited to as I mentioned uh you know see increasing amount of work in this area. [03:34:33] Great. Thank you. Well, let me start off with the first paper which is on the non uh uh non-employee workforce that Martha presented with many co-authors. So, I think this is an incredibly important new re uh resource for research and [03:34:48] policy. Um their major shortcoming of the LEHD and related products is is that the analysis is limited to workers uh in employee W2 employment arrangements and the new data is going to use uh [03:35:02] information from tax filings to help in for self-employed businesses and the workers um uh whom we might call self-employed or non-employees. [03:35:12] uh for many self-employed will be able to link to demog uh graphic information and to the businesses using their services. So we'll get that that link that you have nice link that you have uh with the LHD along with the characteristics of those businesses. [03:35:25] It'll enable insights into workers holding both employee and non-employee jobs arrangements within a year and enable us to see some really interesting uh analyses of transitions between and across employee and non-employee [03:35:40] statuses. Um, and I want to underscore the different types. This will be available for the different types of non-employee business owner arrangements across years as Martha described and I won't go into that. So the one thing I [03:35:54] do want to flag is is that this new um, you know data product will help capture non-employees who are not business owners. So um there is a a ma this is a major gap in standard measures of [03:36:07] self-employment which focus on business ownership. Um the authors called it a blind spot in administrative and surveybased measures of self-employment. [03:36:18] Um in this paper these workers are identified as receiving 1099 NEC's but do not file a schedule C. Um they are termed latent businesses. Um I you know [03:36:31] Martha mentioned that she got push back on calling everybody business owners and I might suggest that they drop the latent business owner um uh language. [03:36:40] These are people who are probably most likely working uh as independent contractors for businesses and don't think of themselves as business owners and probably will never set it up. [03:36:51] Analysis of characteristics shows that these workers are younger, disproportionately black. Um, I'm going to kind of start my remarks here by mentioning some shortcomings of the [03:37:04] product. Um, I don't mean this to be a criticism, but rather I think it's important to recognize what it does and what it's not doing. Obviously, it's going to miss some people who don't receive any uh 1099. [03:37:17] That may be uh because these individuals receive cash payments and are part of the underground economy. Of course, you're not going to uh catch those, but they may also be paid through a third-party payer like PayPal, Stripe, Venmo, or large platform companies like [03:37:31] Uber, Lift, Upwork that issue 1099Ks. And as Dimmitri explained, for the most part in most states, um the threshold for the 1099K is much higher, $20,000. [03:37:45] And if they're earning less, they're not going to be captured here. uh the current analysis also misses anyone who receives a 1099 payment even even if it's above the threshold and a key question is how big is this phenomena in [03:37:59] cases also I want to point out where third party payers issue the 1099s we can't link the worker to the business sector that's kind of a really interesting part of the analysis that's [03:38:12] coming out here to the extent that businesses are increasingly paying people through uh you know, systems like PayPal, we're just going to miss that link. We won't know. We'll capture, we may capture them in the tax, we will capture them in the tax state, but we [03:38:27] may not know who they're working for. I want to quickly note that there's a comparable uh problem on the employee side. When employers hire workers through a contract company, they may be [03:38:39] a employee of the contract company um and they're paid a W2, but we don't know where their labor services are actually provided. We don't know where they're actually working. The prototypical [03:38:51] example of this is staffing companies um like temporary health agencies. So uh another sharp claim is the data frequency is annual which may be inadequate for some purposes uh insufficient for much policym. We [03:39:06] actually can't tell the paper uses the term moonlighting. I'd say we actually can't tell if people are moonlighting because we don't know the sequences of jobs. Uh it may be uh you know a main job for some period of time. Studying [03:39:20] dynamics is limited. it misses income data for those who receive 1099s but who don't schedule the 1090C um and uh demographic characteristics are available and administrative data are far less than what could be [03:39:34] collected in surveys so many possible uses in the interest of time I'm going to skip through this um you can think of them yourselves but um I want to mention that the findings about non-employees [03:39:48] who are not business owners underscores the need to improve worker class questions in government household surveys. So, uh for now, as others, uh Brett and others have have mentioned today, household survey is an important [03:40:01] complement to administrative data on non-employee workforce. They potentially capture those who don't file taxes, uh provide more demographic information, and are often collected with shorter [03:40:13] reference periods. Um the paper mentions uh a a paper by um uh Katherine John and others um that compares the uh tax data with non-employee um uh miss uh [03:40:27] non-employee sorry tax data with the CPS showing that they're missing a lot of uh non-employee employment. Um, but I think we have a pretty good idea of uh the problems with workerclass questions in [03:40:41] surveys like the CPS and uh some work that uh Katherine Abraham, myself and other colleagues at UpJon have been doing have been kind of testing possible questions with the Gallup organization. [03:40:53] One of them is ongoing and we think we're we're you know capturing a lot of these people with better questions. Um incidentally these people like what um Martha and co-authors found uh were less [03:41:06] advantaged you know non-white younger uh less educated and the like. So um uh finally on this paper I just want to mention that the uh NLSY cohort the new cohort I understand will be improving [03:41:20] those questions so they can be uh integrated into uh uh federal statistics surveys. So moving on to Dimmitri Andes and and their co-author's paper. Um it's a broad uh the it ties into the broader [03:41:35] literature on tax evasion uh by examining the effects of receiving a 1099 on filing of schedule C in federal tax returns. Um and exploits exogenous uh cross-state variation in the receipt [03:41:48] of 1099 and whether workers file A and B report how much they report in expenses and profits. [03:41:56] So um as Demetri is uh has explained uh the filing of 109C uh uh sometimes uh is issued by the in sorry I'm a bit ahead of myself but but [03:42:11] it provides really convincing causal evidence that this is having an effect on information returns. [03:42:19] So the empirical setup is uh such that um uh Dimmitri explained it and again is as in the interest of time I'm not going to go through the results but they find a small but insignificant effect on uh [03:42:34] filing of uh schedule C and significant effects on the reporting of receipts and profits. I want to close by saying by talking a bit about the interpretation of these findings. I think the causal [03:42:46] effects are clear. uh interpretations a bit uh a less a little less. So on the surface the finding the first finding in particular is a bit puzzling. Why is it affecting people who file? Um I think [03:42:59] you know they they kind of outline some some possible uh uh uh possible mechanisms. One is that the controls those are residents outside of Massachusetts don't behave as expected. [03:43:11] Um the analysis sample only includes incumbent gig workers. they're used to filing schedule C and they continue to do so even when they stop receiving them. The other another one is a treatments those insights uh [03:43:23] Massachusetts don't respond maybe because uh many of them just reserve a receive a state return and they know it's not going to be shared with the federal government so they don't file a [03:43:36] schedule C um but the and the analysis sample may be too small to detect an effect or finally no one responds [laughter] right we always have some people file taxes and comply with the law they know what it is and others [03:43:50] don't. Um uh and so uh you know that's not going to change. Um it would be uh useful to see the full distribution of this percent filing schedule C among recipients by their kind of level of of [03:44:05] of uh uh revenues. It's probably concentrated amongst the the low earnings folks. Um the other one interpreting uh the fact that it does have an effect on uh 1099 [03:44:20] uh K uh is conditional on on filing a 10 uh a schedule C. It does have an effect on the receipts and expenses reported. [03:44:29] Possible mechanisms, the filing without uh a 1099K. [03:44:34] Those filers um who don't have a 1099 K uh uh K uh information return or maybe they're cheating a little on their taxes. and uh filers with uh without a [03:44:48] 1099K and an alternative explanation is they just don't maintain good records and that having an information return is very important for getting accurate tax records. Um uh and I'm also going to say [03:45:01] that the simulations we should be very you know careful about uh I have some comments which I'll skip over in the interest of time. um but encouraged the authors to be explicit about assumptions that must hold for their accuracy when [03:45:16] they go beyond and and are and are are uh projecting the effects. My guess is is that because of this nature of the sample and other factors, it's possible that they're understating the effects of [03:45:29] these more recent changes. Um if you'll give me 30 seconds, I just want to say this is my final slide. I wanted to step back and in in the context of of both of these papers um [03:45:44] kind of ask the a bigger question research question is there a future role for private data to supplement government survey and administrative data. We have spent a lot of time at this conference already talking about [03:45:57] the use of thirdparty payer data to uh estimate uh W2 employment. [03:46:04] um these uh data potentially could also be used for 1099s and that would be a useful area to explore. We might also be able to use from these 1090s uh thirdparty [03:46:19] payment processors um might help shed some light on uh the size of the 10 1099K workforce which is a you know a big question mark. So thank you very much. ## Q&A (03:46:34 – 03:50:23) *Shared floor discussion for Session 5: The Nonemployee Workforce.* [03:46:34] [applause] Uh so we are running a little behind [03:46:43] schedule. Um maybe I could give Martha 30 seconds if she wants to say anything, Dimmitri Andy 30 seconds if you want to say anything and then we could continue [03:46:57] our conversations about this over lunch. Uh thank you for the great comments. Uh the 1099K is uh actively being linked this summer. [03:47:08] Uh there there's this fixed cost that sends us of releasing data. It's called disclosure review and uh it's as soon as you do one, you have to be very careful what you release next. So anyway, we opted not to release our 1099Ks in a [03:47:22] hurry. Uh but we definitely recognize that's an important component uh and want to to bring them in. And then I will say that I was very interested and this is something I did not know that the 1099 uh NEC's and KS are sometimes [03:47:35] issued by the same company. So thank you for bringing that to our attention. [03:47:47] >> Yeah. So I thanks for the comments and and for the presentation. I just want to say that we've been Demetri and I and and and Emily have been turnurning out um work in this area at the IRS for a long time uh in a way that um we hope [03:48:01] has been uh really kind of shed a lot of light on how these forms work, but it's not a kind of sustainable research infrastructure. It's not what the IRS JSRP was made for. So we really think the effort that's going on at census um you know not just what we're doing but [03:48:15] the but with the broader effort to kind of build these uh resources that can be um for uh uh research going forward is really important. Um I would say that you know most of the tabulations that that you guys present today we have put [03:48:28] out in various forms um and and in a way that we won't be able to do going forward. So it's very important that you can and and you know we've promoted these a lot. I think understanding it would be very helpful since we've done the same thing to do it for like 2018 we [03:48:43] have the demographic cuts um in 2016 we have um and and a couple of earlier years the most comprehensive cross tabs of all the various filings um and um you know you may have seen maybe one or two of our papers we actually have a a lot [03:48:58] of things up there um and and and seeing um do you match what we're getting I mean it should be the same universe what decisions matter and documenting that in a sustainable way will be really important for the research community to be able to actually use these returns um [03:49:12] as opposed to just kind of starting from scratch without any kind of regard. And it's not just us. There's actually other work that was going on when we were at IRS by um Katie Lim and Elanor Wilking and Max Rich who um you know we did less on who was hiring them because that was [03:49:27] their turf. and they obviously have a work a working paper that has almost like a hundred tables and and and graphs um that kind of take what you had started doing but kind of for 2016 go banan I mean it's it's it's um about the [03:49:41] you know the dynamics of the relationships and and do people go and come back a lot of information out there and we're happy to point you to these resources but the more you're able to kind of go back and and look at you know which definitions matter and which don't [03:49:54] I think will be um useful for these statistics to have impact that going forward. So, look forward to working with you on this. Yeah. [03:50:01] >> Great. Um, lots lots to talk about over lunch. Lunch today will be upstairs in on the penthouse level for those of you who were here for dinner last night. [03:50:12] It's uh that floor. You can go up on the elevator. We are in the jewel room. [03:50:18] Okay. >> Whatever room it was we were in last night, but there's signs on the doors.