Zev Eigen: Hi everybody, very excited about today's webinar.
Zev Eigen: Talking about SB 973 in California is new data reporting law.
Zev Eigen: We have an exciting panel, we have a lot to talk about today. And so we'll just dive right in.
Zev Eigen: I might I'll introduce myself first I'm serving as moderator and panelists. So I'll introduce myself first and then introduce our esteemed panelists.
Zev Eigen: So my name is F. I. Again, I'm the president of the fair pay workplace alliance, who is hosting this webinar. And I'll tell you a little bit more about the fair pay workplace alliance in a second.
Zev Eigen: I'm also the founder and chief science officer at Cindy, do which is if you don't know. Cindy, or the world leader in equity tech solutions empowering organizations to treat people fairly and equitably
Zev Eigen: Previously I've served as the head of data science analytics, one of the world's largest labor and employment law firms, I was a full time professor for six years at Northwestern law school and business school.
Zev Eigen: And was a visiting professor at Yale Law School and NYU Law School. I was also previously senior Labor Council at 20 Century Fox I hold a BS from Cornell School of industrial Labor Relations a JD from Cornell Law School, and a PhD from MIT.
Zev Eigen: Joining us today are five feldblum and Kevin Kish
Zev Eigen: I will. I guess I'll just go in the order on the screen.
Zev Eigen: Kevin Kish we're very, very happy to have both these panels Kevin is a civil rights attorney whose career has been dedicated to public service.
Zev Eigen: And Advancing Justice for disadvantaged communities, he was appointed by Governor Brown as Director of the California Department of Fair Employment and Housing the DFA H
Zev Eigen: In February of 2015 and confirmed by the California Senate in January 2016 he was reappointed to that position by Governor Newsome in February of 2020
Zev Eigen: Kevin has a long track record of working tirelessly advocating for the rights of people of the state of California serving as director of the employment rights project at Bates SEC legal services.
Zev Eigen: One of the nation's premier public interest law firms, he has been recognized for a creative approach to advocacy that complements legal strategies with innovative collaborations involving nonprofits law schools, public agencies and organizing campaigns.
Zev Eigen: He's been named as California is one of California has super lawyers, the daily Journal's top 75 Labor and Employment Lawyers he's basically a rock star and
Zev Eigen: Just an amazing person to he is a graduate of Swarthmore College and Yale Law School, and he began his legal career as a scat and fellow and as a law clerk for Judge Myron Thompson of the US District Court know district of Alabama.
Zev Eigen: Bye fell Bloom is a partner and director of the workplace culture consulting group at Morgan, the law firm Morgan Lewis, where she helps employers create safe respectful diverse and inclusive workspace workplaces. Excuse me.
Zev Eigen: I served as the Commissioner of the Equal Employment Opportunity Commission the EEOC from 2010 to 2019 where she co lead to select task force.
Zev Eigen: On the study of harassment in the workplace and CO authored a widely read report on preventing workplace harassment.
Zev Eigen: highly relevant to today's discussion. She also she also served on the Commission when it voted to change to the EEO one form to collect pay data.
Zev Eigen: Prior to serving on the EEOC. I was a law professor at Georgetown University Law Center for 18 years where she established and ran a federal legislation legislation clinic that worked on behalf of nonprofits to advance civil rights and social justice goals.
Zev Eigen: High is a leader in both the disability and LGBT communities. She was the lead lawyer involved in drafting negotiating the ADA in 1990 and the ADA amendments X in 2008
Zev Eigen: I also is an amazing person and an a group of rock star in this space and just really thrilled to have her as well.
Zev Eigen: She attended Barnard College and Harvard College law school and clerked for
Zev Eigen: Judge Frank M coffin on the First Circuit Court of Appeals and for the US Supreme Court Justice Harry Blackman, so please
Zev Eigen: join me in welcoming our awesome panel today I'm personally honored that you both can make it. And really, really excited to have you on the on this webinar.
Zev Eigen: So, and I think just for housekeeping purposes. If you have audience members have questions for the panel, we're going to be talking through a lot of stuff today. If you have questions, please put it in the chat.
Zev Eigen: You can just type your questions there. And hopefully we'll have some time at the end.
Zev Eigen: To to bring those questions up. Okay. So, just as I said, I want to. So just keeping up. What we're going to do today. I'll give you a little bit of background about the fair pay workplace alliance what it is.
Zev Eigen: why we're here talking today and then dive right into questions and answers. I'll also key up to fun, exciting things. We have a small game show esque game to play in the middle of the with an opera, which hopefully will be fun. And I don't want to spoil anything but Kevin has
Zev Eigen: A hot exciting preview exclusive maybe preview.
Zev Eigen: never before seen. I don't know. I'm trying to make it sound very tantalizing for the audience of something that's forthcoming on the DHS website so stay tuned for that seem like a like a game show host. I'm very excited. This is like I missed my calling.
Zev Eigen: Do all this data science and not enough game show hosting. So, real quickly on what the fair pay workplace alliances, for those who may or may not know it, it is
Zev Eigen: The Fair Pay workplace alliances a not for profit, whose mission is to advance the interests of ensuring transparent consistent and valid pay equity.
Zev Eigen: For all based on gender, race and the intersection of gender and race. So it's alliance. It's an alliance of experts on pay equity.
Zev Eigen: Who really have created this set of rules and standards by which organizations can hold themselves accountable to ensure that that they're, when they're committing to
Zev Eigen: The pay equity forward thinking organization that they're doing. So following best practices that have been set by by leading experts and please check us out online.
Zev Eigen: Therapy workplace.org is the website where you can find the rules and standards, the methodological white paper detailed facts and other information.
Zev Eigen: Okay, so let's without further ado, let's jump right in. So today's topic of discussion is SB 973 or Government Code Section 12 999 be one.
Zev Eigen: For the legislative nerds in the audience, but this is California is new law that went online very recently that mandates submission of data to
Zev Eigen: Kevin's organization to D, F, G, H, as well as the de El se the division of labor standards enforcement.
Zev Eigen: And the basic requirements are to submit an annual pay data report to those organizations on or before March 31 of 2021 and every year thereafter.
Zev Eigen: And you can see on the slide. The report will include race, ethnicity, and sex of employees in 10 specifically enumerated categories and will we have a slide with the 10 categories.
Zev Eigen: Race, ethnicity, and sex of employees whose annual earnings fall within specified pay bands and will show you those 12 pay bands as well. And it also specifies that the data.
Zev Eigen: should also have covered the earnings. The total earnings and we'll ask questions about that. What are the total earnings from the previous reporting year
Zev Eigen: And it also specifies the data must be transmitted in a form that may be searched and sorted using software.
Zev Eigen: So that's sort of our starting point for this conversation. And just for the sake of transparency and we'll share the slides with the audience.
Zev Eigen: But you've got the 10 categories. We've got a slide with the 10 categories. If you want to see what those are and another slide with the the pay balance. So without further ado, I love to ask you, Kevin first
Zev Eigen: What, what's the background of how SB 973 came to be and maybe bring us up to current date and if you have any thoughts on where it's going, moving forward.
Kevin Kish: Sure, thank you, thanks for the introduction. It's great to be here with everyone. Um, let me start just by saying that anybody who went through the
Kevin Kish: EO one component to reporting is not going to be surprised. I don't think by anything that we're doing in California. This is explicitly based on that process.
Kevin Kish: And that really comes from a focus by the legislature in California on pay equity over the past few years, as many of you know the California Equal Pay Act was amended.
Kevin Kish: In 2015 going into effect in 2016 to make it one of the strongest in the country. And it's been amended every year since 2017 race and ethnicity were added as characteristics.
Kevin Kish: Upon which it's unlawful to pay people differently, unless justified by by a factor that isn't related to sex, race or ethnicity and and i think folks on this call.
Kevin Kish: You're in California, you know, in a lot of ways, our industries or some of our industries are way out ahead of the pack on pay equity.
Kevin Kish: And the legislature is as well. So for the past few years, the legislature has introduced bills about a data collection. These are based, you know,
Kevin Kish: There are various other jurisdictions throughout the world that have mandated pay data collection of course the EEOC had its process.
Kevin Kish: And this is the year that it passed and the purpose really is to eliminate pay inequality. And so I think a major purpose of the law is to have companies collect this data look at it and
Kevin Kish: Decide whether there's anything that needs to be done internally. Right. It's kind of a self compliance type of exercise and something that I want to highlight for folks.
Kevin Kish: Is that when we talk about the usefulness of this of this process or this data.
Kevin Kish: There are other jurisdictions where it has been shown to have reduced pay disparity. So for example, in Denmark after implemented a
Kevin Kish: A data collection obligation, the gender pay disparity shrank by seven percentage points. So I think the California legislature is very interested in seeing
Kevin Kish: What effect this will have in terms of voluntary changes that employers will make to reduce what we know to be a pay disparity based both in gender and and rates.
Kevin Kish: And so do you want me to go through all of these questions, or do you want to
Zev Eigen: Know, so that that's how, in terms of future plans are there plans that you know about or that you can share with the group on
Zev Eigen: Expected changes to the law. Like, do we expect the law to stay in its current state. Is there anything else you can talk about in terms of like what what we expect for California or California and employers.
Kevin Kish: Yeah. So something to keep in mind for the for the folks on this webinar is unlike the data collection at the federal level. This was done through statute in California.
Kevin Kish: And so changes to the statute will obviously go through the legislature and I think I think we'll see. I think we'll talk about on this webinar, some of the ways in which
Kevin Kish: You know the categories. They are what they are. The, the job categories are a little outdated, I think, personally, they're very broad and so
Kevin Kish: I think the legislature will be interested interested in seeing what the data looks like.
Kevin Kish: And whether to further tweak the law in future years. Based on what we see after this first collection of the first collection or two to make it more useful. And when I say useful and useful for the purpose of eliminating pay disparities.
Zev Eigen: So I guess I mean that gets an I want to ask lie about the the comparison or your experience guy with the federal at the federal level, but there are definitely ways that this, like you said, Kevin. It's very similar to the experience of EO one. Hi, maybe you could talk a little bit about
Zev Eigen: The experience at the federal level and and how that works. And what you see as the similarities here.
Chai Feldblum: Sure, and welcome to everyone on this webinar.
Chai Feldblum: I think, as Kevin said a lot of folks in California already ahead of the game. And so I think it's sort of looking at this as a way of
Chai Feldblum: Reinforcing what you've already been doing. And then maybe doing some additional things. So I'll say a little bit about
Chai Feldblum: How the EEOC requirements came about. And then there are at least two things that are different that California did that Kevin, I think is important for people to know about that I think is useful.
Chai Feldblum: So in terms of the EMC, I mean, this was all initially driven by the Obama administration that set up early on in the administration a pay equity task force.
Chai Feldblum: That had representatives from a whole range of different agencies, including the EEOC and every agency sort of took on with they were going to do in terms of advancing pay equity.
Chai Feldblum: And we do see Trayvon was collecting pay data certainly something man and discuss, but it really came out of that task force.
Chai Feldblum: And the first thing they use see did in fact it was one of the first things I voted on was a contract to as the National Academy of Sciences and as to put together an analysis of how should the pay data be collected
Chai Feldblum: What should we, what should the UFC be using it for so that took a year and some. And one of the things they report said is, be very clear about what you're going to use this for before you start collecting
Chai Feldblum: Because it sort of makes sense to put a burden on employers. If you're not going to get something from it.
Chai Feldblum: And they recommended having a pilot actually or something that would give some concrete information. So that's what the UFC did. I mean, the whole process was about five years.
Chai Feldblum: And because it was the one form that is it's a form that collects data from the public.
Chai Feldblum: It was subject to the Paperwork Reduction Act, which was a law passed specifically to make sure that the benefits of collecting data.
Chai Feldblum: You know, were enough to outweigh any burdens. So it actually is a week that ultimately is issued by the Office of Management and Budget, I won't be
Chai Feldblum: At the White House, but under Paperwork Reduction Act, there's a greater responsibility than just under the Administrative Procedure Act, which requires. Notice of Proposed reg
Chai Feldblum: Taking in comments doesn't require a public hearing and it's only one round and do the Paperwork Reduction Act. It's two rounds and you have
Chai Feldblum: Putting up a comment. You have to have a public hearing you have to put out modifications, based on that another time for input and then it goes over the own be so
Chai Feldblum: You know, I think, EMC did the best it could based on the information and had I've now spent the last two years in a law firm that advises clients, I can assure you I was able to see some of the difficulties. So I think Kevin's point when something actually goes into effect.
Chai Feldblum: It's important to see what's working and what's not, and
Zev Eigen: What, what, that's very helpful. What, so I'm curious, both at the federal level and at this at California State level.
Zev Eigen: Like, what is the point like Kevin, you said, you know, part of the purpose is to shine a light and hope employers respond favorably.
Zev Eigen: Like, what is the agency's plan on what they're going to do what it's going to do with the submitted data. And I guess high at the federal level what maybe you could just say, you said that the yo said this bike. What was the EEOC stated purpose for the one data.
Chai Feldblum: Well, I think the stated stated purpose for the one data is similar to what California is purposes so
Chai Feldblum: KEVIN I you know love in terms of hearing what you're going to say what you said one thing. Ready, which was really one of the main purposes, which is
Chai Feldblum: You can't know about fixing something until you know if there's a problem. So I think it was very clear that
Chai Feldblum: The data was going to serve a limited purpose, certainly in terms of enforcement for the EEOC, but the main purpose it served really was getting people to collect the data and then doing their own self analysis. I mean, I would say that's one major purpose.
Chai Feldblum: The second purposes. The way you'll see us do when data all the time, you know, which was about representation
Chai Feldblum: Which is not you got an ear one report. And then if the representation look weird. You sort of went after that entity etc and never done that they really didn't even have the resources to do it.
Chai Feldblum: But instead, if there was a charge. For example, if there was a charge of discrimination based on race and promotions, they investigated doing that charge would often go pull the one report, you know, was just something else to look at
Chai Feldblum: But again, even if the report showed disparity that wasn't enough to show reasonable cards that discrimination was occurring, he still had to dig in. But it was one piece. So, so those are really the two I think purposes of having the data collected
Kevin Kish: And I'll add you know as a FIFA Fair Employment Practices agency. We are the state contractual partner of the EEOC and as everybody on this call knows I mean we've been collecting er one data for more than 60 years at this point. And so the agencies have always use that data.
Kevin Kish: Exactly as high has said, so you are going to look at that data. If you get an individual complaint or charge. We also use it to figure out where we should invest our affirmative
Kevin Kish: Investigation resources, right. So, especially in the context of
Kevin Kish: You know, gender or race segregated workplaces or unequal pay. These are types of complaints, you know. We know, for example, from the sexual harassment context that the most common
Kevin Kish: Response to experiencing sexual harassment is nothing right is not to file any type of formal complaint. This was an highs report.
Kevin Kish: From the EEOC. That's so much more true in the context of a failure to hire or essentially gender pay disparities, right. These are not
Kevin Kish: A violations where they exist that people are even in a position to know about. So there is a role.
Kevin Kish: For government. It's explicit in our statute and we've been very clear about our, our view of it will affirmatively investigate these types of violations, to ensure that we aren't just complaint.
Kevin Kish: Right, that if there are violations that are not going to be uncovered through the complaint process that they nonetheless don't go on repressed. So we look at this data in the same way that we look at the EO one data again for individual charges, but also to see
Kevin Kish: You know, serious disparities that might prompt an inquiry. I think it's important to say, although I think everybody on the on the webinar knows this.
Kevin Kish: This data does not create a case and it does not lead to litigation. This is the starting place.
Kevin Kish: Right, this is the starting place to see first for the employer whether there are disparities that need to be addressed. And then for the government enforcement agencies to see, hey, it should be look into this and understand what's underlying a disparity that we see.
Zev Eigen: Yeah, that's, I think that's super helpful and I, it is. I mean it is I see it as a paradigm shift as you're both describing
Zev Eigen: Really from the regime that you're describing Kevin and I both like of putting the onus on the the putative plaintiff or group of, you know, people who are potentially
Zev Eigen: Wronged to identify whether there's a problem to a shift more towards a
Zev Eigen: Burden on employers to submit data to the government. And frankly, more of a burden on the government to surface those claims to so it's
Zev Eigen: It's a trade off right because it's nice that we have plaintiff Attorney General act kind of claims being that are very robust in California, and it does create incentives on for the plaintiffs bar.
Zev Eigen: To look for those claims. But when you have a regime change like like this purports to do like you're describing it does put more of a burden on the government
Zev Eigen: And I mean some. I think one of the questions that were already asked was, like, you know, does the government
Zev Eigen: You know what's California is plan for you know how they're going to handle the review of the data which is going to be voluminous. And what you know what, more specifically, does the government plan to do
Zev Eigen: To kind of meet that that heavier burden that's being put on on agencies like the FBI agent and deal is eight.
Chai Feldblum: And yeah.
Kevin Kish: There's always a resources and I do see in the comments. I don't know how we're
Kevin Kish: Going to do this. There is a question about this right about
Kevin Kish: How are you going to ingest them analyze all the data.
Kevin Kish: So the first thing that I will say is the process that took the EEOC A COUPLE YEARS TO DO WE HAVE 13 weeks to implement and we're going to get it done.
Kevin Kish: But there's going to be some work on the back end right after the after the data collection and the there are resources included and Governor nuisance budget that was announced on Friday.
Kevin Kish: To two to DEF, eh, for a number of different things but including for the analysis of this data. Um, but yeah, I mean, it's going to be there.
Kevin Kish: We have the job of the agency that collects the data is to clean it right, make sure that that we have the data in a form that's actually usable look at non compliance look at non filers and see what kind of actions we need to take to ensure compliance.
Kevin Kish: We have the authority and the statute to issue reports of aggregate data. Keep in mind that this data is explicitly protected from disclosure under the California Public Rights Act and the statute, which is the state equivalent of oil.
Kevin Kish: So, you know, no individually identifying data is to be made public, but we do have the authority to issue aggregate reports as EEOC has done in the past. So there's some public education and outreach and around that.
Kevin Kish: And then again, as we've discussed using it and enforcement actions and so it'll be a it's going to be a process and it's going to be a yearly
Kevin Kish: A yearly process that I expect will become smoother, year after year. And again, going back to the legislature, if there's something that isn't working. I do see your colleagues up
Kevin Kish: Asking a question about why the collection forum is such a bear. I'm in California, they put into statute, what the EEOC did right and so we'll see both at the state level, but also at the federal level, if there is a revisiting of that.
Kevin Kish: And there may be things that should be revisited. And we'll see as the as the years go on.
Zev Eigen: Yeah, that's super helpful and before we move on to the second cluster of discussion point there was one point that we had in our prep conversation that I just wanted to service, but I think it's a good one.
Zev Eigen: About the ways in which SB 973 depart from the EEOC requirements. So I just wanted to cue that up for either fire or or Kevin, because there were two points that I have in my notes that I thought were kind of important on this for panelists to hear, I think.
Chai Feldblum: Yeah, Kevin. Would you do those two differences. And then I have some responses to the comments that people put in which I think are very good and connected to what we're talking about.
Kevin Kish: Is other i'm sorry i can you help me remember what
Zev Eigen: You're sure you're sure. All right. I like is like a law professor is like giving it so sorry, I didn't mean to do that so that
Zev Eigen: The two ways that I had in my notes for the W two verses Box five and then the reporting hours on on the paid leave. So I just think they're important points and I'll make sure that I just took notes on what you said.
Kevin Kish: Yes, yes, yes. Thank you. Thank you for
Kevin Kish: Me, I was
Kevin Kish: Always really bad.
Kevin Kish: Socratic
Kevin Kish: So there are two places, you know, in the statute, the statute requires us to collect he data that comes from the W two form.
Kevin Kish: Which is what the EEOC did and we are departing at least now. So let me take a step back and say, our approach to this and the short period of time that we have before the march 31 deadline.
Kevin Kish: Is to put out in the form of FAQs. Our approach as we're developing it. And that's been helpful to us because we're getting feedback in real time and we did issue additional FAQs. By the way, on Friday afternoon.
Kevin Kish: That are up there now and our actual landing site where people will access the portal is up as of yesterday afternoon so people can go to our website and see that
Kevin Kish: One of the areas where we've said we're going to do something slightly differently, is we're asking employers to report wages from Box five of the W two form rather than box one and the thinking behind this is that box one excludes wages that have already been
Kevin Kish: Adopted pre tax for certain major categories including retirement.
Kevin Kish: Health Plans health insurance plans and Flex Spending Accounts that people can pay into for things like childcare.
Kevin Kish: So the box five which is Medicare wages actors those wages, which we think is a fuller picture of what
Kevin Kish: An employee is paid right before the the employee makes choices around
Kevin Kish: Around pre tax deductions and the other place where we're asking for something a little bit different, isn't the hours.
Kevin Kish: Work worked. So for the Federal collection.
Kevin Kish: And I think this came from the definition of hours worked in the Fair Labor Standards Act. There was a statutory
Kevin Kish: link there.
Kevin Kish: The ESC asked employers to exclude the hours that an employee spends on paid leave
Kevin Kish: But the pay of course gets reported, but in that collection. The hours were excluded. We want those hours. And so we want employers to report both the paid
Kevin Kish: Leave the pay, but also the hours that were associated with that paid leave and the issue here is to reduce any disparities that that might create right
Kevin Kish: I think that there is a gender disparity in terms of who takes leave at least in certain workplaces. And so if you have
Kevin Kish: Two employees both paid the same amount, but one takes leave and the other doesn't. And that leave isn't reported
Kevin Kish: And then it looks like one employer as an employee is being paid more for fewer hours when in fact there. They may be paid the same. So that's the thinking behind that choice and our data point and
Zev Eigen: I know you have comments but for what it's worth. I'm the reason why I wanted to surface those two points is a I think they're really important for the audience to hear but to I frankly I'm glad.
Zev Eigen: That that that's I think the second one, especially as a really good, a good idea. So just, I think it's a great thing.
Zev Eigen: Hi, glad you had your
Chai Feldblum: Yeah, I think both
Chai Feldblum: Are great we certainly can talk about the first at the second at the OC. I don't remember talking about that. I do remember using W two and you know
Chai Feldblum: What to use and and i actually think health. That's the bitter thought there, but I wanted to go back to the point that if they have that you made that if this data is collected and there is a burden on the government to do something about it, you know, and the question.
Chai Feldblum: Maybe I need to scroll up on my screen right from Rob porcelli says I recall EEOC expressing some concern over how it would adjust and analyze all of this data.
Chai Feldblum: Well, that's correct. You have to have the resources. And I think one of the most important things that Vicki lipnic did during the two years that she was chair of the EEOC is she made a real effort in decision to invest in the research division of the EEOC
Chai Feldblum: You know, a new person had come in with significant experience in in data analytics. They got more money.
Chai Feldblum: And she did this even before the pay data issue just in terms of other year, one for exactly the point of looking at trends or as Kevin said maybe comparing
Chai Feldblum: Companies within the same industry right everyone would have the same problems with job categories etc within the same industry. So we're their differences. So that was a significant change the OC did now once two years of pay data work collected with sitting in the EEOC
Chai Feldblum: That office that division could have done a lot of work, but when Janet doing came in the second republican chair that EMC had she decided to tell that office, not to do anything with that data.
Chai Feldblum: And there are a number of things in the Commission with the whole Commission has to vote. And then there are a number of things that the chair has authority to do unilaterally.
Chai Feldblum: Chair Dylan had that authority, as I'm sure everyone knows I expect very shortly after January 20
Chai Feldblum: You'll see will be getting the letter from the White House saying that Charlotte burrows. I'm sure it'll be Charlotte is the more senior person will be the chair acting Chair of the EMC and so
Chai Feldblum: New chair borrows on day one token, otherwise the age, the research division to start working on that data. And I think they will.
Chai Feldblum: And Rob. Second question of, you know, same thing collection form is a bear and widen UC California collected data in which it's stored by employers.
Chai Feldblum: I do remember asking that question when I was a commissioner, like, will they be able to crosswalk easily between the data we're asking for. Oh yeah, yeah, no problem. They have vendors that do this and actually they were not
Chai Feldblum: Which is certainly something I discovered and I you know I think California, it made sense that you were going from the template.
Chai Feldblum: That you see was using, but I would think one as California moves forward and analyzing it's la Kevin and the way you said and as he Yossi revisits the data collection which it definitely will. As soon as there's a democratic majority on the condition
Chai Feldblum: Hopefully they'll collect information from employers to understand what works and doesn't work.
Zev Eigen: Yeah, so I mean it sounds like there's probably going to be some big changes as I think a lot of people are expecting at the federal level.
Zev Eigen: On on this and I know the EEOC under, under chairperson lipnic was already moving forward with some some aspects of like data analysis with the office that they created. So I mean, partly it's, I mean,
Zev Eigen: There's some evidence that even across the aisle republican concert, you know, Democrats and Republicans are both kind of interested in in some kind of data.
Zev Eigen: analytic approach. But yeah, there is there. It takes a lot of work. And as you're saying, and both of you are saying it you need you need resources to be able to do it in an effective way.
Zev Eigen: I'd like to just an interest of time, move to some of the nuts and bolts stuff that I'm sure our listeners are interested in
Zev Eigen: And and Kevin. You mentioned the the fact the FAQ that's up on on the site and we have I it's been updated that's great too. But we have some of the slides, the questions in the back in the slides. When we share the slides, you'll see the link
Zev Eigen: That that is still the Live Link and and some of the questions that are that are there that are frankly really robust like the questions and answers. I urge the the view the listeners viewers.
Zev Eigen: To check out the fact because they're they're they're they're great. And apparently there are more answers forthcoming too. But Kevin, I'd love to just turn to you for a second and talk a little bit about some of the
Zev Eigen: The kind of nitty gritty or the nuts and bolts that I'm sure folks are thinking about, which is, you know, how do I, how do I comply with this out of the gate, because this is coming up in March.
Zev Eigen: So the first thing that I get asked a lot, and I'm sure fi does too. And maybe you do to Kevin is what about these categories of people part time employees out of state or remote workers definitely in 2020 we've got a much bigger proportion of people who are remote
Zev Eigen: And learning how to use zoom right and employees that only work for part of the qualifying year. I know there's some some guidance on the fact that that we have there, but it would be great if you could just talk through those three categories, just briefly,
Kevin Kish: Yeah, and so folks should know that I am looking at that document as it's hot.
Kevin Kish: And you know what, I know you have access to
Kevin Kish: And the other thing that I'll say just before it's starting is
Kevin Kish: You know, please write us there is actually at the top of the FAQ page an email address, it's paid data reporting at D E F G h.ca.gov
Kevin Kish: It's on the presentation that you guys are going to be getting. And people have been writing us with questions and some of them I tell our staff that's not a fact.
Kevin Kish: It has to actually be within the realm of possibility for it to potentially be a frequently asked question. We have some
Kevin Kish: wild speculation about outlying scenarios that we just aren't going to be able to address. But there are lots of things. And in fact, I saw one
Kevin Kish: Pop up in the chat about I'm partial paid for leave. That's a question that I am not prepared to answer on this webinar but that I strongly encourage you to write to us, posing so that we can, if appropriate incorporated into FAQs.
Kevin Kish: So going back to the specific questions. One thing to keep in mind that the statute in California requires employers of 100 or more employees who also have to file the EO one
Kevin Kish: Folks who have this obligation in California already have to file the EO one, and that's something that I think is important to keep in mind.
Kevin Kish: If you don't have to file the EO one for whatever reason you do not have to file in California.
Kevin Kish: So, part time employees in terms of counting employees. We have existing regulations in California right
Kevin Kish: In a, in a 13 week implementation timeline. There is not time for formal rulemaking, although I anticipate that we will do some in the future, especially as these issues.
Kevin Kish: come to light during the first collection period and but we don't need to do that for counting because we have existing regulations that tell us yes you count part time employees.
Kevin Kish: We are if you have no employees in California, then you don't have to report, but our jurisdiction is over every employee in California. So if you have even one and then you're going to be reporting and so yes you do count out of state employees.
Kevin Kish: In terms of remote workers. I think we're going to be seeing a lot of changes around us and I think that employer practices are changing.
Kevin Kish: And
Kevin Kish: So the way we've framed this is
Kevin Kish: Employees who are working within California regardless of where they report are counted. You need to account for those folks.
Kevin Kish: Employees outside of California who are reporting in California also need to be counted and there's going to be some ambiguity around what that actually means, at least for right now. I think most employers have employees assigned to
Kevin Kish: Some concept of a physical location. Like, I can tell you at the F, G, H, we've got folks working in various counties.
Kevin Kish: Remotely right now who are assigned to our headquarters and Elk Grove and so folks would report if if we were submitting this data, we would
Kevin Kish: include those employees and our headquarters report and the same for everyone else and then employees working for part of the qualifying year our regs do
Kevin Kish: require employers to count employees who are regularly working for various reasons. Under California law regularly working could be seasonal
Kevin Kish: Right, if you think about a farm labor contractor. You don't have to be employing 100 employees, every day of the year.
Kevin Kish: If your regular business practice means that for some part of the or you're employing 100 employees and
Kevin Kish: You're employing 100 employees but keep in mind that other prong which is you already have to submit that yield one Yep, I'm so all of this is explored in greater detail and the FAQ is, but I hope they're not provides at least a little bit of a framework.
Zev Eigen: That's great. And I think, and the one question that I saw was a if you're a P. If you have a PEO who has to do you both have to submit like who submits under those circumstances, or is it both
Kevin Kish: Um, I'm a PEO
Zev Eigen: Yeah, so like if you have like if you're a joint employer with a professional and like a
Chai Feldblum: Some organization that's that's
Providing employee right
Kevin Kish: Okay, so this is interesting the way that we have described this in our FAQs is I mean county employee is defined in California law.
Kevin Kish: And part of the definition is you are that employee is on your payroll and you are paying taxes for the employee.
Kevin Kish: Right. So this comes up in the context of temporary workers as well who reports them.
Kevin Kish: And typically, there's only one entity that is actually paying and withholding taxes on that employee. That's the entity.
Kevin Kish: That's going to be reporting the employee, whether you know it's that kind of direct employer situation or a temporary employment if there are situations where that's not the case, then please write to us.
Zev Eigen: One of the things that I've had conversations with and we've talked about a little bit is this idea of how to assign employees to the 10 categories described in in the act. These 10 categories.
Zev Eigen: And you know, you mentioned this when we first started talking a little bit. It is a bit of a challenge to figure this out. And I've done a little bit of the homework for for folks in this slide here.
Zev Eigen: So if you look at the DHS website and it gives you, it gives you links to this website. So it says, look at the EEO sees website to figure out how to put people in which boxes.
Zev Eigen: This link takes you to this page, which is census.gov and then that page takes you to this one.
Zev Eigen: And how you made the point when we were prepping about those. So these are the EEO occupation that this link gives you a list of occupation code descriptions or iOS, the occupation descriptions and their codes.
Zev Eigen: And you mentioned, I think someone said this earlier in the conversation. They're a little bit out of date. Right. So some of these are from I think 2008, if I'm not mistaken.
Zev Eigen: And maybe work has changed. Just a little bit since 2008
Zev Eigen: But I do you have any or if I or Kevin, do you have any thoughts on that before. Like, how should employers go about classifying and then we'll play our quick game show
Chai Feldblum: Yeah, I mean, there was a bit of an update. I think 2008
Chai Feldblum: He's coming on the for that as well. I mean, I think for the EEOC in terms of pay data. It's the one that already existed. I mean, to try to change the categories.
Chai Feldblum: That would have been a huge undertaking and really would have required a lot more of a burden on it on companies and employers to
Chai Feldblum: Figure out new categories. So I think people just sort of felt okay maybe you're not going to have a lot of craft workers, you know, operatives, but you can still distinguish between your executive
Chai Feldblum: senior level officials and your first or mid level officials and between your administrative support workers so so then when it came to the pay data. I mean really Yossi was just building on what was there because it would have been too much.
Chai Feldblum: To change. I will say, talking to a lot of clients over the last two years with this pressure to report the ER one data, the demographic data. I think that should result in a request to the EEOC to, in fact, we look at these categories.
Chai Feldblum: Yeah, you're going to
Chai Feldblum: Publish your demographic data, but these categories don't line up with how you run your business. That's a problem.
Zev Eigen: That that's definitely a problem that's something that we've encountered for a lot of the folks with whom we work at San Diego and the Fair Pay workplace alliance and just maybe to illustrate that point.
Zev Eigen: And we want to take a crack at these are these are, these are the ones on this this list. These are all copied and pasted from that document that he OC occupation description. So these I didn't make these up. These are straight there and we'll try to match with
Zev Eigen: You see the numbering here one a one b 23456789 and you want to take a guess for hotel.
Zev Eigen: Motel and resort desk clerks.
Zev Eigen: Crying Kevin
Chai Feldblum: You know when you said you were going to play this game. I thought you were playing
Chai Feldblum: With the audience.
Zev Eigen: I know I play the experts on the spy
Kevin Kish: Play Games when
Zev Eigen: It will play some poker later but
Zev Eigen: We don't. I mean, there's nothing bad if you get it wrong. I mean, that's the point is
Chai Feldblum: Like I would want Sharon Maslin
Zev Eigen: Who's my chairman, he is
Zev Eigen: My point is
Zev Eigen: I mean, we're all experts and like I had a hard time like some of these are like, I'm like, whoa, these aren't easy. The, the only point here and we don't have to do. I mean,
Zev Eigen: That's fine, I'll give you a an easy one. Well, maybe not easy. That's that that came under admin computer operators, a tough one but I think, you know, just to illustrate the point, it's also
Zev Eigen: Which may or may not like these are very broad. Right. So, to your point five. I'm statistical assistant
Chai Feldblum: Wait community so computer operator is not a professional and you know that because of the guidance from
Zev Eigen: Yeah, I'm
Zev Eigen: I'm not as a as
Zev Eigen: A like
Chai Feldblum: I get it.
Zev Eigen: I literally just pulled it straight. If you click on this link. You'll see it gives you the exact
Kevin Kish: Exactly what
Kevin Kish: I'm sure
Chai Feldblum: This is incredibly helpful.
Chai Feldblum: And I think everyone in the audience.
Chai Feldblum: As you say the one everyone in the audience should write down the number and we can write it down and Airhead and then put up the number
Kevin Kish: Right there is an answer.
Kevin Kish: And the answer may be outdated, but you can find it. And my advice is to use it.
Chai Feldblum: Yeah, yeah. So those numbers can
Zev Eigen: Write down that
Chai Feldblum: Yes, that's it. Don't wait. Everyone watching the webinar. Right.
Zev Eigen: Now,
Zev Eigen: Let's see, we've got it down.
Chai Feldblum: Okay, now put it up.
Zev Eigen: Okay, let's see we got here for. So this may be a surprise to some, but look at that. We got three fives in a row. So, and again, to illustrate the point
Zev Eigen: Those are all in one group. So we talked about my next question, which is the relationship between this and California fair pay act like this is the point of trauma. Okay, so five. Let's do animal train. I thought it'd be a fun one.
Zev Eigen: So before I put up animals. So I won't put up their numbers out there. We got an eight.
Chai Feldblum: Great. Yes, put it in the chat.
Zev Eigen: That's a good one. Anyone else want to take a guess. No, there is no, there are no wrong answers. Except there are definitely wrong answers.
Chai Feldblum: Okay, what is it,
Zev Eigen: Five, six to eight or seven. I love it. Higher Kevin. You guys have to put your
Zev Eigen: Little bit to come on, you don't want to do we do labors and helpers maybe libbers
Chai Feldblum: Laborers and help
Zev Eigen: Are so high, is just a number. You're saying
Zev Eigen: So high as eight KEVIN, COME ON.
Kevin Kish: I'm gonna say that an animal trainer.
Kevin Kish: Is a and
00:50:57.750 --> 00:50:59.850
Kevin Kish: I'm also going to say labors and helpers, a
Zev Eigen: Oh, it's not
Chai Feldblum: Fish.
Chai Feldblum: Is animal trainer.
Chai Feldblum: But my computer.
Zev Eigen: Oh my gosh. OK, so
Zev Eigen: The more these dental assistant, but some numbers. And guys, we had some way. Some of you got that right. The two who said to Jocelyn Thomas Thompson's excuse me, Jason got that right. Nice job. Jocelyn.
Zev Eigen: You win a prize. Okay, what's a dental assistant
Zev Eigen: Dental Assistant Kevin CLI.
Chai Feldblum: Maybe also professional
Chai Feldblum: We got our
Kevin Kish: Mission.
Zev Eigen: I got a lot of
Zev Eigen: Threes of five technicians three, a lot of threes.
Chai Feldblum: Okay, maybe Chloe. What is it,
Zev Eigen: All right. Let's check it out. Oh, everyone.
Zev Eigen: Lot of missing.
Chai Feldblum: Data work through and I do want to say this, there are specific people in each company who is doing this and they've had some experience right all those years of doing either one.
Chai Feldblum: Yeah, anyway, that's
Chai Feldblum: Day, even if those of us.
Chai Feldblum: That but it does make the point, okay.
Zev Eigen: Guys me so. Oh, sorry.
Kevin Kish: No, I was just gonna say the other point and I think we're going to actually move into this and the next topic.
Kevin Kish: Is
Kevin Kish: Again, this is a starting place and there are answers to these which you should use, but the initial goal or point is for you to be comparing like employees right and seeing, you know, is there something that I need to be paying attention to that. I have not noticed
Zev Eigen: Yeah, I think the. I agree. I just think it creates you know
Zev Eigen: There are definitely challenges because a lot of the jobs aren't right or they're not matching properly, like what a designer is or what a statistical assistant is like the descriptions are a little bit
Zev Eigen: Little bit out of date as as you, as I said, so like it creates challenges because like what we think of as a computer operator from like probably when that was formulated and probably 2006 or 2005 even
Zev Eigen: A computer operator is very different, like that was when you'd ask people what they did for a job and they say, I work, I work in computers. Well, now we all work. I mean, like, it's just not the same.
Zev Eigen: So I gave you a designer that was to lawyer judge magistrate other others.
Zev Eigen: The kind of the point there yet to. So these are also professionals. I see what people are putting into so that one is sort of straight, straight paralegals and legal assistance just it's just a time just moving a little faster.
Zev Eigen: Yeah, those are five so
Zev Eigen: That's five yeah we john got that one right and then medical assistance in life, scientists, those are
Zev Eigen: Those are professionals. So the only. I mean, the point here is, and this is just all the answers together and but bucket ID.
Zev Eigen: You've got this is the question I wanted to put to you, Kevin is. So what's so we've got these if you bucket people and you submit your data you've got people who I think are doing very
Zev Eigen: Different things based on the sub they're not doing substantially similar work based on skill effort and responsibility, which is the requirement for grouping under CAF BA
Zev Eigen: So what's the deal if I'm submitting people who are doing stuff that's that are different, like all these fives are all these tues
Kevin Kish: Mm hmm.
Zev Eigen: What's the relationship, if any, between SB 973 and Katha
Kevin Kish: Well, look, I mean I am. I'm a little bit of a broken record on this point about it being the starting place. But I do think that this gives all of us as employers.
Kevin Kish: I am an employer as well, an opportunity to see into our workplaces in a way that on a day to day basis. Most of us simply do not
Kevin Kish: And so yes, if you see a disparity. I think the answer is, look into it now. Maybe that disparity is
Kevin Kish: For multiple reasons but including so that if somebody else sees the disparity, right, including your own employees or the government
Kevin Kish: That you already know what the answer is, and whether it's something that you've needed to take action on or whether it's something that's explained by
Kevin Kish: Legitimate and legal reasons. So if you see a disparity within a job grouping because you've grouped. Let's say you see a gender disparity right between in your
Kevin Kish: Service Workers, but you've grouped five different occupation or job categories into that, that are very different.
Kevin Kish: Well, you may not have depending on what you find when you look at it, you may not have a California Fair Pay Act.
Kevin Kish: Violation but if you're seeing the gender disparity, you may have a job segregation issue right under the house under Title seven
Kevin Kish: Now, maybe that's justifiable and maybe it's not. But it gives you the opportunity to look at it and figure that out in advance. So I'd say, you know, look at the disparities understand what's causing them and make those adjustments that are appropriate depending on what you find.
Zev Eigen: So I you know I gave some examples here the slides with looking at, you know, and this is an scenarios pay equity tool which looks at, you know, any kind of grouping. You want to run, you can look at it by one category which is what you're describing, Kevin.
Zev Eigen: You can look like in the questions we have like this is, you know, mid matters broken down by pay down so you can look at it however you want. And I guess the last question that I have really is for both of you, you know i i said to both you and our prep that this is the
Zev Eigen: A checklist that I put together based on talking to some of the law firms with whom we partner at the alliance and it's in do just asking people to do a lot of pay equity work or work in the space.
Zev Eigen: What would you want, what would you suggest to clients that they look at a review before submitting data to the government. So
Zev Eigen: I've shared this with both of you before, but I really just love to share with the audience. Your comments or thoughts on this checklist and again we'll share all the slides with the attendees, so you don't have to
Zev Eigen: Take you know attendees. Don't take notes. You don't have to but Hi, I'm Kevin please weigh in on best practices are things that on this checklist. You love you. Hey,
Chai Feldblum: I'll say something about the, I guess the first three. I mean it because it's just really reinforcing what Kevin just said of once you see something based on how you need to report it.
Chai Feldblum: And you think that there is a new see a statistically significant pay gap from all your professional. So are you mid level managers, which I think, by the way, one a one b and two are going to be where a lot of your people are and
Chai Feldblum: Those should be what you can sort of put together well. So I think if you see a disparity, just look into it, you know, and pay band is also a very helpful thing to look at because again it doesn't necessarily translate into
Chai Feldblum: You know, similarly, based on responsibility, etc. But it's, it's pretty close. And then I think it also like leads you to three because
Chai Feldblum: You know, if your statistically significant pay gap is because you've got more men at the top of the organization that are making more money than women, right, because there
Chai Feldblum: For some reason, haven't taken that promotion didn't apply for some promotion. That's what you want to dig into. I mean,
Chai Feldblum: This is really just the starting point. And so much and I'm sure lots of law firms do it and love from certainly does is ok take the
Chai Feldblum: Differentiation and pay and then dig down. What's the reason and even find a reason that might not be discriminatory, you know, you only had three women apply among and 15, you know, then you should say what was only three women.
Kevin Kish: What's up with
Chai Feldblum: The low
Chai Feldblum: Well below in the level below. It's 5050, I can tell you how many companies have this they've done a good job in recruiting. They've done a good job and even mentoring and moving women and men up at the same level, sometimes even awake. So that's really an issue.
Chai Feldblum: But then what's going on that they're not moving to the next level. These are things like organizations employers can take affirmative actions on intentional proactive actions if they know
Zev Eigen: That's something we strongly agree with that, Cindy. Oh, for sure. That's something that's in our dashboard.
Zev Eigen: Kevin or before and I want to give Kevin, just a minute to give the preview that I said I've gotten the slides. I want you to queue up but any last comments or thoughts on the checklist. Before we give, Kevin. Just a minute, since we're close on time.
Kevin Kish: Not for me. Thanks. Okay.
Zev Eigen: So I hope that was helpful, I want to give Kevin a chance to queue up some fun.
Zev Eigen: Exclusive preview. I'll call it
Zev Eigen: God, sorry.
Kevin Kish: I think you described it as hot and exciting, which I've never heard the D E F G H website just kind of
Zev Eigen: try my best. I'm trying, I'm trying to be your, your PR person. Yeah.
Kevin Kish: So there have been some questions, appearing in the chat about when is the form GOING TO BE READY WHEN IS WHEN IS WHEN ARE THINGS GOING TO BE READY.
Kevin Kish: And we are now in Week six of working with our contractor on building the portal. So I asked you for a little bit more patience. But this is the site.
Kevin Kish: That I'm showing you this is up now, as of yesterday. You can see that the data submission portal user guide and template are coming soon the FAQs are up. Now we have said.
Kevin Kish: Now that we're going to have
Kevin Kish: Oh, well, this is, this is an old version. This is not what's out there now. I'm the user guide I'm my hope is that it will be ready by February 1
Kevin Kish: In about two weeks and and our goal is to launch the portal with the form the template, which I will show you in a second. By February 15 which will give folks about six weeks.
Kevin Kish: To do their submission. Now this is going to be hard to see, but again, it's in the slides, you'll see that we're offering
Kevin Kish: Two options and the one that I frankly recommend is for you to download the template and an Excel format and fill it out and then upload it.
Kevin Kish: As a CSV file there will be detailed instructions on how to do all of this. And I think that's probably going to work best for most folks, but we all are also offering a
Kevin Kish: Basically a web based form. This is going to be a very long web page because there's so many fields, but you will be able to enter it. And then if you'll go to the next slide and
Kevin Kish: Go to the next one.
Kevin Kish: I can't actually read these
Kevin Kish: That's the text, but you'll see that there's an opportunity to edit what you put up before
Kevin Kish: certifying it to. There we go. There's a screen where you will be able to upload it, you'll be able to go back into it and edit it before you certify. Um, so this is in broad terms, what its gonna look like. Again, you'll be working in Excel or
Kevin Kish: On the on the web. And that's what it is.
Zev Eigen: And then you get it gives you this final certification step.
Zev Eigen: But I think that's. This is like all there and Kevin you you mentioned this in the email before when you were talking and obviously we'll leave this in the slides.
Zev Eigen: And you'll have the access to that everyone will have access to the slides, but so you have that email there and there's lots of information on David's website.
Zev Eigen: Please join me in thanking our panel thigh and Kevin. I really, really, we really, really, really appreciate you taking the time it's I think it's hopefully it's been very helpful and really informative. So we really appreciate you sharing your insights with the audience.
Chai Feldblum: Or just to do a plug for us if it's in. Do I was happy to say yes because of the awesome work you do and I think I can save it. Now, I'm not a government official, you know, but we really appreciate what you do and the alliance is doing.
Zev Eigen: Thank you very much. And anything else we have to do. Are we, how you
Ana Anttonen: Know, we're said thank you all for joining us, Kevin Hi, it's been amazing says Awesome job really fun webinar. And thank you guys. We will be sending a follow up email to everybody shortly with a recording and the downloadable presentation. So thank you very much.
Kevin Kish: Thank you. Take care.
Chai Feldblum: Thank you.