Labor Dept. trying to help states fix pandemic unemployment claims crunch

Michele Evermore, senior policy adviser on unemployment insurance at the Labor Department, talked about some of the efforts now underway via the American Rescue...

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The huge sums of cash Congress allocated for pandemic unemployment assistance went a long way toward staving off an economic collapse during the worst days of the crisis. But the huge surge in claims through state unemployment agencies also highlighted weaknesses in the system — weaknesses the Labor Department is now trying to help states fix. Michele Evermore is senior policy adviser on unemployment insurance at the Labor Department. She talked with Federal Drive with Tom Temin about some of the efforts now underway, with the help of about $2 billion in grants under the American Rescue Plan.

Interview transcript:

Jared Serbu: I certainly want to spend the bulk of our conversation today talking about some of the modernizations and improvements that you all are making to UI. But I thought you could start us out with just a little bit of discussion on what the challenges the pandemic exposed really were. I mean, were they known issues that were just kind of highlighted by all the extra things that were happening during the pandemic, or were there new issues that surfaced?

Michele Evermore: It’s really a combination of both. So prior to the pandemic, I had actually written a paper saying that states weren’t necessarily ready for a recession. But that was largely about benefit amount and access to benefit. And so what happened during the pandemic is Congress last March, in March of 2020, passed benefits that would expand the number of people who are eligible and dramatically expand the amount of benefits. So those two problems were somewhat taken care of over the course of the pandemic with obvious lapses in some coverage and a lapse in the federal pandemic unemployment compensation for a few months in the fall. But by and large, those issues were covered by Congress. What we did find is problems with technology, and some of the issues that states had established to control access to benefits and make benefits a little bit harder to access. Those did actually present problems for claimants getting through the line during the pandemic. But one thing we really couldn’t have planned for is the massive fraud rings that came in around last May to attack state systems. And in general, fraud is somewhat of a problem and unemployment insurance, but not the hugest problem. But these coordinated attacks were truly unprecedented, and the kinds of attacks that states weren’t prepared to deal with it.

Jared Serbu: Did that become a problem? We think just because criminal syndicates recognize the large amount of money that was suddenly available? Talk about why that became a problem and some of the things you’re doing to solve that from a cybersecurity standpoint?

Michele Evermore: Sure. Well, there were two problems. There were two issues that led to criminals attacking the system. Number one, I suppose having a larger benefit did make the system more attractive. Number two, the requirements to get pandemic on unemployment assistance are different than the requirements to get unemployment insurance. So in order to get PUA, there’s not necessarily an employer check. So claimants did not have to necessarily verify their earnings to get the minimum weekly benefit amount under pandemic unemployment assistance. And so that made it a little bit easier to establish a weekly benefit amount. And the other thing was just the challenge of dealing with the crush of new claims left every system vulnerable to any kind of attack, because the states were so concerned with paying out benefits in as timely a manner as possible. And those systems were crashing, that creates a vulnerability in and of itself. So that to set up sort of an understanding of how big a change this was for states. The middle of March last year, there were 3.3 million new seasonally adjusted claims. And the week after that there were 6.6 million, and the week after that there were 6.6 million, and the week after that 4 million. It was over a million new claims a week for more than 52 weeks. By way of comparison, the highest week of new claims in history was 695,000 in October of 1982. So states were seeing a tenfold increase in new claims. At the same time, they’d come off historically low unemployment rates, which also meant that their administrative funding was at a 50 year low. So they had historic low administrative structures to pay out historically high benefits. When that happened, something is going to break.

Jared Serbu: That all makes sense. But to some extent, I can sort of imagine Congress and policymakers making a rational calculation here that the most important thing at the moment is not to prevent fraud, it’s to keep the economy from crashing so it’s okay to have fewer checks in place for a while. But what can you do long term with some of this funding from the American Rescue Plan to stabilize some of the estate systems, make sure they are more cyber secure for the sorts of benefits that are going to continue over the long run?

Michele Evermore: That’s a really good point. When the CARES Act was passed in March of 2020, I think a lot of people assumed that this pandemic would be a real problem for up to a few months. I don’t think anybody anticipated that we’d still be dealing with a new surge in the pandemic in August of 2021. And so this wasn’t designed to be a long term economic stabilizer, and so certain risks weren’t necessarily envisioned. But now we are where we are and we know the things that we know, and there are a few things that we can do to help out. Number one, the department has entered into a Blanket Purchase Agreement with certain ID verification vendors. The Continued Assistance Act signed in December, required identity verification for pandemic unemployment assistance claim. There are several vendors out there in the space that are that are helping states to perform identity verification. So that’s one route. Another route is, essentially the problem in many ways is that unemployment insurance is a 53 state system. And with that brings 53 front doors for criminals to test. I sometimes compare this to velociraptors testing a fence, once they find one spot to get in, that that is a breach. And the problem is, so once the criminals get into one state system, and that state system figures out where they’re coming from and figures out how to walk them out, well they can just move on to the next state. And you can’t take the same team of people that locked the criminals out of one state and bring them into the next state and have them fix the problem there, because with the 53, state system, there are all different technologies in place in every state. And so every safe operating on a different system. One thing that can help to begin to deal with that problem is more central technology solutions. And so one of the things that we’re investing in with the funds that were provided in the American Rescue Plan Act is starting to develop central modular IP solutions for states. We’re currently in the research phase on this project, it’s going to take some time and some effort, but giving states a way to share information across states and a central solution that can be updated as the identity of the criminals is updated, as the tactics are updated. Having a central solution to that would ultimately be a helpful thing. In the meantime, however, there’s also short term solutions, right. And so, another effort that we’ve launched is these tiger teams. So we’ll send teams to states that include fraud experts, equity experts, people who know how push people through a queue. And they they are charged with both eliminating backlogs and also promoting equity and fighting fraud. And a lot of those things can happen all at the same time. A lot of the backlogs are fraud related at this point. So figuring out how to better identify the fraudsters and help the innocent people get through the line accomplishes all kinds of things at once.

Jared Serbu: Do you have any sense yet for how receptive state officials are going to be to really these two main things, the centralized technology solutions and the tiger teams, because especially with the centralized technology, it’s only helpful if states actually use them. So what kind of carrots and sticks can you put in place so that they do get employed, or do you feel like even need to do that?

Michele Evermore: Well, we have the capacity to deploy tiger teams in six states at a time. We had no problem getting volunteers for the first six states, we already have a couple states volunteering for the next round of technical assistance. When I first started at this job, we had a big meeting with with a number of states. And so this is very responsive to the things that we’ve heard that they need. And that is just more help, more sharing information across state lines, more sharing of the lessons learned. And so I think that both of these efforts are popular there. So we already have six states participating with the tiger teams. Another incentive for states to work with these tiger teams is there will be grants available. Once the tiger teams identify potential solutions, there’ll be funds available to states to implement those solutions. So it’s a win-win for states. Not only they get somebody to help them figure out how to make things operate more efficiently, but then they’ll get the money to implement those solutions. So I think that that’s very attractive to states. With regard to the technology, we already have had nine states volunteer, just for the research phase. There will be several other stages. But again, we’ve heard great interest from states in central solutions, or at least at the very least information sharing about how best to modernize the technologies that they currently have. Having said that, there is an actor in the space already doing a lot of work on this front and that’s NASWA, the National Association of State Workforce Agencies. And they do a wonderful job. I guess the thing is scale. There’s just so much to be done. Applying these $2 billion in resources to serve that purpose is necessary.

Jared Serbu: We’ve spent most of our time talking about technology improvements here. But before we let you go, I wanted to bring up the equity grants as well, because there’s a pretty healthy chunk of money there also in the American rescue plan. What do we know about racial disparities, ethnic disparities in the UI system and what at this point is the thinking on how to address them?

Michele Evermore: Well, I have to say, that’s part of the problem. A lot of the data that we have about disparities, it’s just not there. We don’t necessarily have data disaggregated by race, gender, disability, English proficiency. And so I think maybe step one is figuring out how to measure that, you can’t manage what you don’t measure. I think that that’s issue one. I do know that the GAO has issued some preliminary findings that there’s definitely disparities in access, we know that there’s disparities in non-filer rates, people just clean don’t apply because they don’t think that they’re going to be eligible. And we know that states that have the lowest replacement rate and the lowest recipiency, that is the number of unemployed people getting a benefit, those tend to be states that are the most diverse. So just looking at demographics on a macro-level, you can get the sense that recipiency of unemployment insurance benefits is unequal across racial lines. Also, surveys of recipiency throughout the pandemic also demonstrate that black workers had a much harder time getting benefits than white workers, for example. So we have some data, we need better data. And hopefully these equity grants will start to help us begin to measure the problem so that we can manage it. These are very flexible grants for states to employ. They will come to us with a plan about areas of access that they want to address and ways that they want to address them. And we’ll work with them to figure out allowable uses for the for this grant funding.

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