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[Watch] Highlights from the 2025 Survey of Household Economics and Decisionmaking

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Fed Communities Staff

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During this Connecting Communities webinar, researchers from the Federal Reserve Board of Governors shared key findings from the annual Survey of Household Economics and Decisionmaking (SHED), which was fielded in October 2025 and released on May 13, 2026. The survey is a key tool for measuring the economic well-being of U.S. households and identifying potential risks to their finances.

In addition to monitoring household financial circumstances, each year the survey explores a range of related topics. In 2025, it introduced new questions about the use of generative Artificial Intelligence at work, alongside expanded modules on family finances, employment, and economic hardships. The survey also continues to track other financial topics such as access to credit, housing affordability, savings and investments, child care costs, and banking. 

Attendees had the opportunity to engage directly with the panelists during a live Q&A session, gaining deeper context and clarity on the findings.

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Connecting Communities: Highlights from the 2025 Survey of Household Economics and Decisionmaking (video, 59:31)
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Transcript

Sergio Galeano

Good afternoon, everyone around the country, and welcome to Connecting Communities. Thanks for joining us for today’s webinar, during which researchers from the Board of Governors of the Federal Reserve System will share findings and key insights from the annual Survey of Household Economics and Decisionmaking, or SHED for short. I’m Sergio Galeano, a community and economic development advisor with the Federal Reserve Bank of Atlanta, and it’s my pleasure to serve as moderator for today’s session. 

Today, we’re fortunate to be joined by three economists from the Board of Governors who helped produce today’s SHED report. They’ll walk us through the findings and discuss what the survey reveals about the financial experiences of US individuals and households. They’ll host their presentations and then we’ll follow up with a panel and Q&A. Now, about our speakers and format, first, Alicia Lloro will provide an overview of the SHED report itself. 

She’ll then give us a brief overview of how people are faring financially and what their main financial concerns are. Second, Mike Zabek will discuss employment and job quality. His section will explore how people are faring in today’s labor market, from finding work and paying for childcare, to emerging workplace trends, including the use of generative AI. 

Third, we’ll hear from Ellen Merry, who will walk us through the reports finding on economic hardship. She’ll give us a view into the challenges that individuals face when financial resources are stretched, overall preparedness for unexpected financial challenges, and what we generally know about the measures that households take when dealing with economic hardship. 

Now, before we get started, just a few housekeeping items, views expressed during today’s webinar are those of the speakers and are intended for informational purposes only. They do not necessarily represent the views of Fed Communities, the Board of Governors, or the Federal Reserve System. 

Now, microphones have been muted, but we still want to hear your voice and questions. Please engage with the Q&A feature to ask your questions. In fact, you don’t even have to wait till the Q&A session. If you came in with a burning question, please go ahead and start adding those questions now. You can also connect with us online wherever you get your social media. 

Please use #ConnectingCommunities, and you can also get more familiar with Fed Communities on the website, fedcommunities.org for a ton of resources on past events and content across economics and public policy. 

And finally, this webinar will be recorded, and the presentation, video, and podcast will be available on the fedcommunities.org website in about two weeks’ time. 

Now, before I turn it to Alicia to help kick us off, I’d like to ask everyone in the audience, just to get us started and engaged, it’s helpful to know how you each interact with the SHED report. 

So the question is, which of the following best describes how you use information from the SHED? Now, with that, I’ll return in a moment. I’d like to pass the floor, the mic to Alicia, who will kick off our presentation. Thanks for being here, everyone. 

Alicia Lloro

Thanks so much, Sergio. Happy to be here today. We can go ahead and move to the next slide. The SHED has been conducted annually since 2013. We’re now in our 13th year. The most recent survey included over 12,000 respondents and was fielded in October of 2025. So, the results that we’re presenting here today reflect people’s financial conditions at that time. I know things change very rapidly, so we have to put ourselves in the head space of back in last fall. 

The survey results are also representative of all US adults. Generally, the approach that we take with SHED, when we’re thinking about what questions to ask, is that we like to combine both subjective and objective questions. This lets us get a fuller picture of how people are faring financially. Often, the questions are focusing on the “why” and the “how” people do things, and not just the “what” they are doing. We also like to maintain flexibility to cover new and emerging issues. For example, this year we included some new questions on generative AI in the workplace that Mike’s going to talk about later. 

So next on slide seven, I believe. Yes. So here, we have three of the main takeaways from our presentation. So, the first one comes from my section, which is also one of the main takeaways of the whole report, and that’s that people’s financial situation across a wide variety of metrics looked very similar to what it had the prior year. 

The second takeaway is based on AI, and it’s that workers are split on whether AI is going to improve their career or whether it’s going to replace their job. And then our last takeaway from Ellen’s section is that major unexpected expenses are fairly common among the population, yet many people, particularly those with low income, don’t have enough in savings to cover those unexpected expenses. 

Okay. The next slide. And we can go ahead and skip one more. Thanks. So, to give an overview of people’s financial situation as of fall 2025, this slide shows two series. So, the first, the top line, is a subjective measure of people’s financial wellbeing. It’s based on a question that asks people how well they’re managing financially these days, and people could choose from four options, either living comfortably, doing okay, just getting by, or finding it difficult to get by. 

So, the top line in dark blue is showing the share who answered with the highest two out of those four choices. So that is they were either doing okay or living comfortably. In recent years, the shares remained either at or near 73%, which was down from the series high of 78% in 2021. So, this was when all of the pandemic-era stimulation programs were in place and before inflation had really taken off as it did after that. 

Another thing that I found really interesting about this series is that the current plateau at around 73% is also lower than what it was before the pandemic. Not by a lot, but two percentage points for this series is significant. So, we sort of settled into this new plateau that’s lower than what it was before. The bottom line shows results from perhaps our most well-known question that asks people how they would handle an unexpected $400 expense. 

In 2025, 63% of adults said they would cover such an expense using either cash, savings, or a credit card that they paid off in full at the next statement. So we call this combination of methods cash or its equivalent. This year has also followed a similar pattern to the measure of doing okay or living comfortably, except that the current level now is not statistically different from the 63% right before the pandemic in 2019. 

On the next slide, we also show several measures related to savings, economic hardships, and applying for credit. And nearly all of these measures held steady over the prior year. So, if you look at that one year change since 2024, they’re almost all zero except for the skipped medical treatment. 

So, this is, again, pointing to that key takeaway, where overall, people were seeing very little change in their financial situation over the prior year. I will say that despite that, we do see declines for certain demographic groups like lower income, young adults and Black adults. Moving on to slide 11. 

For the past two surveys, we have included a question asking people whether specific areas of their financial lives were a concern. So nearly everyone was concerned about increases in prices. That’s shown by the bars all the way on the left. About 9 in 10 adults said that price increases were either a major or minor concern, and this was unchanged from 2024. 

That said, we did see the price increases as a major concern, did decline a little bit over the prior year, three percentage points. In contrast, if you look all the way onto the right, concerns about finding or keeping a job, both as a major and minor concern, increased over the prior year. And this finding is consistent with some trends we see with other questions about the labor market that Mike is going to discuss in the next section. 

So, if we take these two things together, that people were slightly less worried about prices, at least as a major concern, with increasing concerns about finding or keeping a job, it’s consistent with the overall theme that in aggregate, people’s financial situation was largely similar from the prior year. 

Next slide. Along with questions about their own financial circumstances that I’ve talked about so far, the survey also asked people to rate their local economy and the national economy. This was also on a four point scale, and they could rate it as excellent, good, only fair, or poor. 

Overall, people’s views on the local and national economy were slightly less favorable in 2025. This was particularly true for the national economy. The middle line shows the share of adults who rated their local economy in the top two categories of good or excellent. This was down one percentage point over the prior year. The bottom line in light blue shows a three-percentage point decline in the rating of national economy as good or excellent over the prior year. 

The other thing I think that really jumps out, well, two things with this figure. The first is that views of the local and national economy remain much lower than they were before the pandemic. They took a dive between 2019 and 2020 and really haven’t fully recovered since. And then the other really striking thing about this figure that we always get asked about is, why is it that people’s rating of their own financial wellbeing is so much higher, and also has seen less movement than that of the national or local economy? 

I will say that the survey itself doesn’t speak directly to this question. However, we can surmise that people do have very good information about their own financial situation. They have firsthand knowledge. However, their information about the local and national economy comes from external sources, which may or may not be as accurate as the information about their own, or they may have different perceptions that way. So, with that, I’ll wrap up my section and pass things along to Mike. 

Mike Zabek

Thanks, Alicia. So, the next slide begins the employment section, where I’ll talk a little bit about different labor market indicators, the impacts of generative AI on work, and a little bit about paid childcare. So, the next slide gets into our picture of the aggregate labor market. And as Alicia noted, she noted that we’ve seen some softening in terms of some other questions. We also see some softening in these measures, but overall we see a pretty solid labor market. 

So, what this is plotting, is this is plotting movements into and out of jobs, which we have a decently long time series for. We have if people applied for a job, started a new job, quit a job, which is actually typically thought of as a measure of labor market strength. If there’s a lot of people who quit a job, so for example, the Great Resignation, which you’re seeing in 2022, was a pretty tight labor market, and there were a lot of people who were quitting jobs at that point. And we also have indicators of people who were laid off. 

As I said, if you look at the fewer people started a new job in 2025 compared to 2024, and particularly compared to 2022, and we saw fewer quits. We saw a few more layoffs. However, you look at the level of those series, so for instance, starting a job or quitting a job, those are very similar to levels that were in 2018, which was widely considered a relatively strong labor market. And so we’re kind of consistent in that view with a lot of other surveys. 

The next slide, however, goes into a particular corner of the labor market where we see a little bit more stress, and that’s particularly for younger adults. And this is something that we generally saw throughout the survey. So what this plot is, is it’s giving the share of all adults in all of these age ranges who quit a job over the past year, or who said that they quit a job over the past year. 

And so notably quitting a job, at least cyclically, is generally seen as a sign of labor market strength because it generally means that someone is either moving into a new job, or they feel comfortable enough financially that they can move away from an old job to do something else, and potentially get another job if they need one again. 

So looking at this figure, there’s roughly two things that I would emphasize about it. The first is just that if you look at people who are younger, so the 18 to 29-year-olds and the 30 to 44-year-olds, you see that those are the highest rates of quits, and you see that the rates of quits go down as people get older. 

And it’s a pretty noticeable difference, so people that are under the age of 30 are much more likely to quit than the other age groups as people are kind of moving their way up the job ladder. And I’ll show you a little bit more about why I would interpret that as moving up the job ladder in the next slide. 

But the other thing that I would just note from this is just looking at the time series. So, if you look at the peak of what people call the Great Resignation in 2022 when there were a lot of quits, you see a pretty big difference between that number and the number today among the 18 to 29-year-olds and the 30 to 44-year-olds. 

You see a more muted difference among the older group of workers, and that’s just because the younger workers are more likely to be out searching for new jobs and potentially getting hired. And so having a relatively low rate of hires, as we’ve seen over the past few years, is going to affect them just by more because they’re more likely to be the people who are going to be hired, is my interpretation of that. You can see a little bit more of that in terms of career progression in terms of the next slide. 

The next slide is introducing a question that we put into effect in 2021. And what we’re asking there, is we’re asking people if they have a new job this year, and if so, if that new job is better. So this is the share of all people in those age ranges who moved to a better job. It shows a pretty similar pattern. 

Notably, we don’t have it before 2021, but you can see a peak in 2022 when, for example, 14% of all 18 to 29-year-olds moved to a better job in that year, or in the 12 months preceding the survey, towards the end of October. And you see that that’s much decreased to 9%, which is still notably higher than the other age groups, but it’s lower than the 14% that it was in 2022. 

Moving on to the next slide, this introduces another development that we’ve been covering, or that we’ve been trying to get at with the SHED, and that’s the impact of generative AI on the labor market. So we introduced a question asking if people had used generative AI as a part of their job in the past month, and we saw that 25% of all workers had used generative AI at some point over the prior month for the survey. 

We also asked if people agreed, disagreed, or were neutral about a series of statements about generative AI, is statements thinking through a series of narratives or a series of potential impacts that AI could have on workers. The first three that you can see in the bars below are relating to the use of AI, how useful AI is potentially in your job, in terms of saving time, improving the quality of your work output, and enabling you to do new things. 

One thing to note is that 44% of all workers agreed that for their job, AI saves time. That’s higher than the share of people who adopted AI. So it suggests that there is some scope for additional adoption of AI, at the very least. And you also note that that’s a lot higher among people who used AI in the prior month, at 81%. In general, people who used AI had more positive perceptions of AI. 

Another way in which this comes through is in terms of these last two categories, where we ask people if they agree that having generative AI available will improve their career, or if they agreed that they worried that AI will replace their job. As Alicia mentioned upfront, we had an identical 20% of all workers who agreed with both of those statements. So in that sense, people were split on whether generative AI will improve their career or will replace their job. So, that’s kind of the net expected career impact. That’s what I’m going to start talking about for the next two slides. 

But if you look at that net expected career impact, so the difference between agreeing that it’ll improve my career and agreeing that I worry that it will place my job, if you look at that among people who used AI in the prior month, that’s pretty positive. So, 48 minus 22% among AI users, specifically. 

Going on to the next slide, we can see differences in adoption of AI by education. And what we’re plotting there, which I guess is kind of telling a somewhat similar story to the prior slide, but it’s also really showing that there’s quite a bit of variation in terms of who’s using generative AI in terms of how much education they have. And that goes all the way up to having a graduate degree. 

So, 43% of workers that have a graduate degree said that they used AI in the prior month. And those workers with a graduate degree are more positive about the kind of career impacts of having generative AI available for them in terms of the difference between saying that having it available will improve their career, minus worrying that it will place their job. You see that workers with a high school degree or less have much lower use rate and they’re also much more negative about the potential career impacts of generative AI. 

The next slide is another really interesting split that I actually found very, very surprising. And so what this figure is showing you is just differences in terms of this use of generative AI in the top line, and then these net expected career effects in the bottom line by age. And so these are kind of smooth 10-year moving averages of these things. So for instance, the number at 25 is the average among all people that are in their 20s for using AI or net expected career effects. 

And the shading is 95% competent in a role, so it’s kind of a margin error. What you see from that is you see the highest levels in people who are roughly in their 30s through early 50s in terms of using AI for work and the net expected career effects. The highest use is actually among people in their mid-30s in terms of use, and the most positive net expected career effects are among people who are actually in their early 50s. 

The thing that I found the most surprising about this, is that if you look at the younger ages, you actually see pretty low rates of using AI for work. Now, notably this is for work. This isn’t necessarily using it in your personal time. We might get different results if we asked about people using it in their personal time. I suspect we would. 

But one thing to point out is just that a lot of people in their 20s are not using AI in their jobs, and they also have more negative net expected career effects of AI. This was really surprising for me, particularly looking at it in the fall. I think it speaks a little bit to some of the concerns about the effects of AI in terms of entry-level jobs. 

And then moving on to the next slide is another look at the variation in terms of where AI is getting used. And in this case, this is by occupation. So these are by broad occupational grouping, so what people are doing for work. One takeaway from this is just that it varies a lot depending upon what your job is. That might not be surprising if you think about it, but just the magnitude of it is, for me, pretty striking. 

So, more than half of people in computer and mathematical occupations think coders, possibly using Claude Code, said that they used generative AI in the past month as part of their job. And then if you look at people who are installation, maintenance and repair occupations, it’s 6%. It’s quite a bit smaller. 

The other thing, is that if you look at the other column, so the share agreeing that AI will improve their career, and also agreeing that they worry that AI will replace their job. If you look at both of those, they both go up as you move down. So as you move down, you have higher use occupations. So with higher use, both thinking that AI will improve your career and worrying that AI will replace your job go up, but one pattern with that is that essentially thinking that AI will improve your career goes up by more. 

So, if you look at people in computer and mathematical occupations, compared with, say, the installation, maintenance and repair occupations, 40% of those computer and mathematical occupation workers think that AI will improve their career, and 27% think or worry that AI will replace their job. 

That’s higher than the 8 and 10% in installation, maintenance and repair in terms of both those numbers, but the 40 is a bigger increase than is… The 40 versus the 8 is a bigger increase than the 27 minus the 10. So that’s kind of what you’re seeing in a lot of cases with these net expected career impacts numbers. 

So, people in computer and mathematical occupations, they are more likely to worry that AI will replace their job. It’s just that they’re more often citing the potential career impact. So they’re kind of emphasizing that, yeah, maybe… So, what I would take from that is basically that, yeah, there could be disruptions, but they do see some opportunities in those disruptions, potentially. 

Another thing to note, a really extreme outlier is workers in arts, design, sports and media occupations, who 43% of them agree that they worry that AI will replace their job. I would also note that office and administrative support professions also have a pretty high level of worrying that AI will replace their job. 

So moving on to the next slide, this presents a different thing that we’ve looked at in the report, and this is actually in a different section of the report. This is just measuring people’s expenditures on paid childcare, which is another thing that we’ve asked about in the report for a few years. 

And this is comparing people’s median expenses on paid childcare. This is for parents of children under the age of 13 who pay for childcare, and it’s plotting their median expense in the top, either by whether they pay for any childcare at all, or if they pay for more of it in the bottom, 20 or more hours per week. 

And it’s comparing that to all parents of children under 13’s median housing expenses. So that’s either their rent or their mortgage. Takeaway from that is just basically that childcare is a pretty significant expense, is more than 50% of housing expenses, if you pay for any at all. And it’s about a little bit more than 75% in terms of comparing those medians if you pay for 20 or more hours per week of it. 

And the next slide shows, I think, something that’s related to the cost of childcare, and that’s just rates of use of paid childcare in the past week. And we’ve split this out by family income and by how much you use paid childcare. The main thing that I’d take away from this is, well, first that the numbers are well below 50%, so less than half of parents of children under the age of 13 use paid childcare, and also that there’s a pretty strong income gradient. 

So in families that are earning more income, possibly because both parents are working, they’re more likely to use paid childcare than are families with lower incomes overall. Okay. And so I’ll advance to the next slide and then turn it over to my colleague, Ellen Merry, who’ll talk through a few of our measures of economic hardships. 

Ellen Merry

Thanks, Mike. And talking in this section about economic hardships, I’m going to be focusing on two different types of hardship. One is people facing major unexpected expenses, and a second is difficulties paying bills. Now, SHED’s long covered these types of topics on the survey, but we added some new questions this year and wanted to share some of the results. 

So moving to the next slide, looking at that gold bar at the top, 59% of adults said that they had at least one type of major unexpected expense in the prior year. We didn’t specify a dollar amount here. We allowed the respondents to tell us what they thought was major, and people could have more than one type. And you can see the types at the bottom in the blue bars, and those add… Because you can have more than one type, they add to more than 59%. The most common types were vehicle repairs, house repairs, and unexpected medical expenses. Now, not shown here, but 31% of adults, or over half of the people who experienced a major unexpected expense, experienced more than one of these types. 

So moving on to the next slide, here we’re looking at some of the demographics of who was likely to have these types of expenses. Starting over at the left, parents were more likely to have them, along with homeowners and people who had access to cars. And that’s consistent with some of the types of expenses, like vehicle repairs and appliance repairs, and things like that. So that’s sort of intuitive with what we saw on the prior slide. 

Over on the right, you can see by family income that those with less than $25,000 in income were less likely to have these types of expenses. Now, this group is less likely to own houses and cars. And because it includes a lot of young adults, they’re also less likely to be parents, so that explains part of the difference. But even controlling for those sorts of factors, the slower income group is still less likely to have experienced these kinds of expenses. 

So moving on to the next slide, here we’re focused on the ability to cope with unexpected expenses using savings. And so where the real hardship comes in, no one likes having these types of expenses, but if you can’t cover it, that’s where the real hardship comes in. And so, several years ago in SHED, we added a question asking people what the largest emergency expense they could cover right now using just savings. And that’s what’s used for this slide. 

And we’ve grouped people by whether or not they have income less than 50,000 or income of 50,000 or more. And down the left side, you can see the size of expenses. So, just to read this from the top, 40% of adults with an income less than 50,000 could not cover an expense of 100 to $499 right now using just their savings. And if you move to the middle of the figure, 72% of this group couldn’t cover an expense of 1,000 to 1,999. 

Now, that middle bend of about $2,000 is the median size of a car repair or appliance repair, or major medical expense from these new questions we added this year. So, that’s a common size expense, and a lot of people aren’t in a position to cover it with savings. Now, if you look at the light blue bars for a minute, the middle and higher income adults were better positioned to cover things with their savings. 

But still, when you get to some of these larger expenses, you can see that down at the bottom, 49% of that group could not cover an expense of 5,000 or more with savings. So, this is not to say they couldn’t cover it some way. This slide is focused on savings. People may have credit cards, be able to pay something over time, but this is just focused on ability to cover it out of what people may have set aside. 

Okay. Moving to slide 27, I’m going to shift gears here and talk about a different type of hardship, which is the ability to pay bills. So, starting with that top bar, overall, 16% of US adults said that they did not pay all their bills in full in the prior month. And an additional 12% said that they paid their bills, but they had some difficulty doing so in the prior month. So in total, 28% of adults had struggled to pay bills in the prior month. So, smaller than the share that experienced an unexpected expense in the prior year, but still a substantial group of folks. 

As you could tell from the bottom bars, there’s a strong correlation with income, as you might expect, and these shares are really substantial. So over half, 53% of adults with income of less than 25,000 struggle with their bills, and about 45% of those with income between 25,000 and about 50,000 had struggled in the prior month. 

So moving on to the next slide. In the survey that we fielded in October, we asked people a new question about if they were struggling with bills in these ways, what kinds of actions they took in the prior month. So as you can see from the top of the table, the most common action was that people cut back on their expenses. Also, very common was paying a bill late. 

Number three and four on the list have to do with borrowing, either on a credit card or getting money from friends and family. And right there in the middle is using savings from either emergency fund or retirement account, something like that. Things like selling or pawning something are on the list, payday loans, but down further at the bottom of the list, less common ways of borrowing. 

So moving on to the next slide, this takes the top five ways of coping that were just on the prior slide, but it’s split out by what level of emergency savings people had available to them. And this is the same question that I used for the question about unexpected expenses a few slides ago, but just split out by whether or not people said that they could cover an expense of $500 or more right now using their savings, that’s the light blue bars, or they could just cover something that’s under $500. 

So, looking first at that top set of bars, pretty much everybody was equally likely to say they cut back on expenses. Both groups gave similar responses along those lines. But where you start to see differences is that the folks with less available savings were more likely to pay a bill late, second pair of bars. They were also more likely to borrow or receive money from friends and family, the fourth set of bars. 

Now, not surprisingly, the folks who had more savings were more likely to say they used money from a savings or retirement account, which is the bottom set of bars there. Now, not shown on this slide, but when you take this whole group of people who struggled with bills, meaning that they either didn’t pay something in full or they said that they had difficulty paying their bills in the prior month, about two-thirds of them said that they couldn’t cover an expense of $500 or more. So this is the group that’s in a pretty precarious financial position, both from the standpoint of covering bills, but also from their savings reserves. 

Okay, moving to the next slide, slide 30. So, we’ve covered a few topics at a high level today from the report, but there’s much more available. This is a slide showing the topics for all the chapters in this year’s report, and it’s available on our website. I believe the link was shared in the chat earlier. 

Also on the website there is an appendix that’s got all the question wording, tabulations of all the questions. And all the data from the survey is publicly available, so you can find that on the board’s website. So that concludes the presentation portion, but I want to turn the floor back to Sergio. 

Sergio Galeano

Ellen, thank you so much. 

Ellen Merry

Thank you. 

Sergio Galeano

And thank you to you, Mike, and Alicia for such a great presentation. I learned so much. There’s so much rich data in this SHED report. And just a reminder for folks who joined us a little bit later than the introduction, very popular question, yes, the slides and the recording will be available two weeks from now, so you can certainly dive in, and we’ll talk a little bit more about the resources there. 

Before we dive into the panel portion, quick reminder for folks, please go ahead and submit your questions. We’ve gotten a few so far. We promise to get to as many as possible, though we’re grateful that we’ve had the chance to answer a few of them already. I just wanted to go back to that polling question from the beginning, the results that I see here. 

So the majority of people are using the SHED report data to understand, A, broad economic indicators, B, beef up their general knowledge in the field, whether any of the subsections or general economic indicators. Next up, third, is to reference specific indicators or statistics, whether in publications, board meetings, nonprofit reports, and so on, or grants. 

And then we have some people who may not be using it or are exploring. Thanks for your curiosity and attendance today. And finally, some folks who may be combining it or chopping up the data for custom research. Thanks, everyone, for those answers. 

All right. And making sure that we have all the presenters here, I want to dive in a little bit. We’ve got a couple of minutes to dive in with the three of you, and Alicia as well, and a chance to also host some questions for the audience. So, we’ll get a chance to dive in a moment, but I just wanted to step back. 

Each of you chose one report of a total of nine or more from the SHED report. And all of you, as economists, have had such rich history with this, and working with this data and its familiarity with its change over the years. 

But before we get started, as you’ve worked through this year’s results, I’m curious what stood out, what maybe challenged your expectations, what’s something that confirmed an insight that you suspected, or something that just stood out as particularly interesting. 

And for this question, you can speak more on the sections you presented on, but please feel free to riff on other items that we didn’t touch base on yet. And let’s start in the same order we presented, so we’ll start with you, Alicia. 

Alicia Lloro

Yeah, thanks for the question. There’s always so much in the report that I find interesting and I want to talk about. If I stick around the content that I presented, I think the one thing that continues to, it’s not surprising, but I think that stands out and really speaks to the experiences of consumers, is the results we continue to see on price increases and inflation, which has persistently been the top financial concern or a major financial concern among people. 

Even when it was the case that the official inflation rate had fallen from the high that we saw after the pandemic, people’s concerns about prices persist. And I think it really speaks to a lot of this… You hear in the media often, “Why is consumer sentiment so poor, when at least before, it was like the economy seems to be humming along?” 

I mean, now we have a little bit more concern about the labor market, but nonetheless. And I think a lot of it is this persistence of concern about prices and price increases. And although the official inflation rate, it’s above the Fed target, but it’s not quite as high as it was. There’s a really long half-life, so to speak. And I think that is sticking with people, and we’re seeing it in the survey results. 

Sergio Galeano

Thank you for that. Yeah, absolutely. I thought of that same. There’s a bit of a, not always disconnect, but there’s a gap between how people fare on themselves and how they see the national economy. So thanks for talking about that. Let’s go to Mike. What stood out for you in this year’s report? 

Mike Zabek

So I mean, I’d say that I think that the report is really interesting. I think I’ve seen it many times, and been in many of these issues for quite a while. So I think I’m biased towards things that are relatively new, so I’m going to talk about a new question. 

But I would just highlight that there’s just, in lots of different areas, including areas that I don’t know really well, I think that it’s a really good way to see a lot of really interesting patterns, that I think that if you look at them, they make sense, but it’s kind a cool aha eureka moment. 

For me, I mean the thing that I was not so much expecting was this AI result that I think I tried to talk a little bit about in my presentation. And we tried to get some measure of how much people were using AI, and we just broadly wanted to see what people thought about the potential implications of AI for their career, because there’s just been a lot of discussion and thoughts about that. 

And the thing that was very surprising to me was just that the people who have more exposure to AI generally are highlighting the positive career effects as much or more as the potential negative things in terms of them being replaced. 

And so it was surprising to me that people who are having exposure to this big thing that, for instance, we’ve heard a lot about the potential negative implications, including from some of the major people who are developing AI, particularly in the media. 

But then if you look at people who are on the ground trying to implement AI, they’re kind of like, “Yeah, there is some possibility that it could replace things, but I can also see some opportunities that are there.” 

That’s no way a final say about what’s going to happen for the labor market, and I’m really just speaking for myself with this, but it made me want to dig in and think more about why that is and if that says something about how the development’s going to go. 

Sergio Galeano

Yeah, I’m particularly grateful for the chance to see how that cut down by perceptions about their own field in different industries. It would be interesting to track that over time. 

And since I got you there on AI, if we could already maybe get at someone’s question from the audience, we have Ulrika asks about, “Are the people who think AI is improving their job the same or different as those who worry it will replace them?” 

Mike Zabek

Yeah, so I think that’s a really interesting question. And I said that that’s there in terms of the occupational breakdown. So in terms of people who are in occupations where there’s more use, they’re more likely to say both of those things. 

I actually went through and opened up the data and tried to get the exact percentage. So if I’m getting my numbers correct on the fly, I think we’re getting something like 4% of people agree to both of them. 

Sergio Galeano 

Okay. 

Mike Zabek

And we said that 20% agree to any one of them. So actually, it’s pretty common for people who agree that AI will improve their career to also say that they agree that they worry that AI will replace their job. And I think that that kind of correlates with being in an area where AI is more relevant because you can see both of those things. And I think that’s what you’re seeing with the occupational-level differences. 

So yeah, it is somewhat common for people to say that. There are a lot of people who just say, “Oo, I’m not worried about either thing.” Or, “No, I don’t see any positive career impacts and I do worry that it will replace my job,” I should have said. So, you kind of see a lot of different breakdowns with that. 

Sergio Galeano

Yes, yes. Thank you, Mike. Ellen, over to you on the section on economic hardship. Anything that stood out to you from that section or generally in the report? 

Ellen Merry

I think, like Mike, I think a lot about some of the new questions. I mean, just briefly on the hardship section, as Alicia showed in one of her slides, some of our hardship measures were pretty flat year over year. Like the bill payment question that we’ve had for several years has been unchanged for several years in terms of the share of people. Similarly, we have another question I didn’t discuss on food insufficiency, that’s been sort of flat. So at least it’s consistent with the overall wellbeing, which has also been pretty stable. 

But turning to some things that I didn’t talk about, we added a new question this year on whether or not people received financial help, or help paying bills, or things like that from outside of their household. And it was striking to me how prevalent that is. It was 20 something percent of adults overall, but I think 47% of young adults, 18 to 29. 

So, that kind of dovetails with some of the things that Mike was talking about, about young adults and their struggles in the labor market. So I think seeing that thread through the report of some of the ways that people are coping and challenges that young adults have been facing was an interesting finding this year. 

Sergio Galeano 

Yeah, that resonates. And also, as it pertains to your section on economic hardship, someone in the audience has asked about, I think referring to the challenges you’re talking about, those hardships, they’re asking, “How are major unexpected expenses defined? Is it by a certain financial threshold or is it relative to income?” 

Ellen Merry

The respondent determined that for themselves. We didn’t give them a dollar threshold for what a major expense was, so- 

Sergio Galeano

Relative to their own experience? 

Ellen Merry

Right, relative to their own experience. So probably it would be related to their income, but we didn’t make that judgment call. 

Sergio Galeano

Thank you. Alicia, I want to turn back to you. I loved the beginning that gave us this overall financial wellbeing status for individuals and households across the survey, and what it speaks to across the larger economy. 

And I’m really curious if there’s ability to disaggregate that, especially by education? So, how would the trends that you talk about, how do they compare across education groups? And if possible, is it easy to see that across different demographic groups for folks? 

Alicia Lloro

Yeah, thanks, Sergio. That’s a great question. So in the report itself, we do include breakouts by education and also by race and ethnicity. And for those, we have a full-time series, you can see all the different years. We also include breakouts by age, other things I’m forgetting, geography, metro status. 

And as I mentioned briefly in the presentation, I didn’t have time to really get into it. While overall financial wellbeing was pretty stable from last year to this year, we did see declines among certain groups. One of those was for people without a high school degree. 

And we’re actually seeing that there’s always been this gap between those with at least a bachelor’s degree and those with lower education, particularly those without a high school degree. And because of the decline in financial wellbeing among those without a high school degree, we actually saw that gap increase over the prior year. 

As far as other groups we saw a decline, and we note this, I think in the executive summary. Among lower income adults, they saw declining wellbeing, younger adults, particularly on the, I think it was 18 to 24 or 25. So it points to the struggle of younger adults and younger workers. 

And we also saw a decline among Black adults. And I think one interesting piece there, when we look at what are your financial concerns across these different areas, lower income and young adults, they did not see any improvement in their concerns about price increases. So overall, there was this slight dip as a major concern, but for those groups, it didn’t dip at all. 

And in fact, for Black adults, it actually increased. And in another question we have, we saw an increase in a question that asks if price increases have hurt their finances. So I think this is speaking to this effect where prices and price increases are not impacting everyone equally, and we’re seeing some evidence that certain groups are feeling it more than others, which is coming through in this what we’re seeing by education and income and age, and race and ethnicity. 

Sergio Galeano

Thanks, Alicia. I think that sparks some interest from folks to maybe dive into any of the topics you mentioned, but also I got two questions that also speak on disaggregating. 

I’m assuming we can do this geographically, right? Just to what level? State, census, region? And related on demographic groups. Someone, Janice, Ruth asked if there’s anything on gig and independent consulting workers that you can break down. 

Mike Zabek

So I’ll talk about that. In this version of the survey, or in this report, we don’t have questions. We have a question that asks if you’re self-employed and if you have any workers, if you have any people working for you. In previous years, we’ve asked a series of questions about gig work and other more enterprising activities. So, things that would include what people would typically think of as a gig economy, so like Uber and Lyft, DoorDash, et cetera, but also other things like doing odd jobs for someone, taking care of someone’s kids, babysitting, things like that. 

Last year, we asked people if they agreed or disagreed with a series of statements about these kinds of activities. You can see that in the last year’s version of the report. I thought it was giving an interesting picture of that segment of the economy. We had an idea of how many people do that. It’s a relatively small segment of the economy, but it seems as if it plays some role in terms of helping people to make ends meet, particularly when they are in a little bit more difficult financial situations. 

And people also really emphasize that gig work is extremely flexible for them, but they also didn’t… And some people said that it offered them some level of work-life balance, but smaller shares said that it offered them work-life balance and said that it was flexible. So that’s just one kind of thing that we’ve talked about. 

Sergio Galeano

Thanks, Mike. And on the question of geography, I know that there are thousands of respondents in the survey and it makes it a very strong one, and it keeps growing year by year. Is it strong enough that the sampling would allow folks to make good calculations by state or is it a more responsibly used survey at the national level? 

Alicia Lloro

I can answer that one. There are certain geographic breakdowns we can do. If we’re trying to provide estimates at the state level, we cannot do that for every state. We don’t have enough of sample. We can break down by metropolitan status. So we do metro area, non-metro area. We can also do census region, which breaks it into four areas. So the South, the West, the East, and the Midwest, if I remember my census regions. 

We can also do census, something is odd. It’s called census division, but we use that for the homeowner’s insurance. We break that out. It’s a sort of smaller… It’s groupings of states. Certain states, we have enough of a sample to produce estimates for, but we can’t do them for all. 

So, that’s sort of the quick summary on geographic breakouts. We do something for lives in an LMI track, to low-to-moderate income tract, based on the CRA definition, versus doesn’t live in an LMI tract. So I mean, there are various things that you can do by geography. That’s an overview of some of the things that we’re able to do 

Mike Zabek

And I would also note that if you’re one of the people who wants to go and download the data yourself, you can go through. And I believe we do, in the public dataset, have state level identifiers, so you can go through that. 

Sergio Galeano

Yes. 

Mike Zabek

You just might not have enough people in every single state to have a completely reliable statistic. 

Sergio Galeano

Yes. And for folks online, there’s a very helpful code book that will help guide you on the use and interpretation of the data for this kind of reason. Ellen, I wanted to turn to you, I love surveys that we can track over time. It’s just so helpful to see change, but it’s also interesting to see continuity, stability in the data. And when I heard your section, I thought about the many areas where we see things change, and often, it’s easy to overlook things that didn’t. 

And I’m curious, as an economist working on the SHED report, how do you think about stability in the results? Should someone in the audience or anyone who’s trying to work with this data characterize stability as positive or negative? How do we make that distinction depending on the metric and what it’s trying to show about folks with financial wellbeing and hardships? 

Ellen Merry

Right. This is something we wrestle with as we write the report each year. And I’ll just reference some of the things that Alicia was referring to on prices as an example of this, because we do see persistence in both problems that persist with lower income adults or things like that. 

We cover a lot of statistics like income variability that don’t really change a lot from year to year, but we still feel like it’s important to highlight those because we’re trying to get a picture of people’s financial situation. 

But going back to the prices example, a majority of people say, 58% say that their finances are worse off due to higher prices this year. That’s a lot. And it is down seven percentage points from 2023, so that’s kind of positive. So, there’s just nuance here. 

It’s hard to assign a positive, negative kind of thing because the level is high. That’s sort of a negative, but the change is in the right direction, and that’s kind of positive. 

Sergio Galeano

Thanks, Ellen. All to the healthy use and responsible use of data. 

Ellen Merry

Yeah. 

Sergio Galeano

Everyone, as we get to the top of the hour, I just wanted to give at least one of you a chance. If you can give us a quick brief answer on… You’ve covered three sections out of several, and there’s an even bigger appendix in data. 

Well, there’s many things we didn’t discuss today that in the interest of time we have to end, but I’m curious if you could leave the audience with one insight or one next step to really using the full potential of the SHED data, what would you like to share with them, for anyone? 

Alicia Lloro

I could take this really quickly. I’d like to promote our new-ish questions on fraud and all their results around fraud, which it’s kind of sadly been… We’ve been hearing more and more about it. 

And so in the SHED we have information on dollar losses from fraud, different types of payment instruments, and we break that down by various demographic groups as well. So I think the results are really interesting. So I would point people to the banking section to read about fraud. 

Sergio Galeano

Fantastic. Well, Alicia, Ellen, Mike, thank you so much for your time, for your expertise, and so much that the economist and team do at the Board of Governors. Thank you for your time. And for everyone in the audience, I hope that you walk away just a bit more informed on a new dimension of how folks are doing in the economy. And that you have all the right and availability to jump into this data and the results, whether you work in very local measures on economic mobility or more at regional, state, federal levels on workforce development, credit and access, fraud, like Alicia mentioned, employment, hardship. The SHED report is a fantastic resource, and it’ll continue to grow over the years. 

I invite our panelists to go off camera. Thank you so much for your time today. But before we do go, dear audience, we do have a few requests. Please complete the post-event survey webinars like this. We want to keep them up and we want to always improve the content and quality, and it starts with you letting us know what you liked, and how we can always improve to make this a better platform for you on matters of economics and public policy. 

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Now, finally, again, all the recordings are available. And please, mark your calendars for Thursday, August 6th for our next Connecting Communities webinar. This one is going to actually dive in a little bit deeper into one issue that has come up in the economy for many years, and that’s on Benefits Cliffs. 

This webinar will discuss the economic challenges that benefits clips create for workers and employers when they hit a financial threshold, and efforts that the Federal Reserve and partners across the country have implemented to address the issue. 

We’re going to hear from a recent conference, open questions that remain, and the untapped potential there is to make continued advancement on resolving this key issue. Everyone across the country, thanks for joining us on Connecting Communities. We hope to see you soon. Have a wonderful rest of your day.  

Thank you.