[Transcript]: Who’s Minding the Store? Firm-Level Characteristics and Worker Outcomes


Fed Communities Staff

A chef plating food, a construction worker reviewing blue prints, a librarian

Watch on Demand

Sara Chaganti

Hello, I’m Sara Chaganti from the Federal Reserve Bank of Boston. Welcome to day three of the Uneven Outcomes in the Labor Market Conference. This conference was organized by community development staff from the Federal Reserve Board and the Federal Reserve Banks of Atlanta, Boston, Cleveland, Philadelphia, San Francisco and St. Louis. During this conference, we’ll be convening researchers, policymakers, and practitioners to examine disparities in labor market outcomes and explore policy solutions to address these inequities. We’re hoping to deepen your understanding of disparities in employment, labor force participation, income, and wealth and to learn about their implications for economic growth, the health of communities and individual wellbeing. I’m excited that we open today with remarks from Michelle Bowman, member of the Board of Governors of the Federal Reserve Board. Now I’ll turn it over to Governor Bowman.

Governor Michelle Bowman:

Good afternoon and welcome to the third day of the Federal Reserve’s Uneven Outcomes in the Labor Market Conference. It’s a pleasure to join so many researchers, policymakers and practitioners interested in understanding the labor market and its implications for the overall economy and the financial wellbeing of workers. At the Federal Reserve, the labor market is often top of mind. As many of you are aware, the Federal Open Market Committee has a mandate to effectively promote maximum employment and price stability. My FOMC colleagues and I strive to find the balance between these objectives. And when considering how best to promote a healthy economy, it’s important that the Fed supports a labor market that works for both employees and employers. Today, I’d like to highlight the role of small businesses and entrepreneurship in the US labor market and in turn the importance of access to capital in creating and maintaining jobs in those businesses.

I’m sure many of us know business owners who put their own personal savings, even their homes at risk to start or to grow their businesses. I will discuss why external sources of capital and financial institutions that provide access to capital are vital for both successful small businesses and a robust labor market. There are many pathways for workers to find employment. For some, entrepreneurship offers the opportunity to fulfill a personal goal, to create a new business, or to have more control over their careers. While not all startups are successful, many grow into stable businesses that employ additional workers. In fact, small businesses currently employ nearly half of America’s private sector workers and have accounted for over 60% of net new jobs since 1995. Throughout my experience as a banker, a bank commissioner, and a Federal Reserve governor, I have been inspired by the success stories shared by entrepreneurs from across the country.

At the same time, I’ve also heard about many of the difficulties they face, particularly in hiring and retaining employees and sourcing capital for business operations. For example, a bakery owner in Texas recently shared the challenges her business has faced in hiring and retaining workers. Another small business owner in Maryland shared the difficulties that he’s faced in accessing capital to open new restaurant locations. While these are just two examples that I’ve heard personally, I know that it can be very difficult for many small businesses to find qualified workers and access the capital they need to grow. As policymakers considering making changes to existing rules and expectations that may impact a business’s access to funding sources, we need to identify and to understand any potential unintended consequences of these new policies, especially if those policies may affect the ability of small businesses and entrepreneurs to access capital.

While many of these challenges are typical hurdles for entrepreneurs and startups, the pandemic created new complications for businesses, small and large. Firms owned by women and minorities were particularly vulnerable due to many being newer businesses, more fragile financially and more likely to be part of the sectors hit hardest by the pandemic, including food services, personal services, and retail. Yet while many small businesses shut their doors due to the effects of the pandemic, in 2020 the US saw a boom in business creation, which began shortly after the initial lockdown period. This increase has continued long after the labor market recovery. In fact, the Census Bureau’s November 2023 analysis of business applications shows that business formation since 2021 has remained 32% higher than in the previous 10 years. Many of these new businesses were started by minority business owners. Increased rates of workers voluntarily quitting their jobs and high levels of unemployment during the pandemic appear to have encouraged workers to become self-employed or to become entrepreneurs.

This growth in startup businesses could help to address the challenge of closing employment gaps for workers who typically face the greatest headwinds in the labor market. For example, startups are more likely to hire new workers and workers with lower levels of educational attainment. New businesses have also been responsible for a surprising amount of job growth with an average of nearly one million jobs created each quarter from early 2021 through the beginning of 2023, which is significantly at a higher pace than was typical prior to the pandemic. Of course in order to create jobs, small businesses need access to sufficient amounts of affordable credit and capital to form, grow and succeed. Otherwise, they may underperform, not reaching their growth potential either in revenue or employment. The share of small businesses reporting that they rely on personal sources for capital increased about half in 2019 to two thirds in 2022, according to the Fed’s small business credit survey or SBCS, from that year.

This suggests a high degree of personal risk for owners and their workers. Moreover, personal financial resources are often limited, which can constrain growth. Ideally, small businesses should be able to access external funding, which can be a key to their growth and to their stability. Most businesses that seek external financing turn to banks, community banks and some larger institutions. Community banks remain an essential resource for many small businesses to access capital to support their businesses. These banks are focused on relationship banking, which uniquely positions them to meet the challenge of assessing the credit worthiness of local small businesses. Community banks make greater investments in small business lending relative to larger banks.

Furthermore, according to the 2022 SBCS, small businesses were more likely to be satisfied with their experiences when dealing with small bank lenders than with large bank lenders or other finance companies. I’d like to take a moment to share some of the ways the Fed supports small businesses through community banks. First, the Fed engages community banks through its Community Depository Institutions Advisory Council, or our CDIAC, which advises the board on matters of public policy. CDIAC members include community bankers who provide insight and information about the economy, lending conditions and other issues that they see firsthand in their communities. Second, the Federal Reserve supervises and regulates smaller banks based on a variety of factors specific to their size, their condition, their risk profile, and their business model to assess their safety and soundness.

One of the Federal Reserve’s unique strengths is the localized nature of our supervision, which relies on regional reserve banks that understand their local markets and their community needs. Many banks also have a mission to serve historically underserved population. These include minority depository institutions, or MDIs, women owned depository institutions, WDIs, and community development financial institutions, CDFIs. These banks are vital sources of capital to many underrepresented small businesses. For example, the Philadelphia Fed found that community banks, including these unique banks, are traditionally more successful than larger institutions at small business lending in times of crisis. This finding was well supported by the volume of lending conducted by these banks through the Paycheck Protection Program, or PPP, during the COVID-19 pandemic. As everyone knows here, the PPP provided loans to small businesses to keep their workers employed. The program was implemented in large part through community banks, including these MDIs, WDIs, and CDFIs.

The Fed supported the PPP by providing liquidity through its emergency lending program, the Paycheck Protection Program liquidity facility. Notably, MDIs originated more than 20,000 PP loans to support small businesses during that time. The Federal Reserve has also supported CDFIs and MDIs through their work with the US Treasury Department on the implementation of the ESOP program. This COVID relief program provided $9 billion in capital directly to CDFIs and MDIs. These funds were to be used for loans, grants, and forbearance to small and minority owned businesses, especially in low income and underserved communities. I’d like to close with a brief note on the CDFI fund’s recently released revisions to the CDFI certification application. In addition to ESOP funding, certified CDFIs are eligible to access other federal resources, state and local governmental funds, philanthropic support, and private sector investment.

I look forward to engaging with community banks as they and other financial institutions begin to digest these revised application requirements. I also look forward to learning about how these changes may affect the certification process for renewing or obtaining a CDFI certification. The Fed continues to support the growth of startups and small businesses as a means of creating opportunity for all workers. This afternoon’s discussion will focus on how employer structure and financing may affect employee wages and benefits. I’m excited to see that new research will be presented at this event and I look forward to our ongoing discussions about labor outcomes.

Sara Chaganti

Thank you, Governor Bowman. We titled today’s panel Who’s Minding The Store: Firm Level Characteristics and Worker Outcomes. The relationship between employer or firm characteristics and job quality is not really well understood. Today’s speakers will discuss how firm structure and financing may affect worker compensation and other job characteristics. The full bios for all the speakers are available on the conference website. Today, you’ll hear from the following. First, Nate Wilmers, associate professor at MIT’s Sloan School of Management, will provide framing for today’s topic. Paige Ouimet, professor of finance at the University of North Carolina, will present her research on non-wage compensation and implications for firms and labor markets. Wenting Ma, assistant professor of finance at the University of Massachusetts Amherst, will present her research on access to financing and racial pay gaps inside firms. Andrew Joung, PhD candidate at the University of Michigan, will present his research on identifying alternative work arrangements in the United States.

And Adria Scharf, associate director at Rutger School of Management and Labor Relations will discuss the research and moderate questions and answers. And finally, you’ll hear from Doug Webber, senior economist at the Federal Reserve Board. We’re excited to have Doug here to present initial findings from the Fed’s research on geographic inequality and labor market indicators. And now I’ll turn it over to Nate Wilmers.

Nathan Wilmers

Great. Thanks so much, Sara, and thanks to the organizers of the conference for putting together such an exciting panel and exciting several days of research discussion. So Sarah had asked me to sort of frame the research area that these papers come out of and this is sort of a hard task because although Sara notes that we know little about this general question of how and why firms affect worker outcomes, we’ve actually been working on this question for like 70 years. So this goes back to in the 1940s and early 1950s, researchers were kind of surveying employers in Boston and Cleveland and New Haven and just asking, “How much do you pay for different job titles?” And a surprising thing that they found even way back 70 years ago is that seemingly similar employers, manufacturers and the same producing similar products in the same lines of business and industry were actually paying pretty different wages for workers who had similar experience, were working in the same job title, a similar position.

And this is really puzzling from the perspective of a competitive labor market, where you would expect pay to be a function of worker skills and their marginal product, the stuff that they’re producing. What researchers have found way back then and then since is that there’s actually a lot of differences and inequality between firms in terms of how they set up their jobs, how they remunerate workers for similar work. And this is actually really important for understanding worker outcomes. And so if you can go to the next slide, please. I wanted to frame my remarks in terms of this general question that the conference is addressing. Are there no dots on that slide?

Okay, that’s fine. This slide can give a similar message. Basically, the big macro context that we’re sitting here discussing these worker outcome issues is actually one where there’s a lot of good news for relatively low wage workers. So this chart shows within occupations change in hourly wages during two periods. The blue circles show the change from 1982 to 2011. And so if you see in that early period, you see there’s very big pay increases for doctors, nurses, health practitioners, for managers, for engineers, finance people, for managerial and professional employees. They had really substantial pain increases during that 1980s, 1990s through the 2000s period. But if you look at other occupations, that period does not look so rosy. So if you see toward the left of the graph, food prep, personal care, health support, these kinds of relatively low wage occupations had very little pay growth during that 30 year period.

And if you look down in the middle, drivers, warehouse, production, construction, traditional blue collar type jobs, you actually see that real wages declined within a lot of those occupations during that 1980s through the great recession period. So that’s sort of the era of rising inequality that defined our labor markets, defined our economy more generally. What we’ve seen, though, more recently, say in the last decade since the recovery from the Great Recession really accelerated, is that wage growth trends look quite different across occupations. So you see the engineers and professionals over on the right side of the chart, you see that they still have reasonable wage growth in the order of 5% to 10% real wage increases. But then if you move towards the left and the chart, you see that those traditional blue collar jobs that I mentioned. Instead of having real wage declines over the last 10 years, they’ve actually had meaningful real wage increases on the order, again, of about 5% to 10%.

But then if you go all the way to the left of the chart again to these low wage jobs in food prep and personal services and hospitality, you see that those occupations have actually outpaced managers and professionals in terms of percentage pay increases over the last 10 years. So you see something of the order of 15% to 20% real wage increases for those lowest wage occupations. So from the perspective of thinking about issues of inequality, disparities in the labor market, worker outcomes, we’ve actually seen a lot of good news over the last 10 years. So what’s going on here? Well, some of this you can make sense of in traditional competitive market terms. We’ve had lower unemployment, we’ve had tightened labor markets and we’ve seen wages at the bottom really respond to those changes. If you can go to the next slide, please.

But if you look more closely at what’s going on, all of a sudden all of the weirdness of firms and employers starts to matter again. So this chart zooms in on just two low wage occupations, cashiers, and cooks, two of these occupations that I’ve seen really significant pay increases over the last 10 years. But here, instead of just looking at those occupations as a group, we actually break it up into how specific employers changed wages for those jobs from 2014 through 2022. And what you see here is that there’s a lot of differences. There’s big heterogeneity across different employers in terms of how they responded to these tightening labor markets that we’ve seen recently. So you can see on sort of the bottom left side of the chart there’s Pizza Hut, Dollar General, Subway, relatively low wage employers that did raise wages during this period, but didn’t raise them by that much relative to a lot of their competitors.

And then on the right side of the chart, you see some employers that are traditionally kind of the good jobs, relatively high paying employers, Costco, Lowe’s, Whole Foods. Those employers are paying relatively high wages and still are for these cashier jobs, but you see that their percentage wage increases weren’t actually that big over the last 10 years. They’re not sort of leading the charge in terms of pay increases for these low wage workers. In contrast, what you see sort of in the upper middle of the chart are a bunch of kind of employers that were middle paying employers for cooks and cashiers, but which have really rapidly increased their pay during this period on the order of like 50% real hourly wage increases. So this is sort of Target, CVS, Dick’s Sporting Goods, some of these old line retailers that have really increased their pay.

And so what I take away from this is that when you look within occupation, these wage increases that we’ve seen at the bottom of the labor market have really been driven for some reason by these middle paying firms. We don’t know exactly why this is, but I think it sort of brings back on our intellectual agenda the question of what are the processes that contribute to some firms paying more, some firms raising wages more than others? And if you can go to the next slide, wanted to look at one example of this and that is looking at so-called voluntary corporate minimum wages where we’ve seen just a whole bunch of retailers, large banks institute over the last 10 years so-called voluntary minimum wages that are corporate minimum commitments to pay at least $10 or $12 or $15 an hour as a starting wage, to not pay any workers within your organization below that.

And this is typically above the statutory federal or even state and local minimum wages. And what we’ve been working on is basically tracking what happens to the inequality and disparity outcomes that we’re studying in this conference, been studying what happens to workers who are working at these firms. And so the right side of the slide here basically shows what happens to the working poverty rate at firms over this six year period that we have data for. What you can see is that the working poverty rate basically falls in half among these firms that are adopting corporate minimum wage increases. It’s also declining a bit at other firms, but there’s sort of this really substantial reduction in working poverty at these firms that are raising their wages. If you can go to the next slide, please. Then we also see at those same firms when wages rise, there’s a really significant reduction in the black white wage gap among hourly workers at those employers.

Basically, the black white wage gap disappears altogether as these employers adopt these corporate minimum wages. And so I like this example because it just makes really concrete what we mean by firm differences in wages, by wage policies varying by firms. It means there’s sort of substantial reductions in working poverty in black, white wage gaps depending on the kinds of wage policies that employers adopt. Next slide, please. And so this basically sets up what we’re going to discuss over the course of this panel. So everything I just described, think of that as kind of the vanilla standard research that we have on all this, which is all about basically firms and pay setting. What the papers on this panel are trying to do is essentially push this research forward towards new frontiers. So Paige is going to kick things off by asking we know a lot about wages earning salary, what about non-wage compensation? What about health insurance and retirement benefits?

How does that stuff vary across firms? And understanding those differences in non-wage compensation actually help us understand this broader puzzle of why firms vary so much in their working conditions. Wenting will then sort of shift this basically so far pretty descriptive discussion to a much more causal direction of asking if racial pay gaps vary a lot across firms, what is it that explains that? What is it that drives different firms to have different inequality outcomes for their workers? Then we’ll conclude the sort of formal papers with Andrew’s discussion of self-employment and non-traditional employment. Where throughout my remarks so far I’ve been talking about Costco and Whole Foods and Target these sort of big traditional employers that employ lots of workers directly for the firm, but Andrew points out that working conditions for a lot of people really don’t look like this. Lots of people are working in some version of non-traditional employment, some type of self-employment. And understanding the details of these different types of self-employment can really help us understand how it affects worker outcomes. So super excited for the panel. And with that, Paige, take it away.

Paige Ouimet

Well, thank you. I think we’re having a little trouble with getting my video on. Well, I will get started anyway. So this is joint work with Jeff Tate from the University of Maryland. And if I can go to the next slide. So I’m going to be using confidential data and as such, I need to ensure that no confidential information is going to be disclosed as part of my presentation. Also, any of the opinions expressed in this paper reflect those of my co-author and myself, not of the US Census or the Bureau of Labor Statistics. And if you can go forward.

So when we think about compensation inequality in the US, what we really are thinking about and talking about is inequality in cash wages. And this is an important distinction because cash wages is not the only form of compensation that you receive from your employer. In fact, the Bureau of Labor Statistics estimates that more than one third of total compensation now is coming in the form of non-wage benefits. So the big three you want to think about in terms of non-wage benefits are going to be health insurance, retirement benefits and paid leave. And so we can understand compensation inequality by just looking at cash wages if non-wage benefits effectively mirror the distribution of cash wages. But if they don’t, then just focusing on cash wages, we’re going to have an incomplete picture of inequality in the US. And if you can go forward.

And so it always brings to mind the question: if we should be looking at total compensation, why aren’t most researchers doing that? And so it always reminds me of this economist joke. So there’s two economists, they’re out at a bar, they’re drinking, they’re having a great time. One economist starts looking on the ground and the other economist asks him, “Did you lose your keys?” And she says, “Yes, I lost them over there,” and points out to the darkness. “But I’m looking here under this lamppost because there’s better light.” And I think that really gets at some of the limitations we have faced. It is very hard to find high quality data on wages to at least understand wage inequality, let alone to find high quality data on sort of a broader measure of compensation.

So one of the key innovations of this paper is going to be that we’re going to take the best quality data we have on wages, which is this US census, LEHD data, and we’re going to link it to the best data we have on non-wage benefits, which is this national compensation survey from the BLS. And if you can go forward. So as I mentioned, this is going to matter if we expect non-wage benefits to have different distributional properties as compared to wages. And I’m going to argue this is exactly what we should expect. And the main reason is because of these non-discrimination regulations. So non-wage benefits, they have some tax advantages. And because of that, the government says if you give very generous non-wage benefits to your highly compensated employees, then you have to give similar non-wage benefits to your less well compensated employees.

So effectively, it compresses the distribution of non-wage benefits within affirm in a way that we don’t see equivalent for wages. So put differently, what I would expect to see is more similar non-wage benefits within a firm as compared to wages. There are a couple other reasons why we would expect this. This could have to do with concepts of fairness or administrative costs, but either way, with our data we’re able to explore this and this is indeed what we find. We find this reduced compression within a firm in non-wage benefits relative to wages. So what does that mean for inequality? Can you go forward? So what I’ve done here is I have plotted inequality by decile. So if you go on the X axis, you can see the 10th decile, the 20th decile, the 30th percentile. And then I’m just plotting the mean wages in that black line.

So for the 10th decile, the mean wage is about $12. And if we go up, so around the 50th percentile, it’s going to be about $22 and on. This line is obviously upward sloping by definition. And then above it in gray, what I have plotted is the same thing, by decile distribution, but this is now of wages plus non-wage benefits. So obviously the gray line is going to sit on top of the black line because it’s a more inclusive definition of compensation. It’s also going to be upward sloping by definition. And when we think about inequality, what we care about is the slope. How different are compensation at the high deciles versus the low deciles? And so one way to think about that is what we call the ratio of the 90th decile to the 10th decile, the 90/10 ratio. And if we use this as a measure of inequality, what we see is that by thinking about this broader definition of compensation that also includes non-wage benefits, we see that our estimates of inequality are about 10% bigger.

So in other words, if we just look at cash wages, we’re underestimating the true level of inequality and compensation in the US. So onto the next slide. And so this is going to have greater implications for how we see the distribution of human capital across firms. And to explain this, I need to go through the motivation. And to do that, I have to make one assumption, but it’s a pretty standard assumption that we make in labor economics. And what I’m going to assume is that different workers, either because of their skillset or where they’re willing to work, command different levels of bargaining power. So the example I have in mind is AI engineers. It’s a great time to be an AI engineer. AI engineers are in huge demand. If you happen to be an AI engineer, you’re probably going to get a lot of job offers and you’re going to be able to choose a really generous compensation because there’s so much demand for your skillset. So now, imagine two firms. Both firms employ data clerks, firm A and firm B.

These data clerks are otherwise similar, so they’re both working in the same industry, they’re both working in the same occupation, they’re working in the same state, they’re getting paid the same amount in terms of cash wages. The only difference between these two workers is that the data clerks at firm A, they are working at a firm that also employs AI engineers and the data clerks at Firm B are working at a firm that does not also employ AI engineers. So now going back to the arguments I’ve made, I said if you’re an AI engineer, you’re going to get a generous compensation package. So that’s going to mean a lot of non-wage benefits. And at a firm, if you give high non-wage benefits to some of your employees, such as your AI engineers, you have to also give generous non-wage benefits to the rest of your employees.

And so what I should expect to find is that the data clerks at firm A receive more generous non-wage benefits relative to the data clerks at firm B. And that is indeed what we find in the data. So if I can go to the next slide. And why does this matter? Well, let’s think through what it means for the workers and for the firms. So starting with the workers, if you’re a data clerk and you’re working at firm A and you’re receiving these generous non-wage benefits, you’re more likely to want to stay at firm A. Because if you know leave and you, let’s say, go get employed at firm B, you’re less likely to receive these generous non-wage benefits. So we should see lower turnover at firms with higher non-wage benefits. That’s indeed what we find. And specifically where we really see this to be salient is among the low wage workers.

So the low wage workers at firms with high non-wage benefits have much lower turnover. But now, let’s think about it from the firm’s point of view. If I’m firm A relative to firm B, I’m actually providing greater total compensation to my data clerks than is firm B. And so from firm A’s point of view, this means that if there’s some way that I can replace these expensive data clerks, so if I can either outsource their job or I can replace this tasks with some sort of automation, I’m more likely to do it because it’s more expensive for me to employ these data clerks. And so what we expect to find is that these firms that have very generous non-wage benefits, what they do is they shed their low wage workers at a higher rate relative to firms with less generous non-wage benefits.

And that indeed is exactly what we find. So I encourage you, if you’re interested in these topics, please look at the paper. It’s actually a much more nuanced approach to going through all of this, but of course details that I can’t get into in this presentation. And then if I’ll just conclude on the last slide. So non-wage benefits are increasingly important in the US, so they’re now more than one third of total compensation. And so we document very different distributions among non-wage benefits versus cash wages. So what we find is this compressed within firm variation in non-wage benefits and greater across firm variation as compared to wages. We find that higher non-wage benefits are going to predict lower turnover, so employees are less likely to quit. And this is going to be particularly true for the low wage workers. And then finally, these firms that have these generous non-wage benefits, they appear to shed their low wage workers at a faster rate relative to firms that have less generous benefits. And so with that, I’m happy to pass this along to Wenting Ma.

Wenting Ma

Thank you, Paige. Good afternoon, everyone. Thank you for having me here today and I’m honored to present our findings from our study on access to financing and racial pay gap inside firms. So this study was collaborated with Janet Gao and Qiping Xu. And for the standard disclaimer, that since we use the Census data, so anything expressed here are those of the authors and do not necessarily reflect the view of the Census Bureau. So next slide, please. So this study was motivated by this well-documented fact that there is a persistent earning gap between white and non-white workers. So within our sample, and from 1990 to 2012, the pay gap between white and non-white workers have been increasing from about 12% to 20%. And based on the most recent data published by Department of Labor and the white black worker earning gap is about 31%.

And a key factor that explain this earning gap is that non-white workers disproportionately occupying those low rank, low skill jobs, even conditioning on sorting. And so if we look at the chart on the right hand side which present you the share of non-white workers within each job rank based on the resume data, so we find that the share of non-white workers decrease with the increase in the job rank. So if we look at the chart at the entry level job, we see about 19% of non-white workers and this share declined to 11% when we look at the C-suite. So if those kind of matching is not due to sorting and it could be problematic and also indicate the firms are not reaching their optimal productivity. And so a lot of recent discussion is centering around DI practice to fix this broken run, which is extremely important. But however, we don’t have much evidence on actually access to financing with the role of financial capital in firm’s design in their pay policy towards different group of workers. So next slides, please.

So in this study we ask how does access to financing affect racial pay gap inside firms? The prediction is actually not very trivial. On the one hand, we would expect that when firms got access to financing, they would allocate those financial resources to expand productions and that would lead to increase in their labor demand. And with fixed labor supply and we would expect firms to better utilize their internal workers, especially those underutilized non-white workers. And in that case, we would expect the racial pay gap to reduce. And on the other hand, we could also expect that firms would allocate those financial resources to the majority workers, for example, assigning more tasks or high value tasks to majority workers. Then in that case, we would expect the inequality to increase. So to study this question, we use the individual employment records data along with the workers’ demographic information from the Census Bureau’s LEHD-LBD program and resume data from Revelio Labs and we explored the effect of exogenous shocks to firms’ access to that market. So next slide, please.

So since the access to financing is a key element of our study, so please let me give you a quick introduction to the shocks that we utilize here. So in our study we focus on this anti-recharacterization law adopted by few states in late ’90s and early 2000. And so this law affects firms that incorporated in Texas, Louisiana, and Alabama. And so what does this law do? Under the normal situation, firm’s assets are under automatic stay when they file for bankruptcy, meaning that the creditors cannot immediately seize their assets. So lenders would be worried about lending out money at the first place because they worry about the asset value depreciation before the courts resolve the bankruptcy case. And so to get around this issue, firms, they set up a holding firm called Special Purpose Vehicle and sell their assets to the holding firm to make them bankruptcy remote and allow the lenders to seize the assets if they file for bankruptcy.

And some chapter 11 judges would not actually recognize those sell as true sell and would delay the seizure of the assets. And so the anti-recharacterization law basically prevent the judges from doing so and allow the creditors to immediately seize assets and increase the lender’s confidence in lending money to the firms that incorporated in the states that adopted this law. And so this automatically increased the credit supply to those firms. And based on the previous research, we do find that the after the law firms increase their debt capacity and increase their leverage ratio as well as increase their capex spending, indicating production expansion. And since this law was not adopted by every state, which give us a unique opportunity to identify this treatment effect, so we basically compare workers with similar background in terms of education, gender and work in the same industry stage. And we can compare workers at the firms that incorporated in those treated states and with workers and work for firms that incorporated in the states that have never adopted this law. So next slides, please.

So what we find, so after the firms get better access to financing, we find that the non-white workers observe a 3% to 4% greater increase in their earnings compared with their white worker peers within the same firm’s time with the same gender and education level. So if we convert this percentage increase in earnings into 2018 dollars, that means that after firms get better access to financing, we see that a non-white worker receives about $600 per quarter increase in their earnings. And for white workers, that increase is only about $150. And so we observe the effects are mainly driven by black and Asian workers. And more interestingly, we find the effects actually last for long-term, even after the worker leaves the company, indicating that those now white workers have accumulated certain human capital and allow them to preserve the bargaining power and get a better paying job at the next firm. And next slide, please.

So our study next steps explore through what channel explained this reduction in racial pay gap. So recall that minority workers tend to occupy the positions of lower rank skill jobs. And so if we see this reduction in racial pay gap, we should either expect that the access into finance enhanced the value of those low skill jobs or it could actually mean that the firms increase the demand for higher-skill, high-ranked jobs and require firms to allocate more human capital towards those kind of jobs. And so to disentangle those two channels, we group workers into three groups based on their pay level or their education level into low need, high-skill groups. And we find that the racial pay gap reduction is mainly concentrated among the mid to high-skill worker group, indicating that it’s not because accessing to finance enhanced the value of low-skill jobs, like food processors or cashiers.

Instead, it’s mainly because the expansion of production or adoption of technology require firms to allocate more human capital towards those jobs and ask firms to better utilize their underutilized workers. So to further support this mechanism, we look into the resume data and we find that after firms get better access to financing, the non-white workers are more likely to receive a promotion. And either it’s within the same occupation, but at a higher rank, or it could be that the minority workers got moved to other occupations with better pay. So for example, we find the minority workers experience a greater increase in the likelihood of transitioning into those tech heavy jobs, such as programmers or engineers, which is complimentary to the previous research finding that after firms get better access to financing, they adopt more technology. So next slide, please.

So let me conclude. And so our research highlights the important role of financial markets in the labor market, and we document that with better access to financing. Firms actually organically grow and increase their labor demand for higher-skill, higher-rank jobs, and that would require them to allocate more human capital into those jobs and ultimately reduce the racial pay gap inside firms. So that’s all from our research and thank you again for having me here. So the next presenter is Andrew Joung.

Andrew Joung

Can you see me? I’m just going to start, assume that the video will start working soon. Hi, everybody. My name’s Andrew. I’m a PhD candidate at the University of Michigan Department of Economics and this is joint work with Joella Abramowitz, who’s a professor at the University of Michigan. And we’re going to be talking about opening the black box of self-employment, identifying alternative work arrangements in the United States. Next slide. So we’re motivated by just the first thing is that self-employment is just an inherently difficult thing to measure, but even within self-employment itself there’s a lot of different types of work arrangements we think are pretty different and have very different effects on wellbeing. So just to take a really simple example, consider somebody who’s an employee for a limousine company, an owner operator of taxi cabs, or a Lyft driver. All three of these jobs are classified under the same industry and the latter two would be just generally classified in most data settings as just broadly self-employment.

But we can kind of intuitively look at these three different jobs and say these are probably quite different in terms of their pay, their structure and maybe the kind of workers that select into these jobs and their effects on those workers’ wellbeing. Now, there’s a lot of data that has attempted to answer these questions and there are some issues. A lot of these data are really good, but there could be some gaps that we think that we’ll be able to fill. And in particular with admin data, these admin data are going to have gaps in coverage since they’re not going to be able to capture employment related to income that is not reported to tax authorities and often are going to lack linkages to important demographics and measures of wellbeing. Of course you can turn to household surveys, but a lot of household surveys are just going to be cross-sectional in nature. They don’t probe about detailed employment characteristics and generally focus on primary jobs only.

And there’s substantial amounts of work are not captured or inaccurately captured by these surveys. And of course there’s discrepancies that exist across the surveys and between surveys and administrative data and identifying trends in self-employment just generally let alone differences across different types of work arrangements within self-employment. Next slide, please. And so what we’re trying to do with our project is we’re going to use some novel data to understand trends in the nature and prevalence of different types of work arrangements. So what we’re going to do is we’re going to use the 2003 and 2019 waves of the panel study of income dynamics or the PSID. And the PSID is a longitudinal data set that’s following families over time with over 10,000 families and 24,000 individuals. And for our purposes, the thing that’s really important is that in addition to just standard questions about work, the PSID asks respondents open-ended questions about their job industry, occupation and title along with employer names for all the work for which they were paid since the last interview two years prior.

And so what we can do with this rich kind of textual data is that we can classify these respondent’s jobs by type of work arrangements using a machine learning algorithm trained on those text data. And then we can link that to the public PSID data. In particular, we are going to have categories for platform gig workers, informally self-employed, formally self-employed workers, business owners and employees. And we have a bunch of stuff in the paper about how we define these categories, how our algorithm benchmarks against how our algorithm just does in terms of its accuracy and how our self-employment rates benchmark go to other data sources. But I just want to focus on the findings since we have so little time. So next slide, please.

So just starting out by examining different types of self-employment, we can immediately see that they’re divergent trends. Focusing on panel A, here we’re plotting for among the workers that are presently employed, the share of those workers that are engaged in different types of self-employment. Looking at the solid black line, you can see that those that are informally self-employed, the share of those workers that are informally self-employed has risen over this time period. For business owners, that is the gray dash line. We saw that they rose and then kind of fell during this time period. But importantly for the formally self-employed, their share of the workforce has been declining over this period. If we look at panel B at secondary jobs, you can see that the share of workers that are holding secondary jobs and informal self-employment has been rising quite consistently over our time period.

Whereas for the other two types of self-employment, business ownership and formal self-employment are relatively constant during this period. And you might be wondering how much of this is explained by platform gig work and the expansion of platform gig work. So we plot in that faint kind of gray dotted line what is the share of informal self-employment if you take out the platform gig workers and you can see that they explain a chunk of the rise. However, they don’t explain all of it. And so we think that this result is robust even if you exclude platform tape workers. Next slide, please.

We also see that transition patterns vary across work arrangements and that persistence has risen over time. So in panel A, what we’re going to be reporting there is in between 2003 and 2009. What is the share of, let’s say, employees? People that were employees in the prior survey wave, they remained employees in the current survey wave. And we can see that about 85% of people that were previously employees remained employees in the current survey wave. We can see that employees are pretty likely to remain employees from survey wave to survey wave. However, people that are self-employed are much less likely to persist in their roles. However, relative to the informally self-employed and the formally self-employed, you can see that business owners have about a 50% chance of remaining business owners from survey wave to survey wave. We also want to emphasize that if you look at the people that were previously informally self-employed, they have about a 20% chance of transitioning into not working.

Now, if we look at from 2011 to 2019, we see that broadly these patterns persist, but one thing that we would like to note is that it seems that persistence has risen. That is to say in between 2003 and 2009, somebody that was informally self-employed has about a 37% chance of remaining informally self-employed, whereas in between 2011 and 2019 somebody has a 47% chance of remaining informally self-employed across serving waves. Next slide. We also see that there’s declining average labor earnings among the self-employed and declining health among the informally self-employed in particular. So what I want to point you to is that what we’re plotting here is that for each of our work arrangements we’re plotting the average labor earnings, weekly hours, hourly wages, and the self-reported good health for each of the survey waves. And what I want to focus you on is the gray solid line, which is the informally self-employed and you can see that in levels they are consistently below all other types of work arrangements in earnings hours, wages, and health.

And also in Panel D, what we can note is that across all the workers in our sample there’s a slight decline in self-reported good health. The decline for the informally self-employed is considerably steeper than other types of workers. Next slide, please. We can also see that the composition of self-employment varies across the income distribution and by age and gender. So in panel A, what we’re plotting there is the share of people in the bottom income across income quintiles that are engaged in some form of self-employment. And then we break it out by the type of self-employment, whether it’s secondary self-employment, primarily employed self-employment, but it’s not informal, or if it’s primarily informal self-employment. And we can see that the bottom and the top income quintiles are more likely to be engaged in self-employment than the other remaining income quintiles, increase this kind of U-shaped distribution in the share of workers engaged in self-employment.

But we can see that the composition is very different across these workers, which we might’ve expected. In particular, we can see it’s pretty stark that among those that are in the bottom income quintile, about half of the self-employed workers in the bottom income quintile are engaged primarily in formal self-employment. Whereas we can see that for people in the top income quintiles, relatively few of them are engaged in informal self-employment. Much more of them are engaged in business ownership or formal self-employment. If we look at panel B where we’re just plotting there, the share of self-employment by age and gender, and we can see that men in general are more likely to be engaged in self-employment across the age distribution than women. However, if you conditioned on self-employment, you would see that basically that women conditional on being self-employed are more likely to be engaged in primarily informal self-employment than their corresponding similarly aged male counterparts. Next slide, please.

And with that, I just want to wrap up. Our findings we think can inform discussions about the future of work and provide evidence for policymaking. We find that rising informal self-employment and following formal self-employment and business ownership, you see that there are differences in transitions and changing trends in labor earnings, wages, hours and self-reported health across work arrangements and we see differences across subgroups by type of self-employment. We think our results shed light on how informal self-employment differs from other types of self-employment and wage and salary employment in particular. And then in the future we’re aiming to better understand transitions and effects of different work trajectories over the life course. And we want to emphasize here that our classification approach can be used in a variety of other data settings, so really excited to see what we can do in the future with other survey data. But with that, I want to wrap up and I want to hand it off to Adria Scharf for our discussion.

Adria Scharf

Thank you, Andrew, and thank you to all of the paper presenters and to Nate for the framing comments. Good afternoon. I’m Adria Scharf. I’m an associate director at the Institute for the Study of Employee Ownership and Profit Sharing at the Rutgers University School of Management and Labor Relations and I’m really delighted to have had the opportunity to read these three papers. Delighted to have the opportunity to offer a few comments and summary reflections about them. Each of the three papers very much advances our understanding of unequal labor market outcomes. And I will offer a few comments on each of the papers and then I will moderate the Q&A. And my understanding is that everyone participating in the Zoom is welcome to submit your questions for the Q&A into the Q&A box. So with these three papers, we really have three very different kind of angles on angles into exploring firm effects on worker outcomes.

The papers each focus as you can see on very different sort of slices of the labor market. The first study covered full-time employees, the second studied workers and publicly listed companies in treatment and control states and the third explored the self-employed very broadly defined. So starting with Paige, her and Jeffrey Tate, it’s very interesting paper. Firms with benefits reminds us that non-wage compensation is a critical and under considered component of compensation. And one that, as she explains, has unique dynamics in terms of its impact on firms and on worker outcomes. And as Paige just shared, this is in part because benefits like health benefits and retirement insurance have statutory inclusion and fairness constraints thanks to federal laws that wage scales do not. So they tend to be more standardized or more equalized across employees within a firm.

And also, I’ll add drawing from some of my research at Rutgers that, and the paper very much recognizes this, there are other reasons, too, that firms may voluntarily intentionally structure their benefits in relatively inclusive ways. And as an example from our research on high performing companies with employee stock ownership plans that employ low and moderate wage workers, I found that many such companies strategically adopt broad-based benefits along with the ESOP sort of as part of a high performance work system strategy in which the frontline workers are expected to take responsibility, cooperate, or share information with one another. And those inclusive benefits in combination with the broad-based stock sharing reinforces and includes the frontline workers in that high performance culture. That was just a bit of a side note, but in Paige’s work I was very much struck that low wage workers may access more generous benefits when their firms have well-paid higher bargaining power employees also on the payroll. And I was thinking about that finding very important finding in the context of the literature on job quality and the societal crisis of job quality.

It made me curious whether that variable, having a wage mix on payroll, whether that is a predictor of other potential measures of job quality for low wage workers, too, and be interested to see future studies explore that as a predictor of job quality measures more generally. And then secondly, just have the thought that if higher paid employees lift the benefits for the lower wage workers, then in the firms with concentrations of low wage workers, firms that don’t have that paid mix, what alternative or distinct strategies may be needed to incentivize benefit provision? So I want to just close my comments on this paper just on the note of underscoring the importance and the life altering impact of employer benefits for low wage workers in an economy in which a very significant portion of low wage workers in fact have no access to any healthcare benefit or retirement plan, let alone any other benefits.

Paige and Jeffrey Tate’s work very helpfully clarify important predictors and dynamics of non-wage compensation, including sort of naming and uncovering the important fact, the important finding that your coworkers matter. Turning now to Wenting Ma, et al. Wenting Ma and colleagues and their fascinating paper, they used the staggered adoption of anti-recharacterization laws across states really as a natural experiment. They find that, as you just heard, following the adoption of these ARLs, the minority workers experienced increased earnings and significantly higher increase in job mobility and promotion rates compared to their white colleagues. These effects persist over the long-term, even after workers leave the original company. And interestingly, the workers benefiting the most from these patterns are the medium and high skilled workers, minority employees whose human capital has been underutilized. The results suggest that access to financing strengthens human capital and promotes the careers of skilled minority workers.

I was struck that the reductions in the racial pay gap were most pronounced at firms where white workers earned higher premiums over minority workers prior to the shocks and also among firms with less diverse boards of directors. It made me curious whether some of the observed effects may represent sort of a catch-up effect within organizations that were more rigidly racially stratified organizations historically. And just an observation that these findings are very much consistent with and speak very much to the sociological literature on race and labor cues. I very much appreciated how this paper places racial equity alongside and in the context of financial markets and financing access and the findings potentially have relevance to many different conversations about ways that financing can contribute to socially desirable firm outcomes from ESG to impact investment and beyond. Turning now to Andrew and Joella Abramowitz’s Opening the Black Box of Self-Employment.

As you just heard, older conceptions of self-employment are really no longer sufficient and data are lacking. This paper very much innovates by examining narrative data responses to open-ended questions in order to differentiate multiple different types of the self-employed. With the explosion of the digitalized gig economy and the erosion of traditional long tenure employment relationships, this sort of analysis is overdue. I appreciated how the authors created a typology of self-employment with help from machine learning, and I’m curious about that, out of the narrative responses. Appreciated how they transitions between roles, income levels by types, and looked at wellbeing outcomes. The most striking takeaway for me of course was the stark observed increase in gig work and the informal self-employed and the incomes were lowest and extremely low in absolute terms in this rapidly growing platform gig and informal self-employed group. This group also had markedly lower wellbeing outcomes, good health, and life satisfaction.

And just in closing, societally we need to be thinking proactively about organizational, institutional and policy approaches to economic security for some portion of the gig economy. In our institute for the study of employee ownership and profit sharing at Rutgers, we are connected to some interesting emerging projects and new thinking about cooperative structures, including cooperative franchise models and federations of cooperatives as potential workable institutional structures to help benefit and stabilize independent contractors and gig workers. Andrew’s findings are important to our thinking about our work and I imagine to many others. This is a hugely helpful first cut at utilizing underutilized narrative data to get at a vitally important topic. And with that, I would like to stay on time here and I’d like to invite the paper presenters as well as Nate back on screen for a Q&A.

Is everybody on here? Okay. Did you get Nate? Not sure if you have. Hello one and all. Thank you for all of your incredible work. And I think I’ll start off with one of my own questions. I have several, so I’ll direct this question to Wenting. Wenting, I really appreciated reading your paper. Reading your paper, that was my first introduction to the concept of anti-recharacterization law. And having no prior knowledge, I was curious whether the ARL laws were passed at the state level alone or were they passed together with a platform of tax cuts and other business and financing reforms around the same time? I just imagine that might represent part of a larger policy agenda, so I was curious at our ability to isolate the effects.

Wenting Ma

Thank you. Thank you, Adria. So to my knowledge, it was not part of the large package of the law and it was adopted sequentially across several states. And so it was just a state level law, an isolate law, that only affect the credit supply market.

Adria Scharf

Thank you. That’s helpful to know. A question for Andrew. Andrew, I was just curious reading your paper if you could share sort of an example of what were some of the typical responses that you actually found in the open-ended question? What were some of the typical narrative responses, just to give us kind of a feel of what people wrote?

Andrew Joung

Yeah, I mean, people wrote quite a lot, honestly. So the thing about the PSID and a few other survey data sets as well is that … Well, let me start off by saying most survey data when they are collecting this information on the backend, these kinds of open-ended questions, because that’s kind of how they create the NIX codes or the industry codes for these jobs, is that they’ll ask people these open-ended questions and then they collect. And then somebody goes through and then they classify them as for whatever industry, but the thing about the PSID is that they let people really go on. And so people will have multiple sentences of saying, “I’m a manager, but I’m managing with my cousin.” So then at that point we might define that person as something like an owner manager and that gives us a lot of rich data of seeing what is the kind of exact context in which somebody is working.

So somebody might say I drive on the weekends, but I only do it for a handful of people and I meet with them on some sort of platform. So there’s quite a lot of it and then sometimes the answers can be quite short and curt. They might say, “I’m a driver,” and then that’s all we have. And then they’ll ask follow-up questions about employer name and we’ll use that additional information to kind of simple up things. So it kind of can run the gamut, for sure.

Adria Scharf

Thank you. Paige, we have a question for you from an attendee. It is “should the same conclusions for non-wage benefits apply to non-monetary non-wage benefits, like time off, flexible work arrangements, work-life balance, or agency on how you do your job?”

Paige Ouimet

So thank you for this question. I think this is really important. I think when we think about inequality, we need to be thinking more broad than just wages and even more broad than what we do in the paper, which is wages and these sort of monetary, almost non-wage benefits. I think things like being able to work from home, absolutely this matters. Now to get to your question, in our data we don’t have information on things like alternative work arrangements, but I know from other work that for at least subsamples of firms are able to observe things both like health benefits as well as work from home and other job characteristics that tend to be highly desired, that these tend to be positively correlated. So my assumption would be that the firms that have more generous non-wage benefits are also going to be better on a lot of other criteria. And again, what this is probably going to say is we’re going to have more inequality in the economy than we think we are seeing if we just look at wages.

Adria Scharf

Thank you. A question for Andrew from a participant on Zoom. Did we see the same declines for the self-employed full-time employees as those who are performing self-employment as a supplement to their primary employment situations, which is not really self-employment?

Andrew Joung

Could you read that question one more time? Is the question asking whether or not people that have self-employment as a secondary job they saw a decline as well.

Adria Scharf

Yeah, do we see the same declines for self-employed full-time as those who are doing self-employed as a supplement? Yes, so I think that is the question. Is it the same trend line for full-time self-employed versus folks who are doing self-employed gigs as a supplement to a primary employment?

Andrew Joung

So I think the answer to that is that we see that there’s actually a rise in people that are holding supplemental self-employment. When it’s informal supplement, we’re seeing a rise. But for other types of self-employment, we’re seeing a relatively constant level, but we want to emphasize that for those other two types of self-employment that we classify formal self-employment and business ownership, they’re already quite low. And so it’s kind of like if there was a decline, it would’ve been a very modest decline from maybe 1% of the workforce to closer to zero. So there wasn’t much more for them to go down, if that makes sense.

Adria Scharf

Yeah, that does make sense. It looks like there’s time for one more question. So here is a final question for Paige, which is a question about one of your variables which was significant in a number of your models and that is organization size or whether measured by firm size or establishment size. I was curious about what you see as the sort of the meaning or significance of or how organization sides fit into your overall theoretical model, knowing that organization size is highly correlated with having differentiated wages and such.

Paige Ouimet

So I want to just start by adding one caveat to our results that I didn’t have time to get into, which is the National Compensation Survey over samples large firms. So the sample that I am using does not include a lot of small firms and particularly does not include really any new firms. So I would sort of consider my limits to generalization talking about the very smallest and the very newest companies. Otherwise, we tend to see a positive correlation between firm size and having these benefits. And I think some of this comes from the fact that as companies get larger, they sort of get more formalized. And with this part of formalization, you end up adding additional benefits across your employees. Obviously to the extent that you end up as you get bigger, you’re more likely to pull one of these high wage workers who’s going to demand these generous benefits is also going to lead to the same effect.

Adria Scharf

Thank you for that. So panel, I think that wraps up our Q&A. Thank you one and all. And now we are going to hand off to Douglas Webber.

Douglas Webber

So my presentation today is a high-level look at a collection of research projects that I’m working on with my wonderful and brilliant colleagues, Erin Troland and Jessica Liu, as well as a number of other coworkers throughout my division and here at the Board. I should mention that while everything that I’m talking about today relates to the labor market and employment outcomes, this is just one focus of our research. When we are ready to put something out that’s a little bit more formal, it will include not just employment, but also work on housing, credit and what we are calling community infrastructure as well. Given that so many of the fantastic presentations we’ve seen so far through the first three days of this event, I hope I don’t need to spend any time convincing the audience that the communities that individuals live in have a large and disproportionate impact on their economic and financial opportunities and outcomes.

Put very simply, place matters. I think as one participant at a Fed Listens event in Chicago put it, they put it best when they said, “In some places it’s always a recession.” So our collection of research projects is aimed at first documenting how much place matters across a wide variety of outcomes and data sources. Obviously causally identifying either headwinds or tailwinds to upward mobility would be the ideal and it’s where we hope that some of this research will eventually lead, but at this early stage we think that there’s still a lot of high-quality work to be done on just good descriptive data work. Now, one challenge in work like this is how to summarize geographic differences into something coherent and easily digestible. There are over 3,000 counties and while it may be true that each has a unique labor market, that isn’t really helpful for categorization purposes. So we’ve experimented with many different ways of grouping localities together, but the principle way that we landed on is to divide counties into three income groups based on their median household income.

More details on this in a moment. We can then look at both structural differences across counties that tend to persist over time as well as differences across counties that may vary depending on the point that we are in the business cycle. Next slide, please. Thank you. So we landed on county as the best unit of analysis because it strikes a balance between the timeliness of data availability and the granularity of information. Our three county income groups are the bottom 25%, the 25th to 50th percentiles in the top half of the county income distribution. Now importantly, these groups are population weighted. So this is not taking the bottom 25% of counties, but rather the counties at the bottom of the distribution that add up to 25% of the whole population. So since many of these counties tend to be more rural on average, this means that there will be far more than 25% of the counties will fall into this bottom group. One important thing I want to highlight is that all the conclusions that I mentioned today hold even when we adjust for differences in the local price level and cost of living.

On the next slide, you can see a visualization of the employment to population ratio across US counties. Only the bottom income counties are highlighted to give you a sense of the places that tend to struggle the most. On the right, you can see how the employment to population ratio relates to county income. You also notice how compressed the middle-income group is with a household income of roughly $57,000 separating the first two groups and $68,000 delineating the second from the top income group. On the next slide, we can see how the employment to population ratios have evolved over time across county income groups. The differences are remarkably persistent, and I can’t think of a better illustration of the quote that I mentioned earlier from the Fed listens event about some places always being in a recession. Try to take yourself back to April and May of 2020.

Forgive me for taking you back there, the deaths of the pandemic recession. Top half income counties in the midst of unprecedented shutdowns in 100-year flu saw their employment indicators fall all the way to levels that lower income counties experienced every day prior to the pandemic. We’ll be digging much deeper into headline labor market indicators like this as our research evolves. For instance, breaking things down by industry, showing how the labor market evolved differently during the Great Recession and its recovery relative to the pandemic recession and its recovery. And on the final slide, I want to conclude with some data on what was the major labor market story of really the last two years. So here I present the number of monthly job postings at the county level relative to the number of unemployed workers in that county. This is similar, though not identical, to the vacancy per unemployed worker measure that some of you may be more familiar with.

This is really a way of trying to quantify local labor market tightness. Now, a major narrative of the pandemic recovery is that low wage workers and those with lower levels of education experienced significantly greater wage gains than higher income workers. This slide along with other graphs that I don’t really have time to present right now adds a possible wrinkle to this narrative. So top income counties actually experienced greater gains in labor market tightness than bottom income counties during the recovery. And when I look at listed wages associated with these postings, there’s evidence that a disproportionate amount of the positive wage growth, or at least assuming that the higher wages from job postings is passed on to actual higher wages, so there’s evidence that a disproportionate amount of the positive wage growth for low income workers may have actually gone to low income workers, but who live in higher income areas. In other words, to bring my presentation full circle, place matters. So thanks very much for your time. I look forward to sharing more of this work in the future and I will pass it back to Sara.

Sara Chaganti

Thanks so much, Doug, and thank you everyone for joining us on day three. On behalf of the organizing committee, I want to acknowledge all the participants for their time and insights shared during today’s informative discussion. I hope you’ll join us tomorrow at the same time to continue the conversation on uneven outcomes in the labor market. Tomorrow we’ll discuss the role of policy in addressing labor market disparities. The day will open with remarks from Bill Rogers, vice president and director of the Institute for Economic Equity at the Federal Reserve Bank of St. Louis. We look forward to seeing you tomorrow.