NBER Summer Institute 2022 Round-up: Week 2

Posted on by Emilie Openchowski
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On July 11, the National Bureau of Economic Research kicked off its summer institute, an annual 3-week conference featuring discussions and paper presentations on various topics in economics, including disaggregating income growth, labor market outcomes of technological innovations, and welfare implications of regulating tech companies. This year’s NBER event is virtual and is being livestreamed on YouTube.

We’re excited to see Equitable Growth’s grantee network, our Steering Committee, and our Research Advisory Board and their research well-represented throughout the program. Below are abstracts (in no particular order) of some of the papers that caught the attention of Equitable Growth staff during the second week of the conference.

Check out the round-up from week 1, and come back next week for the round-up from the third and final week.

“Real-Time Inequality”
Thomas Blanchet, University of California, Berkeley
Emmanuel Saez, University of California, Berkeley and NBER, former Steering Committee member
Gabriel Zucman, University of California, Berkeley and NBER, Equitable Growth grantee

Abstract. This paper constructs high-frequency and timely income distributions for the United States. We develop a methodology to combine the information contained in high-frequency public data sources—including monthly household and employment surveys, quarterly censuses of employment and wages, and monthly and quarterly national accounts statistics—in a unified framework. This allows us to estimate economic growth by income groups, race, and gender consistent with quarterly releases of macroeconomic growth, and to track the distributional impacts of government policies during and in the aftermath of recessions in real time. We test and successfully validate our methodology by implementing it retrospectively back to 1976. Analyzing the COVID-19 pandemic, we find that all income groups recovered their pre-crisis pretax income level within 20 months of the beginning of the recession. Although the recovery was primarily driven by jobs rather than wage growth, wages experienced significant gains at the bottom of the distribution, highlighting the equalizing effects of tight labor markets. After accounting for taxes and cash transfers, real disposable income for the bottom 50 percent of the income distribution was 20 percent higher in 2021 than in 2019 but fell in the first half of 2022, as the expansion of the welfare state during the pandemic was rolled back. All estimates are updated with each quarterly release of the national accounts, within a few hours.

Note: This research was funded in part by Equitable Growth.

“The Welfare Consequences of Regulating Amazon”
Germán Gutiérrez, New York University, Equitable Growth grantee

Abstract. Amazon.com Inc. acts as both a platform operator and seller on its platform, designing rich fee policies and offering some products direct to consumers. This flexibility may improve welfare by increasing fee discrimination and reducing double marginalization but may decrease welfare due to incentives to foreclose rivals and raise their costs. This paper develops and estimates an equilibrium model of Amazon’s retail platform to study these offsetting effects and their implications for regulation. The analysis yields four main results. First, optimal regulation is product- and platform-specific. Interventions that increase welfare in some categories decrease welfare in others. Second, fee instruments are substitutes from the perspective of the platform. Interventions that ban individual instruments may be offset by the endogenous response of existing and potentially new instruments. Third, regulatory interventions have important distributional effects across platform participants. And fourth, consumers value both the Amazon Prime program and product variety. Interventions that eliminate either of the two decrease consumer, as well as total, welfare. By contrast, interventions that preserve Prime and product variety but increase competition—such as increasing competition in fulfillment services—may increase welfare.

Note: This research was funded in part by Equitable Growth.

“‘Compensate the Losers?’ Economy-Policy Preferences and Partisan Realignment in the U.S.”
Ilyana Kuziemko, Princeton University and NBER
Nicolas Longuet Marx, Columbia University
Suresh Naidu, Columbia University and NBER, Equitable Growth grantee

Abstract. Why have less-educated voters abandoned center-left parties in rich democracies in recent decades? While much recent literature highlights the role of cultural issues, we argue that, at least in the United States, the Democratic Party’s evolution on economic issues has played an important role. We show that lower levels of education predict strong support for “predistribution” policies (e.g., guaranteed jobs, public works, a higher minimum wage, protectionism, and support for union organizing) much more than for redistribution policies (taxes and transfers). This robust support for predistribution among the less-educated is mostly unchanged since the 1940s. We then move to the “supply side” of economic policies: Congressional roll-call votes exhibit a decline in predistribution legislation while Democrats are in power, whereas redistribution-related legislation has remained steady. We also document changes in the supply of Democratic politicians. Today, Democratic politicians are far more likely to come from elite educational backgrounds than Republicans, whereas the reverse was true before the 1990s, which might help explain why they no longer propose the predistribution policies favored by the less-educated. We then examine the intersection of the demand and supply sides of economic policy by showing that today, the less-educated are more likely than others to say that Republicans are the party that will keep the country prosperous, whereas from 1948 until the 1990s, the reverse pattern held.

“Monetary Policy and the Labor Market: A Quasi-Experiment in Sweden”
John Coglianese, Board of Governors of the Federal Reserve System, Equitable Growth grantee
Maria Olsson, BI Norwegian Business School
Christina Patterson, University of Chicago and NBER, Equitable Growth grantee and former Dissertation Scholar

Abstract. We analyze a quasi-experiment of monetary policy and the labor market in Sweden during 2010–2011, where the central bank raised the interest rate substantially while the economy was still recovering from the Great Recession. We argue that this tightening was a large, credible, and unexpected deviation from the central bank’s historical policy rule. Using this shock and administrative unemployment and earnings records, we quantify the overall effect on the labor market, examine which workers and firms are most affected, and explore what these patterns imply for how monetary policy affects the labor market. We show that this shock increased unemployment broadly, but the increase in unemployment varied somewhat across different types of workers, with low-tenure workers in particular being highly affected, and less across different types of firms. Moreover, we find that the structure of the labor market amplified the effects of monetary policy, as workers in sectors with more rigid wage contracts saw larger increases in unemployment. These patterns support models in which monetary policy leads to general equilibrium changes in labor income, mediated through the institutions of the labor market.

“An Equilibrium Analysis of the Effects of Neighborhood-based Interventions on Children”
Diego Daruich, University of Southern California
Eric Chyn, Dartmouth College and NBER, Equitable Growth grantee

Abstract. To study the effects of neighborhood and place-based interventions, this paper incorporates neighborhood effects into a general equilibrium, or GE, heterogeneous-agent overlapping-generations model with endogenous location choice and child skill development. Importantly, housing costs, as well as neighborhood effects, are endogenously determined in equilibrium. Having calibrated the model based on U.S. data, we use simulations to show that predictions from the model match reduced form evidence from experimental and quasi-experimental studies of housing mobility and urban development programs. After this validation exercise, we study the long-run and large-scale impacts of vouchers and place-based subsidies. Both policies result in welfare gains by reducing inequality and generating improvements in average skills and productivity, all of which offset higher levels of taxes and other GE effects. We find that a voucher program generates larger long-run welfare gains relative to place-based policies. Our analysis of transition dynamics, however, suggests there may be more political support for place-based policies.

“Technology-Skill Complementarity and Labor Displacement: Evidence from Linking Two Centuries of Patents with Occupations”
Leonid Kogan, Massachusetts Institute of Technology and NBER
Dimitris Papanikolaou, Northwestern University and NBER
Lawrence Schmidt, Massachusetts Institute of Technology
Bryan Seegmiller, Massachusetts Institute of Technology, Equitable Growth grantee

Abstract. We construct new technology indicators using textual analysis of patent documents and occupation task descriptions that span almost two centuries (1850–2010). At the industry level, improvements in technology are associated with higher labor productivity but a decline in the labor share. Exploiting variation in the extent to which certain technologies are related to specific occupations, we show that technological innovation has been largely associated with worse labor market outcomes—wages and employment—for incumbent workers in related occupations using a combination of public-use and confidential administrative data. Panel data on individual worker earnings reveal that less educated, older, and higher-paid workers experience significantly greater declines in average earnings and earnings risk following related technological advances. We reconcile these facts with the standard view of technology-skill complementarity using a model that allows for skill displacement.

“Did Racist Labor Policies Reverse Equality Gains for Everyone?”
Erin L. Wolcott, Middlebury College

Abstract. Labor protection policies in the 1950s and 1960s helped many low- and middle-wage White workers in the United States achieve the American Dream. This coincided with historically low levels of inequality across income deciles. After the Civil Rights Act of 1964 was enacted, many of the policies that had previously helped build the White middle class reversed, especially in states with a larger Black population. Calibrating a labor search model to match unemployment benefits, bargaining power, and minimum wages before and after the Civil Rights Act, I find changing labor policies explain most of the rise in income inequality since the 1960s.

“Oligopsony Power and Factor-Biased Technology Adoption”
Michael Rubens, University of California, Los Angeles

Abstract. I show that buyer power of firms could either increase or decrease their technology adoption, depending on the direction of technical change and on which inputs firms have buyer power over. I illustrate this in an empirical application featuring imperfectly competitive labor markets and a large technology shock: the introduction of mechanical coal cutters in the 19th century Illinois coal mining industry. By estimating an oligopsony model of production and labor supply using rich mine-level data, I find that the returns to cutting machine adoption would have increased by 28 percent when moving from one firm to 10 firms per labor market.

“Industries, Mega Firms, and Increasing Inequality”
John C. Haltiwanger, University of Maryland and NBER
Henry R. Hyatt, U.S. Census Bureau
James Spletzer, U.S. Census Bureau

Abstract. Most of the rise in overall earnings inequality is accounted for by rising between-industry dispersion from about 10 percent of four-digit North American Industry Classification System, or NAICS, industries. These 30 industries are in the tails of the earnings distribution and are clustered especially in high-paying high-tech and low-paying retail sectors. The remaining 90 percent of industries contribute little to between-industry earnings inequality. The rise of employment in mega firms is concentrated in the 30 industries that dominate rising earnings inequality. Among these industries, earnings differentials for the mega firms relative to small firms decline in the low-paying industries but increase in the high-paying industries. We also find that increased sorting and segregation of workers across firms mainly occurs between industries rather than within industries.

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Equitable Growth welcomes R. Jisung Park as a visiting scholar to examine economic impacts of climate change

Posted on by Maria Monroe
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Wildfires are blazing across Europe and temperatures are soaring around the world, exemplifying new extremes that are set to become normal as a result of climate change. But the effects of global warming certainly will go beyond hotter days and more intense natural disasters. It will also affect the U.S. economy—and indeed, already is.

Researchers have been assessing and debating the economic damages from climate change for some time, predicting how rising temperatures may affect economic growth and worker productivity, and how best to navigate the all-but-inevitable health and financial shocks for workers amid rising climate-related hazards. This research couldn’t be more important as our understanding of climate change and how to mitigate its effects evolves, and as new data become available and modeling techniques are modernized.

Against this backdrop, Equitable Growth is pleased to welcome R. Jisung Park as a visiting scholar to continue his work examining the impacts of climate change on the U.S. labor force and broader U.S. economy. Park’s interests lie in how environmental factors shape economic opportunity, including the effects of climate on social inequality. His research to date includes examinations of how high temperatures affect human capital outcomes, such as learning and student performance, and how those effects translate into racial and economic achievement divides.

Park also examines the role of adaptive investments, such as air conditioning in schools, in alleviating or exacerbating inequality and disparities in well-being and other outcomes. He likewise studies the labor market outcomes of high temperatures, and excessive heat specifically, for workers in the United States. His previous research with Nora Pankratz at the University of California, Los Angeles and A. Patrick Behrer at Stanford University, published last year in Equitable Growth’s Working Paper series, finds that workplace injuries in California increased by 6 percent to 9 percent on days warmer than 90 degrees Fahrenheit, and by 10 percent to 15 percent on days that exceeded 100 F. This finding applies to workers in occupations based both indoors and outdoors, with widespread implications for worker productivity across industries.

Park will be with Equitable Growth for 18 months, while retaining his academic appointments. As a visiting scholar, Park will work to expand Equitable Growth’s institutional research capacity and policy knowledge in the environmental economics space.

While at Equitable Growth, Park will undertake two independent research projects. One is on labor market frictions and climate change adaptation. The other is on natural disasters and human capital. The findings from these two studies will be published in peer-reviewed journals and presented at academic conferences, contributing to Equitable Growth’s status as an organization for researchers examining inequality and economic growth in the United States. Park will be integral in boosting the organization’s capacity to produce in-house research and analysis on climate change, which policymakers can turn to when crafting environmental legislation and regulations.

Additionally, Park will support Equitable Growth in its role as the bridge between academia and policymakers as we expand our work addressing the economic impacts of climate change. As a former grantee, he is well-positioned to support this important aspect of Equitable Growth’s work. He knows firsthand the value of policymaker connections in reaching a wider audience. He also is experienced at detailing his research to the press and has testified before the U.S. Congress in a House select committee hearing on advancing environmental justice through climate action.

Furthermore, Park will help to grow Equitable Growth’s broader network of scholars, particularly environmental economists studying the disparate impacts of climate change and how it may exacerbate existing inequalities. His role advising the organization in this area will be vital as we seek to fund new projects and connect with researchers making important contributions to the existing climate economics literature through our annual Request for Proposals.

Bringing Park on board is a crucial step forward in Equitable Growth’s plan to examine in more detail the economic impacts and consequences of climate change, as well as reinforce the organization’s status as a trusted resource for evidence-backed policy solutions to various drivers of economic inequality in the United States. We look forward to his tenure with us over the next year and a half, and hope you’ll join us in welcoming him on board!

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New working paper examines the evolution of economic thought on the impact of technological change in the labor market

Posted on by Maria Monroe
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How have automation and digital technologies contributed to wage inequality in recent decades? It may seem like a straightforward question, but it is one that has been difficult for economists to answer. A new NBER working paper from economist David Autor, an Equitable Growth grantee and a member of Equitable Growth’s Research Advisory Board, explains what we can learn from the evolution of mainstream economic thinking on this question over the past four decades—and shows how much we still have to uncover.

In the working paper, “The Labor Market Impacts of Technological Change: From Unbridled Enthusiasm to Qualified Optimism to Vast Uncertainty,” Autor describes the changing economic approaches to studying the relationship between technology and wage inequality through four frameworks. These are the “education race,” the “task polarization model,” the “automation-reinstatement race,” and the “era of Artificial Intelligence uncertainty.” Throughout, Autor examines key developments in a large body of economic research, and the questions they raise as he and other economists seek to chart the impacts of technologies yet to come.

This exploration of these frameworks of economic thought highlights the ongoing challenges of measuring the impact of new technologies in the labor market. This column will briefly present Autor’s four frameworks and his analysis, and then will examine how the role of worker power and policy can influence the impact of these new technologies in U.S. workplaces.

The “education race”

The first framework Autor discusses, the metaphor of a “race” between education and technology, juxtaposes an ever-increasing rise in demand for educated workers and the ability of a labor market to meet that demand with rising supply. In this model, technology’s role in the workplace is generally assumed to advance over time, but as Autor notes, it is not directly measured. This model also shows the rise of the college wage premium and a similar rise in earnings inequality by educational attainment.

The “education race” framework could illustrate some dynamics of wage inequality in the 20th century, but it cannot explain it on its own. Evidence does not show that there is or has been an ongoing shortage of college-educated workers in practice, and Autor also notes that pay for workers without college degrees in the United States has fallen in ways that would not be predicted by this framework for seeking to understand this “education race.”

The “task polarization model”

Over time, economists have developed methods that attempt to directly or indirectly measure and model the growing presence of new technologies in a variety of ways, from tracing supplier records of industrial robots to analyzing text from patent applications. Researchers also have taken a more granular look at the tasks involved in jobs themselves to understand why the use of technology has affected some jobs more than others, and how tasks shift between and within the jobs themselves.

In the “task polarization model” that Autor describes, a more fine-grained examination of occupational change suggests increasing polarization in the growth of jobs in low- and high-wage occupations. This framework posits that such polarization is due to how technology is used to automate routine work—and displace workers—in many middle-wage occupations while complementing the work of those in higher-wage occupations, especially those that require advanced credentials or high levels of educational attainment. Meanwhile, the influx of formerly middle-wage workers leads to this polarization and depresses wages at the bottom of the pay distribution, even though technology is not necessarily used to displace the work of those in many low-wage occupations.

The “automation-reinstatement race”

This discussion of occupational change underscores another challenge for economic research, which is how to capture changes not only in the jobs themselves, but even the types of tasks that workers may need to do. Autor describes how this leads to the “automation-reinstatement race,” a paradigm that attempts to navigate the ways automation technologies can be used to not just replace human output, but also to enhance human labor and even create new types of work. Whether and how each path is taken has important implications for inequality in wages and other aspects of job quality.

Indeed, some other evidence suggests that employers are deploying technologies to replace workers for automation’s sake rather than because of any gains in productivity or quality. Companies also may deploy technology in ways that appear to create “new” forms of work, such as ridehail drivers, which differ from the “old” work not in their composition of tasks but in their rights, protections, and legal classifications.

The “Artificial Intelligence uncertainty” era

Going forward, can the labor market effects of new technological capabilities in machine learning and “artificial intelligence” be captured by previous models of economic thinking? Autor thinks that the potential implications for work and wage inequality are broad and nuanced enough that new frameworks will be needed. Referring to this present era as one of “Artificial Intelligence uncertainty,” he discusses some of the questions that new frameworks will need to confront, such as the potential paths that the development of artificial intelligence and other technologies will take, and what new roles and areas of specialization will open for workers as these innovations enter workplaces.

Raising these questions does not mean Autor expects a jobless future, whether utopian or dystopian. Rather, he writes, “I do not foresee a moment when labor scarcity (and hence, labor income) is eliminated.”

The role of worker power and policy in shaping the future of work

Autor’s working paper examines how economists have explored whether technological change plays at least some role in widening wage inequality, but also notes that technological change is only one of the many forces at work. In understanding the college wage premium, for instance, Autor mentions that the worsening outcomes for workers without college degrees has taken place in the larger context of declining minimum wages, weakened unions, and other broader structural changes to the U.S. labor market.

The importance of this broader context is reinforced by recent research that shows the importance of policies that strengthen workers’ bargaining power in reducing inequality. Not only is the United States unique among comparison countries in the poor outcomes for its workers without college degrees, but this divergence also can be traced to the countries’ policy choices relating to labor protections, wage setting, and social policies over the past four decades.

Many challenges remain for economic researchers as they seek to understand how technological advances have been used in the workplace, as well as their impact on workers and work in all its forms. Industrial robots can be counted, for example, but technology’s presence elsewhere in workplaces and work processes is often invisible. One case in point: The question of whether management jobs will be automated it still debated, but technology is already deeply layered into many management processes through algorithmic management, a practice that already has widespread consequences for workers and on the organization of work itself.

Similarly, even the question of what tasks make up a job is not driven by technological necessity, but by human decisions within the present policy landscape. Many employers have responded to possibilities opened by technology by reorganizing and deskilling work to be more easily outsourced and automated. And digital technologies have made it easier for employers to monitor and manage workers across these increasingly fractured work arrangements.

Expert Focus

Expert Focus: Advancing our understanding of new technologies and the future of work

By Christian Edlagan, Maria Monroe, Shonda Williams, Kathryn Zickuhr,

December 15, 2021

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New research shows the importance of worker power for addressing U.S. wage inequality

By Equitable Growth,

November 9, 2021

report

Women and the future of work in the United States

By Susan Green,

August 28, 2020

Yet these decisions around the deployment of new automation and digital technologies in the workplace—and even the design of the technology itself—are not a natural “consequence” of new technologies, but rather a series of choices enabled and shaped by existing labor and employment law.

Autor in his new working paper cautions against the dangers of assuming there is only one path of technological advancement, writing that “forecasting the ‘consequences’ of technological change treats the future as a fate to be divined rather than an expedition to be undertaken.” In fact, new technologies could be used to benefit workers, such as by making physically demanding jobs less strenuous or enhancing workers’ health and safety, rather than being used to undermine job quality and destabilize work in a downward job-quality spiral.

Policymakers can make real and actionable policy choices that can affect how employers implement automation and digital technologies in their workplaces—choices that can build “high-road” supply chains and strengthen the rights of workers so that they can navigate these technological changes more effectively.

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NBER Summer Institute 2022 Round-up: Week 1

Posted on by Maria Monroe
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On July 11, the National Bureau of Economic Research kicked off its summer institute, an annual 3-week conference featuring discussions and paper presentations on various topics in economics, including monopsony power, inflation, and discrimination in housing policy. This year’s NBER event is being held virtually and is being livestreamed on YouTube.

We’re excited to see Equitable Growth’s grantee network, our Steering Committee, and our Research Advisory Board and their research well-represented throughout the program. Below are abstracts (in no particular order) of some of the papers that caught the attention of Equitable Growth staff during the first week of the conference.

Come back next week and the following for round-ups from weeks 2 and 3.

“The Effects of Federal “Redlining” Maps: a Novel Estimation Strategy”
Luca Perdoni, Yale University, Equitable Growth grantee
Disa M. Hynsjo (deceased), Yale University, Equitable Growth grantee

Abstract. “This paper proposes a new empirical strategy to estimate the causal effects of 1930s federal “redlining” – the mapping and grading of US neighborhoods by the Home Owners’ Loan Corporation (HOLC). Our analysis exploits an exogenous population cutoff: only cities above 40,000 residents were mapped. We employ a difference-in-differences design, comparing areas that received a particular grade with neighborhoods that would have received the same grade if their city had been mapped. The control neighborhoods are defined using a machine learning algorithm trained to draw HOLC-like maps using newly geocoded full-count census records. For the year 1940, we find a substantial reduction in property values and homeownership rates in areas with the lowest grade along with an increase in the share of African American residents. We also find sizable house value reductions in the second-to-lowest grade areas. Such negative effects on property values persisted until the early 1980s. Our results illustrate that institutional practices can coordinate individual discriminatory choices and amplify their consequences.’

Note: This research was funded in part by Equitable Growth.

“Measuring Common Ownership: the Role of Blockholders and Insiders”
Amir Amel-Zadeh, University of Oxford
Fiona Kasperk, University of Oxford
Martin C. Schmalz, Oxford University

Abstract. “We construct a novel data set to show that, between 2003-2020, up to one-fifth of America’s largest firms had a non-financial blockholder or insider as their largest shareholder. Blockholders and insiders tend to be less diversified than institutional investors. Measures of `”universal” and “common” ownership of firms are therefore lower than previously believed based on analyses of institutional investors’ holdings alone, and the heterogeneity in ownership structures across firms is greater. Consolidation in the asset management industry increases universal ownership and common ownership of industry rivals. Extant results claiming indexing alone explains the rise of universal ownership cannot be confirmed with the new, more comprehensive data.”

Note: This abstract is from an earlier version of theis paper.

“Wealth and Property Taxation in the United States”
Sacha Dray, London School of Economics
Camille Landais, London School of Economics
Stefanie Stantcheva, Harvard University and NBER, Equitable Growth grantee

Abstract. “We study the history and geography of wealth accumulation in the US, using newly collected historical property tax records since the early 1800s. The property tax in the US was a comprehensive tax on all kinds of properties (real estate, personal property, and financial wealth), making it one of the first “wealth taxes.” Our new data allows us to reconstruct wealth series at the city, county, and state levels over time and to study the effects of property taxes on property values, migration, and investment. We first document the long-term evolution of household wealth in the US since the early 1800s, offering new fine-grained and high-frequency estimates of household wealth over a long period of time. The US had significantly lower wealth than Europe and only caught up with Europe after WW1, despite GDP per capita having been larger than that of France or the UK since the late 1870s. Second, we study the spatial allocation of wealth in the US over the long run. The geography of wealth is highly persistent and factors related to geography and demographics correlate strongly with wealth at the city, county, and state levels. Finally, we study the role of the property tax (i.e., a “wealth tax”) on wealth accumulation, using the large variation in property tax rates across more than 300 municipalities. We find an implied elasticity of capital income with respect to the net-of-tax rate on income of about .70 after 10 years. This elasticity can be broken down into an (extensive) elasticity of migration of about .26 and an (intensive) elasticity of per capita income of about .44. The intensive margin elasticity appears to be driven in part by reporting and avoidance responses, but also by significant capitalization of property taxes in local real estate prices.”

“Learning About the Long Run”
Leland Farmer, University of Virginia
Emi Nakamura, University of California, Berkeley and NBER, Equitable Growth grantee
Jón Steinsson, University of California, Berkeley and NBER, Equitable Growth grantee

Abstract. “Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and forecast revisions predict forecast errors. Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have precise knowledge of the data in question. We propose that these anomalies arise because professional forecasters don’t know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the data generating process (low frequency behavior) can generate all the prominent aggregate anomalies emphasized in the literature. We show this for two applications: professional forecasts of nominal interest rates for the sample period 1980-2019 and CBO forecasts of GDP growth for the sample period 1976-2019. Our learning model for interest rates also provides an explanation for deviations from the expectations hypothesis of the term structure that does not rely on time-variation in risk premia.”

Minority Unemployment, Inflation, and Monetary Policy
Munseob Lee, University of California at San Diego
Claudia Macaluso, Federal Reserve Bank of Richmond
Felipe Schwartzman, Federal Reserve Bank of Richmond

Abstract. “Persistent income inequality between Black and white households has generated a vigorous debate on what policy instruments may be effective to reduce such disparities. One such instrument to be evaluated is monetary policy. In this paper, we study precisely how monetary policy affects the real income volatility of Black and white households, in a framework where the monetary authority faces a trade-off between unemployment and inflation. Our assessment is informed by four empirical regularities: (i) the unemployment rate for Black individuals is about twice as high than the unemployment rate for white individuals, at all times; (ii) though the levels differ, the unemployment rates for Black and white individuals move closely over the business cycle; (iii) labor income represents a larger portion of overall income for Black than for white households; (iv) Black households experience higher price volatility with respect to white households. We argue that (i) and (ii) imply that proposals postulating that monetary policy targets Black unemployment are equivalent to a policy accepting larger inflation fluctuations. At the same time, (iii) and (iv) imply that the real income volatility of Black households is more directly affected by inflation changes. In our quantitative evaluation, we find that Black households gain disproportionately more from accommodative monetary policy so long as inflation expectations remain anchored. With unanchored expectations, on the other hand, the benefit from reducing unemployment become smaller and the erosion of real income from higher inflation dominates.”

“Minimum Wages, Efficiency and Welfare”
David W. Berger, Duke University and NBER, Equitable Growth grantee
Kyle F. Herkenhoff, University of Minnesota and NBER, Equitable Growth grantee
Simon Mongey, University of Chicago and NBER, Equitable Growth grantee

Abstract. “It has long been argued that a minimum wage could alleviate efficiency losses from monopsony power. In a general equilibrium framework that quantitatively replicates results from recent empirical studies, we find higher minimum wages can improve welfare, but most welfare gains stem from redistribution rather than efficiency. Our model features oligopsonistic labor markets with heterogeneous workers and firms and yields analytical expressions that characterize the mechanisms by which minimum wages can improve efficiency, and how these deteriorate at higher minimum wages. We provide a method to separate welfare gains into two channels: efficiency and redistribution. Under both channels and Utilitarian social welfare weights the optimal minimum wage is $15, but alternative weights can rationalize anything from $0 to $31. Under only the efficiency channel, the optimal minimum wage is narrowly around $8, robust to social welfare weights, and generates small welfare gains that recover only 2 percent of the efficiency losses from monopsony power.”

Note: This research was funded in part by Equitable Growth.

“New Pricing Models, Same Old Phillips Curves?”
Adrien Auclert, Stanford University and NBER, Equitable Growth grantee
Rodolfo D. Rigato, Harvard University
Matthew Rognlie, Northwestern University and NBER
Ludwig Straub, Harvard University and NBER

Abstract. “We show that, in a broad class of menu cost models, the dynamics of aggregate inflation in response to arbitrary shocks to aggregate costs are nearly the same as in Calvo models with suitably chosen Calvo adjustment frequencies. We first prove that the canonical menu cost model is first-order equivalent to a mixture of two timedependent models, which reflect the extensive and intensive margins of price adjustment. We then show numerically that, in any plausible parameterization, this mixture is well-approximated by a single Calvo model. This close numerical fit carries over to other standard specifications of menu cost models. Thus, the Phillips curve for a menu cost model looks like the New Keynesian Phillips curve, but with a higher slope.”

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COVID-19 recession and ensuing economic recovery reveals why Unemployment Insurance fails to reach marginalized U.S. workers

Posted on by Maria Monroe
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Policymakers at the state and federal levels of government in the United States who are steeped in the nation’s Unemployment Insurance system are well aware that many unemployed workers fail to access this critical income support program. Since the Great Recession of 2007–2009, attention on the demographic disparities in accessing these joint federal-state programs has grown. Research consistently finds that members of marginalized racial and ethnic groups and people with lower levels of education and income are far less likely to tap into Unemployment Insurance when they lose their jobs through no fault of their own than their more-advantaged counterparts in the U.S. workforce.

These problems faced by marginalized workers when dealing with the nation’s Unemployment Insurance system undermine U.S. economic growth and prosperity. A key role of the UI system during a recession is to provide income support to individuals who have lost employment, automatically reducing the severity of the recession. If Unemployment Insurance does not reach these individuals, then disadvantaged communities and the economy as a whole suffer.  Yet policymakers and academics alike debate the reasons why these marginalized groups of U.S. workers are less likely to receive Unemployment Insurance when they lose their jobs.

Some say it’s because low-income workers have less incentive to apply for Unemployment Insurance since the income support they receive if they successfully apply is too meager. Others argue that differences in eligibility status for the income support program, due to the kind of jobs lost by marginalized workers is the problem. And still others say union membership is pivotal in determining which workers tap the UI system. Finally, structural disparities in the design of the UI system mean that low-earning, gig, and seasonal jobs are often excluded from UI eligibility, disproportionately excluding disadvantaged workers.

Much attention has been paid to how the dramatic COVID-19 recession of 2020 and ensuing economic recovery sorely tested the nation’s Unemployment Insurance system. But changes to the UI system’s eligibility criteria enacted by the U.S. Congress in response to the crisis also made it far easier for marginalized racial and ethnic groups and people with lower levels of education and income to gain access to this key income support program amid the sharpest rise in unemployment since the Great Depression more than 70 years ago.

Were these changes to eligibility requirements enough to erase the demographic disparities that have plagued the UI system for so long? My new working paper with Hesong Yang at the University of Illinois Urbana-Champaign, “Understanding Disparities in Unemployment Insurance Recipiency,” examines these disparities in UI recipiency and the policy experiment that occurred when pandemic-era UI programs granted eligibility to workers who did not satisfy traditional UI eligibility criteria. We investigate whether these changes, in addition to increases in UI income support to all workers, were enough to close disparities in access.

We conclude from our analysis of pre- and post-COVID-19 recession data that even with greater access to Unemployment Insurance before and after the recession, members of disadvantaged demographic groups continue to be less likely to access this income support program than their more-advantaged counterparts. Specifically, we find:

  • Black and Latino workers receive Unemployment Insurance at lower rates than White non-Latino workers.
  • Younger workers access Unemployment Insurance at lower rates than older workers.
  • Less-educated workers are less likely to apply for Unemployment Insurance than workers with higher levels of education.
  • Heterosexual workers are more likely to access Unemployment Insurance than lesbian, gay, and bisexual workers.
  • Citizens access Unemployment Insurance at higher rates than noncitizens.
  • People who are highly affected by stress are less likely to receive Unemployment Insurance than people who are less affected by stress.
  • Union members are more likely to receive Unemployment Insurance than nonunion members.

Crucially, many of these groups that are less likely to receive Unemployment Insurance are the same groups that were more likely to lose work during the pandemic. Thus, disparities in access to UI benefits further exacerbate inequalities in the labor market. (See Figure 1.)

Figure 1

Unemployment Insurance recipiency rates for likely UI-eligible workers by demographic group

We find in our research that the two greatest barriers for workers to access Unemployment Insurance are incorrect information about their eligibility and uncertainty about how to apply for this key income support—at one of the most critical times in workers’ livelihoods. For all demographic groups, incorrect beliefs about eligibility are the key reason eligible individuals did not apply, with a smaller role for being unsure how to apply, in particular for younger workers and those without a high school degree.

In our analysis, we are careful to focus on individuals who are very likely to be eligible for UI benefits. Thus, these mistaken beliefs about ineligibility are largely incorrect. We find a similar pattern both before and during the pandemic, indicating that poor knowledge of the UI system is an enduring issue. 

These findings are important as policymakers in Washington and in statehouses around the country assess the pandemic UI programs and look toward longer-term structural reforms. Our findings suggest expanding eligibility and increased benefit levels are insufficient to reach all eligible workers. Rather, states need to be more proactive in identifying potentially eligible workers and providing them with information and assistance.  (See Figure 2.)

Figure 2

The reasons why nonemployed U.S. workers do not apply for Unemployment Insurance

The U.S. Department of Labor’s recent pilot program to provide UI “navigators” to help potentially eligible workers navigate the UI system is an important step in the right direction. I encourage states to continue to innovate in finding ways to identify potentially eligible workers and target information and assistance to these individuals. Such policies offer an opportunity to target assistance to disadvantaged groups and to narrow disparities in UI receipt.

Although we document that states with historically low UI recipiency rates continue to have lower recipiency rates during the pandemic, these state differences are unable to explain the differences across demographic groups. Instead, disparities in UI recipiency are a problem for all states, even those that are more successful at providing high levels of access to benefits overall. Thus, all states should work to improve equity in their UI systems, a perspective that has been embraced by the U.S. Department of Labor. (See Figure 3.)

Figure 3

Rates of Unemployment Insurance access in 2020 and 2021 in states with low (<20%), medium (20-30%), and high (>30%) rates of pre-pandemic (2019) Unemployment Insurance receipt

In short, this paper shows that even with the loosened eligibility requirements and increased benefit levels during the COVID-19 pandemic, workers from disadvantaged demographic groups were less likely than workers from other groups to access Unemployment Insurance. For our nation’s UI program and other parts of our social infrastructure tied to employment to work effectively and efficiently for all workers, we must make sure that all workers are aware of the program and can apply for it easily.

— Eliza Forsythe is an assistant professor of economics at the University of Illinois Urbana-Champaign.

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Equitable Growth’s Jobs Day Graphs: June 2022 Report Edition

Posted on by Carmen Sanchezcumming

On July 8, the U.S. Bureau of Labor Statistics released new data on the U.S. labor market during the month of June. Below are five graphs compiled by Equitable Growth staff highlighting important trends in the data.

Total nonfarm employment rose by 372,000 in June, and the employment rate for prime-age workers decreased slightly to 79.8 percent.

Share of 25- to 54-year-olds who are employed, 2007–2022. Recessions are shaded.

The unemployment rate stayed at 3.6 percent again in June, and remains higher for Black workers (5.8 percent) and Latino workers (4.3 percent) compared to White workers (3.3 percent) and Asian American workers (3.0 percent).

U.S. unemployment rate by race, 2019–2022. Recessions are shaded.

The rate of growth for average hourly earnings slowed in June to 5.1 percent over the previous year, down from 5.3 percent in May.

Percent change in U.S. wages from previous year, as measured by two surveys. Recessions are shaded.

Employment in construction, manufacturing, retail, and educational services is now back to or surpassing pre-pandemic levels, but employment in leisure and hospitality has yet to fully recover.

Employment by selected major U.S. industries, indexed to industry employment in February 2020 at the beginning of the coronavirus recession (shaded).

The number of unemployed workers remained steady in June. The share who are unemployed due to job loss fell to 30.7 percent, and 13.9 percent are on temporary layoff; 14.0 percent left their jobs, 33.6 percent are re-entering the labor force, and 7.8 percent are new entrants.

Percent of all unemployed workers in the United States by reason for unemployment, 2019–2022
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Canada is the first country to release subannual statistics on the distribution of income. Here’s how it was done.

Posted on by Emilie Openchowski
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Statistics Canada, the umbrella agency for national statistics in Canada, is the first statistical agency in the world to release subannual data on how economic growth is distributed among rich and poor households. In January of this year, it released a quarterly dataset of these distributional statistics for the first quarter of 2020 through the third quarter of 2021 called the Distributions of Household Economic Accounts, or DHEA. Then, in April, it released new statistics for the fourth quarter of 2021. Statistics Canada plans to continue releasing these quarterly snapshots on a one-quarter lag.

The production of these statistics at a quarterly frequency with relatively little lag is a watershed moment for the worldwide effort to produce more comprehensive and useful statistics on income inequality. For decades, reporting on income inequality has been dominated by the production of opaque “Gini coefficients,” which are difficult for nonspecialists to understand and are often constructed using incomplete measures of income.

This new vintage of inequality statistics will be easier for nonspecialists to interpret, while also offering more comprehensive measures of income. These data build on the pioneering work of Joseph Stiglitz at Columbia University, Amartya Sen at Harvard University, and Jean-Paul Fitoussi at the Institut d’Etudes Politiques de Paris—an expert group at the 38-member-nation Organisation for Economic Co-operation and Development on distributional national accounts—in their “Report by the Commission on the Measurement of Economic Performance and Social Progress,” as well as the many economists who have participated in the WID.world project to create comparable metrics of inequality for a large number of countries.

The U.S. version of these statistics, which does not currently offer subannual estimates and is released on a 2-year lag, provides simple answers to questions such as “What share of income is earned by middle-income Americans?” or “What percentage of annual growth accrued to the richest 10 percent of households?” The chart below shows how aggregate economic growth was divided in each year between the lowest-income half of U.S. households, the upper-income 40 percent above that, and the richest 10 percent of households. (See Figure 1.)

Figure 1

Real growth in disposable personal income from 2000 to 2019, divided by income category

A number of other countries are also beginning to produce these statistics, mostly in an experimental capacity. Reducing the amount of lag in the production of these statistics and increasing the frequency of their release is the next frontier in inequality statistics. Statistics Canada’s example could provide a template to produce inequality statistics that provide up-to-date guidance for the public, businesses, and governments to act on.

In this column, I describe how Statistics Canada produced these data and detail some remaining caveats. Notably, Statistics Canada also produces distributional statistics for consumption and wealth, but I focus only on income here.

Overview of datasets and methods used to create Canada’s DHEA

A key advantage Statistics Canada has over the U.S. statistical agencies is that they start with their Social Policy Simulation Database. The SPSD is a high-quality synthetic dataset based on a blend of survey and administrative data that Statistics Canada has maintained in some form since 1990. It is primarily based on four data sources:

These data sources are blended, and disclosure avoidance techniques are applied to ensure privacy.

The SPSD is publicly available and comes packaged with a simulation program that can be used to look at the impacts of various policy options on Canadian households. Analysts can simulate different kinds of tax changes, for example, or changes in levels of social spending using the simulation program.

This database is fairly different from what the U.S. Bureau of Economic Analysis has access to in the United States. There is no current synthetic dataset that blends administrative and survey data that is widely available to agencies and other researchers. This may change soon, as the U.S. Census Bureau is currently working on some large data blending projects, as well as disclosure avoidance strategies that may make blended synthetic data files available.

Nor can BEA start with confidential administrative data, such as IRS tax returns, because the tax code specifically prohibits this sharing. It is a serious flaw in the U.S. tax code that our economic statistical agencies are prohibited access to these data, which would allow them to construct more innovative and ambitious statistical products that could be informative for the public. Consequently, BEA starts with one part of the Census Bureau’s Current Population Survey, the Annual Social and Economic Supplement, which is somewhat comparable to the Canadian Income Survey.

Statistics Canada follows the lead of the OECD expert group on distributional national accounts, as does BEA in the United States. The aggregate income concepts targeted by each agency are a bit different, but both come relatively close to reproducing the System of National Accounts’ definition of disposable income (BEA’s statistics are available for both personal income and disposable personal income).

Because both methods are grounded in the OECD approach, they have some core similarities. Both make some use of a technique called scaling, for example, to ensure that aggregate income in the microdata match aggregate income in the national accounts. This approach has been criticized, but more research is necessary to determine exactly why these aggregates disagree and how to correct for discrepancies. For now, scaling is the best answer we have. Statistics Canada also makes adjustments to the SPSD data to ensure compatibility with System of National Accounts’ income definitions. These include deriving imputed rent estimates and adjustments for tax-sheltered income.

Methods used in the subannual DHEA

Statistics Canada goes beyond any other country in offering subannual distributions. The agency began producing these in response to demand from the public and government officials to better understand the effects of the COVID-19 pandemic. For the most part, the datasets used to create the SPSD are available only at annual frequency, so statisticians had to get a little creative to provide subannual measures.

An easy way to create subannual distributions is to take existing distributions of income in wages, business income, and other income categories and simply apply them to new national accounts aggregates. This can be an inaccurate approach. In this approach, if the first quintile of households by income earned 10 percent of wage income in the previous year, statisticians would simply assume this distribution continues to apply in current-year quarters, but economists know that these distributions change, so this is not generally going to produce very accurate results.

If agencies want to construct accurate subannual estimates of household income, then it is important for them to try to redistribute at least some income sources on a subannual basis. In other words, they must find a data source that can be used to make a new estimate of how some sources of income are spread across the income distribution. As I have previously explained, the vast majority of income for the lower 90 percent of households comes from wages and government transfers. So, these are the most important categories of income to redistribute.

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More frequent disaggregated data on U.S. income inequality would improve economic research and inform policymaking

By Austin Clemens,

February 24, 2022

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GDP 2.0: Measuring who prospers when the U.S. economy grows

By Austin Clemens,

July 11, 2023

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The U.S. economy is in its fourth decade of rising inequality amid the need for more accurate data on its consequences

By Austin Clemens,

January 27, 2022

Statistics Canada’s solution is very close to the one adopted by the website Realtime Inequality. Realtime Inequality is a project from University of California, Berkeley economists Thomas Blanchet, Gabriel Zucman, and Emmanuel Saez that reports distributional measures of growth in the United States on a quarterly cadence. Both Statistics Canada and Realtime Inequality redistribute government transfers using rules-based simulation. That is, they look at known data on households, such as income, household size and composition, and other relevant information, and use those criteria to determine whether or not the household is eligible for government transfers, such as the stimulus checks issued by the U.S. government during the pandemic.

For wages, another data source is necessary. Statistics Canada uses the monthly Canadian Labour Force Survey. This survey asks respondents a number of questions about employment status and earnings from employment. Statistics Canada uses the responses to these questions to simulate the number of weeks worked and wages for individuals each quarter.

Additionally, the Labour Force Survey asks questions about respondents’ business income. This allows Statistics Canada to redistribute that category of income as well. Unfortunately, in the United States, there are no similar sources of high-frequency business income data to draw on.

Realtime Inequality distributes wages using the Quarterly Census of Employment and Wages. The QCEW does not provide disaggregated data on the wages of particular employees, but it does report out the total money spent on wages in highly disaggregated cells. Pioneering work by Byoungchan Lee of the Hong Kong University of Science and Technology demonstrates that the QCEW can be used to proxy for the distribution of income.

For other sources of income, among them interest and dividends, both Statistics Canada and Realtime Inequality use old, known distributions mapped onto current quarter aggregates. These sources of income account for relatively small percentages of household income outside the top 10 percent of households, making them less important as contributors to inequality.

Implications for the methods of disaggregating U.S. economic data

One significant caveat is necessary. To date, Statistics Canada has not released a public analysis of the accuracy of their method. Subannual estimation of inequality necessarily relies on a modeling approach that may make these estimates less accurate. So-called nowcasting techniques, such as the one described above, require revisions when more accurate annual data are released.

Until that analysis is released, some caution is warranted. The Realtime Inequality website, which uses a similar approach to nowcasting, has shown that the approach can be relatively accurate. In their methods paper, the creators of Realtime Inequality show that they only rarely make errors in the direction of growth. That is, they very rarely find that income for a particular income group—say, the bottom 50 percent—is shrinking when it is, in fact, growing. This is an encouraging sign for the methodology, but more error analysis is necessary.

Statistics Canada may have some advantages that are unavailable to the U.S. Bureau of Economic Analysis. Canada’s Social Policy Simulation Database provides blended administrative and survey data to use as a base for the statistics. The U.S. Bureau of Economic Analysis has no similar starting point and begins instead with the Annual Social and Economic Supplement to the Current Population Survey. The Realtime Inequality team starts with IRS tax data. It is not entirely clear how these differing data sources affect accuracy. More work is necessary to better understand why all these data sources fall short of national income aggregates.

In short, considerable research is still necessary. In 2021, BEA issued a report on the feasibility of creating quarterly distributions. At the time, no one had demonstrated a working prototype of quarterly reporting using current distributions of government transfers and earnings. BEA did not investigate the possibility of using the QCEW data or another dataset to make those redistributions.

In light of continued interest from policymakers and proof of concept from both Realtime Inequality and Statistics Canada, it would be appropriate for BEA to revisit this decision. A prototype dataset blending existing BEA data with QCEW data would provide an opportunity to test the accuracy of more timely predictions and decide whether they meet the necessary standards of national statistical reporting.

JOLTS Day Graphs: May 2022 Edition

Posted on by Carmen Sanchezcumming

Every month the U.S. Bureau of Labor Statistics releases data on hiring, firing, and other labor market flows from the Job Openings and Labor Turnover Survey, better known as JOLTS. Today, the BLS released the latest data for May 2022. This report doesn’t get as much attention as the monthly Employment Situation Report, but it contains useful information about the state of the U.S. labor market. Below are a few key graphs using data from the report.

Quits have tempered from their recovery peak, declining slightly to 2.8 percent in May, but remain well above pre-pandemic levels.

Quits as a percent of total U.S. employment, 2001–2022. Recessions are shaded.

Job openings decreased to 11.3 million in May as total hires remained at 6.5 million over the month.

U.S. total nonfarm hires per total nonfarm job openings, 2001–2022. Recessions are shaded.

The ratio of unemployed workers-to-job openings increased slightly to 0.53 in May, potentially signifying a plateau of this rate following a rapid tightening of the labor market in the recovery.

U.S. unemployed workers per total nonfarm job opening, 2001–2022. Recessions are shaded.

The Beveridge Curve moved downward in May as openings declined and unemployment remained steady, but remains outside its typical cyclical range.

The relationshi between the U.S. unemployment rate and the job openings rate, 2001-2022

Job openings remain above pre-pandemic levels in most sectors, but openings decreased significantly in manufacturing in May.

Job openings by selected major U.S. insudtries, indexed to job openings in February 22020
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Reduced job turnover in small U.S. firms is an overlooked benefit of paid sick leave

Posted on by Maria Monroe
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For those U.S. workers fortunate enough to have access to it, paid sick leave is a vital tool for protecting both the public’s health and their own economic stability when an illness strikes. Under most sick leave policies, including those voluntarily provided by employers, workers accrue paid time off as they work—typically 1 hour of sick leave for every 30 hours worked—which they can use to cover short periods away from their jobs if they or a loved one falls ill.

Without this benefit, many workers are forced to choose between going into work sick, leaving their loved ones who are in need of assistance alone, or forgoing a necessary paycheck to recover safely at home. Unfortunately, millions of U.S. workers must face these difficult choices regularly. Unlike workers in nearly all economically comparable nations, U.S. workers have no national guaranteed sick leave.

The United States’ patchwork approach to paid sick leave—made up of state or city mandates, as well as those employers that voluntarily provide this benefit—is increasingly inconsistent with a growing body of literature on the relatively low costs of providing such leave, and the high public health and economic benefits of doing so, including my new working paper on one overlooked perk of this benefit: reduced job turnover for small businesses.

Below, I detail my research findings and the implications for paid sick leave policy in the United States. First, though, let’s turn to the proven health benefits of sick leave for both workers and the public.

Paid sick leave protects workers’ health and the public

Workers without sick leave, many of whom earn low wages, are 1.5 times more likely to go to work while sick. They also are more likely to report receiving threats from their employers for leaving work when sick and have higher levels of psychological distress, relative to workers who can take paid sick leave when they need it. Policies that guarantee access to this workplace benefit can alleviate these challenges and others. Research shows that access to paid sick leave is linked to lower rates of absenteeism and lower levels of psychological distress and occupational injury.

Moreover, sick leave policies protect the public’s health. Research shows reductions in the transmission of influenza-like illness following the implementation of paid sick laws. During the early phase of the COVID-19 pandemic, for example, access to emergency sick leave through the federal Families First Coronavirus Response Act was associated with a significant reduction in COVID cases in states that previously did not have a sick leave guarantee. (See Figure 1.)

Figure 1

Estimated average new daily cases, relative to March 8, between states with existing and new paid sick leave guarantees after passage of the Families First Coronavirus Response Act

Paid sick leave policies benefit employers and employees alike by reducing turnover

In addition to the public health benefits associated with paid sick leave, these programs also have economic benefits both for workers and their employers by reducing job turnover, which can be costly to both firms and workers. A recent study by the University of California, Santa Barbara’s Pete Kuhn and Lizi Yu of the University of Queensland shows employers face many expenses associated with employee turnover, such as the recruitment and training of new employees, short staffing, and low employee morale surrounding departures—all of which affect a firm’s productivity. For workers, job loss or churning through jobs is linked to adverse health outcomes, economic insecurity, income volatility, and, for working parents, adverse behavioral outcomes among their children.

My recent working paper builds on the literature of job turnover, as well as the research on paid sick days. I examine a paid sick leave policy implemented in Seattle in 2012 to determine its impact on workers’ job turnover. While there is considerable research on paid sick leave policies and employment or earnings levels, there have been relatively few studies that look at the effects of local laws on turnover.

I find that Seattle’s paid sick leave policy reduced job turnover by 4.7 percent for workers earning low wages in small firms mandated to comply with the law. Critically, these reductions in job turnover were not accompanied by losses in employment or earnings, suggesting that employers did not pass the costs of the policy down to their workers in the form of earnings reductions or job losses.

Including small businesses in paid sick leave policies is an effective way to expand access and reduce turnover costs

While I find that Seattle’s ordinance led to modest declines in turnover for workers earning low wages at small firms, I also show that the broader local labor market was unaffected, positively or negatively, by the ordinance. The reductions in turnover were concentrated among low-wage jobs in small firms because these workers were less likely to have access to paid sick leave prior to the policy’s implementation—highlighting the importance of targeting public policies toward businesses less likely to provide such fringe benefits.

Small firms are less likely to offer paid sick leave relative to large firms, in part due to employer concerns that the benefit may be too costly. Policymakers, in turn, often exempt small employers from these policies, severely limiting the intended impact of the policy change. This leaves low-wage workers at small firms unprotected, as they often lack the savings, assets, or access to credit required to buffer against earnings losses from missed work.

working paper

Mandated Sick Pay: Coverage, Utilization, and Welfare Effects

By Catherine Maclean, Stefan Pichler, Nicolas R. Ziebarth,

January 12, 2022

working paper

Support for Paid Family Leave among Small Employers Increases during the COVID-19 Pandemic

By Ann Bartel, Maya Rossin-Slater, Christopher Ruhm, Meredith Slopen, Jane Waldfogel,

January 14, 2022

working paper

Uncertain Time: Precarious Schedules and Job Turnover in the U.S. Service Sector

By Joshua Choper, Daniel Schneider, Kristen Harknett,

October 16, 2019

Yet research shows that small businesses are increasingly supportive of paid leave policies. A new working paper by Ann Bartel at Columbia University’s Graduate School of Business and her co-authors finds that support for paid leave among small firms with fewer than 100 employees has grown during the pandemic. Their research documents that nearly 71 percent of small businesses supported paid sick leave policies in the fall of 2020, compared to just below 62 percent in the fall of 2019.

In my own work, too, I find that by reducing job turnover, paid sick leave could save these small employers money. My back-of-the-envelope calculations show that Seattle’s sick leave policy has the potential to save employers roughly $2,300 per year in turnover costs, while prior research shows the costs of implementing these programs are minimal. Small firms thus need not worry too much about the added costs and should instead focus their attention on the savings they will reap through reductions in turnover and employing a healthier workforce.

Other research corroborates the minimal negative impact of paid sick leave on employment and earnings across the broader U.S. economy. Evidence from survey and administrative data, for instance, finds that city- and state-level paid sick leave policies—which includes Seattle’s sick leave policy—had minimal effects at all on aggregate employment and earnings in the affected geographies.

Conclusion

My latest research joins a growing body of literature showing that there is a better way to support sick workers and their employers than the nation’s current patchwork system. As policymakers at the local, state, and federal levels look to strengthen their economies amid the ongoing COVID-19 pandemic—and guard against the next public health crisis—they should consider paid sick leave guarantees as a low-cost, high-reward policy proven to protect workers’ economic security and health of the public, as well as the broader U.S. economy.

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