Does education lead to political engagement?The empirical literature is mixed.Theory suggests economic context matters.Individuals unable to take advantage of education in the labor market are more likely to engage in political activity.We find support for this channel during the rapid expansion of NAACP branches in the South around WWII. Branch growth was stronger where Black workers were denied returns to schooling due to Jim Crow occupational discrimination.We further show that a pre-1931 large-scale school construction program caused greater NAACP activity during the 1940s and 1950s when many former students were in their prime working years.

This paper uses minimum wage hikes to evaluate the susceptibility of low-wage employment to technological substitution. We find that automation is accelerating and supplanting a broader set of low-wage routine jobs since the Financial Crisis. Simultaneously, low-wage interpersonal jobs are increasing and offsetting routine job loss. However, interpersonal job growth does not appear to be enough – as it was prior to the Financial Crisis – to fully offset the negative effects of automation on low-wage routine jobs. Employment losses are most evident among non-Asian people of color who experience outsized losses at routine jobs and smaller gains at interpersonal jobs.

Abstract A newly digitized panel of county-level branch activity of the National Association for the Advancement of Colored People (NAACP) is used to describe the potential factors underlying the expansion of political participation in the American South, with a particular emphasis on the short period from the late 1930s through the 1940s. This period has long been recognized for its significant progress in reducing sizable racial gaps in labor market outcomes. But little work in economics has considered the role of political participation in shaping that progress. As the preeminent civil rights organization prior to the 1950s, the NAACP provides a natural lens in which to explore the expansion in political activism during this crucial period. Associative evidence suggests that a few potential channels could be especially worthy of future study, including the role of demographics, increased human capital, expansion in labor demand driven by wartime efforts, reduction in racial violence, latent political activism, and expansions in political and social networks, all of which have been highlighted in a variety of history and social science literatures. However, careful causal empirical work does not currently exist on these factors. Filling in this hole is important for providing compelling evidence on the origins of the 20th century’s most important U.S. political movement, as well as adding to a growing literature in political economy and development economics which examines the role that grassroots activism has played on economic growth and income inequality around the world.

We estimate the long-run effects of the 1930s Home Owners Loan Corporation (HOLC) redlining maps by linking children in the full count 1940 census to 1) the universe of Internal Revenue Service (IRS) tax data in 1974 and 1979 and 2) the long form 2000 census. We use two identification strategies to estimate the potential long run effects of differential access to credit along HOLC boundaries. The first strategy compares cross-boundary differences along HOLC boundaries to a comparison group of boundaries that had statistically similar preexisting differences as the actual boundaries. A second approach only uses boundaries that were least likely to have been chosen by the HOLC based on our statistical model. We find that children living on the lower-graded side of HOLC boundaries had significantly lower levels of educational attainment, reduced income in adulthood, and lived in neighborhoods during adulthood characterized by lower educational attainment, higher poverty rates, and higher rates of single-parent households. (JEL G21, I26, I32, J13, N32, R23, R31)

Using both a structural and statistical model of pay, performance, and performance-enhancing drug (PED) use, we show that increasing pay in minor league baseball would reduce the financial incentives players face when deciding whether to use PEDs. In addition, we show that the largest decline in cheating would occur at the lowest minor league levels, where pay is currently well below the effective minimum wage. The gains from such a change are economically large and could potentially offset the higher salary costs for major league teams through an increase in franchise values driven by fewer costly PED suspensions. Our findings are salient to a large literature that considers the design of contractual obligations to prevent undesired behavior when information is costly to acquire, as is the case in many employer-employee relationships.

Leveraging the increasing availability of ”big data” to inform forecasts of labor market activity is an active, yet challenging, area of research. Often, the primary difficulty is finding credible ways with which to consistently identify key elasticities necessary for prediction. To illustrate, we utilize a state-level event-study focused on the costliest hurricanes to hit the U.S. mainland since 2004 in order to estimate the elasticity of initial unemployment insurance (UI) claims with respect to search intensity, as measured by Google Trends. We show that our hurricane-driven Google Trends elasticity leads to superior real-time forecasts of initial UI claims relative to other commonly used models. Our approach is also amenable to forecasting both at the state and national levels, and is shown to be well-calibrated in its assessment of the level of uncertainty for its out-of-sample predictions during the Covid-19 pandemic.

We estimate the long-run effects of the 1930s Home Owners Loan Corporation (HOLC) redlining maps on census tract-level measures of socioeconomic status and economic opportunity from the Opportunity Atlas (Chetty et al., 2018). We use two identification strategies to identify the long-run effects of differential access to credit along HOLC boundaries. The first compares cross-boundary differences along actual HOLC boundaries to a comparison group of boundaries that had similar pre-existing differences as the actual boundaries. A second approach uses a statistical model to identify boundaries that were least likely to have been chosen by the HOLC. We find that the maps had large and statistically significant causal effects on a wide variety of outcomes measured at the census tract level for cohorts born in the late 1970s and early 1980s.

Using a compiled data set of 441 censuses and surveys from between 1787 and 2015, representing 103 countries and 51.4 million mothers, we find that: (i) the effect of fertility on labour supply is typically indistinguishable from zero at low levels of development and large and negative at higher levels of development, (ii) the negative gradient is stable across historical and contemporary data, and (iii) the results are robust to identification strategies, model specification, and data construction and scaling. Our results are consistent with changes in the sectoral and occupational structure of female jobs and a standard labour–leisure model.

Joseph Altonji is one of the pioneers of modern labor economics. To mark Joe’s sixty-fifth birthday and honor his illustrious career, a conference was held at his alma mater and employer, Yale University, on September 7–8, 2018. This special issue contains seven of the papers presented by former students, coauthors, and colleagues. Joe has been central to the empirical revolution that has brought vast amounts of individual-level data to some of the most important questions ofbothmicroandmacroeconomics.His seminal breakthroughshave changed thewaywe think aboutnumerous topics, including the studyof consumption behavior and labor supply over time, intrafamily insurance, discrimination, wage determination and learning, school choice and school quality, returns to higher education, and the stochastic process of income. The hallmark of all thiswork is a thoughtful combination of theory anddata, the development ofnewempiricalmethods, thediscoveryofnovel identificationstrategies, and extreme care with extensive robustness checks and attention to data details. Early on in his career Joe was one of the first to develop our understanding of the relationship between intratemporal and intertemporal allocations and the implication this distinction has on estimating elasticities. These

The Covid-19 pandemic and associated recession have had dramatically different effects across industries, with some, including large parts of the leisure and hospitality sector, truly devastated and others, like much of the manufacturing sector, able to recover quite quickly. This has led some analysts to describe the pandemic as a reallocation shock, requiring substantial movement of labor across industries. Such a process likely requires substantial time, during which the natural rate of unemployment may be elevated. In this Chicago Fed Letter, we consider two questions: First, has the need for labor reallocation risen, and second, has there been an increase in the amount of reallocation that is actually occurring?

This study uses a boundary design and propensity score methods to study the effects of the 1930s-era Home Owners Loan Corporation (HOLC) “redlining” maps on the long-run trajectories of urban neighborhoods. The maps led to reduced home ownership rates, house values, and rents and increased racial segregation in later decades. A comparison on either side of a city-level population cutoff that determined whether maps were drawn finds broadly similar conclusions. These results suggest the HOLC maps had meaningful and lasting effects on the development of urban neighborhoods through reduced credit access and subsequent disinvestment. (JEL G21, J15, N32, N42, N92, R23, R31)

School and day care center restrictions during the Covid-19 pandemic have presented enormous challenges to parents trying to juggle work with child-care responsibilities.Still, empirical evidence on the impact of pandemic-related child-care constraints on the labor market outcomes of working parents is somewhat mixed.Some studies suggest the pandemic had no additional impact on the labor supply of parents, while other studies show not only that it did but that the negative impact was disproportionately borne by working mothers. 1 In this Chicago Fed Letter, we describe estimates of the impact of the pandemic on prime-age (25 to 54) parents' labor market activity through the fall of 2020, with a particular emphasis on mothers.We show that the labor force participation (LFP) of mothers, i.e., the share of working-age mothers employed or seeking employment, declined by an additional 0.6 percentage points in the spring and 0.3 percentage points in the fall, above and beyond the negative toll that the pandemic had on labor market attachment of prime-age adults without kids.The impact translates to roughly 120,000 and 60,000 fewer prime-age mothers in the labor force in the spring and fall, respectively.This estimate is more than fully driven by a decline in employment.Indeed, we estimate roughly 200,000 fewer prime-age mothers were employed throughout the pandemic.The largest impact has been on Black, single, and non-college-educated mothers, mirroring widening employment disparities in the broader labor market since March. Data and methodsWe use a conventional statistical model to estimate how labor market activity was impacted by the pandemic.The model takes the general form:In words, a person i 's labor market outcome-say, whether they are participating in the labor forcein a given month t (which we denote Y it ) depends on whether that person is being observed in one of three pandemic periods: March to May 2020 (which we refer to as the spring school semester), https://doi.org/10.21033/cfl-2021-450The largest impact has been on Black, single, and non-college-educated mothers, mirroring widening employment disparities in the broader labor market since March.

We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, foot traffic, gasoline sales, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implement a novel benchmarking method that uses a mixed-frequency dynamic factor model to constrain the weekly index to match the monthly MRTS. We use the index to document several interesting features of U.S. retail sales during the Covid-19 pandemic, many of which are not visible in the MRTS. In addition, we show that our index would have more accurately predicted the MRTS in real time during the pandemic when compared to either consensus forecasts available at the time, monthly autoregressive models, or other commonly-cited high-frequency data that aims to track retail spending. The gains are substantial, with roughly 50 to 75 percent reductions in mean absolute forecast errors.

How does school quality affect health amid multiple behavioral responses? The Rosenwald schools transformed school quality for rural southern African Americans in the early 1900s. Research shows that the schools made black migration northward more likely and that the Great Migration shortened life expectancy for these migrants. Besides the hypothesized health-enhancing effects of school quality, negative health effects might also occur through migration. We disentangle behavioral mechanisms and find complete exposure to the Rosenwald schools increased life expectancy by 2–3 months; a more naive approach finds no relationship. Results are robust to heterogeneous treatment effects and various measurement issues.

We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic “unemployment,” we show that out-of-sample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the COVID-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty surrounding these estimates and demonstrate their use within a broader forecasting framework for US economic activity.

Between 1907 and 1914, the ?Galveston Movement,? a philanthropic effort spearheaded by Jacob Schiff, fostered the immigration of approximately 10,000 Russian Jews through the Port of Galveston, Texas. Upon arrival, households were given train tickets to pre-selected locations west of the Mississippi River where a job awaited. Despite the program?s stated purpose to locate new Russian Jewish immigrants to the Western part of the U.S., we find that almost 90 percent of the prime age male participants ultimately moved east of the Mississippi, typically to large Northeastern and Midwestern cities. We use a standard framework for modeling location decisions to show destination assignments made cities more desirable, but this effect was overwhelmed by the attraction of religious and country of origin enclaves. By contrast, there is no economically or statistically significant effect of a place having a larger base of immigrants from other areas of the world and economic conditions appear to be of secondary importance, especially for participants near the bottom of the skill distribution. Our paper also introduces two novel adjustments for matching historical data ? using an objective measure of match quality to fine tune our match scores and a deferred acceptance algorithm to avoid multiple matching.

This article looks at the relationships between internet searches for unemployment-related terms, unemployment insurance (UI), and the public health orders issued in the U.S. during the Covid-19 pandemic.We find that Google searches for unemployment-related subjects surged before the record increase in initial UI claims, which in turn peaked before the public health orders were implemented.As of mid-April 2020, these orders covered the vast majority of the U.S. population.Since then, the rates of increase in both search activity and initial UI claims have slowed.In this Chicago Fed Letter, we explore the relationships between daily Google searches for unemployment-related subjects, weekly initial unemployment insurance claims, and public health orders (stay-at-home, shelter-in-place, and nonessential business closure orders) imposed by U.S. state and local governments in response to the Covid-19 pandemic.A sizable increase in search interest in unemployment and similar terms preceded the historic increase in initial UI claims in late March and early April of 2020 by several days.Growth in initial UI claims in turn peaked several days before state and local public health orders went into effect.By mid-April 2020, these orders had covered close to 95% of the U.S. population. 1 Since then, the rates of increase in both Google search activity for unemployment-related subjects and initial UI claims have slowed. A historic surge in new UI claimsAs the Covid-19 pandemic led to the widespread shutdown of nonessential businesses, over 30 million new unemployment insurance claims (seasonally adjusted) were filed during the six-week period between March 15 and April 25, 2020.By comparison, just over 1.2 million claims were filed in a similar six-week period during February and early March of this year.Previously, the six-week record for seasonally adjusted initial UI claims was 3.9 million

For much of the recent expansion, real wage growth was surprisingly sluggish, by some measures never reaching its pace prior to the 2008 financial crisis, despite tight labor markets that drove the unemployment rate to 3.5%. However, on average, the lowest-earning workers fared substantially better, consistently experiencing real wage growth of 6% or more for much of the late 2010s, a pace well above the previous two decades.

In August 2019 the unemployment rate was roughly 1 percentage point below the Congressional Budget Office?s (CBO) estimate of its long-run or natural rate, nearly matching the unemployment rate gap that developed during the historically tight labor market of the late 1990s. Nevertheless, real wage growth remains well below its pace of the late 1990s and even that of the milder 2000s expansion.

To what extent are low-wage jobs in the United States being replaced by technology? Our research suggests that low-wage jobs that are intensive in routine cognitive tasks, such as cashier, were supplanted by automation during the 2000s. Moreover, since the Great Recession, jobs intensive in both routine manual and routine cognitive tasks have been negatively impacted by automation. Nevertheless, the overall effect on individual low-wage workers has been surprisingly small.