Explaining the Decline in the US Employment-to-Population Ratio: A Review of the Evidence
Katharine Abraham & Melissa Kearney
Journal of Economic Literature, September 2020, Pages 585-643
This paper first documents trends in employment rates and then reviews what is known about the various factors that have been proposed to explain the decline in the overall employment-to-population ratio between 1999 and 2018. Population aging has had a large effect on the overall employment rate over this period, but within-age-group declines in employment among young- and prime-age adults also have played a central role. Among the factors with effects that we can quantify based on existing evidence, labor demand factors, in particular increased import competition from China and the penetration of robots into the labor market, are the most important drivers of observed within-group declines in employment. Labor supply factors, most notably increased participation in disability insurance programs, have played a less important but not inconsequential role. Increases in the real value of state minimum wages and in the share of individuals with prison records also have contributed modestly to the decline in the aggregate employment rate. In addition to the factors whose effects we roughly quantify, we identify a set of potentially important factors about which the evidence does not yet allow us to draw clear conclusions. These include the challenges associated with arranging child care, improvements in leisure technology, changing social norms, increased use of opioids, the growth in occupational licensing, and declining labor market fluidity. Our evidence-driven ranking of factors should be useful for guiding future discussions about the sources of decline in the aggregate employment-to-population ratio and consequently the likely efficacy of alternative policy approaches to increasing employment rates.
Public Policy and Participation in Political Interest Groups: An Analysis of Minimum Wages, Labor Unions, and Effective Advocacy
Jeffrey Clemens & Michael Strain
NBER Working Paper, October 2020
Why do individuals join interest groups? Through what channels do interest groups and public policy affect one another? We study these questions by analyzing the interplay among labor unions, minimum wages, news coverage, and public opinion. Over the past decade, labor unions have played a significant role in advocating for state and federal minimum wage increases. Over this period, we find that each dollar in minimum wage increase predicts a 5 percent increase (0.3 pp) in the union membership rate among individuals age 16-40. We document four additional facts that shed light on the mechanisms that may underlie this finding. First, while we find increases overall in union membership, we find declines among the minimum wage's most direct beneficiaries. This is consistent with a classic "free-riding" hypothesis. Second, we find increases in union membership among much broader groups that are not directly affected by the minimum wage. Third, we find that minimum wage increases predict increases in unions' favorability ratings among the public. Fourth, we find that events in the legislative histories of minimum wage increases predict increases in counts of newspaper articles that simultaneously discuss the minimum wage and key players in the labor movement. Overall coverage of organized labor shifts towards articles that discuss the minimum wage. These facts are consistent with models in which a desire to affiliate with "effective advocacy" is an important driver of the decision to participate in unions and other politically oriented groups.
The Surprising Impacts of Unionization: Evidence from Matched Employer-Employee Data
Journal of Labor Economics, forthcoming
This study presents new evidence on the impacts of unionization using administrative data matching workers to employers in a regression discontinuity design. Close union elections exhibit substantial nonrandom selection or manipulation. Estimates accounting for this selection show that unionization substantially decreases payroll, employment, average worker earnings, and establishment survival. Payrolland earnings decreases are driven by composition changes, with older and higher-paid workers leaving unionizing establishments and younger workers joining or staying. Worker-level effects on earnings are small, and are reconciled with large negative establishment-level effects in a model of employer and employee selection into union jobs.
City Limits: What do Local-Area Minimum Wages Do?
Arindrajit Dube & Attila Lindner
NBER Working Paper, October 2020
Cities are increasingly setting their own minimum wages, and this trend has accelerated sharply in recent years. While in 2010 there were only three cities with their own minimum wages exceeding the state or federal standard, by 2020 there were 42. This new phenomenon begs the question: is it desirable to have city-level variation in minimum wage polices? We discuss the main trade-offs emerging from local variation in minimum wage polices and evaluate their empirical relevance. First, we document what type of cities raise minimum wages and we discuss how these characteristics can potentially impact the effectiveness of city-level minimum wage policies. Second, we summarize the evolving evidence on city-level minimum wage changes and provide some new evidence of our own. Early evidence suggests that the impact of the policy on wages and employment to date has been broadly similar to the evidence on state and federal-level minimum wage changes. Overall, city-level minimum wages seem to be able to tailor the policy to local economic environment without imposing substantial distortions in allocation of labor and businesses across locations.
Metropolitan Reclassification and the Urbanization of Rural America
Kenneth Johnson & Daniel Lichter
Demography, October 2020, Pages 1929-1950
We highlight the paradoxical implications of decadal reclassification of U.S. counties (and America's population) from nonmetropolitan to metropolitan status between 1960 and 2017. Using data from the U.S. Census Bureau, we show that the reclassification of U.S. counties has been a significant engine of metropolitan growth and nonmetropolitan decline. Over the study period, 753 - or nearly 25% of all nonmetropolitan counties - were redefined by the Office of Management and Budget (OMB) as metropolitan, shifting nearly 70 million residents from nonmetropolitan to metropolitan America by 2017. All the growth since 1970 in the metropolitan share of the U.S. population came from reclassification rather than endogenous growth in existing metropolitan areas. Reclassification of nonmetropolitan counties also had implications for drawing appropriate inferences about rural poverty, population aging, education, and economic growth. The paradox is that these many nonmetropolitan "winners" - those experiencing population and economic growth - have, over successive decades, left behind many nonmetropolitan counties with limited prospects for growth. Our study provides cautionary lessons regarding the commonplace narrative of widespread rural decline and economic malaise but also highlights the interdependent demographic fates of metropolitan and nonmetropolitan counties.
Getting a Job, Again: New Evidence against Subjective Well-Being Scarring
Social Forces, forthcoming
Previous research finds that unemployment leaves permanent "scars" on subjective well-being (SWB) that remain even after reemployment. However, this research systematically overweighs long-term unemployment, inaccurately measures employment transitions, often does not track individuals long enough to substantiate scarring, and does not always account for age-related changes in well-being. This paper uses event history calendars from the Panel Study of Income Dynamics to track complete monthly employment histories of prime working age Americans over a 17-year period, and accounts for the temporal relationships between SWB, age, and employment transitions using a novel fixed-effects formulation. The results suggest that there is some variation in patterns of recovery by employment stability after job loss, but no significant differences were observed by the duration of unemployment spells. Within 2 years of reemployment, average SWB levels reverted toward baseline trajectories across all groups, showing no evidence of scarring. This study brings unemployment literature into better alignment with research on resilience and adaptation. The findings also highlight some limitations of the construct of SWB for assessing the long-term costs of unemployment.
Does a Guaranteed Basic Income Encourage Entrepreneurship? Evidence from Alaska
Robert Feinberg & Daniel Kuehn
Review of Industrial Organization, November 2020, Pages 607-626
While the concept has been around for years, recently the policy notion of a "guaranteed basic income" (GBI) - or universal basic income - has had a resurgence of interest. In addition to rationales that relate to fairness and response to structural employment shifts due to automation and globalization, another motivation that is sometimes put forward for these plans is to encourage risk-taking by providing a safety net: There would be greater entrepreneurial activity if an unsuccessful entrepreneur had the GBI to fall back on. In this paper we investigate a rare long-standing example similar to a GBI in the US: the Alaska Permanent Fund Dividend program. This was not put forth as a GBI, and the annual amount is too small to allow an individual to rely fully on these funds; but for a moderate-to-large family the APF can replace a large share of a poverty-level income. Receipt of the APF also does not preclude a family from receiving other safety net benefits - food stamps, unemployment compensation - which suggests that the downside risk for a potential entrepreneur may be lower than in other US states. We initially examine trends in small-firm births in Alaska over time from the Census Bureau's Business Dynamics Statistics 1977-2014 - before and after the institution of the APF program (the first payment was in 1982) - relative to other US states to investigate a possible impact on entrepreneurship; the results suggest a positive effect - which appears to dissipate over time. We then turn to micro data from the Current Population Survey to examine changes in self-employment behavior in Alaska, with somewhat similar findings.
Industrial Robots, Workers' Safety, and Health
Rania Gihleb et al.
University of Pittsburgh Working Paper, September 2020
This study explores the relationship between the adoption of industrial robots and workplace injuries using data from the United States (US) and Germany. Our empirical analyses, based on establishment-level data for the US, suggest that a one standard deviation increase in robot exposure reduces work-related injuries by approximately 16%. These results are driven by manufacturing firms (-28%), while we detect no impact on sectors that were less exposed to industrial robots.We also show that the US counties that are more exposed to robot penetration experience a significant increase in drug- or alcohol-related deaths and mental health problems, consistent with the extant evidence of negative effects on labor market outcomes in the US. Employing individual longitudinal data from Germany, we exploit within-individual changes in robot exposure and document similar effects on job physical intensity (-4%) and disability (-5%), but no evidence of significant effects on mental health and work and life satisfaction, consistent with the lack of significant impacts of robot penetration on labor market outcomes in Germany.
Behavioral and Mental Health outcomes from an RCT of a Youth Entrepreneurship Intervention among Native American Adolescents
Lauren Tingey et al.
Children and Youth Services Review, forthcoming
Methods: This randomized controlled trial included N=394 Native Americans ages 13-16. Participants were randomly assigned 2:1 (n=267:127) to the Arrowhead Business Group intervention versus a control condition. Logistic mixed effects regression models examined within group and between group differences in trajectory from baseline to 24 months follow-up.
Results: Fewer intervention vs. control participants used marijuana at 6-, 12- and 24-months post-intervention (19.6% vs. 28.0%, p=0.032; 20.4% vs. 31.8%, p=0.01; and 24.1% vs. 31.4%, p=0.047). All violence-related measures (suicide attempts, carrying a weapon, missing school because felt unsafe, fighting, and fighting at school) statistically significantly declined between baseline and 24 months for both groups. Positive between group differences favoring intervention participants were observed at 6-months for missing school because felt unsafe, and at 24-months for fighting at school. While alcohol use increased for both groups over time, control participants experienced a two-fold higher increase in binge alcohol use than intervention participants (control: 7.1% to 16.7% vs. intervention: 8.1% to 13.0%).
Demand-Aware Career Path Recommendations: A Reinforcement Learning Approach
Marios Kokkodis & Panagiotis Ipeirotis
Management Science, forthcoming
A skill's value depends on dynamic market conditions. To remain marketable, contractors need to keep reskilling themselves continuously. But choosing new skills to learn is an inherently hard task: Contractors have very little information about current and future market conditions, which often results in poor learning choices. Recommendation frameworks could reduce uncertainty in learning choices. However, conventional approaches would likely be inefficient; they would model previous (often poor) observed contractor learning behaviors to provide future career path recommendations while ignoring current market trends. This work proposes a framework that combines reinforcement learning, Bayesian inference, and gradient boosting to provide recommendations on how contractors should behave when choosing new skills to learn. Compared with standard recommender systems, this framework does not learn from previous (often poor) behaviors to make future recommendations. Instead, it relies on a Markov decision process to operate on a graph of feasible actions and dynamically recommend profitable career paths. The framework uses market information to identify current trends and project future wages. Based on this information, it recommends feasible, relevant actions that a contractor can take to learn new, in-demand skills. Evaluation of the framework on 1.73 million job applications from an online labor market shows that its implementation could increase (1) the marketplace's revenue by up to 6%, (2) contractors' wages by 22%, and (3) the diversity of new skill acquisitions by 47%. A comparison with alternative recommender systems highlights the limitations of approaches that make recommendations based on previously observed learning behaviors.