Productive Inquiry
Automation and unemployment: Help is on the way
Hideki Nakamura & Joseph Zeira
Journal of Economic Growth, June 2024, Pages 215-250
Abstract:
This paper examines the evolution of unemployment in a task-based model that allows for two types of technical change. One is automation, which turns labor tasks into mechanized ones. The second is addition of new labor tasks, which increases specialization, as in the expanding variety literature. The paper shows that in equilibrium the unemployment caused by automation converges to zero over time.
Artificial Intelligence and the Skill Premium
David Bloom et al.
NBER Working Paper, May 2024
Abstract:
How will the emergence of ChatGPT and other forms of artificial intelligence (AI) affect the skill premium? To address this question, we propose a nested constant elasticity of substitution production function that distinguishes among three types of capital: traditional physical capital (machines, assembly lines), industrial robots, and AI. Following the literature, we assume that industrial robots predominantly substitute for low-skill workers, whereas AI mainly helps to perform the tasks of high-skill workers. We show that AI reduces the skill premium as long as it is more substitutable for high-skill workers than low-skill workers are for high-skill workers.
Employment Protection and Venture Capital Investment: The Impact of Wrongful Discharge Laws
Wei Wang & Chris Yung
Management Science, forthcoming
Abstract:
Wrongful discharge laws (WDLs) provide limits to the employment-at-will doctrine, and thus impair operating flexibility, increasing expected financial distress costs by making it costly to fire employees. This impairment is detrimental to start-ups, leading to a decline in venture capital (VC) investment. Using a difference-in-differences framework enabled by the staggered adoption of WDLs across the U.S. states, we show VC investment declines after a state adopts the good faith exception (the strongest form of WDL). This decline is most pronounced in sectors with high labor dependency.
Fast and Slow Technological Transitions
Rodrigo Adão, Martin Beraja & Nitya Pandalai-Nayar
Journal of Political Economy Macroeconomics, forthcoming
Abstract:
Do economies adjust slowly to certain technological innovations and more rapidly to others? We argue that the adjustment is slower when innovations mainly benefit production activities requiring skills that are more different from those used in the rest of the economy. When such skill specificity is stronger, the adjustment of labor markets is driven less by the fast reallocation of older incumbent workers and more by the gradual entry of younger generations. We first document that the US labor market adjusted differently to early twentieth-century manufacturing innovations than to recent information and communications technologies (ICTs). We then build an overlapping-generations model of technological transitions and characterize how skill specificity affects equilibrium dynamics. Skill specificity helps explain why the ICT transition was slower, driven entirely by the entry of younger generations.
Capital-Skill Complementarity in Manufacturing: Lessons from the US Shale Boom
Victor Hernandez Martinez
Federal Reserve Working Paper, May 2024
Abstract:
This paper tests the existence of capital-skill complementarity in the manufacturing sector using quasi-experimental increases in the relative price of low-skill labor induced by the US shale boom. I find that in response to the shale boom, local manufacturing firms decreased their relative usage of low-skill labor while increasing their capital expenditures. These endogenous changes in the input mix allowed manufacturers to maintain the value added despite the increase in the price of low-skill labor, avoiding the potential short-term crowding-out effects of the natural resource boom. Combined with the findings of previous work, my results indicate that the degree of skill substitutable with capital in manufacturing has increased over the last several decades.
New Gig Work or Changes in Reporting? Understanding Self-Employment Trends in Tax Data
Andrew Garin, Emilie Jackson & Dmitri Koustas
NBER Working Paper, April 2024
Abstract:
Rising self-employment rates in U.S. tax data that are absent in survey data have led to speculation that tax records capture a rise in new “gig” work that surveys miss. Drawing on the universe of IRS tax returns, we show that trends in firm-reported payments to “gig” and other contract workers do not explain the rise in self-employment reported to the IRS; rather, that increase is driven by self-reported earnings of individuals in the EITC phase-in range. We isolate pure reporting responses from real labor supply responses by examining births of workers’ first children around an end-of-year cutoff for credit eligibility that creates exogenous variation in tax rates at the end of the tax year after labor supply decisions are already sunk. We find that exposing workers with sunk labor supply to negative marginal tax rates results in large increases in their propensity to self-report self-employment -- only a small minority of which leads to bunching at kink-points. Consistent with pure strategic reporting behavior, we find no impact on reporting among taxpayers with no incentive to report additional income and no effects on firm-reported payments of any kind. Moreover, we find these reporting responses have grown over time as knowledge of tax incentives has become widespread. Quantification exercises suggest that changes in taxpayer reporting behavior are a major driver of discrepancies between self-employment trends in self-reported and third-party reported data. Our findings suggest caution is warranted before deferring to self-reported tax data over other data sources when measuring labor market trends.
The Labor Market Impacts of Ridesharing on American Cities
Tucker Omberg
Labour Economics, October 2024
Abstract:
This paper examines the impact of the emergence of the “gig economy” on the broader labor market by exploiting the staggered introduction of the ridesharing service Uber to American cities between 2013 and 2018. Using difference-in-differences methods, Callaway and Sant’Anna’s doubly robust difference-in-differences estimator, Chaisemartin and D’Haultoeuille’s time-corrected Wald estimator, and Abadie et al’s synthetic control method, I estimate that Uber’s arrival to a city resulted in decline in the unemployment rate by between a fifth and a half of a percentage point. This suggests that Uber allowed many workers to supplement their earnings during periods of unemployment, framing the ridesharing service as a complement to, rather than a substitute for, traditional employment. I also find some evidence that Uber had a very small positive effect on wages at the lower end of the wage distribution, suggesting that Uber may have altered worker search behavior or affected bargaining power.
The Effect of Minimum Wage Changes on Restaurants and the Service Elasticity of Demand
Samsun Knight & Yakov Bart
Northeastern University Working Paper, June 2024
Abstract:
Using a panel of restaurant revenue and data on 55 local minimum wage increases, we present generalizable evidence of the effect of labor cost increases on restaurant demand. We estimate a distribution of effects with synthetic differences-indifferences and find a median restaurant revenue decline of 2.3% after a minimum wage hike, but with a wide dispersion. Attributing these effects to a mix of price increases and quality declines, we use this evidence to identify a structural model of restaurant demand in Los Angeles and provide novel estimates of the elasticity of demand to service quality and to price, respectively.
Why Does Working from Home Vary Across Countries and People?
Pablo Zarate et al.
NBER Working Paper, April 2024
Abstract:
We use two surveys to assess why work from home (WFH) varies so much across countries and people. A measure of cultural individualism accounts for about one-third of the cross-country variation in WFH rates. Australia, Canada, the UK, and the US score highly on individualism and WFH rates, whereas Asian countries score low on both. Other factors such as cumulative lockdown stringency, population density, industry mix, and GDP per capita also matter, but they account for less of the variation. When looking across individual workers in the United States, we find that industry mix, population density and lockdown severity help account for current WFH rates, as does the partisan leaning of the county in which the worker resides. We conclude that multiple factors influence WFH rates, and technological feasibility is only one of them.
Location-Specificity and Relocation Incentive Programs for Remote Workers
Thomaz Teodorovicz, Prithwiraj (Raj) Choudhury & Evan Starr
Organization Science, forthcoming
Abstract:
The precipitous growth of remote work has given rise to a new phenomenon: the emergence of relocation incentive programs that localities use to compete for the physical presence of remote workers. Remote workers with high general human capital may create value for their new destinations and reverse net talent outflow from smaller cities in middle America and globally. However, localities seeking to attract, retain, and create value from remote workers face significant challenges because such workers may have a low attachment to their new destination. Analogizing these challenges to the problem of creating and capturing value from workers with general human capital, we argue that localities can benefit from using relocation incentive program by leveraging location-specific attributes that create value for the individual and the locality. We examined these ideas in the context of Tulsa Remote, a program that provides relocation incentives and a bundle of services to increase engagement and embeddedness in Tulsa, Oklahoma. We found that Tulsa Remote increased community engagement, real income, and entrepreneurship of remote workers, benefiting both the community and the individual. Tulsa Remote increased the worker’s willingness to stay, and local community engagement is a key driver of this relationship. This work thus suggests that location specificity enables localities to both create and capture value from remote workers.
Wage Insurance for Displaced Workers
Benjamin Hyman, Brian Kovak & Adam Leive
NBER Working Paper, May 2024
Abstract:
Wage insurance provides income support to displaced workers who find reemployment at a lower wage. We analyze wage insurance in the context of the U.S. Trade Adjustment Assistance (TAA) program by merging linked employer-employee Census data to TAA petitions and leveraging a discontinuity in eligibility based on worker age. Wage insurance eligibility increases short-run employment probabilities and leads to higher long-run cumulative earnings. We find shorter non-employment durations largely drive increased long-term earnings among workers eligible for wage insurance. Our results are quantitatively consistent with a standard non-stationary partial equilibrium search model. The program is self-financing even under conservative assumptions.
Does Occupational Licensing Reduce Job Loss During Recessions?
Peter Blair & Bobby Chung
NBER Working Paper, May 2024
Abstract:
Licensed workers could be shielded from unemployment during recession since occupational licensing laws are asymmetric -- making unlicensed workers an illegal substitute for licensed workers but not the reverse. We test our hypothesis using a difference-in-differences event study research design that exploits cross-state variation in licensing laws to compare the unemployment rate between licensed and unlicensed workers before and after the COVID-19 recession and the Great Recession. Controlling for worker ability, we find that licensing shields workers from a recession-induced increase in the unemployment rate of 0.82 p.p. during COVID-19 and 1.11 p.p. during the Great Recession.