This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

  1. Home
  2. ASSA Annual Meeting
  3. 2022
  4. Preliminary Program
  5. What Makes the Jobs of Tomorrow? The “What” and “Why” of Labor Outcomes from Technological Change
« Back to Results

What Makes the Jobs of Tomorrow? The “What” and “Why” of Labor Outcomes from Technological Change

Paper Session

Sunday, Jan. 9, 2022 3:45 PM - 5:45 PM (EST)

Hosted By: American Economic Association
  • Chair: David Autor, Massachusetts Institute of Technology

How It’s Made: A General Theory of the Labor Implications of Technological Change

Laurence Ales
,
Carnegie Mellon University
Christophe Combemale
,
Carnegie Mellon University
Erica R.H. Fuchs
,
Carnegie Mellon University
Kate S. Whitefoot
,
Carnegie Mellon University

Abstract

We present a novel theory on the relationship between technology and skill demand that is capable of describing the labor impact of various forms of technology change that have been observed in the last 200 years. Performers (human or machine) face stochastic issues that must be solved in a given time to complete tasks. Firms choose how production tasks are divided into steps (sets of tasks), the rate at which tasks need to be completed, and the type of performer assigned to a step. Performers differ in the breadth of issues they can solve (generality) and in their tolerance for working at higher rates (intensity). Human performers tend to be generalists with low intensity: solving complex steps (variety of issues) at low rates. Machine performers tend to be specialists with high intensity. Central to the theory are the trade-off between step complexity and rate, the costs associated with separating tasks into steps, and the costs associated with assigning a performer across discontinuous sets of tasks. With this construction we are able to derive the conditions for the optimal division of tasks and for optimal automation of production, generating demand for workers of different skills. We test our theory across three empirical contexts: production data on optoelectronic semiconductors for communications and automotive body assembly; and the Hand-Machine Labor Study covering mechanization and process improvement at the end of the 19th century. Our theory predicts the following: the division of tasks is skill polarizing; automation is skill polarizing at lower production volumes and skill upgrading at higher volumes; and that consolidation increases the demand for mid-level skills. We find that these predictions hold true in our empirical settings.

Has COVID-19 Accelerated the Digital Transformation of the Labor Market?

Georg Graetz
,
Uppsala University
Terry Gregory
,
IZA-Bonn
Florian Lehmer
,
Institute for Employment Research-Nuremberg

Abstract

Using a longitudinal survey of German firms, we study the impact of the COVID-19 pandemic on technology adoption. Our survey contains detailed questions about firms’ technology choices, including frontier technologies such as machine learning, cloud computing, and robotics. In the latest wave, conducted in spring 2021, we additionally ask to what extent the COVID-19 pandemic prompted or accelerated technology adoption as well as organizational changes, and whether these shifts are expected to be permanent. A unique feature of our longitudinal data is the elicitation of technology adoption plans during the first wave five years ago. This allows us to assess how actual choices compare with plans, and to see whether deviations from plans are systematically associated with exposure to COVID-19, using sectoral and regional exposure indices. Linking our survey to administrative data, we are further able to explore the impact of the pandemic on firm performance, labor demand, as well as individual workers.

Occupational Change: Automation and Reskilling Risks

Sebastian Steffen
,
Massachusetts Institute of Technology
Erik Brynjolfsson
,
Stanford University

Abstract

We study how much occupational skill compositions changed over the last decade and determine the implications for the values of skills. To do so, we create a novel occupation-level panel of detailed skill compositions based on the skill demands of over 200 million US online job postings from the last decade. We derive a theoretical foundation for measuring distances between occupational skill compositions by leveraging the Aitchison geometry. Using compositional data analysis (CoDA) we then show that low- and medium-wage occupations' skill demands changed more than those of high-wage ones. Thus, lower-wage workers face not only higher risks of direct technological displacement but also increased risks of reskilling in order to stay productive. While consistent with RBTC, the vast majority of these changes are only poorly explained by recent measures of direct technological change and are likely due to indirect effects or unintended consequences of technological change. Finally, we identify skills that are likely to be valuable in the future of work and which may serve as important (re)skill opportunities for workers. These include technical skills, such as machine learning, business, software, and data skills as well as social skills and creativity.

Digging into the Digital Divide: Workers’ Exposure to Digitalization and its Consequences for Individual Employment

Sabrina Genz
,
Utrecht University
Claus Schnabel
,
University of Erlangen-Nuremberg and IZA-Bonn

Abstract

While numerous studies have analyzed the aggregate employment effects of digital technologies, this paper focuses on the employment development of individual workers exposed to digitalization. We use a unique linked employer-employee data set for Germany and a direct measure of the first-time introduction of cutting-edge digitalization technologies in establishments between 2011 and 2016. Applying a matching approach, we compare workers in establishments investing in digital technologies with similar employees in establishments that do not make such an investment. We find that the employment stability of incumbent workers is lower in investing than non-investing establishments, but most displaced workers easily find jobs in other firms, and differences in days in unemployment are small. We also document substantial heterogeneities in the employment effects across skill groups, occupational tasks performed, and gender. Employment reactions to digitalization are most pronounced for both low- and high-skilled workers, for workers with non-routine tasks, and for female workers. Our results underline the importance of tackling the impending digital divide among different groups of workers.

Discussant(s)
Pascual Restrepo
,
Boston University
Jeffrey L. Furman
,
Boston University
Daniel Rock
,
University of Pennsylvania
David Deming
,
Harvard University
Daniel Tannenbaum
,
University of Nebraska-Lincoln
JEL Classifications
  • D2 - Production and Organizations
  • E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy