Automation and Labor Market Institutions

March 8, 2022
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Automation and Labor Market Institutions

Automation and Labor Market Institutions

The optional reading will be for extra credit. But! To earn extra credit, it will require more than just summarizing what you read. It is required that you apply what you have learned so far in the class to understand how automation will affect labor markets across the globe. I will be looking for more than just “Oh this author says this…” or “It is interesting what the author says…”. Now you are asked to be more critical. Based on what you have read in the past weeks and what we have discussed in lecture, how do see the author’s assessment in the context of global inequality and globalized labor. Simply put, to earn the full extra credit it will require you to be more critical about the impact of automated labor on our global society.

The effect of automation and artificial intelligence (AI) on future employment is a subject of substantial interest. There are some disagreements about future net employment effects of these technologies, but most researchers broadly agree that nearly all occupations will eventually be affected. Less is known, however, about the interaction between automation and key labor market institutions—policies and organizations that shape the operation of labor markets—and how these institutions might possibly evolve in the face of continued adoption of the newest technology. For example, is the current structure of unemployment insurance programs sufficient for a world with increased job-to-job transitions? What role will private-sector unions play? Or, considering current conversations among presidential candidates, is a universal basic income (UBI) program the best approach to deal with the wage and employment consequences of technological progress? Understanding how such features interact with technological change will have important implications for shaping labor market policies.

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marcus casey
Marcus Casey
Nonresident Fellow – Economic Studies
@MarcDCase
At a recent public event hosted by the Future of Middle Class Initiative at Brookings, experts presented new papers on the role these institutions currently play and will continue to play in modern labor markets. The papers spanned a broad set of important topics including the role of unemployment insurance and transfer policies in mitigating harm associated with technologically-induced job disruption, balancing redistributive policy with economic growth goals, how union membership influences the careers progression among people in occupations particularly susceptible to automation, and how minimum wage expansions interact with automation to determine net employment. We summarize these papers below, focusing on their central findings and what they might tell us about current and future directions for labor market policy.

Automation and employment policy
In “Automation: A Guide for Policymakers,” Bessen, Goos, Salomons, and van den Berge (2019) study the impacts of automation on individual workers. First, Bessen et al. argue that beliefs that automation in an industry portends mass unemployment events are largely unsupported by the empirical evidence. The evidence instead points to heterogenous effects across industry sectors—employment in the manufacturing sector typically suffers but employment in service-related industries tends to improve.

They draw on complementary empirical work using unique data on firms from the Netherlands. They present a key stylized fact that guides their empirical work: firm investments in these technologies typically resemble “spikes” wherein the company experiences periods of high automation investment purchase but little expenditure in this area otherwise. Their strategy compares net employment and other worker outcomes for firms that automated earlier in their observation period to outcomes of similar workers in firms that automated at a later point. Figure 2 from their paper illustrates the complementary nature of automation with firm growth. Automating firms were growing and dynamic; by contrast, firms that did not automate were largely stagnant.

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