Der Arbeitsmarkt war seit 1990 so stabil wie 100+ Jahre nicht mehr. Seit Covid hat sich das aber drastisch geändert

Drei führende Ökonomen haben ein neues NBER-Arbeitspapier veröffentlicht, das es in sich hat:

We find that—contrary to popular imagination—the pace of labor market disruption has slowed in recent decades.

The changes in the structure of US employment at the end of the nineteenth century were greater than in any decade of the digital era, including the most recent one.

Even more disruptive was the period between 1940 and 1970, when agricultural employment was still disappearing, manual labor was shifting into production and away from railroads, and clerical and administrative work were growing rapidly.

The years spanning 1990 to 2017 were the most stable period in the history of the US labor market, going back nearly 150 years.

At the dawn of the twentieth century, 40 percent of US employment was in agriculture, compared to less than 2 percent today.

Nearly half of all workers held blue-collar production and manual-labor jobs in 1960, compared to only 20 percent today.

While the occupation structure of the US labor market has changed since 1980, that change has been relatively modest in historical perspective.

Das stellt auf den Kopf, was viele von uns denken: Die Arbeitswelt ändert sich so schnell wie nie zuvor. Das Gegenteil ist der Fall: Im historischen Vergleich änderte sie sich seit ich auf der Welt bin, also seit 1990, ungewöhnlich langsam.

Das heißt übrigens nicht, dass es nicht trotzdem eine große Herausforderung für die Politik, Unternehmen und viele Arbeitnehmer:innen ist. Denn die wirtschaftliche Dynamik ist seit der Industriellen Revolution groß.

Es heißt schlicht, dass die Dynamik am Jobmarkt früher größer war als 1990-2019.

Diese Grafik zeigt den “Labor market churn” an, also die Veränderung der relativen Beschäftigungsanteile verschiedenster Berufe.

Eine Ergänzung von ChatGPT:

• Besonders hohe Churn-Werte wurden für die 1880–1900-Periode (Industrialisierung) und für die 1940–1970-Periode (Übergang von Agrar- zu Industriegesellschaft) festgestellt.

• Überraschenderweise war der Arbeitsmarkt zwischen 1990 und 2017 stabiler als in jeder vorherigen Epoche.

• Nach 2020 nimmt der Churn jedoch wieder zu, was möglicherweise auf technologische Umbrüche (insbesondere KI) zurückzuführen ist.

Hier die Studie zum letzten Punkt:

Although the 2010s were very stable pre-pandemic, the post-pandemic labor market has changed dramatically.

A key outstanding question is whether the labor market disruption of the past few years is a temporary response to the changes wrought by COVID-19 or an early sign of AI-fueled labor market disruption.

Weiter:

According to ACS data, labor market churn between 2010 and 2022 (still only two years after the COVID-19 pandemic) was greater than that during any period since the 1970s.

Using more recent data from the CPS shows that churn was slightly greater when measured over the period between 2010 and 2024.

This finding is notable since churn from 2010 to 2019 was lower than it had been during almost every decade since the 1880s.

This fact suggests that the post-COVID labor market has been especially volatile by historical standards, in part due to the four trends we identified earlier in the paper.

A key unresolved question is whether the post-COVID changes to the labor market are here to stay.

Was sind die vier Trends?

First, the labor market is no longer polarizing—employment in low- and middle-paid occupations has declined, while highly paid employment has grown.

Second, employment growth has stalled in low-paid service jobs.

Third, the share of employment in STEM jobs has increased by more than 50 percent since 2010, fueled by growth in software and computer-related occupations.

Fourth, retail sales employment has declined by 25 percent in the last decade, likely because of technological improvements in online retail.

Warum wandelt sich der Arbeitsmarkt überhaupt?

In the past, labor market disruption was fueled by breakthrough general-purpose technologies (GPTs) like steam power and electricity, which enabled the mechanization of agriculture.

Mechanization destroyed farming jobs, but it also created factory jobs by increasing labor productivity in manufacturing.

Similarly, computer-based manufacturing techniques developed in the 1970s replaced precision production jobs, while also increasing the availability of digital data and the value of analytical and managerial skills.

Ein wichtiger Punkt und warum Ökonom:innen weniger Angst vor Veränderungen haben als Nicht-Ökonom:innen: Jobs verschwinden, neue entstehen. Unter dem Strich schaffen neue Technologien eher mehr neue Jobs als sie alte vernichten.

Although technological breakthroughs often happen suddenly, technology adoption and the pace of labor market change are often gradual.

The share of employment in US agriculture fell steadily by 20 percent per decade between 1880 and 1970, and the decline in blue-collar work after 1970 was equally deliberate.

Wir haben also auch bei KI Zeit, uns vorzubereiten. Auch wenn relativ wenig passiert. Die meisten Medien verschlafen das Thema, die meisten Politiker:innen ebenso.

Is AI a GPT? And if so, will it create any long-run labor market disruption on the same scale as the technologies of the past?

Broadly speaking, AI—and machine learning (ML) in particular—is a technology that improves our ability to analyze and interpret data.

In this sense, it exists downstream of the information technology revolution that began in the 1970s and 1980s.

Yet the novelty of AI/ML is the way in which data are used. Rather than following a set of explicit instructions (for example, software code) that are scripted in advance, AI/ML algorithms "learn" about the world by studying and copying actions and implicit rules that are inferred from patterns in the data (Autor 2015; Brynjolfsson, Rock, and Syverson 2019).

AI can predict legal liability from contract language, the likelihood that a medical image indicates a specific pathology, or the next word or phrase in a standard office document, among many other possibilities.

In this sense, AI is best understood as a prediction technology (Agrawal, Gans, and Goldfarb 2019).

Ich habe die Arbeit von Agrawal, Gans und Goldfarb hier ausführlich besprochen.

Since most jobs require some prediction and decision-making, AI will augment or automate aspects of nearly all jobs in the US economy (see, for example, Deming 2021; Eloundou et al. 2023).

Thus, the impact of AI is likely to be widespread and long-lasting, fitting the mold of past GPTs.

However, history teaches us that even if AI disrupts the labor market, its impact will unfold over many decades.

For these reasons, it is still too early to forecast the impact of AI on the labor market.

TECHNOLOGICAL DISRUPTION IN THE LABOR MARKET
David J. Deming, Christopher Ong, Lawrence H. Summers

Die Arbeit ist noch nicht peer-reviewed.