Autor-Levy-Murnane Hypothesis

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An Autor-Levy-Murnane Hypothesis is a labor market hypothesis that jobs are made up of multiple discrete tasks and that the impact of technology on employment is best understood by analyzing these tasks' characteristics, particularly their routine or non-routine nature.

  • Context:
    • It can (typically) be used to analyze the dynamics of job evolution in the face of technological advancements.
    • It can (often) highlight the distinction between routine and non-routine tasks within jobs, emphasizing how automation and technology tend to replace routine tasks.
    • It can reveal the changing Labor Market Educational Demands, particularly the increased preference for college-educated workers over non-college-educated workers since the 1980s.
    • It can include insights into how Middle-Pay Jobs are increasingly affected by automation, leading to changes in the labor market structure.
    • It can provide a framework for understanding the shift from skill-based to task-based analysis of labor market changes.
    • It can underscore the need for policy and educational system adjustments to meet the changing demands of the labor market in the age of automation.
    • ...
  • Example(s):
    • ...
  • Counter-Example(s):
    • Skill-Biased Technological Change, which focuses more on the level of skill required for a job rather than the nature of tasks within it.
    • Sectoral Shift Hypothesis, which attributes changes in employment patterns primarily to shifts between sectors (e.g., from manufacturing to services) rather than changes within job tasks.
  • See: Technological Underemployment, Skill-Biased Technological Change, Non-Routine Task.

References

2024

  • GPT-4
    • It posits that understanding the diverse range of discrete tasks within each job is crucial for analyzing the impact of technology on employment.
    • It highlights how computer technology has shifted job skill demands, substituting for routine cognitive and manual tasks while complementing non-routine problem-solving and interactive tasks.
    • It reveals that the shift in task composition due to technological change impacts educational demands in the labor market, favoring college-educated labor over non-college-educated labor since the 1980s.
    • It includes updates suggesting that technology continues to replace routine tasks, particularly affecting middle-pay jobs, thus maintaining the relevance of the hypothesis in current labor market dynamics.
    • It underscores the importance of considering the vulnerability of various tasks to automation, rather than just classifying jobs as skilled or unskilled, in determining how technology impacts employment opportunities.
    • It provides a nuanced understanding of the labor market by emphasizing the role of task composition and technological change in shaping job skills and employment patterns.

2020

  • (Susskind, 2020) ⇒ Daniel Susskind. (2020). “A World Without Work: Technology, Automation and how We Should Respond.” Penguin UK.
    • BOOK OVERVIEW: ... More recently, things have changed. Since the 1990s, both skilled and unskilled workers have benefitted from automation (in terms of employment opportunities). Meanwhile, middle-skill occupations—and with them, the social base of advanced capitalist economies—have quite rapidly shrunk. The ‘ALM’ (Autor–Levy–Murnane) hypothesis explained this puzzle by pointing out that jobs are, in fact, made up of multiple discrete tasks. Many middle class professions bundle routine tasks with complex intellectual and emotional labour. Susskind provides an overview of new digital technologies to demonstrate the vulnerability of many of these tasks to automation—from driving lorries to conducting legal reviews and making medical diagnoses. But the ALM hypothesis, argues Susskind, whilst an advance on the skill/unskill-bias dualism, reproduces its optimism bias—by assuming that middle-skill job displacement will boost incomes in general by expanding productivity and consequently lead to new employment growth areas. ...
    • QUOTE: ... The ALM hypothesis built upon two realizations. The first of these was simple: looking at the labor market in terms of “jobs,” as we often do, is misleading. When we talk about the future of work, we tend to think in terms of journalists and doctors, teachers and nurses, farmers and accountants; and we ask whether, one day, people who have one of these jobs might wake up and find a machine in their place. But thinking like this is unhelpful because it encourages us to imagine that a given job is a uniform, indivisible blob of activity: lawyers do “lawyering,” doctors “doctoring,” and so on. If you look closely at any particular job, though, it is obvious that people perform a wide variety of different tasks during their workday. To think clearly about technology and work, therefore, we have to start from the bottom up, focusing on the particular tasks that people do, rather than looking from the top down, looking only at the far more general job titles.

      The second realization was subtler. With time, it became clear that the level of education required by human beings to perform a given task—how “skilled” those people were—was not always a helpful indication of whether a machine would find that same task easy or difficult. Instead, what appeared to matter was whether the task itself was what the economists called “routine.” By “routine,” they did not mean that the task was necessarily boring or dull. Rather, a task was regarded as “routine” if human beings found it straightforward to explain how they performed it—if it relied on what is known as “explicit” knowledge, knowledge which is easy to articulate, rather than “tacit” knowledge, which is not.23 Autor and his colleagues believed that these “routine” tasks must be easier to automate. ...

2003

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