The Post-COVID World, Inequality and Automation
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Bibliographic details
- Authors: DARON ACEMOGLU
- Published: March 1, 2021
Summary of central argument
- Automation—substitution of machines and algorithms for tasks previously performed by labor—has been a major driver of declining shared economic growth, rising inequality, disappearance of good high-paying secure jobs, and declining real wages for less-educated workers in the United States.
- The next phase of automation, driven by machine learning and artificial intelligence (AI), could either exacerbate inequality or, if redirected by government policy, contribute to a resumption of shared growth.
- Current technological change is excessively focused on automation rather than a diverse portfolio of innovations complementary to human labor.
Evidence and diagnostics
- Research with Pascual Restrepo attributes much of the loss of shared growth to automation, alongside globalization and declining labor power (Acemoglu and Restrepo 2019).
- Despite rapid advances in information and communication technology, total factor productivity growth in the US is very low and has been much lower over the past 20 years than in the decades after World War II (Gordon 2017).
- Industries more intensive in information and communication technologies have not performed better in total factor productivity, output, or employment growth (Acemoglu and others 2014).
- Some common automation technologies (self-checkout kiosks, automated customer service) are not generating substantial total factor productivity growth and may have social costs via lower employment and worker wages.
- The pandemic has accelerated employer incentives to substitute machines for workers; recent evidence suggests employers are doing so (Chernoff and Warman 2020).
Mechanisms causing excessive automation
- Private decision-making by employers does not internalize social disruption from job loss, biasing choices toward excessive automation.
- Corporate strategy shift: a handful of very large tech companies with tiny workforces and automation-centered business models influence the broader direction of technology.
- Big Tech responsibility: Amazon, Alibaba, Alphabet, Facebook, and Netflix are responsible for more than $2 of every $3 spent globally on AI (McKinsey Global Institute 2017).
- Declining public support for fundamental research has exacerbated concentration in technology directions (Gruber and Johnson 2019).
- Tax policy asymmetry encourages automation by treating capital more favorably than labor:
- Labor has paid an effective tax rate of more than 25 percent via payroll and federal income taxes over the past 40 years (Acemoglu, Manera, and Restrepo 2020).
- About 20 years ago, equipment and software investment faced tax rates of about 15 percent.
- Today, investments in software and equipment are taxed at rates of less than 5 percent in many cases, and some corporations can even derive net subsidies when investing in capital.
- This differential creates a powerful motive for excessive automation.
Potential positive trajectories for technology
- Human-friendly technological directions that can expand employment opportunities and good jobs include:
- Education: AI for adaptive and student-centered teaching combined with better-trained teachers.
- Health care: AI and digital technologies empowering nurses and technicians to provide more and better services.
- Modern manufacturing: augmented reality and computer vision increasing human productivity on the production line.
- Digital communication technologies (e.g., Zoom) broadening human communication and capabilities.
Policy recommendations
- Governments should not block technology but should provide incentives that tilt innovation toward human-friendly technologies that increase labor demand and shared prosperity.
- Correct tax-code imbalances that favor capital over labor to reduce the incentive for excessive automation.
- Use targeted R&D subsidies to promote technologies that help human productivity and increase labor demand.
- Address market dominance of a handful of big tech companies to diversify technological paths, ensure greater competition, and protect privacy.
- Foster societal recognition of unbalanced technology choices and a clear federal commitment to redress imbalances.
Political and institutional considerations
- Historical precedent: governments have shaped the direction of technology through tax policies, research support, and government procurement; transformative 20th-century technologies relied on government support.
- Renewable energy is cited as an example where modest government subsidies and changing consumer norms redirected technology: renewables make up 19 percent of energy consumption in Europe and 11 percent in the United States, with costs declining toward fossil-fuel energy levels (IRENA 2020).
- Concerns about government direction of technology evoking Hayek’s critique of the administrative state are addressed by arguing that stronger state roles can coexist with deepened democracy and societal mobilization (Acemoglu and Robinson 2019).
- A key challenge is weakened democratic institutions: misinformation, AI-powered social media creating filter bubbles and echo chambers, and waning political engagement may undermine society’s ability to keep the state in check—but inaction is not an option.
Source: DARON ACEMOĞLU, F&D Magazine, March 2021 — "The Post-COVID World, Inequality and Automation" (IMF F&D).
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