## What Pandemics Mean for Robots and Inequality

_IMF Blog, April 19, 2021_

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**Canonical URL:** [What Pandemics Mean for Robots and Inequality](https://www.imf.org/en/blogs/articles/2021/04/19/what-pandemics-mean-for-robots-and-inequality)

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## Bibliographic details
- Authors: Tahsin Saadi Sedik, Jiae Yoo
- Published: April 19, 2021

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### Research overview
- Study focus: effect of past major pandemics on industrial robot adoption.
- Pandemics analyzed: SARS in 2003, H1N1 in 2009, MERS in 2012, and Ebola in 2014.
- Data and methods:
  - Robot data from the International Federation of Robotics at the sectoral level.
  - Coverage: 18 industries in 40 countries.
  - Time period: between 2000 and 2018.
  - Outcome measure for robot adoption: new robot installations per 1000 employees.
- Additional country-level analysis linking robot adoption to inequality measured by the Gini coefficient.

### Key empirical findings
- Pandemic events are followed by increases in robot adoption (measured by new robot installations per 1000 employees).
- The increase in robot adoption is especially pronounced when:
  - the health impact of the pandemic is severe; and
  - the pandemic is associated with a significant economic downturn.
- Where new robot adoption has increased more following a pandemic, the medium-term rise in inequality (measured by the Gini coefficient) is larger.
- Conclusion: acceleration of robotization is an important channel through which pandemics lead to higher inequality.

### Mechanisms identified
- Firms restructure after large shocks (such as recessions) and adjust production toward technologies that lower labor costs.
- Firms prefer robots because they are immune from health risks during pandemics.
- Pandemic-induced uncertainty increases incentives for automation as firms try to withstand future pandemics.

### Distributional effects
- Low-skilled workers are more at risk of displacement by robots than high-skilled workers.
- The differential displacement reinforces existing inequality dynamics.
- While automation and robotization are accelerating from still-low levels, they will likely become even more important drivers of inequality in the future.
- Potential social consequences if disparities grow: long-lasting grievances and ultimately social unrest, producing a vicious cycle.

### Policy implications and recommendations
- Policymakers should act to prevent scarring effects on the livelihoods of the most vulnerable, including through appropriate labor market policies.
- Mitigation measures include:
  - revamping education to meet demand for more flexible skill sets;
  - lifelong learning and new training—especially targeted to the most affected workers (example cited: Singapore’s SkillsFuture initiative, which promotes learning in all stages of life).
- Acknowledge limitations of training policies:
  - Training that requires acquiring a substantively different and challenging set of skills may lead to dropouts.
  - Therefore, policymakers should consider additional measures to address medium-term social challenges, including strengthened social safety nets.
- Distributional outcomes of robotization depend on policy choices: a society willing to provide support to those left behind can accommodate a faster pace of innovation while ensuring broader wellbeing.

### Engagement
- Reader action invited: click here for a 3-question survey on IMFBlog.

*Source: IMF blog post “What Pandemics Mean for Robots and Inequality” by Tahsin Saadi Sedik and Jiae Yoo, April 19, 2021.*

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## References

- [increasing inequality](https://blogs.imf.org/2020/07/07/teleworking-is-not-working-for-the-poor-the-young-and-the-women/)
- [pandemics lead to higher inequality](https://blogs.imf.org/2020/05/11/how-pandemics-leave-the-poor-even-farther-behind/)
- [social unrest](https://blogs.imf.org/2020/12/11/when-inequality-is-high-pandemics-can-fuel-social-unrest/)

_Source: https://www.imf.org/en/blogs/articles/2021/04/19/what-pandemics-mean-for-robots-and-inequality_