The Future of Work in Developing Economies

Although every country should look for ways to respond to the effects of automation, it’s especially critical for developing nations, which will be hit hardest and have the fewest resources to cushion the blows.

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Much has been written about the rise of automation in developed countries. Economists have been busily creating models seeking to quantify the likely impact of automation on employment.1 However, far less has been written about the potential effects on work in developing nations. This is surprising, given that automation may be especially troublesome for developing economies.

We know that economic growth brings significant shifts toward higher-skilled occupations and that the economies of many developing nations rely largely on manual labor and routinized manufacturing work. Because some types of manual and routinized work can be easily handled by computers, machinery, and artificial intelligence, it’s clear that large-scale automation could have significant and wide-reaching effects on workers in developing countries.

We wanted to get a more detailed understanding of how automation might affect developing economies compared with those of the developed world. To do this, we examined a database of more than 13,000 workers from 10 countries that contained the workers’ descriptions of the tasks they completed at their jobs and in their households.2 We combined this data with an occupation-level assessment of which jobs would most likely be automated, in order to quantify the risk of displacement.3

Taking a Task-Oriented Approach

Our findings were dramatic and surprising. In the countries we studied, the percentage of jobs susceptible to automation ranged from a low of 11% (Armenia) to a high of 42% (Ghana). For comparison, according to a 2016 study, the average number of jobs at risk of automation among member countries of the Organisation for Economic Co-operation and Development was 9%, with a range of 6% (South Korea) to 12% (Austria).4

We also compared occupational categories to see which ones were the most highly automatable. For example, it’s widely assumed that some jobs (for instance, driving buses and trucks) will eventually be automated, but others (such as teaching in secondary schools and graphic design) are less vulnerable. However, a key insight of our study — and one that has broad implications for both developing and developed economies — is that separating jobs into their component parts provides a much richer way of looking at jobs than estimating the effects of automation by occupation category.

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Frontiers

An MIT SMR initiative exploring how technology is reshaping the practice of management.
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References

1. See, for instance, C.B. Frey and M.A. Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Technological Forecasting and Social Change 114, issue C (January 2017): 254-280; and M. Arntz, T. Gregory, and U. Zierahn, “The Risk of Automation for Jobs in OECD Countries,” working paper 189, OECD Publishing, Paris, June 2016.

2. The countries in our sample were Armenia, Bolivia, China (Yunnan province), Georgia, Ghana, Kenya, Laos, North Macedonia, Sri Lanka, and Vietnam.

3. P. Egaña del Sol, “The Future of Work in Developing Economies: What Can We Learn From the South?” SSRN, Dec. 3, 2019, https://ssrn.com.

4. Arntz, Gregory, and Zierahn, “The Risk of Automation.”

5. In the literature, workers facing an automation risk higher than 70% are labeled as “high risk.” See, for instance, Arntz, Gregory, and Zierahn, “The Risk of Automation”; or L. Nedelkoska and G. Quintini, “Automation, Skills Use and Training,” working paper 202, OECD Publishing, Paris, March 2018.

6. It is important to note that current technologies are also able to automate nonroutine cognitive tasks, and this was incorporated into our analysis. We used data from Frey and Osborne’s “The Future of Employment,” which incorporated the likelihood of automation on a variety of occupations relying on nonroutine cognitive tasks. As these authors argue, occupations involving largely nonroutine analytical or nonroutine interpersonal tasks (such as financial advice, teaching, or diagnostics in the health care sector) also face a high risk of automation.

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