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Thailand has among the highest risks of automation to its older workers globally



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Although it’s a difficult issue to predict, the threat that automation poses to older workers must be quantified. Is it just older workers that are at risk? What job categories are most vulnerable? And what geographical areas are ripe for this workforce disruption?

To examine the risk automation poses to older workers around the world, Mercer measured the extent to which older workers are employed in low- and medium- skilled work across a sample of countries, in its latest whitepaper, The Aging and Automation Resilience Index.

It combined this measure with the percentage of automatable tasks done in an occupation (across more than 700 occupations), to end up with weighted average data to show the average risk of automation to older workers in 15 major economies.

Among the 15 economies, the following are the 10 most at-risk economies when it comes to automation of older workers (score* in bracket):

1. China (76)
2. Vietnam (69) | Thailand (69)
3. South Korea (63) | Chile (63)
4. Japan (59)
5. Italy (58)
6. Germany (57)
7. Singapore (54)
8. USA (52)

*The average risk of automation to older workers score depicts, on average, the percentage of tasks done by older workers that can be automated in a nation based on the types of occupations those nation’s older workers are employed in.

The results show that the average risk of automation to older workers generally sits in the mid- to high ranges of risk (scores of 30% and above). This indicates that older workers in these nations are doing work where 30% or more of all tasks are automatable on average.

Older Thai workers, for example, are doing jobs that are on average 69% automatable.

ALSO READ: 49% of Malaysians are not optimistic about ageing: How can HR help?

Additionally, across the majority of the 15 sampled countries, the report found older workers were at disproportionately higher risk of automation compared with younger workers – particularly so in Asian markets such as Singapore, South Korea, and China.

Per the insights provided, the key driving forces behind these dynamics are: education levels, size of manufacturing industries, welfare system strength, and financial system strength. As such:

  • Countries with higher education levels and smaller manufacturing sectors have fewer older workers at risk of automation.
  • Countries with stronger welfare systems will also have fewer older workers at risk of automation – the stronger the welfare and pension systems in a country, the less likely an older worker will be to remain in low-skill work.
  • The stronger the financial system strength, the more likely it is for an older worker to work independently or start their own business because they can be ensured that they’ll be paid by owners and repaid by creditors.

Image / Mercer

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