As tech threatens jobs, we must test a universal basic income


Technology | Comment 17 September 2015

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Robotics is still muscling in on manufacturing (Image: Jasper Juinen/Bloomberg via Getty Images)

Not so long ago, the idea that technology would destroy jobs and leave us unemployed was considered a fallacy. Economists believed technology always creates new opportunities, and hence new jobs.

Today, with the rise of machine-learning algorithms and advanced robotics, many of them have changed their view. It’s possible that within 20 years almost half of all jobs will be lost to machines forever, and nobody really knows how we are going to cope with that.



So how would the millions of telemarketers and taxi drivers, for example – whose jobs are at high risk of being automated – survive in this new landscape? One of the most interesting proposals, and one that does not live in the fanciful world of “the market will figure it out”, is the creation of an unconditional basic income (UBI).


No incentive-killer

The biggest UBI experiments, involving a whole town in Canada and 20 villages in India, have confounded a key criticism – that it would kill the incentive to work. Not only did people not stop working, but they were more likely to start new businesses or perform socially beneficial activities compared with controls. In addition, there was an increase in general well-being, and no increase in public bads such as alcohol and drug use, and gambling.

These early results are promising but not conclusive. We don’t know what would happen in other countries, and whether the same results would apply if millions of people were involved. Forthcoming experiments may give us a clearer picture.

The city of Utrecht in the Netherlands has started a new UBI pilot, with a view to extending it to 300,000 residents; Finland has announced it is seriously looking into it; and Willem Buiter, chief economist of banking giant Citi, recently endorsed the idea.

But for now, any country implementing a nationwide UBI would be going out on a limb. There are so many variables involved that if it goes right, it will be difficult to analyse and understand exactly why it worked and how it can be replicated elsewhere. If it goes wrong, it will deter further research and experiments.

It would be better to run experiments on a random sample of 1000 cities of 10,000 people each, than in one country of 10 million. A network of citywide experiments in many countries is the perfect set-up to learn, iterate fast, and be ready for larger-scale projects, right before the wave of automation hits us hard. We need to be ready.

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