Which technologies drive concern over the future of work?
Biotech. Nanotech. Cleantech. Gene tech. Cloud computing tech. Successive waves of technological change are the norm for those of us in the developed world. These waves typically rise, peak and ebb while barely raising a concern about wider labour market effects.
Further, it seems strange to be worrying about unemployment – technologically induced or otherwise – when, according to The Economist, across the rich world, an extraordinary jobs boom is under way. In New Zealand, workforce participation is at a historic high while unemployment is also very low (in its second-lowest dip in nearly 40 years).
So, what is this “tech” that has everyone worried? And how does it differ from the regular, garden variety tech? Most of the noise is around robots, autonomous vehicles, bots and artificial intelligence (AI). Broadly speaking, these are automation technologies, that is, those that potentially replace human workers with machines. That’s nothing new in itself – it was the source of the Luddite’s complaints in 1811–16.
Robots, autonomous vehicles, bots and AI have been around in one form or another for many decades:
- Unimate, the first industrial robot, was installed in 1959.
- The London Underground’s Victoria line has had automatic trains from 1968.
- ELIZA, the first chatbot, was developed in 1966.
- AI research dates from 1956.
What is different is that the software that controls their behaviour has recently got much, much better at making decisions. Starting from around 2009, Artificial neural networks (an “old” AI technique dating back to 1965) became a practical technique for pattern recognition and image classification. And there have been ongoing significant improvements since then.
These improvements are due to four factors:
- better algorithms that allow systems to “learn” from large datasets, and then apply this training to making decisions about data not previously encountered;
- better and cheaper hardware (including special-purpose hardware for training and classification);
- larger, more reliable and cheaper datasets to use for training; and
- the commodification of the preceding three factors as on-demand services, allowing low-cost experimentation and widespread application.
Exploding interest in this combination of software, hardware and datasets started around 2012. Furman and Seamans (2018) document a huge jump in venture capital funding for AI since 2014, a significant climb in global industrial robot shipments starting in 2013, and a spike in AI-related US patent applications starting around 2014. The widespread commodification of AI tech is even more recent.
Systems incorporating AI (or more specifically, an AI technology generally called “deep learning”) can substitute for human decision makers. Put it into a car or plane, and you have an autonomous vehicle. A bot, living in a data centre, might substitute for humans in a call centre.
Why has this spawned a “future of work” industry?
For three reasons, I think.
First, this time around, automation appears to threaten service-sector employment. Automation has hit the agricultural and goods-producing sectors of the economy over the past century or so. The share of jobs in the agriculture has plummeted in New Zealand since 1891; and goods-sector jobs have had a less dramatic but significant decline since they peaked (as a share of employment) in 1975. Jobs moved to the service sector – but now at least some of those seem threatened.
Source: NZIER’s Data1850 project
Second, service-sector employment includes many well-paid professional jobs. Professionals make a big investment in their careers (eg, through years of tertiary study) and are naturally fearful of anything that might devalue their skills. Such professionals are generally comfortable with using technology but not with the idea they may be replaced by it. Professionals are very influential in society and expect to have their opinions heard and respected.
Third, fears of job insecurity due to technology have been with us for at least two centuries. Joel Mokyr et al. (2015) point out that “from generation to generation, literature has often portrayed technology as alien, incomprehensible, increasingly powerful and threatening, and possibly uncontrollable”. They document concerns about machines replacing human workers dating back to 1772. More recent concerns hit peaks in the 1920s and 1970s. Jobs seem central for both income and identity, and it doesn’t seem to take too much to fan the flames of the fearful.
Can automation tech keep improving at this rate?
Is the post-2012 acceleration in automation tech sustainable? I’ll punt up my view on this in my next post.
Further reading
- AI and the Economy, Jason Furman, Robert Seamans. in Innovation Policy and the Economy, Volume 19, Lerner and Stern. 2019.
- Employment by industry sector breakdown for the US, 1850-2015.
- Mokyr, Joel, Chris Vickers, and Nicolas L. Ziebarth. 2015. "The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?" Journal of Economic Perspectives, 29 (3): 31-50.
- Some (deeply technical) breakthrough papers on deep learning: Hinton (2007) Learning multiple layers of representation; Raina, Madhavan & Ng (2009) Large-scale Deep Unsupervised Learning using Graphics Processors; and Krizhevsky, Sutskever & Hinton (2012) ImageNet Classification with Deep Convolutional Neural Networks.
Image (top): Life’s changed from 1980, when I was using an Apple IIe. Source: Mystère Martin, Wikimedia Commons
Have your say
Comments
Robert 25 Jun 2019, 21:50 (4 years ago)
Hi Dave. I think that there are four policy issues here. (apologies for the length)
The first is a more general policy issue - that in a stage of rapid change & high uncertainty the objective is not to predict the outcome, but to create time/space for more effective options to become clearer. What we don’t know is whether or how much disruption will be caused by new technologies, nor the speed at which the latter may occur.
I've seen this “time creation” point well expressed in a rebuttal of the "trolley problem" with respect to autonomous vehicles - it shouldn't be a split second choice about whether it is "better" to make a decision to kill one person vs more, but to identify small actions that may give you time to find subsequent maneuvers that avoid hitting anyone. That example doesn't translate well into the policy issue the Commission is facing, but highlights the point of not rushing too quickly to a ultimate solution in a dynamic situation. However, that's very hard in the real world when policy makers need to be seen to be doing something.
The key policy aim associated with the future of work should be to reduce income insecurity for the populace. To my uneconomic eye Colin & Palier (https://www.foreignaffairs.com/articles/2015-06-16/next-safety-net) make sense by suggesting the focus should be on providing “flexible security” – “If the government can guarantee citizens access to health care, housing, education and training, and the like on a universal basis without regard to their employment status, the argument runs, people won’t be so terrified of switching jobs or losing a job.” The downsides are that such an approach can suck up a lot of money when the economy is doing poorly, and isn’t much help if there aren’t new jobs to move into.
Others (as I’m sure you are aware) suggest focusing on a more cohesive package of initiatives, involving supporting on-the job training schemes that better align with emerging skills & jobs, improved access to capital & other financing for small businesses and entrepreneurs to encourage them to establish and grow, wage insurance to help with retraining, and a financial tax or tax reform to help fund these.
The third policy area is ensuring that economic inequalities are reduced. Will existing, or older, policy approaches do the job (if better applied), or will new measures (eg, “robot taxes” – heaven forbid) be required?
Lastly, policies that encourage regional distribution of employment opportunities may be necessary.
Robert 24 Jun 2019, 16:58 (4 years ago)
Setting aside the often stupid speculation of imaginary future job titles (eg, Robot psychologist) it is usually easier for most people to imagine what is lost rather than what is gained. As agriculture became mechanised people migrated to the towns for the expanding manufacturing and service job markets. As manufacturing became more automated, people moved to service industries. Now, it's harder to see what new types of work (if any) will emerge. Is this time different (in terms of new job creation)? No one knows, so into that uncertainty marches angst (and a market for speculation).
Editor 25 Jun 2019, 09:13 (4 years ago)
Thanks Robert. Psychologists (not the robot sort) call the effect you describe "loss aversion bias", that is, people are overly influenced by what they expect to lose relative to what they expect to gain. We can add that to the fact that current job titles are concrete and future ones unknowable. For example, the term "social media marketer" didn't exist 20 years ago, so who could have imagined that it would come to describe a real job? So angst is to be expected. My question for you: how do we best get sensible policy amidst the angst and market for speculation? Dave
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