So does increasing productivity really destroy jobs?

An automated car factory. Image: Getty.

There’s been a lot of debate about productivity in the last couple of mnonths, off the back of our new report on the ‘long tail’. In and amongst this discussion, one thorny question that has been raised is whether improving productivity is bad for inclusive growth. In particular, do improvements in productivity come at the cost of jobs?

The concern about these issues is understandable – in some sectors, improvements in productivity have come through the introduction of machines which destroy jobs. Manufacturing is a case in point. There are many fewer people employed in manufacturing today than in the past – 4m, which is 60 per cent fewer than 1978. What is less well known, however, is that the amount that UK manufacturing produces has actually gone up in that period by 17 per cent, as a result of improvements to productivity.

There are two reasons why productivity improvements don’t mean job losses across the economy. The first is that, while the sectors that have been responsible for productivity growth in recent years have not been directly responsible for jobs growth, they have spurred employment in other sectors. As the chart below shows, ‘exporting’ sectors (those that sell to a regional, national or international market), for example in the manufacturing and finance sectors, have seen large productivity growth but a fall in employment.

Those sectors that have been responsible for employment growth, on the other hand, tend to be local services: accommodation and food services, and arts, recreation and entertainment. These firms, as our briefing shows, have seen little or no productivity growth in recent decades. The one clear exception to this is information and communications, which has managed to provide both productivity and employment growth.

But the performance of these sectors is linked. While exporting businesses don’t create jobs directly, research suggests that the wage-increases they create through productivity growth also increases demand for local services, which in turn boosts employment in these sectors. This is known as the multiplier effect.

Growth in productivity and jobs, 1990-2017. Source: ONS.

The second reason is that these fears are based on what the economy looks like today, rather than what it will be tomorrow. Different sectors grow and decline through time; and it’s the rise of new sectors that historically have more than replaced jobs lost in those declining ones.

Looking at 100 years of economic development in UK cities, as we did in Cities Outlook 2018, shows this. There have been huge changes in the economy over that period, including large increases in productivity and an unabated rise of automation. Despite this, there are 60 per cent more jobs in urban Britain today than there were in 1911. And few would argue that we aren’t better off than our great-grandparents.


Increases in productivity may well mean that jobs decline in some sectors. It’s easy to envisage that retail will employ fewer people in 10 years’ time than it does today. But these productivity improvements also create new opportunities, new types of economic activity and new jobs. And they ultimately lead to improvements in standards of living – again, compare life in 1911 to today.

While this is good for the economy overall, it obviously isn’t good for individuals who lose their jobs as a result of these changes. The challenge, then, for policy makers, is not only to address the UK’s poor recent productivity performance – it is to ensure that people who miss out can benefit from the new jobs that this growth would create.

Paul Swinney is head of policy & research at the Centre for Cities, on whose blog this article first appeared.

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Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.