Poor productivity and high housing costs are driving a “living standards exodus” from London

Good advice. Image: Getty.

As a Londoner, I think it’s fair to say that as a city we’re quite good at giving ourselves a pat on the back (though apparently self-loathing Londoners are a thing ,too). It’s often suggested that London is an economic powerhouse, productive, innovative and leaving the rest of the country in its wake. But new research by the Resolution Foundation suggests that London could do with a bit of self-examination, too.

London’s economy is different, and in a good way: the average worker in the capital produces a third more per hour than the UK average. As a share of the workforce, twice as many people work in professional, scientific or technical roles than in other major UK cities. London’s economy has grown faster than the UK as a whole since the crisis.

But wait. When it comes to productivity growth – probably the most pressing economic challenge facing the UK – far from racing away, London’s economy is actually holding the country back. Productivity growth in the capital has been negative since the crisis.


How can we reconcile these two stories of economic success and failure? The answer is that London’s economic growth has been entirely driven by increasing employment and hours worked, rather than productivity improvements. This economic shift has had a profound effect on the capital’s living standards over the last decade.

The positive side of London’s new economic growth model is that it has been very good for jobs. Employment is up 5 percentage points since 2011. The capital’s employment rate is at a record high, and closing in on the UK average for the first time since the late 1980s. At a time when other major cities – particularly Birmingham – face low employment challenges London is breaking that mould.

But there’s a flipside to this story of strong employment growth – the quality of new jobs created. The big growth areas in employment across London have been low-paying, low-productivity sectors such as hospitality (up 35 per cent) and administrative services (up 29 per cent). This helps to explain London’s recent productivity problems, and why it’s experienced the biggest pay squeeze of any region of Britain. Depressingly, typical hourly earnings in London are still 7 per cent lower than they were a decade ago.

So, in some senses, London’s economy since the crisis has been a bit like the UK’s on steroids:  lack of productivity growth, a sharp pay squeeze, but lots of jobs.

But just as important as these shared challenges are the relatively unique issues London faces, particularly for those on low-incomes. The most obvious one is housing (though this is now being exported to other cities across Britain).

It will not come as a shock to anyone to learn that housing is expensive in London. But to show just how much housing acts as a drag on living standards, it’s worth noting that it turns Londoners from having the highest incomes in the country (£28,600) to being below average the UK average (£21,350, compared to the UK figure of £22,250).

London’s other unique challenge is inequality. Income inequality is around 25 per cent higher in London, and wealth inequalities are even starker. London is now the only part of the country where the typical family has no net property wealth.

Faced with high housing costs and levels of inequality, the response of many Londoners is to leave. London’s population has exploded since the millennium, driven by international migration – but it’s actually a net exporter of people to the rest of Britain. The number of people leaving London climbed to 90,000 last year, driven by rising numbers of people in their early 30s moving out. The triumph of improving London’s schools over the last 20 years is being outweighed by the disaster that is housing. If London is complacent then this exodus will likely continue.

In the past the city glossed over its problems by pointing to the fact that on most metrics it was performing better than the rest of the country. Although in many respects this is still the case, it is less true than it once was. We shouldn’t stop the heady parties that are synonymous with the capital. But we could do with a bit more sober reflection too.

Stephen Clarke is senior economic analyst at the Resolution Foundation. You can read the full report here.


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.