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Our tech advances are difficult for productivity stats to compute

Our tech advances are difficult for productivity stats to compute

One of the most depressing aspects of the decade of the 2010s, well before Covid-19 struck, was the apparently very slow growth in productivity.

This is not a mere ivory tower issue.  It is only through increasing productivity that rises in living standards can be sustained. Productivity is the key measure of the efficiency of the economy.

On an everyday level, the previous decade seems to have witnessed a surge in innovative ways of doing things. Companies like Amazon and Netflix make life easier, more enjoyable. Computing power has made dramatic advances. In the past few years, there have been major new developments in the science of artificial intelligence.

But hardly any of this seems to be reflected in the official statistics. Between 2010 and 2019, these show that productivity in America grew by only 0.6 per cent a year. In the UK, growth was even lower, at just 0.3 per cent a year.

An important paper by Stanford’s Erik Brynjolfsson, in the latest issue of the American Economic Journal, goes a long way to resolving this paradox.

The analysis is based on the concept of what is known in economics as a general purpose technology (GPT).

GPTs are technologies which have a large and pervasive impact on both the economy and society. The steam engine was the first, during the first Industrial Revolution in the late 18th and the first half of the 19th century. Electricity was another, around a hundred years later.

Such technologies are far more efficient than the competitors which they replace. In 1830, for example, the crack London to Edinburgh stagecoach took 39 hours. By the middle of the century, it had been driven out of business by the railways.

They revolutionise many aspects of life. Steam power enabled factories to be built. These in turn led to huge shifts in population from rural to urban areas.

Computers have had a major impact since around 1980. But as economics Nobel Laureate Bob Solow remarked, “one can see the computer age everywhere but in the productivity statistics”.

Brynjolfsson and colleagues argue that GPTs need a lot of complementary investment in order to realise their full impact.

Computers, for example, require firms to develop new business processes, develop the experience of management, retrain workers and the like.

The key point is that many of these investments are intangible and do not appear on the balance sheet. They are particularly difficult for national accounts statisticians to deal with when they estimate the size of an economy.

The authors estimate that productivity levels in the US were 15.7 per cent higher in 2017 than the official numbers suggest. This means that the size of the American economy has been potentially underestimated by some 3 trillion dollars.

A similar exercise has yet to be done for the UK. But we can reasonably expect it will boost the numbers by between £200 and 300 billion.

So we are, in another piece of good news for the New Year, much better off as a nation than the Office for National Statistics says we are.

As published in City AM Wednesday 13th January 2021
Image: Numbers via Pixabay
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Lockdown 2.0: A creative destruction revolution, or the death knell of innovation?

Lockdown 2.0: A creative destruction revolution, or the death knell of innovation?

So Boris Johnson has failed to follow his own government’s guidelines on cost-benefit appraisal.

Study after study by economists show that the costs of lockdown far exceed the benefits.

The NHS — the “envy of the world” — has conspicuously failed to develop sufficient capacity to deal with a second wave, despite having had months in which to prepare.

The Prime Minister’s cronies have failed to deliver on his claims of various “world-beating” devices. We are reminded of Glendower’s boast in Henry IV Part 1: “I can call the spirits from the vasty deep”, and Hotspur’s withering reply: “Why, so can I, or so can any man. But will they come, when you do?”

From this litany of failure, one certainty emerges. The economic recession will now be even longer and deeper than it need otherwise have been.

Perhaps there is a silver lining. In the 1930s, the Harvard economist Joseph Schumpeter coined the memorable phrase “gales of creative destruction”.

He developed an original insight of Karl Marx, who argued that under capitalism firms are under constant pressure to innovate. Failure to innovate increases the chances of going under.

Schumpeter emphasised the “cleansing effect” of recessions. In an economic downturn, trading conditions are hard. The less efficient companies are at greater risk of closing down. This creates an opportunity for new, more dynamic firms to enter the market.

The very deep recession generated by the policy response to Covid-19 might therefore simply sweep out the dead wood. The performance of the economy will be even stronger when the upturn comes.

There is, however, an alternative view to this positive outlook, also developed by a major American economist in the 1930s, which is far less well known outside of economics.

Irving Fisher of Yale set out his theory of debt-deflation while the Great Depression was raging in the west and unemployment rates rose above 20 per cent in many countries.

In the light of the financial crisis of the late 2000s, his perspective is strikingly modern. Fisher regarded major recessions as being created by the balance sheets of companies, and in particular by debt.

In his view, innovation was not stimulated by economic downturns, as Schumpeter thought. On the contrary, it was when the economy was growing rapidly that balance sheets were strong enough to finance innovation, especially of the trail-blazing kind whose outcome is of necessity very uncertain.

In the current circumstances, a lot depends upon who was correct, Schumpeter or Fisher. Is the Covid recession stimulating or reducing innovation?

A timely paper on this has been published in the latest issue of the American Economic Association’s economic policy journal.

Jorge Guzman and Scott Stern, of Columbia and MIT respectively, make excellent use of big data to estimate the quality and the quantity of entrepreneurship and innovation over a 25-year period in the US.

The analysis is complex, and the authors make very proper qualifications to their results. But on balance, their results support Fisher’s view.

Innovation flourishes when the economy is doing well. Yet another reason to exit from lockdown as quickly as possible.

As published in City AM Wednesday 4th November 2020
Image: Pxhere
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The national productivity recovery depends on getting people back to the office

The national productivity recovery depends on getting people back to the office

Office workers continue to display reluctance to return to their workplaces, despite encouragement from the government for them to head back.

The immediate consequences for the service jobs in cities which depend on people commuting into the office are apparent, hence the government drive. But is office work a good thing for the workers themselves?

An important concept in economics is that of revealed preference. Economists believe that preferences are revealed not in surveys, but by the decisions which people actually make.

So could it be that the Covid crisis has given office workers the chance to reveal their true preferences in terms of work-life balance?

This is almost certainly true for the senior staff who make the decisions. They will typically live in large houses in agreeable surroundings, with space to dedicate a room as an office.

It is much less obviously the case for many younger staff. Working with your laptop on your knees in a bedsit, unable to socialise with colleagues, may lead you to prefer the commute instead. But there are constraints on being able to reveal this preference, such as potentially annoying your boss who enjoys working from home.

Furloughed workers, meanwhile, have merely revealed a preference to be paid a large part of their regular pay and not work at all. This is particularly true of public sector workers, who receive their full salary regardless. They have no incentive to change their work patterns.

An obvious incentive for companies is that, if working from home persists, they can save on office costs. They may even be able to adjust salaries downwards, especially for staff who commuted long distance from cheaper locations.

In the very short term, there will be little, if any, loss of productivity to offset this. Most office jobs consist of performing routine, well understood tasks. Within the discipline of an established framework, some people may even be able to do their jobs more efficiently at home, encountering fewer distractions.

This is true even for jobs which require analytical skills. The business model of a number of large consultancies, for example, can be thought of as follows.

The company hires bright young graduates, who come equipped with a stock of the latest ideas — what economists call human capital.  The short-term pressures in the big consultancies to make money are so intense that they have little chance to refresh this during their careers. Essentially, they run down their human capital. By the time they make partner or director in their forties, they are operating on half-remembered ideas from graduate courses.

This is why the company requires a steady flow of new recruits, to refresh the business. The challenge with remote working over time will of course be to not only integrate new young staff into the organisation, but to ensure that their ideas percolate.

Productivity will grow more slowly over time if extensive homeworking persists. In part, this will be due to factors internal to the firm. The tacit knowledge and creativity sparked by informal exchanges will be lost.

The real loss, though, is through factors external to any individual company. A huge amount of evidence shows that the higher the density of employment in an area, the higher its productivity.

The drive to get people back into their offices, therefore, is about far more than saving city cafes and restaurants. The government should incentivise firms to get staff back to work, in order to avoid lost productivity — for the entire economy.

As published in City AM Wednesday 2nd September 2020
Image: London Underground by via Pikist
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Great expectations: The Darwinian wars of economic and epidemiological forecasting

Great expectations: The Darwinian wars of economic and epidemiological forecasting

A key concept in modern economics is, to use the jargon term, rational expectations.

The idea has dominated orthodox macroeconomics over the past 30 years. Not all economists have been persuaded of its merits by any means, but nevertheless, its influence has extended far beyond academia, into finance ministries and central banks around the world.

The basic idea is simple, even though the maths of the macroeconomic models which use rational expectations can rapidly become hair-raising.

When forming a view about the future, an individual chooses the model which best describes how the economy works. It is then simply a matter of running the model and using the values which it outputs as your expectations.

An obvious criticism seems to be that economic forecasts are very often wrong, but this is easily handled by the rational expectations enthusiasts. Each individual forecast in a sequence of months or years can be wrong. The key thing is that the errors over time cancel out. On average over time, the forecasts are correct.

The idea is not as absurd as it may appear to the layperson. For example, the Federal Reserve Bank of Philadelphia has published the Survey of Professional Forecasters since 1968.

This does what it describes on the label. It takes a wide range of forecasts by academic, commercial, financial and governmental bodies. Information on the average forecast for, say, GDP growth one year ahead is published, as well as details of the spread around the average.

Remarkably — and exactly in line with rational expectations — comparing the predictions with the actual growth over many years, the errors do indeed balance out. Spectacular errors have been made for individual years, but the over and under-predictions cancel out over time.

A more telling attack is that economists themselves do not seem to agree on what constitutes the correct model of the macroeconomy. Different groups have different models.

The standard defence is that the best model will eventually prove its superiority and will drive the others out of existence. But the problem here is that this has just not happened.

The concept of rational expectations can be applied directly to the predictions of the epidemiological models. These purport to describe how a virus spreads. So to form a view about the future, make a set of assumptions about the key inputs, and use the forecasts generated by the model.

It should be much easier in epidemiology for the best model to eliminate its competitors than it is in economics. Economics has a wide range of variables to predict, such as inflation, GDP, unemployment, public borrowing, interest rates. The focus of epidemic predictions is much narrower and their models are in general mathematically simpler than those of economics.

The Covid pandemic set up a competition between epidemiological modelling groups of fierce Darwinian intensity. The efforts of many years of academic debate have been concentrated into a handful of months.

But no group seems to have admitted yet that its model is not up to scratch — and huge differences in forecasts persist.

As published in City AM Wednesday 29th July 2020
Image: Opposites via Pxfuel
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Office clusters are as crucial to productivity as they ever were

Office clusters are as crucial to productivity as they ever were

The Prime Minister is now demanding that offices reopen to revive economic activity in the centres of towns and cities.

But there is not much sign of a return to work.

The preferences of the workforce are an important factor in the very slow pace of return. Fears expressed about the safety of public transport may or may not be genuine, but it is certainly true that many prefer to avoid the time spent commuting and enjoy the extra leisure time this brings.

But why do offices cluster together in urban centres anyway?

It is easy to see that in the old days industries such as steel and coal clustered geographically. One was a key supplier of the other. Being near at hand minimised transport costs.

Today’s offices span a wide range of diverse industries, from consulting to law to oil companies. The reasons why they locate in close proximity are more subtle.

The views of economists on this are still shaped by the writings of Alfred Marshall. He established the faculty of economics at Cambridge in 1903 and was then probably the world’s leading economist.

Marshall described the tendency of businesses to cluster near each other as “agglomeration”. He gave three key reasons why this colocation is observed.

In addition to the savings on the costs of transporting the materials needed in industrial processes, Marshall developed a theory of labour market pooling, in which firms located near one another can share labour.

Further, he believed that “intellectual spillovers” were important. Firms locate near each other in order to learn and speed up the process of innovation. Think of Silicon Valley, formed nearly a century after Marshall wrote.

A large number of detailed studies in recent decades confirm that these are not just mere theories. They have strong empirical support. The Harvard economist Ed Glaeser, for example calculated that in the US in the 2000s each of Marshall’s three reasons were of roughly equal importance.

There have been very distinct benefits to agglomeration. Throughout the developed world, the greater the density of employment in an area, the higher is its productivity. Head offices contain more highly skilled staff and so will be more productive than the average. But in city centres, their productivity is even higher than their skill levels suggest they should be.

Has Covid-19 changed all this? Or more specifically, has the crisis enabled people to see that new technology could overturn two centuries of experience in urban centres in industrialised countries?

Certainly, tech platforms such as LinkedIn offer the potential for efficient hiring of relevant skills and for employees to discover opportunities through their networks. But new recruits need to be integrated. And younger people probably still need a combination of social and remote interaction to develop their own professional networks.

It is less clear that remote working can encourage innovation in the same way. Much of the informal contacts needed for this cannot be captured by video conferences.

Yes, there will be an increase in working from home. But Marshall’s insights into the benefits of agglomeration still hold true.

As published in City AM Wednesday 15th July 2020
Image: The City of London via Wikimedia CC BY-SA
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Innovation is the only way to recover from the Covid crisis

Innovation is the only way to recover from the Covid crisis

One silver lining of the Covid-19 crisis has been a surge in innovation.

Enterprising firms have invented both new products and different ways of delivering existing ones.

Innovation is the life blood of any prosperous economy. Innovation is much more than a scientific invention. It turns inventions into things of practical and affordable use to people.

The ability to deliver innovation in a sustained way is the one single quality which distinguishes capitalism from all other forms of social and economic organisation.

Yet our understanding of it remains imperfect.

MIT Nobel laureate Robert Solow laid the foundations for the modern theory of economic growth over 60 years ago. His neat mathematical model postulated that growth was caused by increases in the amount and quality of both capital and labour used in the productive process, and by innovation.

But when the theoretical model was applied to real world data, it created a problem for economics. The increases in the inputs which could be readily measured — capital and labour — could only explain a small fraction of the growth which had taken place.

By implication, most of the huge growth experienced in the west was due to innovation. But innovation itself was not explained in the Solow model.

Despite various attempts to do better, including an ingenious one which won the Nobel Prize for another MIT economist Paul Romer, economists are still far short of a convincing explanation of innovation.

Matt Ridley, the author and scientific polymath, has made a valuable contribution in his recently published book How Innovation Works.

Ridley describes how major innovations arose in a wide range of sectors, such as energy, public health, food, transport and computing.

From this mass of detailed, empirical description, he synthesises some general principles, the vital ingredients for success.

A classic image of innovation is Archimedes jumping out of his bath shouting “Eureka!” But Ridley makes clear that such moments are exceptionally rare, even if the story is true.  This is for two reasons.

First, innovation is almost always a gradual process. It involves re-combinations of existing ideas and methods of production rather than single revolutionary events.

Second, innovation is, as Ridley puts it, a team sport. The myth of the isolated genius is deeply ingrained, but it is a myth. Innovation requires collaborations and building on what went before. Even Isaac Newton, one of the greatest minds in world history, acknowledged that he “stood on the shoulders of giants”.

Innovation also requires an acceptance of failure.  When Edison perfected the electric light bulb, he had tried 6,000 different materials for the filament before discovering what really worked.

The book ranges far wider than just the science. For example, Ridley argues that the EU has evolved into a system in which innovation simply cannot flourish. Of Europe’s 100 most valuable companies, not a single one was formed in the past 40 years. What a massive contrast with America.

Innovation is key to a successful recovery from the Covid crisis — and Ridley’s book offers excellent insights on how to make it happen.

As published in City AM Wednesday 1st July 2020
Image: Silver Cloud by lfranks via Pixabay
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