This is what robots mean for the economy

Advances in machine learning and mobile robotics mean that robots could do your job better than you. That’s led to some radical predictions of mass unemployment, much more leisure or a work free future. But labour saving innovations and the debates around them aren’t really anything new. Queen Elizabeth I denied a patent for a knitting machine over fears it would create unemployment, Ricardo thought technology would lower wages and Keynes famously predicted a 15 hour working week by 2030. Understanding why these beliefs proved to be wrong gives us important insights into why similar claims about robotisation might be incorrect. But automation could nevertheless have sizeable distributional implications and ramifications well beyond the industries in which it’s deployed.

Technology can lead to workers being displaced in one particular industry, but this doesn’t hold for the economy as a whole. In Krugman’s celebrated example, imagine there are two goods, sausages and bread rolls, which are then combined one for one to make hot dogs. 120 million workers are divided equally between the two industries: 60 million producing sausages, the other 60 million producing rolls, and both taking two days to produce one unit of output. Now suppose technology doubles productivity in bakeries. Fewer workers are required to make rolls, but this increased productivity will mean that consumers get 33% more hot dogs. Eventually the economy has 40 million workers making rolls, and 80 million making sausages. In the interim, the transition might lead to unemployment, particularly if skills are very specific to the baking industry. But in the long run, a change in relative productivity reallocates rather than destroys employment, even if the distributional impacts of that reallocation can be complicated and significant.

As productivity rises, people could just work fewer hours and enjoy the same level of consumption. But equally, they could work the same hours and devote the productivity boost entirely to raising consumption or, more likely, enjoy a bit more of both. This so-called “income effect” means working hours should fall, but by less than one for one with the rise in productivity.

But that’s not the only thing going on – rising productivity tilts the relative prices of leisure and consumption in favour of the latter – what economists call the “substitution effect”. Gregory Clarke’s fascinating dataset suggests that in 1700 a craftsman needed to work for almost 10 hours to earn the 2 old pence required to purchase a kilo of beef. But by 2014 a median UK worker can earn the ten pounds or so need to buy that kilo of beef in less than hour. And so measured in beef, or goods in general, the reward for working that extra hour is much bigger.

The overall effect on hours depends on the balance of the two. Angus Maddison’s 2001 magnum opus estimates that between 1820 and 1998, real GDP per capita in Western Europe increased 15-fold. Over the same period hours declined by about a half. So the productivity dividend was split about 7:1 in favour of consumption. On that basis, unless automation leads to vast productivity gains, any fall in hours would be modest and slow. It would take a 75% rise in productivity to deliver a 10% fall in hours. Or a 150% rise to knock a day off the working week.

The consensus view amongst economists is that technological change is labour augmenting- i.e. it acts to increase output in the same way as an increase in labour input. If that sounds counterintuitive it is merely the flip side of assuming that innovations are labour-saving. History provides a good testbed for this hypothesis – if technical change is labour augmenting, then technological change should lead to a rise in the wage rate, but leave the interest rate unchanged. The Bank’s own historical datasets suggests long rates have (periods of high inflation excepted) hovered around 4% since the 1500s. And the chart below shows that since 1800, the return to labour – i.e. the real wage rate – has grown by a factor of around 15. Of course these aggregate figures might well mask substantial variation across industry and worker groups. Certain workers may be hit very hard, especially if their human capital is rendered obsolete. Robotisation may not be good news for all workers and may pose important distributional challenges.

Figure 1: Wages and Interest rates over time

So to argue that robotisation will benefit capital at the expense of labour you have to believe there is something intrinsically different about it compared to innovations that went before. One thing that might, and I stress might, be different is the substitutability between labour and capital. Under labour augmenting technological change, if this parameter is less than one, then rising capital to output ratios over time increases the labour share, if it’s equal to one, the labour share is unchanged.

Looking at back data suggesting a constant or rising labour share, for the bulk of the post-industrial era many economists concluded the elasticity was less than or equal to one. So more capital meant its relative price had to fall by more than its quantity increased – hence a lower share of income goes to capital. How might technology change this?

Imagine a taxi and its driver – there is in essence no substitutability between the two. They have to be combined in fixed proportions, and so having a taxi with two drivers, or a driver with two taxis creates no extra output. Earlier technological progress, faster cars, satnav, Uber, didn’t change much on that score. But perhaps robots will make labour and capital much more interchangeable – so the driver can be substituted by a computer, and the passenger rides round in a driverless car. If this pushes substitutability above one, growth in the capital stock over time leads to a higher share of income for capital. Indeed Piketty and Zuchmann argue this applies to a much broader range of technological change than just robotisation and has driven up inequality in the past two decades.

On the plus side, if you are worried about secular stagnation then robots offer you a couple of reasons to be cheerful. First up, if robotisation does constitute a major productivity gain that raises the marginal productivity of capital, then this should push up on long run-equilibrium real rates, and hence ease fears of secular stagnation. Second, whilst economic theory usually assumes that technological growth means capital is just costlessly melted down and made into newer, more productive machines, in practice, some innovations might require scrapping of old capital, and hence a wave of new investment. If you believe in the loanable funds model (and not everyone does) this increased investment demand should push up on equilibrium rates. This is somewhat speculative but it does make the point that it’s hard to believe in a dystopian world of robot induced productivity growth where secular stagnation also occurs. You have to at least pick where you are going to be pessimistic…

But perhaps the most important historical lesson of all for economists is to remember that often the biggest implications of an innovation occur far away from that good’s own industry. Take the humble shipping container. Transporting goods in pre-packed locked containers, which can be lifted straight onto a lorry or train, yielded enormous savings relative to having cargo transported in crates which needed loading and packing individually at each port. Their inventor estimated that the combined savings on labour costs, time at the dockside and insurance for breakage and theft reduced the price of a tonne of cargo 39-fold. Bernhofen et al calculate this led to an eight fold increase in bilateral trade between countries with container ports. Whilst employment fell, productivity of labour increased nearly 20 fold. For the shipping industry this wasn’t a massively disruptive technology- though trade patterns changed, the industry became more concentrated and ironically less profitable.

But by reducing the cost of trading, containerisation opened up the possibility of new supply chains and trading arrangements that were previously too expensive to undertake. And, inso doing, the resultant trade flows led to a substantial spatial reallocation of economic activity. The real macro impact of containerisation didn’t occur at sea or on the dock side. Perhaps the biggest effect of robotisation might occur far away from the industries which adopt the robots, and in ways which today’s macroeconomists could never imagine.

So in the long run, labour-saving technological change means we can make more stuff. And that is a generally a good thing. In the long run it doesn’t create unemployment and might even help avoid secular stagnation. But it might alter how that output is divided up. Working out the theory behind that, and unpicking the effects of particular innovations will probably keep economists in human or robot form occupied for years to come.

This post originally appeared on Bank Underground.