Productivity growth: unravelling the slowdown and forecasting a pick-up

All investment strategies have the potential for profit and loss, capital is at risk. Past performance is not a guide to future returns.

Productivity is a critical component of the Long-Term Returns Expectations (LTRE) exercise we carry out twice a year on behalf of our multi-asset portfolios. For many asset classes, the expectation of productivity gains creates anticipation for stronger returns. ‘Productivity’ defines how much output we get from any given input. 

The output is effectively how much the economy grows. Inputs can be labour, capital or any other resource. 

Over the past two decades, global productivity has been very low, confounding expectations that technology and digitalisation would cause it to boom. 

We collaborated with the Institute for New Economic Thinking at the Oxford Martin School (INET Oxford) to understand why this has happened and to inform our forecasts.

How big a productivity slowdown have we seen?

We often forget that a ‘slowdown’ is a relative measure. In the 1990s, we saw exceptionally high productivity growth. So one could argue that the slowdown in the recent decades is just a normalisation.

But for many countries, productivity growth has been lower than at any time in the 20th century.

Declining rates of labour productivity gains

Source: Bergeaud, A., Cette, G. & Lecat, R. (2016), ‘Productivity trends in advanced countries between 1890 and 2012’, Review of Income and Wealth 62(3), 420–444.

Explaining the slowdown

Productivity, as discussed in this paper, relates to the US unless otherwise specified and can be broken down as follows:

The data indicates that reduced ‘capital deepening’ has been a significant factor. The term describes increased investment in technology and other fixed assets – eg robots, tools and other machinery – per worker.

As we'll discuss later, growth slowed because of the Global Financial Crisis and the secular (moving in a single direction over several years) decline of investment. These accounted for 44 per cent of the slowdown since 2006.

Reduced ‘allocative efficiency’ – which happens when the distribution of an economy’s resources is misaligned with producers’ needs – explained another 24 per cent.

Mismeasurement is also a factor. One example is the failure to properly account for massive improvements to software and communications technologies, which aren’t captured in spending data. Even so, that only accounted for 13 per cent of the reported productivity slowdown.

Some argue that the slowdown resulted from industries with superior efficiency-saving technologies shrinking in size relative to those that rely on trained workers. This is known as Baumol’s Cost Disease.

For example, manufacturing relies heavily on machines and technology, so it tends to make rapid productivity gains. By contrast, the performing arts and healthcare are more people-dependent and find it more challenging to improve productivity.

So if the arts and healthcare grow quicker than manufacturing, the argument goes that they will pull overall productivity down.

However, INET Oxford's analysis shows manufacturing is, in fact, a strong contributor to the slowdown, indicating other factors have been at work.

Capital deepening

After major financial crises, Oulton et al (2016) found that ‘scarring’ occurred in the ‘capital deepening’ side of investment because of financial constraints and reduced spending on domestic goods and services. INET Oxford estimates the impact accounts for 22 per cent of the total productivity slowdown.

Notably, much of the investment slowdown started in 2000, ie before the GFC. INET Oxford suggests these secular trends are equally as important as scarring from the financial crisis.

Secular trends reducing capital investment include:

• Increased short-termism – companies undertaking greater buybacks at the expense of investment (Lazonick et al, 2014)
• More common ownership – caused by the rise of passive index funds (Gutiérrez & Philippon, 2017)
• Globalisation – companies making the bulk of their profits overseas often invest less.
• Competition – as market power concentrates, companies gaining market share may be incentivised to restrict output to raise prices and increase profits.
• The growing importance of intangible capital – this is relatively difficult to finance and accumulate. For instance, it’s hard for one firm to sell its intangible assets – eg branding, research and training – to another (Haskel & Westlake, 2018). Moreover, because intangible capital is often uniquely linked to the firm that created it – eg Google’s Search facility – the sizeable fixed cost involved deters potential rivals from investing in the same field.

Business dynamism

A common way to measure business dynamism is to calculate the rate businesses enter and exit the market. In recent years, it has been in decline.

There’s a growing concern that this development, alongside increases in profits, market concentration and productivity dispersion, are all symptoms of an underlying reduction in competition and allocative efficiency.

One area of focus has been the lack of creative destruction. Business entry and exit rates are falling across the US and OECD members.

Business and job dynamism in the US and 18 OECD countries

Source: Goldin, I., Koutroumpis, P., Lafond, F. & Winkler, J. (2022). 'Why is productivity slowing down?', forthcoming in the Journal of Economic Literature, available as INET Oxford Working Paper No. 2022-08.

Copyright American Economic Association; reproduced with permission of the Journal of Economic Literature.

Underlying data: US Census Bureau Business Dynamics Statistics and OECD’s DynEmp3 database.

McGowan et al (2018) defined ‘zombie firms’ as businesses that can’t generate enough operating income to pay their interest expense. The study observed such companies had become more prevalent and increased their share of capital.

Schwartz (2021) raised concerns about the world’s largest companies increasing their market share and profits. He argued that the industrial economy was moving to a tripartite structure composed of:

1. High profit volume firms (those with high profits relating to their volume of sales) with monopolies based on intellectual property rights

2. Physical capital-intensive firms protected by an investment barrier to entry

3. Low profit volume labour-intensive firms

Profit data indicates these latter firms invest a lower proportion of their profits than other firms, because they fear creating excess capacity in a generally slow-growth environment.

Combined, these factors account for 24 per cent of the productivity slowdown.

Trade and globalisation

Historically, trade has been a critical stimulant to productivity.

That can result from:

• Direct effects – the least productive firms go out of business because of the added competition, while the most productive benefit from selling to an export market.
  
  
• Human capital – ‘knowledge spillovers’ occur when companies learn from each other via alliances or competition. These can arise when foreign-owned firms enter domestic markets. There can also be knowledge transfers when overseas companies take over local businesses to acquire skilled workers and technological expertise.
  
  
• Technology – protection of intellectual property rights encourages companies to base more of their research and development abroad and can spur innovation, particularly in hi-tech industries.
  
  

INET Oxford's analysis shows considerable variation across countries. But it highlights that falling trade has been a relatively important factor in Germany and the US, with a midpoint impact of 9 per cent of the total fall.

Labour market changes

There are several channels through which labour markets may have contributed to the slowdown. They include direct channels, such as how an ageing workforce could reduce productivity, or indirect channels, including changes to savings levels and consumption preferences.

But the critical point is that in aggregate, INET Oxford believes the net effect is close to zero. However, it thinks several patterns affecting labour markets might show up in TFP instead.

The Institute’s study examines ‘labour composition’ – a metric that assumes changes in the salaries paid to different groups of workers reflect whether the broader workforce has become more productive or not.

Source: Goldin, I., Koutroumpis, P., Lafond, F. & Winkler, J. (2022). 'Why is productivity slowing down?', forthcoming in the Journal of Economic Literature, available as INET Oxford Working Paper No. 2022-08.

Copyright American Economic Association; reproduced with permission of the Journal of Economic Literature.

The financial crisis of 2008 accelerated a shift towards high-skilled workers. So a slowdown in educational attainment does not explain the global productivity slowdown. The next decade might be more negative, given Covid’s impact on education.

The reduction of friction in the labour market – the mismatch between the availability of workers and what employers are looking for – has continued, but the pace might have slowed.

Hsieh et al (2019) show how friction has declined over time. That made it possible for more talented men from minority groups and women more generally to enter professions.

In their estimates, this increasingly better allocation of human resources was responsible for 20 to 40 per cent of the increase in labour productivity between 1960 and 2010. More recently, it is conceivable that a slowdown in reducing discrimination might have contributed to the productivity slowdown.

Lisenkova (2018) sees plenty of academic studies at a macro level suggesting that an ageing population materially reduces productivity. In the EU, an increase in the population of workers aged 55 to 641 has lowered TFP growth by about 0.1 per cent each year from 1984 to 2007. That’s likely to be an even more significant problem in the future.

Demographic changes can also induce shifts in how the workforce is divided up between different sectors of the economy.

Siliverstovs et al (2011) found that as the broader population ages, agriculture, manufacturing, construction, and mining/quarrying all account for a smaller slice of the pie.

At the same time, community, social, and personal services grow significantly as society ages. So too, does the financial sector, where it has long been recognised that it is more challenging to make significant productivity gains.

INET Oxford argues that ageing has not accelerated evenly in all countries. Moreover, because there wasn’t a marked change in ageing before the productivity slowdown, it can’t be the cause.

Baillie Gifford's Multi Asset Team would err on the side of labour markets being a more significant performance drag historically and from now on.

1The median US innovators' age has been stable around 48 for the last few decades, while the median age of managers adopting new ideas is lower at 40.

Technological progress

This is where the bull case for productivity comes into its own. But it is tough to forecast how much individual innovations should boost productivity.

We have historical information from prior revolutionary innovations, known as ‘General Purpose Technologies’ (GPTs).

These have had pervasive, longstanding impacts on a wide range of industries. In our opinion – and using the qualification test of Jovanovic & Rousseau (2005) – there is growing evidence that we are indeed seeing evidence of a new GPT: computer-controlled automation.

Advances in robotics, artificial intelligence and big data are feeding into each other, and we think they have the potential to increase productivity significantly.

So far, we have not seen a surge in capital investment, which JPMorgan observes typically happens alongside investment booms. That would be an important milestone for a broader productivity surge from these new GPTs.

Some wild cards

There are three areas that INET Oxford doesn’t discuss that could weigh on productivity:

1. An expectation that government debt will rise even higher

Reinhart and Rogoff (2010) highlighted how adverse growth effects occurred when the debt-to-GDP ratio was over 60 per cent. Although the paper required a review because of some methodological errors, a re-run of its data largely resulted in the same conclusion but with a lower impact.

A European Central Bank study in 2010 found similar results: a non-linear impact of debt on growth, with a turning point when the debt-to-GDP ratio was about 90 to 100 per cent.

So potentially, the TFP drop can be partly explained by the policy uncertainty commonly associated with high debt. That leads to poor investment choices and less efficient investment ideas, which reduce skills transfer between sectors etc.

2. The effects of social media

Social media existed before the mass adoption of smartphones from about 2008 onwards, but its use has accelerated.

More broadly, the widespread use of messaging services that share video and audio content, and people’s dependence on handsets to manage their lives call into debate how much of their working time is being spent on non-work related activities, reducing productivity.

Duke & Montag (2017) highlighted the ‘interruption potential’ of smartphones to the ‘state of flow’, in which workers become more productive by being fully absorbed in their tasks. It suggested a “moderate relationship” between smartphone addiction and decreased productivity in owners’ work and daily lives.

Marketing company Reboot Online’s survey the same year indicated that almost two days a month of productive work time was ‘lost’ on social media. That would be an incredible 4.3 per cent loss in output if we categorised all that time as unproductive, a big number.

This is a nascent area of research, but the initial ‘direct’ implications seem very negative.

3. The surge in the number of people working from home (WFH)

Barrero et al (2021) believe WFH could result in a one-off boost to productivity of 5 per cent based on their data of employer plans and relative productivity data. They noted that most of this results from time saved by employees not having to commute to work. They added that conventional productivity measures don’t pick this up, and only recognise a 1 per cent boost.

Productivity expectations for the next decade

The central expectation of the Multi Asset Team and INET Oxford is that the next decade will see productivity growth improving, but only by a small amount. We forecast a 1 to 1.3 per cent gain, largely confirming our prior predictions. The biggest contributor will be from general purpose technologies.

Investments in technologies including robotics, health innovation and driverless cars will play a role. And there will also be a ‘lagged’ boost from existing technologies becoming more widely adopted.

We also view the pandemic as an accelerant to business dynamism. So as monetary and fiscal policy normalises, the success of better-performing firms and the failure of less successful ones will further improve productivity.

We also discussed the idea that tight labour markets should raise productivity because companies would be forced to become more efficient and make better use of available technology.

However, there are structural negatives that will weigh on productivity growth. These will be the same or worse than the prior decade.

They include:

• Human capital, which is negatively affected by ‘scarring’ from the pandemic
• Demographics
• The climate transition, which is making some skills obsolete
• Deglobalisation, which looks like it will accelerate over the next 10 years
  
  

The team also debated whether the carbon transition and the focus on ESG (environmental, social and governance) factors would reduce productivity in the formal GDP release in the coming decade. That might happen because costs will rise, and positive externalities – third parties indirectly benefiting from activities – won’t be recorded.

Productivity forecasts for 2021–30 versus historical data

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