As the UK and other countries face some of the worst days of the COVID-19 health crisis, the economic crisis may seem far off or even averted. But the measures taken to support economies battered by lockdown and other restrictions will eventually have to be paid for.
In the UK, the initial business loan package unveiled in March 2020 alone amounted to 15 per cent of GDP, and further spending on furlough and other measures to ward off rising unemployment will have added considerably to the coronavirus bill. These have helped to prevent immediate economic collapse but have drastically increased the ratio of national debt to GDP, storing up economic uncertainty for the future.
Although interest rates are currently at historically lows and servicing the debt burden is not an immediate concern, excessively high ratios can shake investor confidence and draw resources away from national development in the medium to long term. Moreover, studies have shown how a high debt-to-GDP ratio, coupled with liberal labour market regimes, have reduced long-term employment growth.
Debt-to-GDP ratios before the onset of the pandemic were already high across developed economies, with advanced economies in the G20 projected to exceed 120 per cent of debt to GDP. Combined with the decline in productivity growth in recent years, this was a growing problem before COVID-19. Now, it is a critical one.
There are few good options for addressing this debt burden. Spurring higher inflation through monetary policy would exacerbate inequality. So would suppressing interest rates, and anyway, many across developed economies are already at historic lows. Tax rises would hurt the needed recovery. Austerity or government default are politically unthinkable. That leaves GDP growth as the only way out.
Technological innovation is not enough
There have been two well-documented instances when the debt-to-GDP ratio was lowered without substantial economic disruption — in Britain following the Napoleonic Wars, and in the United States after the Second World War.
In the case of Britain, the concentration of wealth due to regressive taxation policies to pay off the debt led to the financing of many capital-intensive projects and novel technological applications, which spurred the Industrial Revolution. In the case of the US, the military technological investments which were commercialised after the war spurred industrial changes and contributed to productivity gains.
Today, emerging next-generation digital technologies offer a way to unlock similar gains and reverse the growth in national debt. Artificial intelligence (AI), 5G connectivity, cloud computing, and 3D printing could be part of a spur to faster productivity and economic growth and a lowering of the debt-to-GDP ratio as a result. But the technologies themselves are not sufficient.
Technology-led research and development is often pointed to as an underlying driver of economic growth, but attributing all productivity gains to R&D greatly simplifies the story. In technological revolutions throughout recent history, there has often been a lag between the introduction of technology and its effect on productivity.
In the late 19th century, when electric motors first replaced steam engines, there was very little initial improvement to productivity until firms completely changed their business processes and corresponding business models.
In information and communications technology, though the ‘computer age’ could be said to have begun in the 1970s, it took until two decades later for long-expected productivity growth to begin to materialise, as personal computers fuelled the spread of more generic software that was customisable to a greater number of business contexts. It was when many firms were able to change what they do — and what they choose to do in-house or in linkages with others — that the efficiency and effectiveness of the industrial system, and in turn productivity, improved.
This is common to many technological changes in the economy, where a small number of first-movers gain a competitive edge early, but the benefits are not consistent across firms. It is only when the technology spreads that widespread benefits are felt across the entire economy.
Today, the combination of technologies like additive manufacturing, distributed ledgers and sensors have the potential for wholesale disruption of the conventional manufacturing, by manufacturing things closer to the consumer. But to realise this potential requires a transition to new business models.
Transitions like this involve a lot of experimentation in the application of new technologies. Innovation in the business models of firms, however, can help smooth the transition by exploring novel ways for implementing new technologies quickly in order to locate profitable avenues for development and encourage widespread adoption.
Moreover, discussions about productivity growth often implicitly assume the absolute necessity of such ground-breaking technological change; yet insufficient attention is given to how smarter deployment, more effective management, and changes in business models can significantly improve productivity under existing technological capabilities. Such organisational changes may even accelerate the pace of technological development.
In a new paper, we argue that debt-to-GDP ratios can be reduced to manageable levels by focusing on this business model innovation, combined with the adoption of digital technologies.
Taking the US as a reference, if the combination of new digital technology and business models result in productivity growth increasing from the current projected 1 per cent per annum to 3.5 per cent, which is comparable to various high growth periods throughout history, this would reduce the US debt-to-GDP ratio to the pre-pandemic level of around 80 per cent by 2045. By contrast, the ratio will significantly expand if productivity growth remains at 1 per cent.
In light of this potential for business model innovation to unlock growth, governments in developed economies should consider various policy initiatives to accelerate its development.
Subsidies and grants, not only for these new digital technologies but also for their business applications, are a good first step. Many tax regimes already provide rebates for firms that invest in cutting-edge new technologies. This, however, may only be sufficient to induce firms to introduce piecemeal changes — there is little incentive for firms to go the extra mile and implement corresponding organisational changes.
Governments should therefore consider subsidising firms that have, or have plans to implement, organisational and business model changes designed specifically to complement their technological assets.
Tax schemes can go even further and subsidise changes that take place between firms. Firms could jointly apply for finance if they alter their current interactions with other firms in response to their technologies. This would encourage firm coordination not just within but across entire industries.
Governments could also permit tax rebates if firms are able to demonstrate productivity improvements via independent audits. Although firms clearly have an incentive to improve productivity, the incentive may not be sufficiently strong for two reasons.
First, productivity, as conventionally defined via accounting principles, is rarely measured directly by firms and does not drive their day-to-day decisions. Second, firms are ultimately motivated by profit, and profitability can be achieved not only via productivity improvement but also through appropriating a greater proportion of value from competitors and consumers.
Arguably, the latter approach is often easier to achieve than productivity increases, aggravated by a lack of competition in many markets. With tax rebates, for example, set to a given proportion of productivity increase, the financial incentives for improving objectively measurable productivity would become stronger.
Governments should also provide relevant training. Skills shortages in scientific, technology, engineering, and mathematics (STEM) areas have been identified as a bottleneck to technological innovation. There may be an equivalent skills deficit relating to the identification and exploitation of opportunities in business model innovation.
Hence, states could provide training in relevant skills, centred specifically around transformational opportunities provided by the next generation of digital technologies.
Finally, governments could seek to encourage the emergence of common standards across the industrial chain, aiming to encourage greater cross-firm and cross-industry collaboration.
This should begin with the legal framework being redesigned, which is currently inadequately equipped to address the concerns of emerging technological use. Legal processes, in general, evolve very slowly, and yet the technological and business model changes that they regulate often happen relatively quickly.
Hence, governments could consider adopting a much nimbler legal framework for use exclusively in technological and business-model-related matters, which is less subject to the complicated political checks and balances in conventional law. Such a legal framework must also respond rapidly to any international technical standards that emerge.
Race against time
The post-pandemic era is likely to feature nearly unprecedented levels of debt to GDP. The only sustainable and sound means of reducing this ratio is via substantial productivity growth.
Historically, new technologies have not immediately grown productivity. Only when complementarity was established between the new technology and business processes was major productivity growth realised.
Economic policy should be focused on encouraging this development for the next generation of digital technologies. By doing so, governments may be able to shorten the time from technological research to commercialisation. As we enter a race against huge debt and interest payments in the wake of COVID-19, this is of critical importance.
This article is adapted from ‘Repaying the National Debt: Post-Pandemic Prosperity through Business Model Innovation and Productivity Growth’ by Chander Velu and Yifeng (Philip) Chen.