Advanced economies’ progress: Dismal and dazzling
Ian Goldin, Chris Kutarna 04 October 2016
Some economists see currently faltering GDP growth within a longer-term trend for advanced economies, reflecting their belief that the majority of technological innovation is currently behind humankind. This column argues that neither history nor the present-day pace of scientific discovery supports the idea of diminishing returns to know-how. The task for growth economists is that analytic models are poorly suitable for capturing and setting society’s expectations for these impending disruptions.
Advanced economies in the 21st century are caught between two giant, competing truths: economic growth is slowing, and science is flourishing.
Per-capita GDP growth is trending downward over the OECD (Figure 1). Among other explanations (rising inequality and debt levels, for instance), economic theory variously attributes this trend to diminishing returns – from the one-time transition of women in to the labour force (Bloom et al. 2003), from human capital improvements via advanced schooling (Goldin and Katz 1998), or from the exploitation of natural capital (Costanza et al. 1997).
Figure 1 . Real GDP per capital, annual percentage change
Recently, Robert Gordon (2012) highlighted another site of diminishing returns, namely, know-how. With a longitudinal study folks labour productivity from the 19th century to the 21st, he showed that the advent of computing and digital communications, for almost all their seemingly transformational impact upon society, haven’t generated much economic growth when occur historical terms. The innovations to that your generations at the start of his study bore witness (public sanitation, electricity and combustion engines) drove rapid US labour productivity improvements for 80 years. Computers and information technologies, however, drove equivalent rates of improvement for only a decade. Average US wages rose 350% in the 40 years between 1932 and 1972, but only 22% over another 40 years. The pattern holds similar over the developed world. Quite simply, for almost all their hype, the computer and the web did less to lift economic growth compared to the flush toilet.
The implication is that the truly transformational innovations may already be behind us. Before, we couldn’t harness electricity – now we are able to. We couldn’t maintain sanitary living conditions – now we are able to. We couldn’t get from any A to any B – now we are able to. We couldn’t speak to anyone anywhere anytime – now we are able to. Whatever’s left – driverless cars, as well as quantum teleportation – might prove incremental compared. This type of argument imagines know-how to end up like pulling balls from an urn, each ball representing a fresh idea. Initially, the urn was full and the spheres were large, but every time we’ve gone back again to the urn, we’ve had to attain deeper compared to the last, and the spheres have dwindled into marbles.
In a fresh book, we argue that metaphor, while intuitive and compelling, is backwards (Goldin and Kutarna 2016). Innovation is similar to mixing compounds within an alchemist’s lab. Each compound can be an existing idea or technology, and initially we had just a couple of – maybe some salt, sugar, and common liquids. But we tried mixing them together, plus some of these reacted with each other to create new compounds. In a short time, our once-sparse workbench was crowded with acids, alcohols, and powders. Each time we enter the lab to cook up something new, we are faced with a wider selection of compounds compared to the last. We are in need of never fear running out of powerful new combinations to try. Worries, rather, is that new compounds and their possible combinations are multiplying so fast that people may neglect to find the most readily useful reactions that lie buried included in this.
This metaphor is far nearer to today’s experience in research laboratories. Over the sciences, the pace of discovery is normally rising, not falling. For reliable evidence, consider the pharmaceuticals industry (an excellent litmus test since it invests more into R&D than any other industry, except aerospace). The entire year 2013 set a fresh record for total drugs launched world-wide (48) – an archive that was promptly beaten in 2014 (61). With another 46 drugs launched in 2015, the last 3 years have already been the industry’s three most productive in its history. Recent major discoveries include new weapons against heart failure, which within an aging world is currently the leading reason behind death; immunotherapies, that assist to defeat cancers by boosting the body’s own immune response; and a viable pathway to effective Alzheimer’s medications within ten years. In part because of the accelerating pace of pharmaceutical achievements like these, average life span across advanced economies is currently rising an unprecedented four to five hours each day.
Of course, for a hardened growth sceptic, such evidence can do little to overturn the broader thesis that returns to know-how are diminishing (indeed, it could strengthen that thesis, since among the consequences of a ballooning population of healthy retirees, ceteris paribus, will be deteriorating output per capita). From an economics standpoint, the transforming pharmaceutical breakthrough was the invention of a pharmaceuticals industry – drug discoveries made and distributed within that paradigm, regardless of how important or frequent, are incremental gains by definition.
However, imagine if the complete paradigm were to shift? ‘Paradigm shifts’, in the sense that Kuhn (1962) described in his Structure of Scientific Revolutions, re-introduce the chance of transformational change, by shifting scientific endeavour out of theoretical frameworks whose limits are being approached and into new ones whose limits have not yet been explored. The classic example may be the Copernican Revolution, which by challenging medieval theories of motion ultimately gave birth to the Newtonian physics where most modern machines are actually based.
Such paradigm shifts are underway at this time, and can undoubtedly disrupt and reconfigure advanced societies over another 30 to 50 years. Medical science isn’t merely discovering new drugs. The advent of synthetic biology – the capability to create and modify organisms at the genetic level – promises to eventually shift the societal role of medicine from the procedure paradigm which has prevailed for 5,000 years to 1 of transforming organisms to provide them overwhelming natural advantages against disease and aging. Within this new paradigm lifespan, intelligence, and other basic human characteristics may quickly evolve beyond ranges that people consider normal today. Because they develop, such medical technologies may also raise the most challenging ethical questions that science has ever presented society – how about us makes us human? Should (and will) humans and trans-humans coexist?
Another way to obtain societal disruption will be artificial intelligence, which is rapidly shifting the role of computers from being tools for calculation to tools for cognition. This shift has near-term implications for the structure of the labour market (in line with the Oxford Martin School, almost half of most current jobs in america have a high odds of being automated away by 2050; see Frey and Osborne 2013), however the more profound disruption is to prevailing conceptions of free will. A lot of the choices we neglect inside our daily lives today – which routes we drive, what products we purchase, what media we eat – will rapidly become at the mercy of AI scrutiny that may unveil a variety of individual and social consequences to which we are presently ignorant. As this new cognitive layer over collective private action increases in strength and reliability, how will that domain of private action be affected? Will we be permitted to keep socially harmful activities in defiance of such evidence? Will elaborate incentives arise that cause our individual behaviours to comply with optimisation algorithms? And who’ll hold the authority to create and adjust such algorithms?
Similarly, profound transformations to society as we realize it now are suggested by present pathways of inquiry into quantum mechanics, nanotechnology, and neuroscience. The proximate cause for these paradigm shifts is digital computers, that may peer deeper and accurately into data than any prior analogue instrument. Because of the (exponentially increasing) capacity to crunch giant datasets and discern ‘signals’ from ‘noise’, computers have previously done, and will continue steadily to do, more to advance astronomy compared to the invention of the telescope, more to advance biology compared to the microscope, and more to advance physics compared to the particle accelerator (Robertson 1998, 2003). The golden age of labour productivity growth to which Gordon’s longitudinal research points – that single lifetime where planes, automobiles and electricity arrived together to define modernity – was founded upon a comparatively simple group of discoveries: abundant oil beneath the ground, internal combustion engines, germ theory, etc. Now we contain the tools to begin with exploring the genuinely hard questions that reality presents. All science today stands close to the base of a steep learning curve.
The broader cause for these emerging paradigm shifts may be the inflation in human brainpower which has taken place in the last 25 years. Because of giant medical successes against childhood disease and aging in the last quarter-century, today’s global cohort of adults is humanity’s largest and healthiest ever. Additionally it is the best-educated. In only a generation, illiteracy has fallen from nearly half to just one-sixth of humanity. In 30 years, we’ve added three billion literate brains to your ranks. Meanwhile, the rapid expansion of higher learning in Asia implies that the amount of people alive at this time with a university degree is higher than the full total number of degrees awarded ever sold prior to 1980. Most of all, today’s generation is history’s best-connected, thanks principally to a quartet of big events – the finish of the Cold War, waves of democratisation across Latin America, a lot of Asia and sub-Saharan Africa, China’s emergence from autarky, and the advent of digital communications.
Neither history, nor the present-day pace of scientific discovery supports the idea of diminishing returns to know-how. The task for growth economists is that analytic models are poorly suitable for capture, and set society’s expectations for, these impending disruptions. Some consequences will be too pervasive and long-term showing up clearly in the immediate data. Some changes our behaviours, and in so doing invalidate prevailing economic assumptions. Plus some will transcend the economic sphere entirely to touch higher human values.
Growth economics is powerful. At its best, it really is an empirical science that helps regulate how to lift human wellbeing – among civilisation’s most significant tasks. But it struggles to capture the dynamism of our modern of discovery for grounds. Much that matters continues to be beyond its sight.
Bloom, D E, D Canning, and J Sevilla (2003), “The demographic dividend: A fresh perspective on the economic consequences of population change”, Population Matters, Monograph MR-1274, RAND, Santa Monica .
Costanza, R, R d’Arge, R, R de Groot, S Farber, M Grasso, B Hannon, K Limburg, S Naeem, R V O’Neill, J Paruelo, R G Raskin, P Sutton, and M van den Belt (1997), “The worthiness of the world’s ecosystem services and natural capital”, Nature, 387(6630), 253-260.
Frey, C and M Osborne (2013), "The continuing future of Employment: How Susceptible Are Jobs to Computerisation?", Oxford Martin School Programme on the Impacts of Future Technology.
Goldin, C, and L F Katz (1998), The Race Between Education and Technology, Cambridge and London: Belknap Press of Harvard University Press.
Kuhn, T S (1962), The Structure of Scientific Revolutions, (1st ed) Chicago: University of Chicago Press.
Robertson, D S (1998), THE BRAND NEW Renaissance: Computers and another Degree of Civilization, Oxford: Oxford University Press.
Robertson, D S (2003), Phase Change: The Computer Revolution in Science and Mathematics, Oxford: Oxford University Press