Artificial Intelligence

Do workers really need to fear robots? Not according to these charts

Robotic arms building a car

Robotic processes have been making their way into the work environment. Image: Unsplash/Lenny Kuhne

Ippei Fujiwara
Professor of Macroeconomics, Keio University and Australian National University
Ryo Kimoto
PhD student, Pennsylvania State University
Shigenori Shiratsuka
Professor, Faculty of Economics, Faculty of Economics, Keio University
Toyoichiro Shirota
Associate Professor, Aoyama Gakuin University
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  • There are concerns that robots could reduce employment opportunities.
  • But the pace at which robots are improving has slowed significantly since 2010, new research shows.
  • This appears to be in line with the findings of recent studies by the likes of the IMF and US Federal Reserve indicating that “ideas are getting harder to find”.
  • However, the new research does not capture expanding range of robot applications due to advances in software, including algorithms.

As the introduction of robots into the workplace increases, there is a growing concern over whether robots will cause human jobs to disappear. In response to this societal fear, academics have tackled this issue from both theoretical and empirical angles (e.g. Acemoglu and Restrepo 2017, Baldwin 2019, Dauth et al. 2017, Michaels and Graetz 2015).

However, to date, no study has specifically investigated the rate of technological progress, namely, the quality improvement of robots. For any attempt to predict how robots will affect the macroeconomy, in recognition of society’s existing anxiety, it is vital to understand the progress of robot production and the quality improvement path of robots. If the pace of quality improvement in robots slows down or has already diminished, fear regarding robots taking human jobs away may dissipate. In a new paper (Fujiwara et al. 2021), we aim to fill in this gap.

Our study uses two novel datasets – Production and Shipments of Manipulators and Robots collected by the Japan Robot Association and the Corporate Goods Price Index from the Bank of Japan – to measure the amount of progress made in improving robot quality in Japan between 1990 and 2018. First, we construct quality-unadjusted robot price indices using the Production and Shipments of Manipulators and Robots dataset and three techniques: index number, stochastic, and structural approaches. We then measure quality per robot by dividing this quality unadjusted price index by the Corporate Goods Price Index, an industrial robot price index that is quality-adjusted.

Figure 1 shows the evolution of quality per robot estimated using the three approaches. Despite different approaches being used, there is no significant difference in trends. The pace of quality improvement per robot has slowed or decreased significantly since 2010. The rate of quality improvement per robot in the 2010s was around three percentage points per annum lower than in the 2000s.

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Figure 1

a) Index number approach

A chart showing estimating the evolution of quality per robot
It seems quality improvement per robot has slowed or decreased significantly since 2010 Image: VOXEU

b) Stochastic approach

A chart showing robot quality using the stochastic approach
Robot quality seems to be decreasing Image: VOXEU

c) Structural approach

A chart showing robot quality using the structural approach
Robot quality seems to peak between 2005 and 2010 Image: VOXEU

The result of the decline in the rate of quality improvement of robots may be in line with the findings of the recent studies by economists at the IMF and Federal Reserve such as Byrne and Pinto (2015) and Lian et al. (2019), which point to a decline in investment-specific technological progress, i.e. a slowdown in the pace of decline in the relative price of capital goods to consumer goods. The main conclusion also implies that the hypothesis that ‘ideas are getting harder to find’, advocated by Bloom et al. (2020), may apply to robot production.

As the estimates are based on various assumptions, the results should be treated with a certain degree of caution. Micro-level data for prices and product characteristics for individual robots are needed for more rigorous quality adjustments. Furthermore, this analysis does not capture the expansion of the range of robot applications due to advances in software, including algorithms and other factors. Measuring service flows from such intangible capital remains an issue for future studies.

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Related topics:
Artificial IntelligenceFuture of WorkFuture of Work
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