Productivity and Innovation

Nicholas Bloom, Chad Jones, John Van Reenen, Michael Webb, 20 September 2017

The rate of productivity growth in advanced economies has been falling. Optimists hope for a fourth industrial revolution, while pessimists lament that most potential productivity growth has already occurred. This column argues that data on the research effort across all industries shows the costs of extracting ideas have increased sharply over time. This suggests that unless research inputs are continuously raised, economic growth will continue to slow in advanced nations.

Wolfgang Dauth, Sebastian Findeisen, Jens Südekum, Nicole Woessner, 19 September 2017

Recent research has shown that industrial robots have caused severe job and earnings losses in the US. This column explores the impact of robots on the labour market in Germany, which has many more robots than the US and a much larger manufacturing employment share. Robots have had no aggregate effect on German employment, and robot exposure is found to actually increase the chances of workers staying with their original employer. This effect seems to be largely down to efforts of work councils and labour unions, but is also the result of fewer young workers entering manufacturing careers.

Jan Hanousek, Anastasiya Shamshur, Jiri Tresl, 18 September 2017

Bribery and corruption still present a significant cost to many countries today. This column examines how the efficiency of Eastern European private firms is affected by the level of corruption in their operating environment. An environment of high corruption has an adverse effect on firm efficiency, with ‘honest’ firms – typically foreign-owned and/or with female CEOs – penalised even more.

Yoko Konishi, 15 September 2017

The latest AI boom that started in 2012 shows no signs of fading, thanks to the recent availability of big data and widespread adoption of deep learning technologies. This column argues that this new combination of data and technology offers an unprecedented opportunity for society. AI will develop sustainably only if systems are in place to collect relevant data, and AI is not adopted for its own sake.

Peter Robertson, Longfeng Ye, 11 September 2017

The conventional wisdom is that labour reallocation has been a key driver of China’s growth miracle, and slowing migrant labour flows and rapid wage growth have raised concerns over whether this source of growth has run its course. This column argues that the literature on growth and labour reallocation in China has been dominated by a method that, relative to the now standard growth accounting model, substantially overstates the gains. Allowing for this and for human capital differences across sectors, sectoral labour reallocation has not been a key source of productivity growth in China.

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