Report - How Robots Change the World_watermark
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How Robots Change the World
We converted this into the average impact of one additional robot on the number of jobs using the average robots per capita ratio across regions. Using these results, we find that a 1-unit increase in robot stock reduces the number of jobs by 1.3 in the short run and 1.6 in the long run across all regions. The impact is lower in high-skilled regions and higher in low-skilled regions. (Table A1)
Model 2: Local labour productivity
Our preferred specification for estimating the productivity impact of robotization implies that a 1% increase in robot stock leads to a 0.1% increase in productivity in the shortterm and a 0.3% increase in productivity in the long-term. Due to the range of factors influencing the productivity impact in a longterm timeframe, we use the short-term impact as a basis for our analysis.
Table A2: Change in productivity (GVA per worker) due to a 1% increase in robots
COMPARISONS
Table A3 presents a comparison of our results with previous academic literature. All results regarding the employment effect have been converted into the change in number of jobs due to one additional robot. Our results are the same sign but slightly lower than other studies, which find the impact of one robot displaces between 3 to 6 jobs depending on the sector, time period and country covered. Graetz and Michaels (2015) and Dauth et al (2017) also find that an increase in robots increases productivity.
|
Short-term impact |
Long-term impact |
|
|
|
Average across |
0.1% |
0.3% |
all regions |
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|
|
|
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Table A3: Change in the number of jobs due to one additional robot
Study |
Geography |
Time frame |
Impact on jobs (of one robot) |
Impact on labour productivity |
Graetz and |
17 countries (US, four- |
1993-2007 |
No effect on total hours |
Increase in total factor |
Michaels (2015) |
teen European coun- |
|
worked, but a reduction |
productivity |
|
tries, South Korea, and |
|
in hours of low-skilled and |
|
|
Australia) |
|
middle-skilled workers. |
|
Acemoglu and Restrepo |
US |
1990-2007 |
Loss of 3 to 6 jobs. |
- |
(2017) |
|
|
|
|
Dauth et al (2017) |
Germany |
1994-2014 |
Loss of 2 manufacturing jobs |
Increases productivity |
|
|
|
offset by a gain of 2 additional |
|
|
|
|
jobs in the service sector. |
|
Chiacchio et al (2018) |
6 EU countries |
1995-2007 |
Loss of 3 jobs. |
- |
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How Robots Change the World
About Oxford Economics
Oxford Economics was founded in 1981 as a commercial venture with Oxford University’s business college to provide economic forecasting and modelling to UK companies and financial institutions expanding abroad. Since then, we have become one of the world’s foremost independent global advisory firms, providing reports, forecasts and analytical tools on more than 200 countries, 250 industrial sectors, and 7,000 cities and regions. Our best-of-class global economic and industry models and analytical tools give us an unparalleled ability to forecast external market trends and assess their economic, social and business impact.
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June 2019
All data shown in tables and charts are Oxford Economics’ own data, except where otherwise stated and cited in footnotes, and are copyright © Oxford Economics Ltd.
This report is confidential and may not be published or distributed without the prior written permission of Oxford Economics Ltd.
The modelling and results presented here are based on information provided by third parties, upon which Oxford Economics has relied in producing its report and forecasts in
good faith. Any subsequent revision or update of those data will affect the assessments and projections shown.
To discuss the report further, please contact:
James Lambert: jlambert@oxfordeconomics.com
Edward Cone: edcone@oxfordeconomics.com
Oxford Economics,
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Tel: +44 207 803 1400
www.oxfordeconomics.com
The image on page 2 is by Field Robotics, and all other images in this report are by Shutterstock (www.shutterstock.com).
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The ‘Pepper’ robot assistant in Italy, 2018.
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