Applied Online Micro Seminar presented by Reka Juhasz
01 October 2020, 3:00 pm–4:15 pm
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"Technology Adoption and Productivity Growth: Evidence from Industrialization in France" (with Mara Squicciarini and Nico Voigtländer)
Event Information
Open to
- UCL staff | UCL students | UCL alumni
Organiser
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Attila Lindner, Suphanit Piyapromdee, Michela Tinacani & Krill Borusyak – Economics
Abstract:
We construct a novel dataset to examine the process of technology adoption during a period of rapid technological change: The diffusion of mechanized cotton spinning during the Industrial Revolution in France. Before mechanization, cotton spinning was performed in households, while production in firms only emerged with the new technology around 1800. This allows us to isolate the firm productivity distribution of new technology adopters. We document several stylized facts that can explain the well-documented puzzle that major technological breakthroughs tend to be adopted slowly across firms and – even after being adopted – take time to be reflected in higher aggregate productivity: The productivity of firms in mechanized cotton spinning was initially highly dispersed. Over the subsequent decades, cotton spinning experienced dramatic productivity growth that was almost entirely driven by a disappearance of firms in the lower tail. In contrast, innovations in other sectors (with gradual technological progress) shifted the whole productivity distribution. We document rich historical and empirical evidence suggesting that the pattern in cotton spinning was driven by the need to re-organize production under the new technology. This process of ‘trial and error’ led to widely dispersed initial productivity draws, low initial average productivity, and – in the subsequent decades – to high productivity growth as new entrants adopted improved methods of production and organization.
Paper: http://www.rjuhasz.com/research/JSV_FrenchIndustr_062020.pdf
Zoom link: https://ucl.zoom.us/j/91358613362?pwd=UDJFMEJLQW5KWlNFSUwzcmhqcHN1QT09
Meeting ID: 913 5861 3362; Passcode: 637846
Please keep your camera on and your mic off, except when asking questions.