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Abstract
Variations in the frequency and tone of news media are the focus of a growing literature. However, to date, empirical investigations have primarily confirmed the existence of such differences at the country level. This paper extends those insights to the subnational level. We provide theoretical arguments and empirical support for systematic regional variations in the frequency and sentiments of news related to innovation and new technologies. These variations reflect regional socio-economic structures. We find that the average newspaper circulating in urban areas features more news on innovation and new technologies than media in more rural areas. Similar findings hold for locations in East Germany and to a certain degree for regions with low unemployment. The sentiments of innovation and new technology news are negatively associated to the unemployment rate, and they tend to be lower in regional newspapers than in national ones. Overall, our results suggest a strong link between the regional socioeconomic conditions and how newspapers circulating in these places report on innovation and new technologies.
Highlights
Innovation is undoubtedly a crucial ingredient of technological and economic growth
We exclusively considered articles to be related to innovation and new technologies if they contained any of the above keywords, and Latent Dirichlet Allocation (LDA) classified them as so related
It turns out that almost all variables with significant coe cients in the baseline scenario remain significant in the other scenarios as well
Summary
Innovation is undoubtedly a crucial ingredient of technological and economic growth. The sheer unlimited potential of ever-growing mountains of data coupled with breathtaking advances in analytical systems, bear huge potentials for future economic growth and, attract the fascination of researchers and businesspeople alike (Aghion et al, 2017; Goldfarb and Trefler, 2018). AI is closely linked to the automation of human tasks and is widely expected to transform and replace the “routine, non-cognitive tasks that have been primarily performed by middle-skilled workers” (Buarque et al, 2019). Many occupations are in danger of being automated or replaced by AI-based systems (Frey and Osborne, 2017; Acemoglu and Restrepo, 2019, 2020), spurring growing public concerns (Furman and Seamans, 2019; Inho↵en, 2018; Fast and Horvitz, 2017)
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