Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, Ansgar Walther, 11 January 2019

The use of machine learning in credit allocation should allow lenders to better extend credit, but the shift from traditional to machine learning lending models may have important distributional effects for consumers. This column studies the effect of machine learning on mortgage lending in the US. It finds that machine learning would offer lower rates to racial groups who already were at an advantage under the traditional model, but it would also benefit disadvantaged groups by enabling them to obtain a mortgage in the first place.

Monica Andini, Emanuele Ciani, Guido de Blasio, Alessio D'Ignazio, 21 November 2018

The impact of a public policy partly depends on how effective it is in selecting its targets. Machine learning can help by exploiting increasingly available amounts of information. Using data from Italy, this column presents two examples of how to employ machine learning to target those groups that could plausibly gain more from the policy. It illustrates the benefits of machine-learning targeting when compared to the standard practice of employing coarse policy assignment rules based on a few arbitrarily chosen characteristics.

Claudia Biancotti, Paolo Ciocca, 23 October 2018

Calls for regulation of big tech are getting louder and louder. This column argues that policy proposals should be evaluated through the lens of their impact on the evolution of artificial intelligence. It proposes a holistic framework that encompasses consumer control over data, competition in product markets, incentives to innovation, and implications for international trade. It also highlights the role played by major big tech companies, and the threat of data and artificial intelligence monopolisation.

Erik Brynjolfsson, Xiang Hui, Meng Liu, 16 September 2018

Recent years have seen dramatic progress in the predictive power of artificial intelligence in many areas, including speech recognition, but empirical evidence documenting its concrete economic effects is largely lacking. This column analyses the effect of the introduction of eBay Machine Translation on eBay’s international trade. The results show that it increased US exports on eBay to Spanish-speaking Latin American countries by 17.5%. By overriding trade-hindering language barriers, AI is already affecting productivity and trade and has significant potential to increase them further.

Michalis Haliassos, Vimal Balasubramaniam, 01 June 2018

The Third CEPR European Workshop on Household Finance took place on 11 and 12 May in London. This column describes the papers that were presented at the workshop.

Katja Mann, Lukas Püttmann, 07 December 2017

Researchers disagree over whether automation is creating or destroying jobs. This column introduces a new indicator of automation constructed by applying a machine learning algorithm to classify patents, and uses the result to investigate which US regions and industries are most exposed to automation. This indicator suggests that automation has created more jobs in the US than it has destroyed.

Josh Angrist, Pierre Azoulay, Glenn Ellison, Ryan Hill, Susan Feng Lu, 17 November 2017

Economics, and economists, are often accused of insularity and hubris, and of talking primarily to themselves in their research. This column uses a recent analysis of citations to and from other disciplines to show that this is no longer the case. Economics papers increasingly cite non-economic research, and other disciplines cite economists more often too. The data suggest that the rising quantity and quality of empirical research in economics has increased the relevance of the field to non-economists.

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.


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