Joshua Blumenstock, 01 October 2021

There is often an urgent need for humanitarian assistance in low-income countries. But how can it be targeted efficiently and quickly? Joshua Blumenstock tells Tim Phillips how, in Togo, a combination of machine learning and mobile phone data dramatically increased the effectiveness of Covid assistance.

Read more about the research discussed and download the free discussion paper:

Aiken, E, Bellue, S, Blumenstock, J, Karlan, D and Udry, C. 2021. 'Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance'. CEPR

Philipp F. M. Baumann, Enzo Rossi, Michael Schomaker, 02 July 2021

The notion than an independent central bank reduces a country’s inflation has been embraced by academics, central bankers, and politicians all over the world. This is somehow puzzling, giving the ambiguity reported in empirical studies. This column argues that overall there is only a weak causal link from independence to inflation, if at all. Even a strong inflation-boosting impact from introducing central bank independence cannot be ruled out. These results are obtained from a statistical approach that has not yet been used in analyses of macroeconomic processes, although it exhibits properties well-suited to this end.

Yusuke Narita, Shunsuke Aihara, Megumi Matsutani, Yuta Saito, 28 April 2021

Machine learning algorithms are increasingly being used in decision making. Web companies, car-sharing services, and courts rely on algorithms to supply content, set prices, and estimate recidivism rates. This column introduces a method for predicting counterfactual performance of new algorithms using data from older algorithms as a natural experiment. When applied to a fashion e-commerce service, the method increases the click through rate and improved the recommendations algorithm.  

Daron Acemoğlu, David Autor, Jonathon Hazell, Pascual Restrepo, 03 March 2021

As artificial intelligence technologies improve rapidly, there is increasing interest in the effects on workers. This column uses data on skill requirements in US vacancies posted since 2010 to examine the impact of artificial intelligence on the US labour market. While the estimates suggest that AI has started to replace workers in certain tasks, it does not yet seem to be having effects on the aggregate labour market.

Augusto Cerqua, Marco Letta, 18 December 2020

There is widespread concern about the toll of the pandemic on local economies, but little causal evidence to assess its real costs. This column presents an impact evaluation of the local economic effects of the COVID-19 crisis in Italy, based on a counterfactual application of machine learning algorithms. It documents that, to date, impacts on employment and firms have been dramatically uneven across the Italian territory and spatially uncorrelated with the epidemiological pattern of the first wave. It shows that this heterogeneity is associated with sectoral specialisation, exposure to social aggregation risks, and pre-existing labour market fragilities. Finally, it argues that such diverging local trajectories call for a place-based approach in the policy response to the crisis.

Guido de Blasio, Alessio D'Ignazio, Marco Letta, 27 November 2020

The use of artificial intelligence in preventing crime is gaining increasing interest in research and policymaking circles. This column discusses how machine learning can be leveraged to predict local corruption in Italy. It highlights how such algorithmic predictions could be employed in the service of anti-corruption efforts, while preserving transparency and accountability of the decisions taken by the policymaker.

Pierre-Philippe Combes, Gilles Duranton, Laurent Gobillon, Clément Gorin, Yanos Zylberberg, 17 November 2020

Applying machine learning to rich historical data sources provides the opportunity to draw novel insights for fields such as urban and spatial economics. Using evidence from France, this column shows how such information might be derived from historical maps to shed new light on the growth of towns and agglomerations, and could inform our understanding of various human behaviours from community evolution to agricultural productivity.

David Bholat, 02 July 2020

Machine learning and artificial intelligence (AI) are at the heart of current transformations that some commentators have dubbed the ‘Fourth Industrial Revolution.’ The Bank of England, CEPR and Imperial College recently organised a virtual event to discuss how machine learning and AI are changing the economy and the financial system, including how central banks operate. This column summarises key topics discussed during the event and introduces videos recorded by some of the presenters, including Stuart Russell, Alan Manning, and the Bank of England’s Chief Data Officer, Gareth Ramsay. 

Satoshi Kondo, Daisuke Miyakawa, Kengo Shiraki, Miki Suga, Teppei Usuki, 13 May 2020

Detecting and preventing accounting fraud is a concern for many policymakers around the world. This column presents a framework that incorporates machine learning techniques to detect and forecast fraudulent behaviour by firms when reporting financial information. The framework relies on a larger set of firm information to achieve better detection performance and, unlike previous frameworks, provides forecasts for potential future accounting fraud.

Jon Danielsson, Robert Macrae, Andreas Uthemann, 06 March 2020

Artificial intelligence, such as the Bank of England Bot, is set to take over an increasing number of central bank functions. This column argues that the increased use of AI in central banking will bring significant cost and efficiency benefits, but also raise important concerns that are so far unresolved.

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In collaboration with CEPR and the Brevan Howard Centre, Imperial College, the Bank of England is hosting a research conference on “the impact of machine learning and AI on the UK economy.” The purpose of the conference is to stimulate academic research and public debate on how machine learning and AI will impact issues that matter to the Bank of England’s policy objectives.

Jacques Melitz, Farid Toubal, 01 August 2019

Artificial intelligence has made spectacular progress in recent years. One particular source of high expectations is automatic translation and whether it will finally bring about the long-predicted death of distance in trade. This column examines the impact of a common language on bilateral trade and finds that the net result of reducing linguistic frictions with a set of trading partners is not apparent.The potential impact of machine translation on foreign trade remains up in the air.

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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