Frontiers of economic research

Neil Gandal, 15 January 2021

What is behind the pinballing price movements of Bitcoin? Neil Gandal tells Tim Phillips how supply and demand works for cryptocurrencies.

You can download Neil's paper, The Microeconomics of Cryptocurrencies (Neil Gandal, Joshua Gans, Guillaume Haeringer, Hanna Halaburda), free here

Paul Oyer, Kathryn Shaw, 09 January 2021

Edward Lazear, the first personnel economist, passed away in November 2020. This column reviews his many contributions to the world of economics and beyond. The authors, two close colleagues and co-authors, highlight some of Lazear’s key research papers, focusing on tournaments, retirement, and piece rate payments. They look at his creation of and contributions to important institutions in the economics community, as well as his work in public policy. Finally, they remember his interests outside of academics.

Charles Wyplosz, 24 December 2020

From early March, it became clear that economists around the world, like everyone else, were mesmerised by the Covid-19 pandemic and trying to make sense of the unfolding events. This column describes how the tradition of pre-prints in physics and the medical sciences inspired the creation of CEPR's “Covid Economics: Vetted and Real-Time Papers”. Beyond its contribution to a faster understanding of the pandemic, the Covid Economics experiment may help the economics profession think about how research is published.

Nicolas Woloszko, 19 December 2020

A pre-requisite for good macroeconomic policymaking is timely information on the current state of the economy, particularly when economic activity is changing rapidly. Given that GDP figures are usually only available on a quarterly basis, the current crisis has prompted a search for alternative high‑frequency indicators of economic activity. This column presents evidence from a new tracker developed by OECD which uses Google Trends and machine learning to provide real-time estimates of GDP growth in countries all over the world.

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.

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