VoxEU: Covid-19 in real time

The widespread digitisation of data has opened up the possibility of monitoring the economy at a higher frequency and along previously unexplored dimensions. Central banks have responded to this opportunity by investing resources in the collection and evaluation of these new and relatively unfamiliar sources of data. The Federal Reserve Board and the Bank of Italy, for instance, have joined forces to host an annual conference on non-traditional data, machine learning, and natural language processing in macroeconomics, where such projects will be presented.

During the onset of the Covid-19 pandemic, attempts to measure economic activity at a high frequency gained a renewed sense of urgency, as monthly and quarterly measures proved insufficient for a crisis that was unfolding on a daily basis. Economists at both public agencies and within academia have since responded with the production of high frequency indicators. For instance, Lewis et al. (2020) constructed a weekly estimate of GDP growth, building on Stock and Watson (2002) and an earlier effort by the Council of Economic Advisers (2013). Chetty et al. (2020) and Andersen et al. (2020) made use of novel, high-frequency data sources to track economic activity at different levels of aggregation. Other projects, such as the Finnish Situation Room, expanded access to granular, high-frequency public data.

In March, Sweden’s central bank responded to the pandemic by initiating an internal project to collect and analyse economic indicators on a daily basis. To extend this effort, its Research Division announced an experimental project that opened participation to the general public (including academics and private companies). This involved the creation of a web application that allowed real-time data sharing and visualisation with external participants.

Rather than requesting sensitive microdata and dedicating staff to learn its underlying structure and documentation, the Riksbank instead decentralised the process by opening up participation. Private companies (which know their own data well and have skilled data scientists on staff) could decide what to contribute and in which form to provide it. Academics could produce model simulations, indices, or scraped data, and contribute them to a site where they could be updated at an arbitrarily high frequency and would then be available to policymakers.

The full and original article first appeared on VoxEU here on 23 July 2020.