How can innovative data collection and analysis methods support evidence-based policymaking in the EU?
Big data in times of crisis – Now-casting under Covid-19
by Luca Barbaglia, JRC’s Economic and Financial Resilience Unit
During the Covid-19 pandemic, economists have struggled to obtain reliable economic predictions, with standard models becoming outdated and their forecasting performance deteriorating rapidly. This paper presents two novelties that could be adopted by forecasting institutions in unconventional times. The first innovation is the construction of an extensive data set for macroeconomic forecasting in Europe. We collect more than a thousand time series from conventional and unconventional sources, complementing traditional macroeconomic variables with timely big data indicators and assessing their added value at nowcasting. The second novelty consists of a methodology to merge an enormous amount of non-encompassing data with a large battery of classical and more sophisticated forecasting methods in a seamlessly dynamic Bayesian framework. Specifically, we introduce an innovative “selection prior” that is used not as a way to influence model outcomes, but as a selection device among competing models. By applying this methodology to the Covid-19 crisis, we show which variables are good predictors for nowcasting gross domestic product and draw lessons for dealing with possible future crises.
The Future of Work – What can we learn from Online Job Advertisements?
by Matteo Sostero, JRC’s Industrial Strategy, Skills and Technology Transfer Unit.
Data from online job advertisements are increasingly used in the emerging area of “skills intelligence” to describe labour market dynamics and the demand for skills in different occupations. Collecting this data involves gathering unstructured information from the internet and processing it into structured datasets, which may provide a biased description of the labour market. We present a framework for these different sources of bias, in terms of representativeness of occupations and their task content. We analyse the Nova UK dataset of online job advertisements from Burning Glass Technologies, containing over 60m individual job ads for the United Kingdom from 2012–2020. We compare the occupation task profiles embedded in this data with the JRC-Eurofound Task Database, through a new Skill-Task Dictionary. The dictionary classifies the rich but unstructured information on “skills” describing individual occupations into the hierarchical Task Taxonomy developed by the JRC and Eurofound, and measured through occupation surveys. In general, we find that the task profile implied in job advertisements is relatively consistent with the EU Task Database across most occupations, especially for intellectual and social tasks, and for tools of work. However, online job advertisements in general (and Nova UK in particular) tend to focus especially on professional occupations, which are relatively better represented in their numbers and in their variety of skills and tasks, relative to less qualified occupations. We enumerate several types of bias that can occur with this data, and discuss possible future applications.
Do Search Engines Increase Concentration in Media Markets?
by Nestor Duch Brown, JRC’s Digital Economy Unit.
Search engines are important access points for newspapers. Google alone is responsible for 35% of online visits to news outlets in the European Union. Yet, the effects of Google Search on market competition and information diversity are ambiguous, as Google indexes news outlets considering both domain authority and information accuracy. Using detailed daily internet traffic data for 606 news outlets from 15 European countries, we assess the effect of Google Search’s indexation on search visits by exploiting exogenous variation in news outlets’ indexation from nine core algorithm updates rolled out by Google between 2018 and 2020. Several conclusions follow from our estimations. First, Google core updates overall reduce the number of keywords that news outlets have in top positions in organic search results. Second, keywords ranked in top search position have a positive effect on news outlets’ visits. Third, our results are robust when we focus the analysis on different types of news outlets, but are less conclusive when we consider national markets separately. Finally, we also analyse the effects of Google core updates on media market concentration.
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