Parallel Session - One of the principal challenges in the measurement of well-being is the difficulty to obtain sufficiently timely, granular and comprehensive data to be able monitor trends in real time and make robust forecasts of future outcomes. The rapid expansion of the availability of Big Data sources (including from social media or search engine usage), coupled with the development of new statistical techniques powered by machine learning such as automatic content analysis, allow to exploit large and detailed datasets to refine trend analysis and predictions of aggregate and individual well-being. This session showcased innovative research and practice examples of projects to use machine learning to measure and forecast well-being, inequality, and poverty outcomes.
This session was moderated by Andrea Brandolini, Deputy Director General for Economics, Statistics and Research, Bank of Italy.
