Gross Domestic Product (GDP) is often treated as shorthand for national economic well-being, even though it was never intended to be; it is a measure of (some) of the marketable output of the economy. This paper reviews several developments in measuring welfare beyond GDP that were recently presented at the Economic Statistics Centre of Excellence (ESCoE) annual conference in May 2019. The papers discussed fall into three broad areas. First, a significant amount of work has focused on incorporating information about the distribution of income, consumption and wealth in the national accounts. Second, the effects of digitisation and the growth of the internet highlight the potential value in measuring time use as a measure of welfare. Third, the digital revolution has spawned many new, often ‘free’ goods, the welfare consequences of which are difficult to measure. Other areas, such as government services, are also difficult to measure. Measuring economic welfare properly matters because it affects the decisions made by government and society. GDP does a reasonable job of measuring the marketable output of the economy (which remains important for some policies), but it should be downgraded; more attention should be given to measures that reflect both objective and subjective measures of well-being, and measures that better reflect the heterogeneity of peoples' experiences.

When economists talk about ‘measurement’ they tend to refer to metrics that can capture changes in quantity, quality and distribution of goods and services. In this paper we argue that the digital transformation of the economy, particularly the rise of cloud computing as a general-purpose technology, can pose serious challenges to traditional concepts and practices of economic measurement. In the first part we show how quality-adjusted prices of cloud services have been falling rapidly over the past decade, which is currently not captured by the deflators used in official statistics. We then discuss how this enabled the spread of data-driven business models, while also lowering entry barriers to advanced production techniques such as artificial intelligence or robotic-process-automation. It is likely that these process innovations are not fully measured at present. A final challenge to measurement arises from the fragmentation of value chains across borders and increasing use of intangible intermediate inputs such as intellectual property and data. While digital technologies make it very easy for these types of inputs to be transferred within or between companies, existing economic statistics often fail to capture them at all.