The global financial crisis is usually seen as a failure of neoclassical economic theory and neoliberal policy, but it also represented an epistemological failure. Forecasters who missed the crisis neglected to include the financial sector in their models, while aggregate indicators such as GDP failed in spite (or perhaps because) of their heavy emphasis on financially driven growth. In contrast to both critics and proponents of GDP who see it as a purely statistical measure, this article argues that GDP is in fact a form of numerical rhetoric. Political messages in such estimates were explicit until the early twentieth century, but have since become implicit in hidden assumptions. To uncover the narratives built-in to GDP's view of finance, the article conducts a thought-experiment comparing GDP with two counterfactual indicators corresponding to historical views of finance as either non-productive or an actual cost to society. The analysis shows how changing this single assumption leads to very different narratives regarding the class-balance of workers vs. capitalists, the relative importance of consumption, and the extent of space that exists for public policy to influence the economy. The article concludes with some thoughts on making the implicit assumptions in GDP explicit, and opening up the debate to the broader public in a transparent way.

As contribution to the debate about the interpretation of the process of economic growth before the Industrial Revolution, we discuss two concerns about the currently available estimates of historical national accounts and the way in which these estimates should be interpreted. Firstly, we argue that estimates of the long-term trends of economic growth should make use of all information contained in time series of Gross Domestic Product (GDP henceforth), and therefore use standard regression analysis to establish those trends. Secondly, we point to the problem that the time series of historical GDP are based on very different estimation procedures, which probably affect the outcome in terms of the level of GDP per capita in the period before 1850. Both concerns imply that we do not entirely agree with Jack Goldstone’s views of pre-industrial growth. In particular, his conclusion that growth was cyclical before 1800 is inconsistent with the available GDP estimates, which point to sustained growth, albeit at a very low rate.

New data on Dutch and British GDP/capita show that at no time prior to 1750, perhaps not before 1800, did the leading countries of northwestern Europe enjoy sustained strong growth in GDP/capita. Such growth in income per head as did occur was highly episodic, concentrated in a few decades and then followed by long periods of stagnation of income per head. Moreover, at no time before 1800 did the leading economies of northwestern Europe reach levels of income per capita much different from peak levels achieved hundreds of years earlier in the most developed regions of Italy and China. When the Industrial Revolution began in Britain, it was not preceded by patterns of pre-modern income growth that were in any way remarkable, neither by sustained prior growth in real incomes nor exceptional levels of income per head. The Great Divergence, seen as the onset of sustained increases in income per head despite strong population growth, and achievement of incomes beyond pre-modern peaks, was a late occurrence, arising only from 1800.

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.

This article provides a comprehensive picture of the National Transfer Accounts (NTA), a project that aims at measuring how people produce, consume, save, and share economic resources at every age. It stands today with a unique dataset that includes 47 countries from around the world, permitting a comparative understanding of economic flows within and between generations and over time.

Many macroeconometric models depict situations where the shares of the major demand aggregates in output are stable over time. The joint dynamic behavior of the considered demand aggregate and output may thus be approximated by a cointegrated vector autoregression. However, the shares of many demand subaggregates in output are rather mobile and changing over time. In order to simultaneously capture the flexibility of the shares of the subaggregates and the long-run constancy of the share of the total aggregate, we consider trivariate systems of two macroeconomic subaggregates and output with error-correction terms that are nonlinear functions of the original variables. The merits of the models are evaluated by means of several forecasting experiments.

This paper gives an overview of the construction of and preliminary results from the EU KLEMS database which contains industry estimates of output, input and productivity growth for EU countries. The paper begins with a discussion of methodology and data sources covering output and intermediates, capital and labour services. The content and scope of the database is then briefly described. This is followed by a discussion of preliminary results focusing on comparisons between the EU and US. These confirm the relatively poor productivity performance of the EU relative to the US since the mid-1990s, mostly driven by low productivity growth in market services.

The objective of the study is to construct forest resource accounts for the state of Maharashtra in India and incorporate the value of depletion and degradation of forest resources into the system of national accounts (SNA). The net state domestic product (NSDP) is adjusted for the depletion of the forest resources to obtain Environmentally adjusted net state domestic product (ESDP). The results show that the value added by forests is 3.56 per cent of NSDP and the value of depletion is 19.8 per cent of the estimated value added. The ESDP of Maharashtra is found to be 99.3 per cent of the estimated NSDP. The study has demonstrated that, although the existing database needs further improvement, forest resource accounting is feasible for the state of Maharashtra in India and can serve as an indicator of economy's performance.