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We describe the theory and practice of real GDP comparisons across countries and over time. Version 8 of the Penn World Table expands on previous versions in three respects. First, in addition to comparisons of living standards using components of real GDP on the expenditure side, we provide a measure of productive capacity, called real GDP on the output side. Second, growth rates are benchmarked to multiple years of cross-country price data so they are less sensitive to new benchmark data. Third, data on capital stocks and productivity are (re)introduced. Applications including the Balassa-Samuelson effect and development accounting are discussed.

The Maddison Project, initiated in March 2010 by a group of close colleagues of Angus Maddison, aims to develop an effective system of cooperation between scholars to continue Maddison's work on measuring economic performance in the world economy. This article is a first product of the project. Its goal is to explain the aims and approach of the project, and, as a first result of this 'collaboratory', to inventory recent research on historical national accounts. We also briefly discuss some of the problems related to these historical statistics and we extend and where necessary revise the estimates published by Maddison in his latest overviews. Most new work relates to the period before 1820; it leads to a reassessment of levels of GDP per capita in western Europe in the early modern period, and to a confirmation of Maddison's previous estimates of Asian levels of real income.

This paper is a methodological guide to using machine learning in the spatial context. It provides an overview of the existing spatial toolbox proposed in the literature: unsupervised learning, which deals with clustering of spatial data, and supervised learning, which displaces classical spatial econometrics. It shows the potential of using this developing methodology, as well as its pitfalls. It catalogues and comments on the usage of spatial clustering methods (for locations and values, both separately and jointly) for mapping, bootstrapping, cross-validation, GWR modelling and density indicators. It provides details of spatial machine learning models, which are combined with spatial data integration, modelling, model fine-tuning and predictions to deal with spatial autocorrelation and big data. The paper delineates “already available” and “forthcoming” methods and gives inspiration for transplanting modern quantitative methods from other thematic areas to research in regional science.

This article describes the contents and the construction of the EU KLEMS Growth and Productivity Accounts. This database contains industry-level measures of output, inputs and productivity for 25 European countries, Japan and the US for the period from 1970 onwards. The article considers the methodology employed in constructing the database and shows how it can be useful in comparing productivity trends. Although growth accounts are the organising principle, it is argued that the database is useful for a wider range of applications. We give some guidance to prudent use and indicate possible extensions.

The system of federal economic statistics developed in the 20th century has served the country well, but the current methods for collecting and disseminating these data products are unsustainable. These statistics are heavily reliant on sample surveys. Recently, however, response rates for both household and business surveys have declined, increasing costs and threatening quality. Existing statistical measures, many developed decades ago, may also miss important aspects of our rapidly evolving economy; moreover, they may not be sufficiently accurate, timely, or granular to meet the increasingly complex needs of data users. Meanwhile, the rapid proliferation of online data and more powerful computation make privacy and confidentiality protections more challenging. There is broad agreement on the need to transform government statistical agencies from the 20th century survey-centric model to a 21st century model that blends structured survey data with administrative and unstructured alternative digital data sources. In this essay, I describe some work underway that hints at what 21st century official economic measurement will look like and offer some preliminary comments on what is needed to get there.

Purpose>This paper aims to examine the levels of integrated reporting disclosure alignment in annual reports by listed firms in Vietnam and the factors influencing these disclosure levels.Design/methodology/approach>Drawing on a sample of 200 listed firms in Vietnam in 2017, the authors constructed a disclosure index based on the content of the International Integrated Reporting Committee (IIRC) Framework. Using this index, the study measures the extent to which Vietnamese listed firms’ annual reports include the content elements required by the integrated reporting (IR) Framework. The study performs ordinary least square regression to investigate the influencing factors.Findings>The study documents that, on average, Vietnamese listed firms disclose about 43% of the information required by the IIRC Framework. The disclosure levels are positively associated with manufacturing firms, board independence, foreign ownership, government ownership, audit quality and firm size.Originality/value>Integrated reports have been widely adopted in many countries, but it is still a new issue in Vietnam. This is the first paper providing some insights into the inclusion of the content elements required by the IR Framework by listed firms in Vietnam. It also contributes to the disclosure literature by providing empirical evidence on the factors influencing these disclosure levels. Deriving from the findings, the authors offer recommendations for policymakers on the issue of regulating and implementing IR in Vietnam.

Using Benford's law, we find evidence supporting the hypothesis that countries at times misreport their economic data strategically. We group countries with similar economic conditions and find that for countries with fixed exchange rate regimes, high negative net foreign asset positions, negative current account balances, or more vulnerable to capital flow reversals, we reject the first-digit law for the balance-of-payments data. This corroborates the intuition of a simple economic model. The main results do not seem to be driven by countries in sub-Saharan Africa or those with low institutional quality ratings.

With the steady progress of social economy on the time axis, regional economic statistics, as an important basis for countries to understand the local economic development and formulate economic development policies, have also made great strides. At present, because of its powerful analysis and statistical function, network big data has been widely used in regional economic statistics. In this paper, the main component analysis method is used as an example, and the application of large data in regional economic statistics can greatly improve the accuracy of statistics, reduce the error, and through the use of computer technology to analyze the existing problems in the application of regional economic statistics in large-scale data, and put forward a solution.

Informatization is the only way for the third-party logistics enterprises to achieve leapfrog development, which is also the key to the development of modern logistics. However, the Internet of things ( hereinafter referred to as IOT) is the extension and development of information technology. Therefore, IOT will be introduced into the third-party logistics enterprises, which is also an important direction of regional economic development in the future. The huge potential market of IOT can better promote the development of regional economy. As an information technology means, IOT can effectively integrate many industries, which will improve the growth of green economy. Through artificial intelligence, IOT technology can play an important role in regional economic statistics. This paper first analyzes the influencing factors of the statistical data collection of IOT. Then, this paper puts forward the application of IOT technology in regional economic development.