Statistical Journal of the IAOS

Inequality and environmental changes are among of the most pressing policy challenges of our century and yet national accounting still largely fails to adequately measure these issues. This paper presents current efforts to distribute National Income and National Wealth in a way that is fully consistent with the National Accounts framework. It also discusses options to extend distributional accounting to the domain of environmental accounts.

This paper describes how the Australian Labour Account supports macro-economic analysis of peoples’ participation in employment and related production over time. Development of Labour Accounts have provided an opportunity to significantly improve the quality of aggregates such as the number of jobs occupied within each industry, measures of hours worked, and labour productivity growth. Finally, it provides an opportunity to further emphasise the household experience within the system of economic accounts, linking from production activities to important demographic and socio-economic issues.

Labour accounts provide an orderly manner to compare, reconcile and built-on data from different sources. The procedures may in principle be applied to topics other than labour. As part of a research project at the Statistical Research and Training Center of Iran, an attempt was made for the first time to develop labour accounts for Iran for the reference year 1390 (March 2011–February 2012). The procedure is described in the first part of the paper. In the second part, comparative tables for Australia, Denmark and Iran are constructed on each of the three main elements (employment, hours of work and employment-related income). In the process of comparing the data, the differences in the underlying methodologies are reviewed and an assessment is made on the way forward. To date, very few countries have attempted to construct labour accounts. The three countries discussed in this paper represent a diverse set of statistical systems, but have in common, particularly, Iran and Australia, the same concepts and definitions in line with the ILO international standards.

Natural capital and its increasing scarcity have been at the heart of concerns over sustainability for many decades. This paper highlights the significant advances in accounting for the stocks and flows of natural capital that have taken place in the statistical community through the ongoing development and implementation of the System of Environmental-Economic Accounting (SEEA). Through description of the history and key components of the SEEA and through presentation of various examples of accounting from around the world, this paper demonstrates not only the theoretical advances but also the feasibility and relevance of SEEA based accounts to policy making. The increasing recognition of the threats of climate change and the importance of halting biodiversity loss and maintaining healthy ecosystems which provide essential contributions to people, make the implementation of the SEEA extremely timely and relevant in supporting policies that take into account the environment. There is now clear support from the official statistics community and a clear role for national statistical offices in using the SEEA to go “beyond GDP”. We can no longer afford to ignore our dependence on the environment, our natural capital. Accounting for it is part of the pathway forward.

The United States’ Bureau of Economic Analysis (BEA) has recently published statistics exploring the size and growth of the digital economy in response to the interests of the data user community and the international statistical community. BEA independently developed preliminary digital economy statistics but has relied on consultation with other statistical organizations and participation in numerous international working groups aimed at advancing coordinated and internationally comparable digital economy measurement. This report describes BEA’s digital economy measurement efforts to date including initial work towards a digital economy satellite account and related research on quantifying the value of “free” digital media the treatment and measurement of data. This report also discusses BEA’s efforts to improve price measures for high-tech goods and services, notably internet and wireless services, cloud services, and ride-hailing services. Lastly, the report provides an overview of BEA’s measurement work related to digital services international trade.

The System of National Accounts (SNA) has adapted, and will adapt, as economic, social and environmental conditions change. The revision process of the SNA2008, which is now underway, will take place in the context of developments such as globalization, digitalization, climate change, biodiversity loss, inequality as well as the COVID19-pandemic. The new SNA will have to make clear how the economy relates to concepts such as wellbeing, sustainability and equity and will also need to be linked to major global initiatives such as the Sustainable Development Goals (SDGs).

I propose a broad accounting framework for Wellbeing, Sustainability and Equity (WiSE). This provides a wider context for the System of National Accounts (SNA) and links to the other frameworks such as the Sustainable Development Goals, and other global initiatives such as the Better Life Initiative (OECD), Changing Wealth of Nations (World Bank) and the Inclusive Wealth Index (UN).

The WiSE framework is not a new system, but rather a combination of existing accounting frameworks which have been proposed in the last five decades. The paper starts off by formulating principles to guide the work on the broader framework. Subsequently, seven accounts are proposed which quantify the various dimensions of the economic, societal and environmental systems. This interdisciplinary accounting framework involves knowledge from many scientific disciplines and multiple units are used (mass, energy, people, time, money etc).

The most controversial part of any discussion about the future of the SNA is the valuation of non-market phenomena such as unpaid household work/care or environmental damages. This paper argues that the discussion is too focused on methods derived from welfare economics. Rather than valuation we should be focusing on evaluation methods from many scientific disciplines which help to assess progress towards wellbeing, sustainability and equity. This interdisciplinary perspective should also guide our thinking to select key indicators to replace GDP.

The Financial intermediation services indirectly measured (FISIM) is a concept used in national accounts to value the activity of banks as intermediaries between depositors and borrowers and widely used in conventional banking. Unlike conventional banking, Islamic banking declared their loans as financing in financial position statement. While interest expense and interest received declared as “profit distributed to depositors” and “income derived from investment”. The terms were compliant by the shariah law in Malaysia and this paper show on how the calculation of FISIM for Islamic banking in the context of the 2008 System of National Accounts. The calculation process is similar to conventional banks methodology, but the difference was only terms of financing instead of loans that have been used in Islamic Banking.

Society’s demand for data-driven, fact-based information continues to increase. National statistical offices play a critical role in providing this demand-driven information to support evidence-based policy making. Thereby transforming from suppliers of official statistics to providers of trusted smart statistics.

The digital transformation, data revolution and emergence of “big data” all influence the way NSOs collect data. Data are everywhere, generated by everything and everyone being stored in numerous locations and devices.

The nature of data collection is bound to change. Using solely primary data collection would be too time-consuming, costly and burdensome to satisfy the increasing demand. NSOs should aim to use the vast amounts of data available in our digital society to be used as inputs for new statistical products, to supplement existing data acquisition or as replacements for existing survey inputs.

Many areas must be taken into account including new data sources, collection methods and collection process redesigns. This comes with consequences with respect to methodology, technology, quality, metadata and standards, confidentiality, privacy etc. Knowledge development requires collaboration between NSOs, governments, end users, academic institutions, research organizations and private sector companies. Social acceptability needs to increase to maximize the benefit of these data sources to produce smart statistics.

This paper highlights the key characteristics and implications of the strategic and data production frameworks designed and progressively implemented by the United Nations Committee of Experts on Business and Trade Statistics (UNCEBTS) to enhance the relevance, accuracy and coverage of business statistics, according to an internationally comparable, result-oriented and sustainable approach. The strategic framework aims to expand the traditional scope of official business statistics by including all relevant environmental and social related issues. NSOs may achieve relevant improvements by focusing their efforts upon specific global goals consistent with their national ones, and sourcing from knowledge sharing with other countries and international coordination. It also highlights the relevance of an enterprise-centered approach for a better understanding of emerging phenomena by official statisticians, and for priority setting in improving the quality of business statistics. The data production framework is dominated by the crucial role of the Statistical Business Register (SBR) as the backbone of any current and future improvements in the relevance and accuracy of business statistics. Its implications, both in terms of sustainability of production lines, data integration and production of new indicators that exploit the variability dimension of business statistics are further investigated in the paper.

With the UN General Assembly’s adoption in September 2015 of the Sustainable Development Goals (SDGs) to be reached in 2030 a new, overarching and prominent policy framework was born. Following the adoption, the Danish government made Statistics Denmark (SD) the responsible authority for national SDG reporting. The strategy to fulfill this task has been to work closely with stakeholders in the business community, public authorities and NGO’s. SD has further worked to implement the SDG-framework on the business sector, in particular inspired by work done in UNCTAD about indicators for business entity reporting on economic, environmental, social and institutional issues. The work in SD has shown that it is possible to provide SDG-relevant information by type of industry using existing statistical data. Emission of CO2 and energy efficiency are examples from the environmental economic accounts. Social and business statistics can provide data on employment and wages broken down by gender, and also follow the development in occupational injuries, just to mention a few examples. However, existing data cannot provide information on how enterprises work with the SDGs. To cover this aspect, a survey among the largest private Danish enterprises was conducted in 2019. A third type of data source may be created by adding additional questions to existing surveys. The article is concluded by a discussion on the lessons learned so far and possible next steps are outlined.

Micro data linking (MDL) has become an important cornerstone in the production of new statistical insights. MDL is now widely acknowledged as a strategic activity to avoid increasing the respondent burden when meeting new user demands on e.g. globalisation. MDL is a strong method in combining micro data on individual entities such as enterprises, people or in a combination of both (Linked Employer-Employee Data). MDL is one of the most powerful methods to answer urgent questions on emerging policy or research topics such as the interconnectedness of the economies and its consequences for jobs, income and growth. Not only for national purposes, where economic behavior and dynamics by enterprises can be expressed in terms of job dynamics, income and welfare for its citizens, but also from an international perspective where consistent and coherent indicators play an important role. Firstly, the article addresses the central role of the Business Register for any MDL approach related to business statistics; secondly the current official statistics based on MDL such as Trade by Enterprise Characteristics (TEC) are described. Thirdly, the most common use of MDL in terms of producing experimental statistics are described, including linked employer-employee data (LEED).

Collecting data from businesses faces ever-larger challenges, some of them calling for an overhaul of underlying methodology, e.g. motivation for participating is low; technology is shaping data collection processes; response processes within businesses are imperfectly understood while alternative data sources originating from digitalization processes push the response process (thus also response quality) further out of our sight. The paper reviews these challenges, discusses them in light of new developments in the field, and proposes directions for future research. This review may help those that collect data from businesses (e.g. national statistical institutes, academia, and private statistical agencies) to reconsider their current approaches in light of what promises to work (or not) in today’s environment and to build their toolkit of business data collection methods.

COVID-19 outbreak has triggered many economic shocks globally. In this study we estimate the role of inter-households transfer in mitigating the impacts of the outbreak on Indonesian economy using a CGE model. The result shows that commodity prices and enactment of physical distancing measures bring negative impacts on the economy. Government response by lowering direct tax rates and increasing transfer to households could not fully compensate the impacts but enlighten it slightly. Households response by increasing inter-household transfers helps the government policy, particularly in reducing the decrease of households’ income and consumption. The result indicates that inter-household transfer could be regarded as an effective instrument to improve the household income distribution quality and reduce the poverty. Regarding that, stakeholders in the economy should improve the collaborative policies to capitalize the policy instruments optimally. Furthermore, the result also indicates that household consumption is not a sustainable engine to boost the economic growth. Prioritizing consumption over saving in the long run could lead to inability of the economy to engage a self-financed investment.

Statistical Business Registers (SBR) have historically underpinned the compilation of economic statistics by providing consistent unit structures and classifications for survey frame production and business demography data. To meet emerging data needs for both regular statistical production releases and for specific questions asked by policy makers, the SBR can also be used as a data integrating framework. This paper outlines the “spine” approach proposed by the Australian Bureau of Statistics (ABS) to support more flexible integration and linking of firm-level data that will also expand the uses of the SBR. The spine is the minimum set of information required to identify an entity and act as the linking variable(s) to other datasets. Its application involves a new approach to management of input datasets and can be applied across statistical registers.

This paper will provide (1) a description of the ABS spine proposal for statistical registers; (2) benefits of a spine approach for both regular statistical production and new data solutions; and (3) an overview of how the ABS BLADE (Business Longitudinal Analysis Data Environment) is used to integrate firm-level datasets to enable policy evaluation and statistical research by analysts from government and academia.

The importance of Multi National Enterprise Groups (MNEs) on the economy is ever-growing and at the same time, it becomes more complex to capture their activities and structures accurately in official national statistics. The establishment of Large Case Units and the introduction of profiling of MNEs are measures to capture the activities of MNEs correctly so that consistency between statistics can be achieved.

At international level, the same challenges can be found. In Europe the existence of the European Statistical System and accompanying legal frameworks make it possible to organize European collaboration, resulting in a EuroGroups Register and European profiling and the Early Warning System.

The benefit from a Global Group Register (GGR) seems evident: providing unique identification of MNEs and insight in the structure of internationally operating MNEs helps to create valuable information for policymakers on many different economic themes. At a global level, we do not have legal facilities like those in the EU, which makes it important to look for other solutions. An initial GGR has to be built upon publicly available sources and upon sources from commercial data providers. The benefits of establishing a Global Group Register are multiple and work in this area should be encouraged.

The professional discussion on “The future of economic statistics” has a practical driver: Economic statistics, produced by national statistical offices, face severe difficulties in describing the national and global economic development in a relevant and coherent manner. This is not only our perception as statisticians – there is a growing criticism towards traditional economic statistics among researchers, policymakers and other users. In this article, we reflect on the factors that have caused the current situation and propose solutions to improving the situation by data sharing. One aspect of the solution relates to the role of national statistical offices. Instead of being solely national institutions, dealing with national data only, they should exploit the possibilities of using statistical data, collected by statistical authorities of other countries, to produce better quality economic statistics. The other aspect of the solution is the sharing of innovative practices to understand and correctly record the activities of multinational enterprise groups (MNEs). The proposals we make in this article are not restricted to MNEs but are applicable to any type of economic activity with a cross-border dimension. The observations we make here are based on the work done when preparing the UNECE Guide to Sharing Economic Data.

As statistical data is becoming more accessible, available in bigger and more complex datasets and can be analysed and interpreted in so many ways, opportunities exist for modernising the development processes for statistical classifications and its responsiveness to emerging user demands. Metadata modelling along with the use of semantic software tools enables significant advances to be explored in the way that traditional statistical classifications are developed, maintained, updated and implemented.

The system of economic statistics is one where there is overlap in concepts, definitions, classifications and metadata which often makes search and discovery by non-expert users challenging. New methodologies for managing and describing data, and the categories to which they are classified can benefit from a greater uptake of semantic web technology, such as Simple Knowledge Organisation Systems (SKOS), and Resource Description Frameworks (RDF).

This paper explores new approaches to statistical classifications and their role in the future of economic statistics through the use of metadata, conceptual and entity modelling rather than the traditional methodology of hierarchically structured, sequentially code based statistical classifications.

Classification of enterprises by main economic activity according to NACE codes is a challenging but important task for national statistical institutes. Since manual editing is time-consuming, we investigated the automatic prediction from dedicated website texts using a knowledge-based approach. To that end, concept features were derived from a set of domain-specific keywords. Furthermore, we compared flat classification to a specific two-level hierarchy which was based on an approach used by manual editors. We limited ourselves to Naïve Bayes and Support Vector Machines models and only used texts from the main web pages. As a first step, we trained a filter model that classifies whether websites contain information about economic activity. The resulting filtered data set was subsequently used to predict 111 NACE classes. We found that using concept features did not improve the model performance compared to a model with character n-grams, i.e. non-informative features. Neither did the two-level hierarchy improve the performance relative to a flat classification. Nonetheless, prediction of the best three NACE classes clearly improved the overall prediction performance compared to a top-one prediction. We conclude that more effort is needed in order to achieve good results with a knowledge-based approach and discuss ideas for improvement.

This paper describes an approach for combining Landsat and Radarsat satellite images to generate national statistics for urban ecosystem accounting. These accounts will inform policy related to the development of mitigation measures for climatic and hydrologic events in Canada. Milton, Ontario was used as a test case for the development of an approach identifying urban ecosystem types and assessing change from 2001 to 2019. Methods included decomposition of Radarsat images into polarimetric parameters to test their usefulness in characterizing urban areas. Geographic object-based image analysis (GEOBIA) was used to identify urban ecosystem types following an existing classification of local climate zones. Three supervised classifiers: decision tree, random forest and support vector machine, were compared for their accuracy in mapping urban ecosystems. Ancillary geospatial datasets on roads, buildings, and Landsat-based vegetation were used to better characterize individual ecosystem assets. Change detection focused on the occurrence of changes that can impact ecosystem service supply – i.e., conversions from less to more built-up urban types. Results demonstrate that combining Radarsat polarimetric parameters with the Landsat images improved urban characterization using the GEOBIA random forest classifier. This approach for mapping urban ecosystem types provides a practical method for measuring and monitoring changes in urban areas.