Statistical Journal of the IAOS

Reimund Mink, a former employee from the European Central Bank recently published the book ‘Official Statistics – A Plaything of Politics? On the interaction of Politics, Official Statistics, and Ethical Principles’. The experience from Reimund with government finance statistics in European but also in many non-European countries is a rich source for dedicated reflections and lessons to learn from the roles that official statistics, (can) play in politics. His book informs in great detail on the backgrounds for and details of the role of official statistics and their political use. Ivo Havinga, former Assistant Director Economics Statistics of the United Nations Statistics Division, Department of Economic and Social Affairs, managing partner, and academic, and present senior advisor in statistical information systems for sustainable development, was found willing to interview the author Reimund Mink.

Official statisticians managed to quickly adapt to the consequences of the COVID-19 pandemic. Looking forward, an important issue is whether, and how, one should be fundamentally rethinking the way to produce and consume data in a “new normal” state of the world. The pandemic underlined that data producers have to provide more and more varied types of information to their users. It was also a reminder that the statistical landscape has to permanently evolve. As regards central banks’ statisticians, this calls for relying more heavily on data science, making a better use of the large amount of micro-level information available in today’s modern societies, adapting statistical frameworks to meet evolving policy objectives and user needs, and continuing to closely cooperate with other relevant stakeholders

For many people, their gender is the same as their sex recorded at birth. For some, gender and sex recorded at birth may not align, or they may not fall exclusively into the binary categories of male or female. There is growing recognition of the need to have quality estimates and projections of the population in a context beyond binary sex and gender. However, there is currently little demographic literature on this topic and production of such data is limited. In this paper, we use the demographic equation as a framework to describe the implications of considering sex and gender diversity in the production of population projections. In doing so, we consider implications for base population estimates, births, deaths and migration. We also consider implications of acknowledging gender as a concept that can change over time. We outline existing Australian and international approaches to data collection and address implications for the formation of projection assumptions. We conclude by outlining possible future directions for forming population projections that consider sex and gender beyond the binary.

Cause-of-death statistics is an essential part of health information system. Finland has collected statistics on causes of death for more than 250 years. Since 1936 medical experts at Statistics Finland has been in charge of the coding. Changes in ICD-classification and coding praxis as well as the use of different standard populations and short-lists hampers time trend analyses and international benchmarking. The five Nordic countries and three Baltic countries has made cause-of-death coding comparisons since 2001. A random sample of death certificates are regularly reviewed. This exercise has demonstrated that national coding systems have not always agreed on the main causes of death. However, there has been a clear trend towards greater agreement, even for specific diagnostic groups, such as cancers, external causes and respiratory conditions. Most of the international data collection is voluntary, but the European Union has adopted a mandatory Regulation to ensure that cause-of-death statistics provide adequate information for all EU Member States to monitor Community actions in the field of public health. Since 2011 the data on causes-of-death have to be provided within 24 months after the end of the reference year. Therefore, causes-of-death statistics at Eurostat is more up-to-date than in other international databases.

Social cohesion is a multi-dimensional concept referring to social connectedness, or the ‘glue’ that connects members of a society through bonds of solidarity and trust, within and across communities and organizations, and within society at large. The concept of social cohesion continues to garner interest in public and policy circles, perhaps reflecting the intuitive appeal of the concept and the role that cohesion can play in societies’ abilities to respond to challenges, to function effectively, and to support rewarding lives. As a latent concept that is not directly observable or measurable, social cohesion is often measured through key dimensions. In this context, a dimension refers to a constituent part of social cohesion. Using factor analysis and data from Statistics Canada’s 2020 General Social Survey on Social Identity, this study identifies nine key dimensions of social cohesion. Latent class modelling is then used to sort respondents into three latent classes or groups (“Low”, high “Confidence-Belonging” and high “Trust-Participation” cohesion groups) of individuals that share common traits and prioritize certain dimensions of social cohesion. The probabilistic classification of individuals in accordance with latent classes provides valuable insights into social sorting mechanisms and how this extends to cohesiveness within Canadian society.

The big data sources of National Statistical Offices (NSOs) are provided to make a superior platform for decision-making. The household income and expenditure survey is one of the economically important surveys especially when the inflation rate varies to assess the changes in households’ consumption patterns. In this case, big data can be beneficial and help to accurately measure consumption patterns of urban and rural households at every geographical level. This analysis is an exploratory study for the extraction of the size of injustice and imparity of household income and facilities implemented by classifying and clustering all Iranian households. Through this study, classification and soft clustering (Fuzzy clustering) techniques are employed to characterize the Iranian household types from 2011 to 2021, which are supervised and unsupervised approaches, respectively. Moreover, association rule mining techniques are employed to discover and extract consumption patterns for each cluster. Obtained results showed that there was a significant gap between purchasing power/receiving energy between lowest and highest income households from 2011 to 2021, and this gap is increasing day by day.

In his memoir published in 2002, George Dantzig, who had invented the simplex algorithm to solve linear programming problems, praised Wassily Leontief as a great pioneer for proposing a large but simple matrix model that represents the interindustry input-output structure of an economy. Input-output tables, which depict the transactions of goods and services between industries, have been intensively used to prepare the U.S. economy for World War II, and to eventually liberate Europe. This paper not only revisits the early development of structural analysis but also applies it to the 1939, 1947 and 1958 U.S. input-output tables using triangulation and dispersion indices as fundamental tools. The degree of integrity represented in the Leontief inverse significantly increased as the division of work progressed in the time of war to achieve maximum productivity. The structural changes ensured a smooth transition of the American economy from peacetime to wartime, and later, the fast rebuilding of European economies that had been completely devastated during the war.

The purpose of the article is to explore a complementary approach to the analysis of countries’ participation in global production networks. We have analyzed the methodological aspect from the standpoint of the Micro and Macro theory of international production networks and value chains.

In the context of globalization, production networks are actively formed within both individual industries and at the intersectoral level, and they successfully operate not only within limited territories but also at the interstate, interregional and global levels.

Therefore, the study of methods for analyzing the participation of countries in global production networks is relevant.

The article has used statistical analysis methods. Analytical methods have been used to determine the countries’ leading types of economic activity (fields of specialization, qualitative indicators that characterize each of the industries of the countries). The study of this issue was carried out on the example of the EU countries.

One of the methods of analyzing the assessment of bilateral relations of the partner countries’ national economies is complementarity. The article examines the complementarity index as an indicator that determines the trade structure of partner countries.

We received a model of the Global map of the International Production Network (nodes of trade) by specific industries, such as Manufacturing, Chemicals and non-metallic mineral products, Rubber and plastics products, computers, electronic and electrical equipment, and transport equipment. To obtain accurate results, we selected specific countries: Germany, the USA, Japan and China, and examined their statistics in two dimensions: gross exports and gross imports, in specifically selected industries.

The role of the National Statistical Institution (NSI) is changing, with many now making microdata available to researchers through secure research environments This provides NSIs with an opportunity to benefit from the methodological input from researchers who challenge the data in new ways

This article uses the United Kingdom’s Annual Survey of Hours and Earnings (ASHE) to illustrate the point We study whether the use of prefilled forms in ASHE may create inaccurate values in one of the key fields, workplace location, despite there being no direct evidence of it in the data supplied to researchers. We link surveys to examine the hypothesis that employees working for multi-site employers making an ASHE survey submission are more likely to have their work location incorrectly recorded as the respondent fails to correct the work location variable that has been pre-filled.

In the short-term, suggestions are made to improve the quality of ASHE microdata, while longer-term we suggest that the burden of collecting additional data could be offset through greater use of electronic data capture. More generally, in a time when statistical budgets are under pressure, this study encourages NSIs to make greater use of the microdata research community to help inform statistical developments.

Conventional wisdom holds that North American Industry Classification System (NAICS) codes chosen by people not experienced with the system are often mis-specified, but there has been little formal research into the scope of the problem. In this paper we explore prevalence of and patterns in misspecification in NAICS codes self-reported on two kinds of business tax forms. Errors are identified by comparing as-filed codes with codes validated by Statistics of Income. We find that over a third of codes are wrong, but that the errors are not random and often (though not always) seem to have logical reasons behind them.

In recent years there has been much debate about the need to supplement existing economic indicators, most notably gross domestic product (GDP), with a more rounded and balanced set of indicators that better reflect the complexity of today’s economic, societal and environmental needs. To address this need, the United Nations Conference for Trade and Development (UNCTAD) in cooperation with the Eurasian Economic Commission (EEC) developed a first prototype composite index measuring inclusive growth tailored to that region. This paper summarises the conceptual objectives and some of key methodological challenges and considerations faced in compiling such an index. Many of the challenges and methodological considerations are universal and not specific to EEC. A brief outline of results and future work is also detailed.

How to detect financial bubble? In response to this question, a vast amount of empirical research is devoted spanning almost half-century. However, identifying unambiguously the presence of a bubble in the financial time series remains an unsolved problem in standard econometric and financial economic approaches. In this paper, we study the impact of auto-correlated innovations, which is a most common feature of the financial time series, on recently developed unit root tests with varying lag to detect financial bubbles. We apply the more powerful test procedure to identify bubble on the quarterly observations of house price-rent ratios of 4 counties. The results of the study suggest that rolling Max Supremum Augmented Dickey-Fuller (MSADF) test as the best test procedure to detect financial bubbles in the future.

West Java is one of Indonesia’s Provinces with a high disaster risk index. The Province has experienced earthquakes, volcanic eruptions, tidal waves, droughts, landslides, floods and tsunamis. In the event of a disaster, the government’s budget allocation is used for handling losses due to disasters. Seeing these conditions, it is necessary to carry out disaster risk management efforts to reduce losses by using natural disaster insurance by households. Publication of household insurance expenditures is essential to see the community’s ability to pay insurance for handling losses due to disasters. This study aims to estimate the average household expenditure on insurance and the average total household expenditure by district/city in the West Java Province. The analytical method used is the Small Area Estimation using the Fay-Herriot model with logarithmic transformation. The data source is the National Socio-Economic Survey (SUSENAS) for March 2019. The results show the proportion of insurance expenditure to total household expenditure (ability to pay) was relatively low, ranging from 0.93%–2%. This result is still far from the ability to pay insurance in developing countries, which is 5%. This finding can be used to see the power of households to pay for insurance (ability to pay). The government can take policies related to this to minimize losses due to natural disasters in the West Java Province.

Mexico’s National Institute of Statistics and Geography (INEGI) is exploring new opportunities to improve its information search service, with the aim of increasing the accessibility of official statistical data. The upgraded search engine will include a new component that offers more sophisticated search capabilities. These include the ability to conduct intelligent searches that do not require an exact match of the search text, as well as the expansion of searches using related ad-hoc terms. Additionally, the new component will provide feedback through the most appropriate relations. To achieve this, the system will utilize neural network-based distributional word representation systems to identify relationships between related terms. The vector spaces and representation will be manipulated to keep connections within the most relevant vocabulary for the institute’s type of searches. The usability testing department at the institute conducted blind pilot tests to compare the quality reported by users with and without the new enhancements. Although the evaluation survey showed significant improvements in the search engine’s performance, the tool presented is just the first step towards a system that allows continuous interaction and feedback with users to improve the quality of the responses presented. This strategy is not currently implemented by the institute, making this an immediate and easy-to-replicate approach for obtaining useful interactions with users.