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Purpose: is to develop recommendations for the construction of new indices of innovative development designed for cross-country comparisons.Methods: the conventional general scientific methods were used in the article, such as the ones of description, comparison, system analysis and historical comparison. The study was based on the documents describing calculation methods of the economic indices and scientific publications covering the history, specifics and problems of the practical application of those indices.Results: it is shown that since the 1930s, when the creation of economic indices drew attention of economic science, indices have evolved from the relatively simple statistical indicators to the complex tools with sophisticated calculation methods. Using several of the most famous or remarkable indices as an example, the principal methods currently used in assessing the level of innovative development are revealed. Basing on the conducted research, the major requirements for composing innovative development indices are stipulated.Conclusions and Relevance: it has been found out that in order to achieve the maximum objectivity in compiling indices of innovative development, the widest possible range of significant indicators should be used and a pool of experts should be engaged in their selection. It is also advisable to engage the pool in compiling the index formula, in particular the indicators weights. Data normalization should be used to ensure comparability of incommensurable indicators, and statistical outliers are advised to be neutralized through standardization techniques. It is also necessary to take into account the goals of the index compiled. At the same time, in the future it is necessary to take into account relationships between the actors of national innovation systems and between different innovation systems, effects of scale and, when possible, sectoral specialization of countries and other national specifics.

Subject. Reduction of fire safety threats based on the development of an economic and statistical unit as part of the fire monitoring system in the administrative-territorial entities of the Russian Federation, which allows for statistical processing of information on the economic consequences of fires.Purposes. Adaptation and application of economic and statistical methods to identify trends in the number of fires and the material damage they cause to the regional economy, as well as determining fire risk indexes on this basis, identifying the interrelationships of the levels of development of the regional economy by the number of fires and their negative impact on the economy.Methodology. In the applied aspect, methods of data integration, determining averages, identifying trends, calculating risks and determining correlation dependence, as well as a graphical method are used.Results. The identification of bodies of interest at various levels of state management of the socio-economic development of Russia and its regions of economic and statistical information on fire safety issues has been carried out. Data on the number and material damage from fires in the regions of the Southern Federal District (SFD) of Russia for the period 2016-2022 were integrated and trends in these indexes were determined. Fire risks are calculated and these regions are ranked according to the degree of danger. The dependence of the number and severity of economic consequences from fires on the level of economic potential of the studied regions of the Southern Federal District has been revealed.Conclusions. The results of the conducted research can be used to create a specialized economic and statistical unit as part of fire monitoring by the Fire Safety Service and The Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters (Emercom of Russia), as well as the country’s Emergency Commissions (regional and federal levels). The calculated data obtained can be used to assess the fire safety levels of the studied regions and the Southern Federal District as a whole, which will allow solving a range of issues related to the management of these regions and the development of programs for their socio-economic development.

With the rapid development of cloud computing technology, sensor networks are becoming more and more important in the application of economic statistics in enterprises. This paper aims to discuss the application prospect and effect of cloud computing detection based on sensor network in enterprise economic statistics. The research identifies the needs and objectives of enterprise economic statistics, and determines the appropriate scenarios for the application of sensor networks. The sensor nodes are arranged and distributed in the areas that need to be monitored, and the sensor nodes are connected to the cloud computing platform through wireless communication technology. Sensor nodes collect data on a regular basis and transmit it via wireless communication to the cloud computing platform. The cloud computing platform will store and process the received data, and can use machine learning and data mining algorithms to analyze and predict the data. The collected data are tested and analyzed, and the corresponding results are obtained. Results The advantages of cloud computing detection based on sensor network in the application of enterprise economic statistics are summarized, and further research directions and prospects are put forward.

This paper aims to produce an experimental economic statistic for the tourist accommodation services for Belo Horizonte under a flexible geography perspective. The starting point is the definition of economic activities in the tourist accommodation concept. The methodology adopted is up-down divided into four steps including filtering processes to accompany the definition, discounts related to obtaining information only for the tourists and procedures for adjustments to the statistics always being compared to the official disclosures and respecting the sum of regions. This analysis revealed an erratic behaviour of the number of establishments in Belo Horizonte across the years and half of the workforce is concentrated in micro and small enterprises. The data from the perspective of the flexible geography allowed to verify where the almost R$ 86 million indicated by the value added of the tourist accommodations were located in 2015.

A teaching model is a stable teaching procedure established under the guidance of certain teaching ideas or theories. As a methodological major in higher education, economic statistics cross various fields of natural science and social science, showing the characteristics of intersection, integration, and marginality. Therefore, this paper proposes a multi-attribute decision-making (MADM) framework for teaching model evaluation based on heterogeneous information. First, the attribute system of competition–academic research–master of knowledge–practical operation (CAMP) is constructed. Second, heterogeneous information is introduced in the process of teaching model evaluation; Third, a weight determination method based on a trust relationship of the fuzzy–social network is proposed, which provides a better solution to the problem of decision makers’ (DMs’) weight allocation in teaching model evaluation. Furthermore, a combined attribute weights determination method under an intuitionistic fuzzy number is constructed, which improves the shortcomings of the weight method in teaching model evaluation. Finally, through empirical research and stability analysis, the proposed evaluation framework has good effectiveness and feasibility, and policy suggestions for improvements to the economic statistical teaching model are then proposed.

Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level production networks within a wider space of networks that are different in nature, but similar in local connectivity structure. Through this lens, we study a regional and a national network of inferred trade relationships reconstructed from Dutch national economic statistics and re-interpret prior empirical findings. We find that company-level production networks have so-called functional structure, as previously identified in protein-protein interaction (PPI) networks. Functional networks are distinctive in their over-representation of closed squares, which we quantify using an existing measure called spectral bipartivity. Shared local connectivity structure lets us ferry insights between domains. PPI networks are shaped by complementarity, rather than homophily, and we use multi-layer directed configuration models to show that this principle explains the emergence of functional structure in production networks. Companies are especially similar to their close competitors, not to their trading partners. Our findings have practical implications for the analysis of production networks and give us precise terms for the local structural features that may be key to understanding their routine function, failure, and growth.

Uncertainty is an inherent part of knowledge, and yet in an era of contested expertise, many shy away from openly communicating their uncertainty about what they know, fearful of their audience's reaction. But what effect does communication of such epistemic uncertainty have? Empirical research is widely scattered across many disciplines. This interdisciplinary review structures and summarizes current practice and research across domains, combining a statistical and psychological perspective. This informs a framework for uncertainty communication in which we identify three objects of uncertainty—facts, numbers and science—and two levels of uncertainty: direct and indirect. An examination of current practices provides a scale of nine expressions of direct uncertainty. We discuss attempts to codify indirect uncertainty in terms of quality of the underlying evidence. We review the limited literature about the effects of communicating epistemic uncertainty on cognition, affect, trust and decision-making. While there is some evidence that communicating epistemic uncertainty does not necessarily affect audiences negatively, impact can vary between individuals and communication formats. Case studies in economic statistics and climate change illustrate our framework in action. We conclude with advice to guide both communicators and future researchers in this important but so far rather neglected field.

In 1918 the Central Statistical Administration (TsSU) was founded with the support of Lenin. Pavel Illich Popov was its first director (1918-1926). TsSU proceeded to the merging and centralization of the former decentralized statistical system of the zemstva (local governments) inherited from Tsarist regime. Between 1918 and 1928, i.e. during the civil war and the NEP, TsSU was at the heart of Soviet Statistics but it came to be controlled and eventually absorbed by Gosplan. At the TsSU Popov defended his view of social statistics as being a device for the revolution and the dictatorship of the proletariat in the political sphere while resting on strong scientific foundations in accordance with international standards. Rooted in Russian statisticians’ tradition, the objectivity and scientific nature of the works issued by the TsSU was Popov’s credo. In 1926, Popov exposed his views in a collective book he edited and published by the TsSU, The Balance of the National Economy of the USSR, 1923-24. In The Balance was exposed for the first time the modern principles of national accounting and intersectoral macroeconometric analysis. According to Popov, these were the relevant devices for social engineering in the NEP. Against “utopian” authors, Popov stressed the importance of founding social engineering and planning on economic theory and political economy. He thus explained how his statistical balance was an outgrowth of Quesnay’s Tableau économique and Marx’s schema of reproduction. Our article aims at examining Popov’s specific contribution as well as identifying its stakes and its significance in the economic debates during the 1920s.

We publish two among the economic statistic proposals presented by Anton Golopentia to the State Planning Commission (SPC) of Romania during 1949. The first is a Note in which, in answer to the problem raised by the SPC, A.G. considers the possibility of dividing the territory of Romania into four economic regions of comparable strength. The second is the proposal to direct the production, in two months, by a group of former employees of CISR, of a Small Economic and Social Atlas of the P. R. of Romania, whose summary is attached.

The traditional ways of measuring global sustainable development and economic development schemes and their progress suffer from a number of serious shortcomings. Remote sensing and specifically nighttime light has become a popular supplement to official statistics by providing an objective measure of human settlement that can be used as a proxy for population and economic development measures. With the increased availability and use of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and data in social science, it has played an important role in data collection, including measuring human development and economic growth. Numerous studies are using nighttime light data to analyze dynamic regions such as expansions of urban areas and rapid industrialization often highlight the problem of saturation in urban centers with high light intensity. However, the quality of nighttime light data and its appropriateness for analyzing areas and regions with low and fluctuating levels of light have rarely been questioned or studied. This study examines the accuracy of DMSP-OLS and VIIRS-DNB by analyzing 147 communities in Burkina Faso to provide insights about problems related to the study of areas with a low intensity of nighttime light during the studied period from 1992 to 2012. It found that up to 57% of the communities studied were undetectable throughout the period, and only 9% of communities studied had a 100% detection rate. Unsurprisingly, the result provides evidence that detection rates in both datasets are particularly low (3%) for settlements with 0−9999 inhabitants, as well as for larger settlements with population of 10,000−24,999 (28%). Cross-checking with VIIRS-DNB for the year 2012 shows similar results. These findings suggest that careful consideration must be given to the use of nighttime light data in research and global comparisons to monitor the progress of the United Nation’s Sustainable Development Goals, especially when including developing countries with areas containing low electrification rates and low population density.

This study extends previous applications of DMSP OLS nighttime lights data to examine the usefulness of newer VIIRS lights in the estimation of economic activity. Focusing on both US states and metropolitan statistical areas (MSAs), we found that the VIIRS lights are more useful in predicting cross-sectional GDP than predicting time-series GDP data. This result is similar to previous findings for DMSP OLS nighttime lights. Additionally, the present analysis shows that high-resolution VIIRS lights provide a better prediction for MSA GDP than for state GDP, which suggests that lights may be more closely related to urban sectors than rural sectors. The results also indicate the importance of considering biases that may arise from different aggregations (the modifiable areal unit problems, MAUP) in applications of nighttime lights in understanding socioeconomic phenomenon.

Economic statistics are frequently reported in the form of index numbers. This article considers how the field of Index Numbers should be approached in the teaching of a general economic degree. While the topic finds a natural home in statistics modules, it is emphasised that the area can also be referred to in the teaching of other areas of economics. It is also emphasised that the differences between Index Numbers theory and the practice of compiling economic statistics such as inflation can help students gain a better understanding of applied economic statistics. Methods for assessing learning in the area are also considered and available material to support teaching is also summarised.

Considering that the motions of the complex system structural units take place on continuous, but non-differentiable curves, in the frame of the extended scale relativity model (in its Schrödinger-type variant), it is proven that the imaginary part of a scalar potential of velocities can be correlated with the fractal information and, implicitly, with a tensor of “tensions”, which is fundamental in the construction of the constitutive laws of material. In this way, a specific differential geometry based on a Poincaré-type metric of the Lobachevsky plane (which is invariant to the homographic group of transformations) and also a specific variational principle (whose field equations represent an harmonic map from the usual space into the Lobachevsky plane) are generated. Moreover, fractal information (which is made explicit at any scale resolution) is produced, so that the field variables define a gravitational field. This latter situation is specific to a variational principle in the sense of Matzner–Misner and to certain Ernst-type field equations, the fractal information being contained in the material structure and, thus, in its own space associated with it.