Economic Statistics Research Articles

Toward evidence-informed policymaking in Nigeria? A systematic review of agricultural evidence syntheses

This article studies the body of agriculture evidence synthesis in Nigeria as a basis for evidence-informed policymaking (EIPM). EIPM is seen by its advocates as an objective way of identifying problems and proffering solutions that work, given its potential to offer sound bases for choices, helping with more effective decisions, and preventing detrimental policy outcomes. Yet, according to the EIPM literature, policymakers hardly use evidence or use it to justify pre-existing beliefs. At the same time, EIPM is often criticized as being susceptible to bias, especially given the large volumes of research being published that may arrive at different conclusions. One tool that could address these challenges is research evidence syntheses, known to be objective and rigorous, although there is a gap in knowledge on whether they are produced in ways that make them easily usable by decision makers. To bridge this knowledge gap, this study develops an analytical framework from the literature on facilitators and barriers of scientific evidence use to analyze existing agriculture-based evidence syntheses in Nigeria. The analytical framework was used to analyse and identify gaps in these syntheses that may limit their use by decision makers. Based on pre-defined search criteria, we find 19 relevant syntheses which are more qualitative and useful in defining policy-relevant problems; however, they mostly do not provide quotable economic statistics, solutions to address the problems identified, or implementation strategies. Given the currently limited number of evidence syntheses, especially those evaluating effectiveness of policy solutions, stakeholders interested in strengthening EIPM in Nigeria could facilitate collaborations between policymakers and researchers to popularize policy-relevant evidence synthesis and ensure such are made available and accessible to users in timely and usable formats.

The Place of World Economy Research in the Heterodox Analytics of Eurasian Integration

Aim. The article shows the increased productivity of heterodox approaches in the part of economics that studies the problems of the world economy, including regional integration.Methods. Illustrations of the apparent paradoxicity (for representatives of the economic mainstream) of geopolitical and world economic dynamics after 2022 are used to explain the importance of analyzing economic processes in the world economy, taking into account approaches of related social disciplines. The connection of some important scientific results of Russian scholars in the field of economics with the achievements of historians, orientalists and cultural scientists, representatives of economic and political geography and studies of international relations is shown.Results. The article describes the boundaries of the mathematization of the considered segment of heterodox analysis related to the unreliability of world economic statistics, with examples of the successful application of approximate (in a broad sense relative to mathematical terminology) calculations to assess shifts in foreign trade and foreign direct investment in contemporary Russia. The author demonstrates that the heterodox approach reveals the absence of contradiction between Russia’s intensification of Eurasian economic integration (including within the narrow limits of the EAEU) and our country’s efforts to build a more equitable polycentric world order, including strengthening the BRICS format. Greater interdisciplinary transparency for heterodox economists against the background of representatives of the mainstream, among other things, makes it possible to intensify fruitful cooperation between scientists and practical analysts representing analytical centers — think tanks of the post-Soviet space and neighboring countries in the interests of developing integration projects.Conclusions. The proposed methodological framework can enhance the effectiveness of the work of the analytical community of Russia and its friendly countries in obtaining new applied conclusions in the economic sphere, especially in terms of enhancing the effectiveness of integration efforts in Greater Eurasia. At the same time, heterodox economists are more receptive to cooperation with historians, political scientists and other analysts who have focused on Eurasian economic integration.

Curbing household food waste and associated climate change impacts in an ageing society.

We explored the intricate quantitative structure of household food waste and their corresponding life cycle greenhouse gas emissions from raw materials to retail utilizing a combination of household- and food-related economic statistics and life cycle assessment in Japan. Given Japan's status as a nation heavily impacted by an aging population, this study estimates these indicators for the six age brackets of Japanese households, showing that per capita food waste increases as the age of the household head increases (from 16.6 for the 20's and younger group to 46.0 kg/year for 70's and older in 2015) primarily attributed to the propensity of older households purchase of more fruits and vegetables. Further, the largest life cycle greenhouse gases related to food waste was 90.1 kg-CO2eq/year for those in their 60's while the smallest was 39.2 kg-CO2eq/year for 20's and younger. Furthermore, food waste and associated emissions are expected to decline due to future demographic changes imparted by an aging, shrinking population after 2020 until 2040. Specific measures focused on demographic shifts are crucial for Japan and other countries with similar dietary patterns and demographics to achieve related sustainable development goals through suppressing food waste and associated emissions under new dietary regimes.

What does e-invoice data bring to SNA and real-time economy?

This paper explores the potential of electronic invoice data to enhance the System of National Accounts (SNA) and facilitate a Real-Time Economy (RTE). The rise of data science and advancements in information and communication technologies have increased the use of big data in economic statistics. E-invoice data is being digitized in Europe to detect VAT fraud, but it also contains a vast amount of information on economic entities that make up the SNA. This study focuses on integrating electronic accounting information to improve the coverage, granularity, and update speed of SNA. The paper examines the concept of RTE, which emphasizes real-time data processing and automation, particularly in Estonia and Finland. It also highlights the benefits and challenges of using invoice data for economic statistics, including issues related to data storage formats, legislative systems, and the coordination of data linkages. The research demonstrates invoice data’s theoretical and practical applications for constructing Input–Output Tables (IOTs) and monitoring financial situations. Despite the technical and institutional hurdles, the study suggests integrating invoice data can significantly transform economic statistics.

Inside ANEMIA of CKD: Projecting the Future Burden of Anemia of Chronic Kidney Disease and Benefits of Proactive Management: A Microsimulation Model of the Chinese Population.

Anemia is a common comorbidity of chronic kidney disease (CKD) that has been associated with increased risk of complications, healthcare expenditure, and reduced quality of life. In China, the treatment of anemia of CKD has been reported to be suboptimal in part because of a lack of awareness of the condition and its management. It is therefore important to raise awareness of the condition by estimating the future health and economic burden of anemia of CKD and also to understand how it may be addressed through proactive policies. This study aims to project the health and economic burden of anemia of CKD, in China, from 2023 to 2027 and to estimate the impact of a hypothetical intervention on related clinical and cost outcomes. A virtual Chinese population was simulated using demographic, clinical, and economic statistics within a validated CKD microsimulation model. Each individual was assigned a CKD stage, anemia stage, comorbidity status (type2 diabetes, hypertension), complication status (stroke, heart failure, and/or myocardial infarction), and a probability of receiving treatments and therapies. Annual direct healthcare costs were assigned and based on these factors. The hypothetical intervention reduced the prevalence of moderate and severe anemia by 5% annually. This hypothetical scenario was chosen to highlight the impact of implementing policies that could reduce anemia of CKD, and is aligned with the Healthy China 2030 policy, which aims to reduce mortality from noncommunicable diseases by 30%. Interventions could consist of early screening and intervention to reduce the escalation of anemia from mild to moderate or severe. Results were compared with a baseline "no change" scenario which reflects current trends. The number of patients with moderate/severe anemia of CKD was projected to increase from 3.0 to 3.2million patients, with associated costs increasing from ¥22.0 billion (B) to ¥24.4B between 2023 and 2027, respectively. Compared with the no change scenario, the hypothetical intervention reduced the prevalence of moderate and severe anemia of CKD, saving ¥3.9B in healthcare costs in 2027 (¥24.4B vs ¥20.6B, respectively). Consistent with trends in CKD burden in China, the prevalence of anemia of CKD is projected to increase, leading to greater related healthcare costs. The introduction of healthcare interventions designed to screen for and treat anemia more effectively could therefore reduce its future burden and related costs.

A nowcasting model of industrial production using alternative data and machine learning approaches

Recent years have seen a growing trend to utilize "alternative data" in addition to traditional statistical data in order to understand and assess economic conditions in real time. In this paper, we construct a nowcasting model for the Indices of Industrial Production (IIP), which measure production activity in the manufacturing sector in Japan. The model has the following characteristics: First, it uses alternative data (mobility data and electricity demand data) that is available in real-time and can nowcast the IIP one to two months before their official release. Second, the model employs machine learning techniques to improve the nowcasting accuracy by endogenously changing the mixing ratio of nowcast values based on traditional economic statistics (the Indices of Industrial Production Forecast) and nowcast values based on alternative data, depending on the economic situation. The estimation results show that by applying machine learning techniques to alternative data, production activity can be nowcasted with high accuracy, including when it went through large fluctuations during the spread of the COVID-19 pandemic.

NEW PERSPECTIVES ON ECONOMIC DEVELOPMENT

In the first decades of the 21st century, in a period characterized by turbulent, liberalized economic environment, in a world marked by a strong demographic growth, depletion of natural resources, climate change and destruction of ecosystems, we are more concerned than ever about the issues related to economic growth and development. In the first part of the paper, we presented the result of an overview study, based on which we synthesized the dynamics of the determinants of economic growth, after which we carried out a comparative study of economic growth and development. The second part of this paper presents a review of those economic indicators that are considered to be the most relevant in measuring economic development. The conclusions of the paper converge on the idea that, despite controversies regarding the importance of GDP as an indicator of economic development, it continues to remain the most present and important reference indicator in economic statistics. However, in order to obtain an accurate and more complex image of the economic and social performance, it is recommended to use several indicators in combination with GDP.

DIVERSITY OF FINANCING SOURCES OF NON-PROFIT ORGANIZATIONS IN ROMANIA AND THE EU – AN OVERVIEW

Financing is an asset or obstacle to the entity's activity, be it economic or not-forprofit. The impact that funding has on non-profit organizations is closely linked to the specificities arising from service and non-commercial status in society. The main objective of this paper is to present and appreciate diversity in funding non-profit organizations in Romania and the European Union during 2018-2022. In this regard, we are looking at the possibility of funding for NGOs from the European Union. The databases studied are Web of Science – for specialized articles – and Eurostat – for economic data and statistics. For database processing, VOSviewer – for bibliometric analysis – and Excel – for graphs and statistics were used. Making an inventory of the main categories of financial resources available today for Romanian NGOs leads us to conclude that NGOs have access to many funding opportunities. However, specific funding sources are available only to this type of organization

Analisis Faktor Faktor Yang Mempengaruhi Perkembangan Ekonomi Di Kabupaten Sarolangun

Sarolangun Regency, Jambi Province, Indonesia, has great economic potential, but its development still faces obstacles. This study explores the factors that influence economic growth in Sarolangun Regency with a focus on labor, foreign direct investment, government spending, local potential, policies, and infrastructure. This study uses a literature study research method with a secondary data approach. Data was obtained from official sources, including economic journals, articles, and BPS statistics. Data analysis was done descriptively to identify these factors. The results show that labor, foreign direct investment, and government expenditure have an important role in economic growth. Local potential, including the agriculture, plantation, and tourism sectors, also has a significant impact. Policies and regulations that support investment as well as cooperation between the government, private sector, and community are considered crucial. Infrastructure, especially transportation, was identified as an important pillar in supporting economic growth. Continued investment in infrastructure is considered vital to maintaining the competitiveness of Sarolangun District. By understanding and optimizing these factors, it is expected that Sarolangun District can achieve sustainable economic growth. This study provides a foundation for planning effective and sustainable economic development strategies for the region and serves as a reference for future research into the economic potential and challenges of Sarolangun District.

PENGARUH PERKEMBANGAN UMKM, TINGKAT PENGANGGURAN DAN INFLASI TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA

This study aims to analyze the effect of Micro, Small, and Medium Enterprises (MSMEs), inflation rate, and unemployment rate on economic growth in Indonesia using a linear regression model that provides a deeper understanding of the role of MSMEs, inflation, and unemployment in driving economic growth in Indonesia. The results of this study can be used to formulate appropriate policies to support MSMEs, control inflation, address unemployment, and ultimately achieve sustainable and inclusive economic growth in Indonesia. The data used in this study covers a specific time period and comes from various sources, including national economic statistics, MSME data, inflation index, and employment data. The results of the analysis show a significant relationship between the MSME variable and economic growth, suggesting that rapid MSME growth can have a negative impact on overall economic growth. Meanwhile, the Inflation variable indicates that a controlled inflation rate can contribute positively to economic growth. The Unemployment variable, indicates that high levels of unemployment can be detrimental to economic growth. Nonetheless, this study has limitations, including data limitations and regression model assumptions. The global economic context and other external factors may also affect the results of the analysis. Therefore, the regression results should be understood in a broader context.

Returns to labour mobility

Abstract Returns to labour mobility have too often escaped the attention they deserve as conduits of important forces in macro-labour models. These returns are shaped by calibrations of productivity processes that use theoretical perspectives and data sources from (i) labour economics and (ii) industrial organisation. By investigating earlier prominent studies, we conclude that the focus on firm size dynamics and shocks intermediated through neo-classical production functions in (ii) yields large returns to labour mobility that are robust to parameter perturbations. In contrast, the reliance on statistics in labour economics to calibrate per-worker productivity processes in (i) can give rise to fragilities in the sense that parameter perturbations that generate similar targeted statistics can have very different implications for returns to labour mobility.

Application of cloud computing detection based on sensor networks in enterprise economic statistics

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.

Statistical Manipulation: An Analysis of Potential Economic Data Tampering

Information on a country’s economy is important for many parties, including domestic politicians, potential investors, and international financial institutions. However, an individual country’s official statistics are generated and published by the governing body, creating the opportunity for manipulation of these measures. Our research is based on the assumptions that all governments are incentivized to exaggerate economic metrics and that stricter measures on civil liberties, like speech and media, allow for this manipulation to remain. We identified two variables suitable for proxying economic growth, per capita Health Care Expenditure (HCE) and per person electricity consumption to compare to the stated economic data. We utilized a Fixed Effect-OLS regression to find the relationship between these proxy variables and Gross Domestic Product dependent on government type for 151 countries from 2000-2020. Our research revealed that, despite two government-type indicators, the relationship between electricity consumption and GDP was greater with more autocratic governments. This provides evidence to support that autocratic governments may manipulate their economic data. This research expands upon the literature on the role both corruption and government style have on GDP, as well as providing additional evidence that governments may have manipulated economic statistics.

A Conversation With Marc Hallin

SummaryMarc Hallin was born in Ghent, Belgium, on 23 April 1949. He holds a Licence en Sciences mathématiques (1971), a Licence en Sciences actuarielles (1972), and a Doctorat en Sciences (1976) from the Université libre de Bruxelles. He then rose through the professorial ranks at the same institution, being successively Premier Assistant (1977–1978), Chargé de Cours associé (1978–1984), Chargé de Cours (1984–1988), Professeur ordinaire (1988–2009), and Professeur ordinaire émérite upon retirement in 2009. Throughout his career, he supervised 25 PhD students and held invited positions at many institutions of high standing in Austria, Belgium, England, France, Hong Kong, Italy, Portugal, Spain, Switzerland, and the USA (most notably Princeton). A renown expert in time series analysis, econometrics, and non‐parametric inference, Marc is the author or coauthor of over 250 research papers, for which he received numerous awards, including the Medal of the Faculty of Mathematics and Physics of Charles University in Prague (2006), a Humboldt Forschungspreis from the Alexander von Humboldt Foundation (2012), the Pierre‐Simon de Laplace Award of the Société française de Statistique (2022), and the Gottfried E. Noether Distinguished Scholar Award of the American Statistical Association (2022). He gave several distinguished lecture series, including the 2017 Hermann Otto Hirschfeld Lecture Series at the Humboldt Universität zu Berlin, and the 2018 Mahalanobis Memorial Lecture at the Indian Statistical Institute. Over the years, he co‐edited a dozen books and proceedings, and served on the editorial boards of several journals, including the Journal of Time Series Analysis (1994–2009), the Journal of Econometrics (2013–2019), the Journal of Business and Economic Statistics (2018–), and the Theory and Methods Section of the Journal of the American Statistical Association (2005–). He is a Fellow of the Institute of Mathematical Statistics (1990) and the American Statistical Association (1997), as well as a member of the Classe des Sciences of the Royal Academy of Belgium (1999). Marc has been a member of the International Statistical Institute since 1985 and was (co‐) Editor‐in‐Chief of the International Statistical Review from 2010 to 2015.

Real-Time Stock Forecasting: Leveraging Live Data and Advanced Algorithms for Accurate Predictions

This groundbreaking research introduces an innovative stock market prediction methodology that integrates financial modeling, machine learning, and real-time data analysis. Anchored in a deep understanding of stock market dynamics, including economic statistics, company performance, and market sentiment, our approach employs natural language processing (NLP) and predictive modeling to analyze live data for accurate stock price predictions. The method involves collecting and preprocessing a dynamic dataset enriched with financial indicators and historical stock prices. Utilizing Long Short-Term Memory (LSTM) algorithms, our model exhibits an impressive 96% accuracy in forecasting stock movements, showcasing adaptability to diverse market scenarios and responsiveness to economic factors and sentiment shifts. The incorporation of live data proves pivotal in providing timely insights for informed decision-making, establishing our model as a valuable tool for navigating the complexities of the modern financial landscape.

University absorptive capacity: Normative and perceived assessments

Knowledge management in universities is a dual process combining continuous learning and development of academic staff with the simultaneous transfer of knowledge to students. Collective capabilities and motivation of university employees to absorb and effectively apply knowledge underlie the so-called university absorptive capacity. However, characteristics perceived by workers and the level of absorptive capacity may contrast with the assessments based on university performance standards. The paper proposes and tests a dual method for judging on university absorptive capacity based on normative and perceived assessments. Methodologically, the study rests on knowledge management, the fundamental theoretical framework that covers the concept of absorptive capacity of an organization. Among the research methods are comparative analysis of university performance indicators and economic statistics. Empirical evidence includes data from the survey of 187 academic staff members of Ural Federal University (Ekaterinburg, Russia) and the Monitoring of Efficiency of Higher Educational Institutions in the RF in 2022–2023. Having performed content analysis, we identified the following evaluation parameters: the current level of employees’ knowledge and the potential for knowledge absorption in the organization. The first part of the methodology presents an algorithm for comparing the growth rates of normative performance indicators of the university in question with those of analogous universities. The second part deals with assessing absorptive capacity based on academic staff’s perceptions. The testing results indicate that there is a discrepancy between the normative and perceived assessments, which allowed us to propose a set of measures for adjusting the HR management model in a university.

China's Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2.

Accurate estimates of fossil fuel CO2 (FFCO2) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO2 observations, allowing us to combine observation-constrained NOx emissions coemitted with FFCO2 and grid-specific CO2-to-NOx emission ratios to infer the daily FFCO2 emissions over China. The estimated national total for 2016 was 11.4 PgCO2·yr-1, with an uncertainty (1σ) of 1.5 PgCO2·yr-1 that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO2-to-NOx emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO2 emissions in China.

Report of the Committee on Economic Statistics

Report of the Committee on Economic Statistics

VisCI: A visualization framework for anomaly detection and interactive optimization of composite index

Composite index is always derived with the weighted aggregation of hierarchical components, which is widely utilized to distill intricate and multidimensional matters in economic and business statistics. However, the composite indices always present inevitable anomalies at different levels oriented from the calculation and expression processes of hierarchical components, thereby impairing the precise depiction of specific economic issues. In this paper, we propose VisCI, a visualization framework for anomaly detection and interactive optimization of composite index. First, LSTM-AE model is performed to detect anomalies from the lower level to the higher level of the composite index. Then, a comprehensive array of visual cues is designed to visualize anomalies, such as hierarchy and anomaly visualization. In addition, an interactive operation is provided to ensure accurate and efficient index optimization, mitigating the adverse impact of anomalies on index calculation and representation. Finally, we implement a visualization framework with interactive interfaces, facilitating both anomaly detection and intuitive composite index optimization. Case studies based on real-world datasets and expert interviews are conducted to demonstrate the effectiveness of our VisCI in commodity index anomaly exploration and anomaly optimization.

Osnovni zavdannia ta predmet viiskovo-ekonomichnoi statystyky

The main tasks and subject of military-economic statistics as a branch of statistical discipline are proposed. The main challenges and tasks of the national economic system in the conditions of a full-scale military invasion of the Russian Federation into Ukraine are characterized, as the solution which will accelerate Ukraine’s victory in the war. Emphasis is placed on the importance of the following elements of statistical management support, such as accounting, analysis, and forecasting of the vector of development, taking into account all the burdens of war. The key role in this process should be given to the state administration, including state statistics bodies, and, accordingly, to the improvement of statistical literacy at the appropriate level of competence of specialists. Definitions of such concepts as: «military economy», «economic statistics of the Armed Forces», «economic statistics», and «military-economic statistics» are given, and they are also classified and structured among themselves. It was determined that military-economic statistics is a branch of statistical science that studies the quantitative side of mass military-economic phenomena and processes in an inextricable connection with their qualitative characteristics in the conditions of a certain place and time. The subject of the study of military-economic statistics is the quantitative side of mass military-economic phenomena and processes inextricably linked with their qualitative characteristics in the conditions of a certain place and time. Accordingly, the object of the study of military-economic statistics is the quantitative side of mass military-economic phenomena and processes. The structure of this branch of statistical science can be successfully presented in the educational process of higher education institutions for students studying economic and military specialties and specializations (general and special).