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Economists typically estimate the economic value of wildlife-associated recreation by applying valuation methods to individual data collected from mail and internet surveys, however, surveys are limited to what respondents choose to report as well as who chooses to respond. Government records and mobile applications that track recreational activity can also provide data for economic valuation if activities can be linked back to individual decision-makers. One particularly intriguing source of government information is harvest check-in data, which tracks hunting success. In this paper, we examine the comparability of economic values based on surveys and harvest check-in data using a site choice model. While we find evidence the datasets yield different models, large and substantial differences are the exception rather than the rule, especially once we accurately account for individual trip costs. Our findings support the conclusion that data generated through surveys and harvest records are capable of producing similar valuation estimates. [ABSTRACT FROM AUTHOR]

This study examines empirically the impact of civil conflict on cross-border lending. Our sample covers 165 countries over the period 1984-2019 with loan data disaggregated at the economic sectoral level (primary, secondary and tertiary sectors), an analysis that is absent in existing literature. Our results indicate that cross-border lending to the primary sector is not significantly influenced by civil conflict, whereas cross-border lending to secondary and tertiary sectors is negatively impacted by the outbreak of civil conflict, leading to a decreased volume of loans or reversals of existing loans. [ABSTRACT FROM AUTHOR]

The study explores the effects of data transparency on economic growth for developing economies over a unique time period ‐ at the onset of the 2007–2009 global financial crisis and thereafter. Data transparency is defined as the timely production of credible statistics as measured by the statistical capacity indicator. The paper finds that data transparency has a positive effect on real gross domestic product per capita during a period of considerable uncertainty. The estimates indicate an elasticity of the magnitude of 0.03 percent per year, which is much larger than the elasticity of trade openness and schooling in the estimation sample. The empirics employ a variety of econometric estimators, including dynamic panel and cross‐sectional instrumental variables estimators, with the latter approach yielding a higher estimated elasticity. The findings are robust to the inclusion of several factors in addition to political institutions and exogenous commodity‐price and external debt‐financing shocks. [ABSTRACT FROM AUTHOR]

Analyses of the informal economy are vital to understanding economic activity in developing countries, but challenges abound when analysing something that is so difficult to measure. In this article, we explore these challenges and what they mean for research and research design in studies of the informal economy. We review key economic theories developed to study the informal economy, before critically discussing the range of methods of data collection available for researchers. All approaches to data collection have limitations, yet many studies fail to reflect adequately on these in their design and presentation. In reviewing data sources for economic analyses of informality, we give particular attention to surveys designed and led by academic researchers, the consideration of which is omitted from leading official organisations' own reports on data collection in the informal economy. In the absence of a unifying theory and data sources that deliver the 'comprehensive and accurate' data called for by some authors we argue, first, that no one source of data is intrinsically superior; and second, that research on the informal economy must embrace transparency—around the data used, their alignment with the underlying theory adopted, and the detailed arrangements for data collection and analysis. [ABSTRACT FROM AUTHOR]

In the dynamic and intricate shared economy, efficient resource management and forecasting are still crucial. In this research, a new prediction model is presented that aims to improve the operational efficiency of several shared economy services. Our method creatively combines Long Short-Term Memory (LSTM) networks with Genetic Algorithms (GA) to assess user demand and optimize resource deployment. We apply this methodology especially to e-scooter sharing services, but the underlying ideas and methods can be applied to other shared economy platforms as well, like peer-to-peer lending services, car sharing, and vacation rentals. The model starts with a GA to adjust the hyperparameters of the LSTM network, making the network better suited to handle specific characteristics of common economic data. Capturing the complex temporal and spatial patterns of user behavior and demand on these platforms requires this optimization. The LSTM element then predicts changes in service demand due to its capacity to analyze sequential records. Further to being useful for analyzing sequential data and predicting destiny wishes, this predictive functionality is important for a shared economy platform to correctly manage stock, allocate assets, and predict personal wishes. We use a large dataset to check our technique, demonstrating the predictive accuracy of the model and demand and its potential to aid strategic choice-making. Compared to traditional fashions, the consequences show a large development in forecast accuracy and resource allocation efficiency. Our methodology creates a robust basis for statistics-driven insights that decorate customer happiness and decorate the long-term increase of the shared financial system. This work highlights the blended ability of GA and LSTM inside the shared economy and paves the manner for future enhancements in using modern-day gadget mastering techniques to optimize and alter various shared services. In quick, effective useful resource control and forecasting in the shared economic system is tough, however our specific forecasting model combining GA-LSTM gives a manner ahead. Our technique, as it should be, predicts fluctuations in provider demand; it was first refined the usage of GA to regulate the LSTM hyperparameters. The consequences show how correct and powerful our version is and spotlight how it can enhance customer pleasure and operational performance. This research paves the manner for future trends within the software of gadgets, gaining knowledge of methods and supports the continuing enlargement and development of shared economic system offerings. [ABSTRACT FROM AUTHOR]

The SARS and COVID-19 pandemics represent the two largest economic shocks to human society in the past two decades. This paper investigates national economic resilience to these pandemics while 25 countries (region) and 46 countries are involved in the two studies, respectively. The study uses year-over-year quarterly economic growth data to measure the national economic resistance and economic recoverability in the recession-recovery cycles caused by the pandemics. The results show that: (i) the two pandemics caused evident economic fluctuations; (ii) there exists a negative relationship between economic resistance and economic recoverability across those infected countries; (iii) the typology of national economic resilience has shifted from weak resistance-good recoverability during the SARS period to good resistance-weak recoverability in the recent years of COVID-19; (iv) big variations of national economic policy response to the pandemic are found between high-income and middle-income countries. We call for building a more resilient world where sustainable and inclusive growth dominates the development pattern in the future. [ABSTRACT FROM AUTHOR]

Objective Data Sources and Study Setting Study Design Data Collection/Extraction Methods Principal Findings Conclusions To characterize the landscape of policies that determine eligibility for family planning services through Medicaid programs and describe trends in eligibility and its determinants over time.Secondary data were collected for all states in the United States for the years 2008 through 2023. Data on economic and demographic characteristics came from the American Community Survey (ACS).Our descriptive study characterized state adoptions of Medicaid family planning section 1115 waivers and state plan amendments (SPA) and their eligibility criteria. We then estimated the proportion of women aged 19–44 years who were eligible for family planning services through Medicaid and identified the key determinants of changes in eligibility, by state and year.Information on state Medicaid policies was extracted from documentation on the Centers for Medicare & Medicaid Services website. When estimating the eligible population sizes, the denominator was women aged 19–44 years, the group most likely to be eligible for Medicaid family planning programs. Supplemental data on program enrollment or utilization were collected from states' websites and reports.Though eligibility limits for family planning through Medicaid generally increased over time, the proportion of women aged 19–44 years eligible for at least limited benefits decreased from 45.0% in 2012 to 39.4% in 2022, largely because of increases in household income. Trends varied considerably across states and by eligibility pathway. Among women with incomes below the poverty level, the proportion who were not eligible for Medicaid family planning services decreased from 6.3% in 2013 to 1.5% in 2022.Our data demonstrated substantial geographic and temporal variation in eligibility for family planning services through Medicaid. We identified key drivers of eligibility changes that may have important implications for health services analyses of means‐tested public programs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As environmentally extended input–output tables are increasingly used for footprint and supply chain analysis, there has been a growing demand for precise, reliable, and intertemporally consistent environmental accounts. Energy accounts are particularly relevant. As well as providing insights into embodied energy, in the case of the multi‐regional input–output (MRIO) database EXIOBASE, they are the basis of the air emissions from fuel combustion. In this study we review previous methods and provide a more integrated and robust implementation for the EXIOBASE energy accounts that ensures higher levels of consistency between economic and physical data. Our results show that the new procedure significantly improves the precision of the energy use coefficients and multipliers, while reducing the number of outliers. The procedure is made publicly available for full transparency and made open source such that users and developers may raise issues and suggest improvements to the procedure. Furthermore, the procedure is standardized so it can be applied to any MRIO model with only minor adjustments to the input data. [ABSTRACT FROM AUTHOR]

In this paper we examine the changing demographics of overdose deaths in West Virginia based upon death certificate and economic data in West Virginia for the years 2001–2016. During the period under study, opioid overdose deaths accounted for 7% of deaths overall, and a significant number of premature deaths. Those affected were an average of 41 years old and were more likely to be unmarried and less than college educated. Eighty percent were employed at the time of death and individual employment in specific industries was associated with an increased risk of opioid overdose death. Previous state and national level policies attempting to address the opioid crisis may have lagged behind the data trends for this time period and thus been less effectual. Future policies intended to ameliorate the harms associated with the opioid crisis should reflect current data trends to help the communities and individuals at high risk for overdose death. [ABSTRACT FROM AUTHOR]

Objective: To investigate the impact of Medicaid expansion on state expenditures through the end of 2022. Data Sources: We used data from the National Association of State Budget Officers (NASBO)'s State Expenditure Report, Kaiser Family Foundation (KFF)'s Medicaid expansion tracker, US Bureau of Labor Statistics data (BLS), US Bureau of Economic Analysis data (BEA), and Pandemic Response Accountability Committee Oversight (PRAC). Study Design: We investigated spending per capita (by state population) across seven budget categories, including Medicaid spending, and four spending sources. We performed a difference‐in‐differences (DiD) analysis that compared within‐state changes in spending over time in expansion and nonexpansion states to estimate the effect of Medicaid expansion on state budgets. We adjusted for annual state unemployment rate, annual state per capita personal income, and state spending of Coronavirus Relief Funds (CRF) from 2020 to 2022 and included state and year fixed effects. Data Collection/Extraction Methods: We linked annual state‐level data on state‐reported fiscal year expenditures from NASBO with state‐level characteristics from BLS and BEA data and with CRF state spending from PRAC. Principal Findings: Medicaid expansion was associated with an average increase of 21% (95% confidence interval [CI]: 16%–25%) in per capita Medicaid spending after Medicaid expansion among states that expanded prior to 2020. After inclusion of an interaction term to separate between the coronavirus disease (COVID) era (2020–2022) and the prior period following expansion (2015–2019), we found that although Medicaid expansion led to an average increase of 33% (95% CI: 21%–45%) in federal funding of state expenditures in the post‐COVID years, it was not significantly associated with increased state spending. Conclusions: There was no evidence of crowding out of other state expenditure categories or a substantial impact on total state spending, even in the COVID‐19 era. Increased federal expenditures may have shielded states from substantial budgetary impacts. [ABSTRACT FROM AUTHOR]

[Objective] The aim of this study is to quantitatively assess the evapotranspiration trends and analyze its response to environmental changes in the Weihe River Basin, so as to be conducive to the study of water resources change in this region. [Methods] Based on the Budyko framework and combined with NDVI, land use type. GLEAM evapotranspiration remote sensing data, meteorological and hydrological data and social and economic data, the trend of each driving factor was analyzed, an empirical model of time-varying Budyko parameter was constructed, and the elastic coefficient method was adopted to quantitatively evaluate the influence of each factor change on evapotranspiration. [Results] (1) From 1990 to 2020. the actual evtranspiration in the Weihe River Basin showed a significant growth trend, with the overall growth rate of 1.98 mm/a, and the spatial trend was gradually decreasing from southeast to northwest. (2) The hydrothermal parameter of the underlying surface of the Weihe River Basin showed an increasing trend. and parameter had a high correlation with the NDVI, population, cultivated land area. grassland and GDP of the region. In the simulation of evapotranspiration, the results of the empirical model with time-varying parameter n are better than those with constant parameter n. (3) Evapotranspiration in Weihe River Basin was the most sensitive to precipitation, but not to underlying surface parameter n, which was most sensitive to NDVI. (4) Precipitation contributed the most to evapotranspiration, followed by underlying surface factors, and potential evapotranspiration contributed the least. [Conclusion] Evapotranspiration in the Weihe River Basin showed a significant increase trend during the study period, and climate change played a leading role in the change of evapotranspiration in this region, but the influence of human activities on evapotranspiration became more and more obvious. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to analyse the institutional threshold effects of foreign aid on foreign direct investment (FDI). Design/methodology/approach: This paper develops a theoretical model from an extended Solow model that introduces the conductive effect of institutions in an aid recipient country towards the capacity of attracting FDI. This study evidences threshold effects with the most recent panel threshold models that consider endogeneity issues. The data on economic institutions and foreign aid are decomposed into disaggregated level to reveal the detailed threshold pattern. Several sample subsets are used for a heterogeneity analysis. Findings: Conducting empirical research on a sample of 62 countries during the period 2003–2016, this study finds robust evidence of the existence of an institutional threshold in the aid–FDI nexus which a country must attain to reap the full attraction of FDI by foreign aid providing financial resources. Furthermore, foreign aid tends to promote FDI in institutions characterized by a right-sized government, a strengthened legal system and an appropriate regulatory environment. On the other hand, aid may crowd out FDI. The results are robust to regional combinations and a subset of low and lower-middle-income countries. In addition, this study finds that aid targeted at social infrastructure and services has a positive effect regardless of institutional threshold. Originality/value: This paper contributes to the literature by introducing a non-linear and discontinuous effect of aid on FDI, i.e. a threshold effect, highlighting the relevance of legal systems and regulations and the possibility of a crowding-out effect on FDI for specific institutional regimes. The thresholds provide a guide for donor countries to ensure aid effectiveness at the risk of being counterproductive and for recipient countries to better assess the institutional dimensions that need to be improved. [ABSTRACT FROM AUTHOR]

The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the synthetic control method. However, most of the literature has ignored the time-series properties of the data. The work of Gonçalves and Ng fills this gap by proposing a simple correction for existing estimators to take into account serial and cross-correlation in the data. This note provides some thoughts on Gonçalves and Ng's method. [ABSTRACT FROM AUTHOR]

The use of smart contracts in areas such as finance, supply chain management, and the Internet of Things has significantly advanced blockchain technology. However, once deployed on the blockchain, smart contracts cannot be modified or revoked. Any vulnerabilities can lead to severe economic losses and data breaches, making pre-deployment vulnerability detection critically important. Traditional smart contract vulnerability detection methods suffer from low accuracy and limited reusability across different scenarios. To enhance detection capabilities, this paper proposes a smart contract vulnerability detection method based on heterogeneous contract semantic graphs and pre-training techniques. Compared to the conventional graph structures used in existing methods, heterogeneous contract semantic graphs contain richer contract information. By integrating these with pre-trained models, our method exhibits stronger vulnerability capture and generalization capabilities. Experimental results show that this method has improved the accuracy, recall, precision, and F1 value in the detection of four widely existing and harmful smart contract vulnerabilities compared with existing methods, which greatly improves the detection ability of smart contract vulnerabilities. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Method Results Conclusion Lyme borreliosis (LB) is the most common tick‐borne disease in Germany; however, data on the economic burden of LB are limited. In this study, we aim to report healthcare costs, healthcare resource utilisation (HCRU) and diagnostic consumption associated with LB by clinical manifestation.Using specific case definitions, patients with localised disease (erythema migrans [EM]) or disseminated disease (Lyme arthritis [LA], Lyme neuroborreliosis [LNB] and other rarer manifestations [OTH]) were identified from a claims database in 2016 and followed up for 3 years (2016–2019). After propensity score matching, excess costs and HCRU were calculated as the differences between each LB cohort and the matched control cohort.On a per‐patient basis, the excess all‐cause healthcare cost was €130 for EM during Quarter 1 of Year 1, and €1539 for LA, €3248 for LNB and €4137 for OTH during Year 1. Only for OTH, additional €1860 was observed in Year 2. No increase in costs was observed in Year 3. When extrapolated to all German patients with statutory health insurance, LB was associated with €64.5 million in excess costs. Although disseminated manifestations only accounted for 7.8% of all LB cases, they were responsible for 66% of overall costs. In addition, LB patients consumed healthcare resources of 1.4 million excess outpatient visits, 13,000 excess hospitalisations, 96,000 ELISAs and 65,000 Western blots.This study shows the substantial economic burden of LB to the German healthcare system. [ABSTRACT FROM AUTHOR]

The Human Development Index is a useful measure of a country's overall prosperity and standard of living (HDI). The Human Development Index (HDI) provides data on the social and economic progress of a nation by accounting for variables such as life expectancy, education attainment, and per capita income. This research delves into the intricate correlation of a nation's historical background, policy framework, and advancements on the Human Development Index. The remarkable journey of South Africa, from apartheid to inclusive growth, is an important example. The study uses an average yearly growth rate to examine how changes in political ideologies, ongoing development initiatives, and historical contexts of inequality have shaped South Africa's HDI trajectory. By examining crucial turning points and policy influences, the study aims to reveal the complex relationship of factors affecting human development results. Despite improvement, challenges like unemployment and poverty continue. The study stresses the relevance of understanding historical context and policy changes in shaping HDI outcomes. Ultimately, the study emphasises the need of maintaining a long-term commitment to effective and inclusive human development policies. Understanding the complex relationship of factors influencing South Africa's HDI will help policymakers make better informed decisions that will lead to a more prosperous and fair society for all South Africans. [ABSTRACT FROM AUTHOR]

Kink regression model which assumes continuity at the threshold point has wide applications in statistics and economics. Existing estimation methods are obtained under a rather important assumption that the errors are mean independent of the threshold variable, namely, it is exogenous. However, endogeneity can arise as a result of omitted variables, lagged dependent variable, measurement error and many other sources. In this paper, we consider the estimation and testing for the kink regression model with endogenous threshold variable and possible other endogenous regressors. We find that the conventional form of 2SLS estimator is inconsistent as the expectation of a nonlinear function is not generally the function of expectations. The continuity feature of the regression prompts us to try method based on the GMM principle using nonlinear instruments. We derive the asymptotic properties using a different way from the usual GMM estimators as the objective function is not smooth with respect to the threshold parameter. A sup-Wald test for the presence of kink effect is established and a bootstrap procedure to gain the p value is introduced. Monte Carlo simulations show that the proposed estimator and testing procedure perform well. The proposed procedures is also illustrated using an empirical application. [ABSTRACT FROM AUTHOR]

The Philippines has had high levels of unemployment for years. During the 2000s, the unemployment rate hovered between seven and ten percent. High unemployment can have adverse effects on individuals and society. The question that this paper analyses is how unanticipated money growth affect the unemployment situation in the Philippines. There has been literature on the relationship between unanticipated growth on the money supply and unemployment. The paper proposes that only unanticipated money movements will affect real economic variables like unemployment and the output level. In order to test our hypothesis, it is important that we need to quantify the concepts of anticipated and unanticipated money movements. This paper uses time-series data on several economic variables as well as a model based on Geetha et al. (2023). Using an error-correction model, the results show that an unanticipated increase in M2 money is a factor that contributes to unemployment in Philippines. [ABSTRACT FROM AUTHOR]

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This document is an editorial from the Journal of Payments Strategy & Systems that covers a wide range of topics related to payments data. It discusses legislative actions, transparency for retail payments end users, data for international supply chains, tools to combat financial crime, synthetic data for fraud prevention, non-Latin data in cross-border payments, the role of public and private sectors in payment systems, developments in the African payments industry, and US consumer payments behavior. The editorial also includes announcements and references to relevant publications. It cites two sources, one being an editorial on page 212 and the other being webpages from the International Standards Organization (ISO) that provide information on AI standards and identifiers for blockchain systems. [Extracted from the article]

The Antarctic region harbors abundant natural resources that constitute a common heritage site for humankind. However, given climate change and global resource scarcity, natural resource management has emerged as a pivotal issue in Antarctic governance. Assessing the value of natural resources is fundamental and crucial for ensuring their sustainable utilization and conservation. This study attempted to promote decision-makers' understanding of the Antarctic using nature's contribution to people (NCP) and provide an evaluation of the value of natural resources in the Antarctic, thereby contributing to sustainable global development. It developed a conceptual framework for evaluating natural resource values in the NCP, which includes scope delimitation, type identification, and value assessment. Economic value assessments were conducted for key NCPs in Antarctica. Based on existing biological, physical, and economic data, our initial estimates indicate that the economic value of the realized NCP is approximately 2.46 trillion CNY, with regulating NCP accounting for roughly 70% of the total value. If the potential NCP in the Antarctic were realized at foreseeable scientific and technological levels, the total economic value would reach approximately 105.62 trillion CNY. Regulating NCP accounted for most of the total value, but material NCP accounted for more than 20%. Overall, the development of natural resources and environmental protection are two sides of the same coin in the Antarctic, and there may be trade-offs between different NCPs. With increasing human activity, many NCP may be lost in the Antarctic region. This study develops a new perspective on the valuation of nature's contributions to the Antarctic. The evaluation results can provide scientific evidence for decision-making and management of the development and utilization of natural resources in the Antarctic. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of the Social Accounting Matrix (SAM) is to improve the quality of the database for modelling, including, but not limited to, policy analysis, multiplier analysis, price analysis, and Computable General Equilibrium. This article contributes to constructing the 2017 national SAM for South Africa, incorporating regional accounts. Only in Limpopo Province of South Africa are agricultural industries, labour, and households captured at the district level, while agricultural industry, labour, and household accounts in other provinces remain unchanged. The main data sources for constructing a SAM are found from different sources, such as Supply and Use Tables, National Accounts, Census of Commercial Agriculture, Quarterly Labour Force Survey, South Africa Revenue Service, Global Insight (regional explorer), and South Africa Reserve Bank. The dataset recorded that land returns for irrigation agriculture were highest (18.2%) in the Northern Cape Province of South Africa compared to other provinces, whereas the Free State Province of South Africa rainfed agriculture had the largest shares (22%) for payment to land. Regarding intermediate inputs, rainfed agriculture in the Western Cape, Free State, and Kwazulu-Natal Provinces paid approximately 0.4% for using intermediate inputs. In terms of the districts, land returns for irrigation were highest in the Vhembe district of Limpopo Province of South Africa with 0.3%. Despite Mopani district of Limpopo Province of South Africa having the lowest land returns for irrigation agriculture, it has the highest share (1.6%) of payment to land from rainfed agriculture. The manufacturing and community service sectors had a trade deficit, whereas other sectors experienced a trade surplus. The main challenges found in developing a SAM are scarcity of data to attain the information needed for disaggregation for the sub-matrices and insufficient information from different data sources for estimating missing information to ensure the row and column totals of the SAM are consistent and complete. Dataset:  https://doi.org/10.38140/ufs.24032457.v1. Dataset License: CC-BY 4.0 [ABSTRACT FROM AUTHOR]

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique high‐frequency electricity consumption data from industrial firms for the second‐largest German state, the Free State of Bavaria, we conduct a pseudo out‐of‐sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find that electricity consumption is the best performing indicator in the nowcasting setup and has higher accuracy than other conventional indicators in a monthly forecasting experiment. Exploiting the high‐frequency nature of the data, we find that the weekly electricity consumption indicator also provides good predictions about industrial activity in the current month with only 2 weeks of information. Overall, our results indicate that regional electricity consumption is a promising avenue for measuring and forecasting regional economic activity. [ABSTRACT FROM AUTHOR]

This article examines the characteristics of entrepreneurship among Scheduled Castes (SCs) in India using the 5th (2005) and 6th (2013) Economic Census data of the Central Statistical Office. The study employs the method of shift-share analysis to compare the growth of the enterprises at all India levels with that of SC-owned enterprises. The analysis reveals that the number of SC-owned enterprises has been increasing, with a significantly higher rate of change compared to the previous period. However, the prevalence of under-representation of SCs in the business field continues. The wholesale and retail trade, manufacturing and livestock are the three major activities in which SCs were concentrated. The emergence of new SC entrepreneurs has been influenced by several motivational factors, while there are also several factors that have a negative impact on the existence of SC entrepreneurs. [ABSTRACT FROM AUTHOR]

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.

Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production. Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of "expert interviews + big data technology" for economic forecasting. Results: The forecasting accuracy on four provincial companies has reached over 90%. Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development. [ABSTRACT FROM AUTHOR]

Background: Honey bees are the principal commercial pollinators. Along with other arthropods, they are increasingly under threat from anthropogenic factors such as the incursion of invasive honey bee subspecies, pathogens and parasites. Better tools are needed to identify bee subspecies. Genomic data for economic and ecologically important organisms is increasing, but in its basic form its practical application to address ecological problems is limited. Results: We introduce HBeeID a means to identify honey bees. The tool utilizes a knowledge-based network and diagnostic SNPs identified by discriminant analysis of principle components and hierarchical agglomerative clustering. Tests of HBeeID showed that it identifies African, Americas-Africanized, Asian, and European honey bees with a high degree of certainty even when samples lack the full 272 SNPs of HBeeID. Its prediction capacity decreases with highly admixed samples. Conclusion: HBeeID is a high-resolution genomic, SNP based tool, that can be used to identify honey bees and screen species that are invasive. Its flexible design allows for future improvements via sample data additions from other localities. [ABSTRACT FROM AUTHOR]

To date, land use structure information has been employed extensively for ecological risk assessment (ERA) purpose in regional/landscape scales; in contrast, land use function (LUF) information-based ERA research is still scarce. Therefore, it is necessary to carry out more ERA case studies in macroscale with the help of pertinent LUF indicators. As an important way to construct production-living-ecology LUF indexes, this study employs the weighted stacking method and related economic statistical data for regional ecological risk assessment (RERA) purpose within Yellow River Delta High-efficiency Eco-economic Zone (YRDHEZ), China. This YRDHEZ-RERA research pointed out that (1) it was rational to use a series of economic statistical data to more comprehensively and precisely characterize regional production and living function grades in YRDHEZ. (2) The Yellow River Delta had lower agriculture and non-agriculture production functions, whereas the rest of the zone had higher production functions. (3) Most people lived in the south part, whereas north coastal zone had very low population density; the east part had higher per capita disposable income of urban/rural households than that of west. (4) The south part of the zone had higher production/living functions and integrated ecological risk source intensity than those of north coastal zone, whereas the coastal zone had higher ecology function, eco-environmental vulnerability, and final integrated ecological risk than those of inland region. As for regional ecological risk management, establishing nature reserve with strict spatial governance for coastal/estuarine wetlands and coordinating production/ecology functions of coastal salterns/breeding ponds are relevant feasible measures. [ABSTRACT FROM AUTHOR]

Background: The per capita net availability of pulses in India has been increased from 15.5 kg per year in 2018-19 to 19.6 kg in 2021- 22. Efforts made to bridge the gap between demand and supply of pulses in the country has resulted in reducing the gap to some extent in recent years and still country is depended on import to meet the growing demand of the pulses such as pigeon pea, lentils and peas. Few studies predicted the demand and supply of pulses as a whole in the country and no studies analyzed the pulse wise demand supply gap in India. Hence, the present study was proposed to predict the demand and supply of major pulses such as chickpea (Gram/Chana), pigeon pea (Tur/Arhar), black gram (urd bean), mung bean (Green gram) and lentil (Masur) in India for a period, 2024-2030. Methods: The present study has been used the crop data on area, production and yield of major pulses including chickpea (Gram/ Chana), pigeon pea (Tur/Arhar), black gram (urd bean), mung bean (Green gram) and lentil (Masur) for a period of 29 years (1985- 2024) collected from the Directorate of Economics and Statistics, Government of India, New Delhi. Household consumption expenditure data was collected from the National Sample Survey office for 2011-12 from the Government of India. The supply projection was estimated by using linear regression model and demand projections were done by using behaviouristic approach. Result: Decadal trend in area, production and yield of pulses from 1970-2010 showed mixed trends of increase and decrease, whereas in 2010-2020, chickpea, pigeon pea, mung bean and black gram showed positive trend in area, production and yield but lentil alone showed negative trend in production. Import dependency of the pulses in the total availability has reduced to 8.92 per cent in 2021-22 from 19.42 per cent in 2009-10. Import dependency of the pulses in the total availability has reduced to 8.92 per cent in 2021-22 from 19.42 per cent in 2009-10. The availability of pulses has grown at a rate of 3.73 per cent from 2009 to 2021. Chick pea, tur, mung bean, urd bean and lentils together contributed on an average 88.7 per cent to the total pulses production and 83.73 per cent of pulses availability from 2013 to 2021. Production of these five pulses has increased to 23.04 million tonnes in 2020-21 from 17.3 million tonnes from 2013- 14. Gram is the single pulse contributed more than 40 per cent of the pulse production of the country in the above period. Demand supply projection showed a net surplus of 0.81-3.89 mt in the case of pulses and 5.64-7.63 mt in the case of gram and 0.89 to 1.91 mt in Urd and 0.68 to 1.57 mt in mung bean during 2024-2030. But in the case of pigeon pea and lentil there may be a net deficit in the range of 3-2.42 and 1.13-1.04 mt, respectively, during 2024-2030. Efforts should be made to bridge the gap in demand and supply of pigeon pea and lentil by the way of bringing new areas under pulses and developing the technologies suitable for the clusters. [ABSTRACT FROM AUTHOR]

On December 5, 2022, Bursa Efek Indonesia resolutely revealed the primary economic data, which will be used as an event study in this investigation to understand the impact of abnormal return on the market and trading volume activity. The research window for this study is 11 days, which includes 5 days prior to the event, 5 days after the incident, and an estimated 14 days after the event. The type of research in this study is an event study. In this case, the independent variable (variable X) is the event of launching the New Board Economic whereas the dependent variables (variable Y) are abnormal return and trading volume activity. The research sample consists of companies listed on the new economic listing board. The companies included in the sample are GOTO, BUKA, and BELI. Hypothesis testing is done using One Sample T-Test and Paired Sample T-Test. The research results show that the significance value of abnormal return on T-5 is <0.05, whereas for other periods it is >0.05. Similarly, all periods of trading volume activity show values >0.05. This indicates that there is a significant abnormal return on T-5, or 5 days before the event of the launch of the New Economic Board. Meanwhile, in other periods, there is no significant abnormal return, and none of the periods show significant trading volume activity. In the difference test, the Sig. (2-tailed) values for both abnormal return and trading volume activity are >0.05, indicating no difference in abnormal return before and after the launch of the new economic board, as well as no difference in trading volume activity. [ABSTRACT FROM AUTHOR]

Burn injuries pose a significant burden on both patients and healthcare systems. Yet, costs arising from the consumption of resources by these patients are rarely examined in Canada. The objective of this study was to assess real-world costs resulting from the initial hospitalization of patients admitted to a major burn unit in Quebec, Canada. A cost study based on a retrospective cohort was undertaken using in-hospital economic data matched to hospital chart data. Our cohort included all burn-injured patients admitted between April 1, 2017, and March 31, 2021, to the hospital's major burn unit during their initial hospitalization. Descriptive statistics were tabulated for sociodemographic and economic data. Costing data were analyzed unstratified and stratified according to burn severity (i.e., ≥ 20% of total body surface area [TBSA] vs. < 20%). Costs were presented in CAD 2021. Our cohort included 362 patients, including 65 (18%) with TBSA ≥ 20%. The average initial hospitalization cost was $32,360 ($22,783 for < 20% TBSA and $76,121 for ≥ 20% TBSA). Conclusion: Findings reveal that the total cost of the initial hospitalization, from a public hospital perspective, was $11,714,348. Our study underlines the substantial burden associated with burns and highlights the need for long-term cost evaluations. • Burn injuries are resource intensive but their exact cost is unknown in Canada. • Real-world cost of the index hospitalization was estimated at $32,360 (CAD 2021). • Heterogeneity of burn patients contributed to a high variability in costs. • Cost of long-term burn injury care remains unknown. [ABSTRACT FROM AUTHOR]

The article focuses on integrating nature into policy-making across various sectors to improve governance and sustainability. Topics include the need for holistic approaches that connect health, national security, and the economy with nature, criteria for mainstreaming nature in decision-making processes, and examples of recent policy changes and their implications for future research and governance.

We introduce t-Digests as an accurate and computationally efficient way to calculate the quantiles of functions of the agent distribution for models in which the full distribution is too large to work with directly; e.g., to calculate the Top 1% of the wealth distribution. When it is possible to fit the entire agent distribution (and a function evaluation on it) into memory, the quantiles can easily be calculated directly. Evaluating a function on the agent distribution can be done much faster using a GPU, however this frequently introduces a memory bottleneck as GPU memory is typically an order of magnitude smaller than CPU memory. For Heterogeneous Agent models the full distribution of agents may fit in CPU memory, but not in GPU memory. We partition the agent distribution into (non-overlapping) subspaces and because these subspaces are much smaller we can fit the subspaces in GPU memory one at a time. For each subspace we then evaluate the function and calculate a t-Digest. t-Digests are a form of data structure that are fast to compute, require less memory, and provide high accuracy for the quantiles of the distribution; t-Digests are a 'sketch' of the subspace. Having computed and stored a t-Digest for each subspace we can then merge them to get a t-Digest for the full agent distribution. The resulting t-Digest is an accurate representation of the quantiles and can form the basis of Lorenz Curves as well as many other distributional and inequality statistics of interest in Economics. [ABSTRACT FROM AUTHOR]

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Economic leakages in tourism are an under-researched area that requires special emphasis from the tourism academia. Leakages persist in a destination and obstruct the economic development of the region. Though mainstream literature highlighted more about the positive side of tourism development, relatively limited literature discussed about leakages. This study critically examines the existing body of knowledge on economic leakages in tourism destinations in order to ascertain the current status of the research and identify any knowledge gaps. This study thoroughly examined theoretical and empirical data on economic leakages in tourism destinations and identified directions for future research. This study further goes into detail on how crucial leakage mitigation is to establishing resilience and self-sufficiency while rebuilding tourism destinations after the pandemic. Finally, theoretical and practical implications are provided. [ABSTRACT FROM AUTHOR]

The coordination and stability of the regional economy (RE) and ecological quality (EQ) are essential for the synergistic development of the Beijing–Tianjin–Hebei urban agglomeration (BTH). In this paper, based on remote sensing data and economic statistics data, this paper analysed the current situation and evolution of the RE and EQ in the BTH region. Based on the coupled coordination degree (CCD) model, this paper has developed an evaluative framework and quantifies the spatiotemporal coupled coordination of the RE and EQ in the BTH region from 2000 to 2020. The results show that: (1) in 2020, the GDP of the poverty-stricken counties (PSCs) around BTH accounted for 4.57% of the total BTH region, and the area of high-quality ecosystems, Net Primary Productivity (NPP), soil conservation, water conservation, and sand fixation accounted for more than 40% of the total BTH region; (2) the GDP growth of the PSCs accounted for 4.45% of the total growth in BTH, the reduced area of high-quality ecosystems accounted for 21.04%, and the increase in NPP, soil conservation, water conservation, and sand fixation accounted for more than 40% of the total growth in the BTH region; (3) the CCD of the RE and EQ in the BTH region showed a tendency to increase, but the gap between the regions gradually expanded, and the development type changed from a lagging RE to a lagging EQ, in which the coupled coordination grade of the PSCs was in a dissonance stage for a long time, and the level of economic development had to improve. Deepening the understanding of the spatiotemporal coupling relationship between the RE and EQ and adopting appropriate development policies is of great significance for achieving the coordinated and integrated development of the BTH region. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the impact of government regulatory institutions on households' natural gas use behavior and suggested that the conventional view of natural gas as a social utility is inappropriate. Pursuing this goal, we applied correlation analysis, regression analysis and the Granger causality test to assess the statistically significant impact of particular factors (environmental temperature, price and tariff on natural gas) on household gas consumption. Our study was based on the data on household gas consumption in 2019–2022. Ultimately, the lowest rate of influence was recorded by the Granger causality test (2.47%), compared to 6.88% in the test for the significance of the correlation coefficient and 9.23% in the t-test for the statistical significance of the regression coefficients. One has to note that the Granger causality test used in our study is considered the most sensitive model for analyzing economic data. Using statistical methods, we concluded that regulatory factors have a negligible impact on the volume of natural gas consumption by households. Our results suggest that the Ukrainian regulatory authorities should be cautious about using non-market mechanisms, such as price caps, in the energy sector. [ABSTRACT FROM AUTHOR]