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This article addresses the economic and fiscal consequences of Russia's full-scale invasion of Ukraine for subnational governments in Russia. Using annual fiscal and economic data, we study how different groups of regions have been affected by the ongoing Russo-Ukrainian war. Despite the Russian economy being more resilient to a sanctions shock than initially predicted, we document several important negative tendencies at the regional level, sometimes overlooked in policy discussions and the literature on economic sanctions. In this article, we present new regional data and focus on the effects of the war on overall regional tax collections in 2022–2023, as well as the dynamics of different types of regional and federal taxes. We identify several groups of "winners" (oil- and gas-extracting regions, weapons-producing regions, and the largest city-regions) and "losers" (regions that share a border with Ukraine and regions with sanctions-prone industrial specializations) and discuss the prospects of their resilience. [ABSTRACT FROM AUTHOR]

This study examines the relationship between education and entrepreneur income in Ecuador, a prototypical small economy grappling with employment challenges. Utilizing quantile regression and neutrosophic statistical methods, the research uncovers varied impacts of education on earnings across different income levels. The findings reveal a decrease in indeterminacy as individuals ascend income quintiles, indicating that higher education levels yield more predictable and substantial economic returns at the upper end of the income spectrum. These insights are vital for policymakers to develop targeted educational interventions. The study's methodological novelty lies in its adoption of neutrosophic statistics, which embraces the indeterminacy intrinsic to economic data, offering a refined lens for understanding the income-education interplay. Future research directions include longitudinal studies to trace the temporal effects of education and cross-comparative analyses across economies. The potential integration of machine learning with neutrosophic statistics promises enhanced predictive models, contributing to data-driven economic policy formulation. [ABSTRACT FROM AUTHOR]

This study examines whether and how public media systems contribute to the health of democracies in 33 countries in Europe, Africa, Asia, North America, the Middle East, Latin America, and South America. We gather national economic data and public media funding levels, audience shares, and regulatory data, primarily for 2018 and 2019 but in some cases earlier, due to lack of available data. We then assess correlations with strength of democracy indices and extend Hallin and Mancini's typology of North American and European media systems through hierarchical cluster analysis of these 33 countries. We find five models of public media systems around the world, ranging from "state-administered" systems with low levels of independence (Botswana and Tunisia) to systems aligning with Hallin and Mancini's "Democratic Corporatist" model, with strong and secure (multiyear) funding, large audience shares, and strong regulatory protection for their independence. In between, we identify three mixed models: a "Liberal-Pluralist" model, a "Direct Funding" model, and a "Commercial–Public" model. Correlations and cluster analyses show that high levels of secure funding for public media systems and strong structural protections for the political and economic independence of those systems are consistently and positively correlated with healthy democracies. [ABSTRACT FROM AUTHOR]

Background: Interprofessional collaborative care such as a split-shared care model involving family physicians and community pharmacists can reduce the economic burden of diabetes management. This study aimed to evaluate the economic outcome of a split-shared care model between family physicians and community pharmacists within a pharmacy chain in managing people with uncontrolled type 2 diabetes and polypharmacy. Method: This was a multi-center, parallel arm, open label, randomized controlled trial comparing the direct and indirect economic outcomes of people who received collaborative care involving community pharmacists (intervention) versus those who received usual care without community pharmacist involvement (control). People with uncontrolled type 2 diabetes, defined as HbA1c > 7.0% and taking ≥ 5 chronic medications were included while people with missing baseline economic data (such as consultation costs, medication costs) were excluded. Direct medical costs were extracted from the institution's financial database while indirect costs were calculated from self-reported gross income and productivity loss, using Work Productivity Activity Impairment Global Health questionnaire. Separate generalized linear models with log link function and gamma distribution were used to analyze changes in direct and indirect medical costs. Results: A total of 175 patients (intervention = 70, control = 105) completed the trial and were included for analysis. The mean age of the participants was 66.9 (9.2) years, with majority being male and Chinese. The direct medical costs were significantly lower in the intervention than the control group over 6 months (intervention: -US$70.51, control: -US$47.66, p < 0.001). Medication cost was the main driver in both groups. There were no significant changes in productivity loss and indirect costs in both groups. Conclusion: Implementation of split-shared visits with frontline community partners may reduce economic burden for patient with uncontrolled type 2 diabetes and polypharmacy. Trial registration: Clinicaltrials.gov Reference Number: NCT03531944 (Date of registration: June 6, 2018). [ABSTRACT FROM AUTHOR]

Background: Dengue is spreading in (sub)tropical areas, and half of the global population is at risk. The macroeconomic impact of dengue extends beyond healthcare costs. This study evaluated the impact of dengue on gross domestic product (GDP) based on approaches tailored to two dengue-endemic countries, Thailand and Brazil, from the tourism and workforce perspectives, respectively. Findings: Because the tourism industry is a critical economic sector for Thailand, lost tourism revenues were estimated to analyze the impact of a dengue outbreak. An input-output model estimated that the direct effects (on international tourism) and indirect effects (on suppliers) of dengue on tourism reduced overall GDP by 1.43 billion US dollars (USD) (0.26%) in the outbreak year 2019. The induced effect (reduced employee income/spending) reduced Thailand's GDP by 375 million USD (0.07%). Overall, lost tourism revenues reduced Thailand's GDP by an estimated 1.81 billion USD (0.33%) in 2019 (3% of annual tourism revenue). An inoperability input-output model was used to analyze the effect of workforce absenteeism on GDP due to a dengue outbreak in Brazil. This model calculates the number of lost workdays associated with ambulatory and hospitalized dengue. Input was collected from state-level epidemiological and economic data for 2019. An estimated 22.4 million workdays were lost in the employed population; 39% associated with the informal sector. Lost workdays due to dengue reduced Brazil's GDP by 876 million USD (0.05%). Conclusions: The economic costs of dengue outbreaks far surpass the direct medical costs. Dengue reduces overall GDP and inflicts national economic losses. With a high proportion of the population lacking formal employment in both countries and low income being a barrier to seeking care, dengue also poses an equity challenge. A combination of public health measures, like vector control and vaccination, against dengue is recommended to mitigate the broader economic impact of dengue. Author summary: Dengue is a viral illness spread by mosquitoes in Southeast Asia and South America, and about half of the world's population is at risk. Burden-of-illness studies on the economic impact of dengue typically focus on medical costs. However, the impact of this infectious disease extends far beyond healthcare costs. This study evaluated the impact of dengue on gross domestic product (GDP) based on approaches tailored to two dengue-endemic countries, Thailand and Brazil. The analyses estimated that the dengue outbreak in 2019 had a profound impact on Thailand's tourism industry, reducing GDP by an estimated US$1.81 billion, equivalent to 3% of annual tourism revenues or 0.33% of total GDP. Dengue also impacted Brazil's workforce in 2019, leading to an estimated 22.4 million lost workdays, of which 39% were associated with the informal sector, and reducing GDP overall by US$876 million (0.05% of total GDP). Informal workers are vulnerable to dengue-related financial burdens, and a low income can be a barrier to seeking medical care for dengue. This study highlights the broader economic impact of dengue beyond healthcare costs and underscores the importance of a combination of public health measures, such as vector control and vaccination. [ABSTRACT FROM AUTHOR]

The potentially catastrophic loss of biodiversity happening around the globe is largely caused by economic activity that is not connected to its environmental impacts. To improve positive and reduce negative outcomes for nature, investment decision-makers in companies, governments, and the finance sector need data on the impacts of economic activity, especially production of food and other commodities, on biodiversity, at fine geographical scales. This Essay argues that the data allowing us to identify the most important factors causing biodiversity loss are already available. However, we need more data to track impacts on biodiversity up value chains into pathways, toolkits, and approaches that will facilitate verified, concrete actions by companies and consumers to reduce threats to biodiversity in particular places. Our current knowledge is insufficient to deliver complete responses to the biodiversity crisis, but this is no excuse for delaying action. Biodiversity is being lost because of economic activity that has no connection its impacts on biodiversity. This Essay argues that we need better spatially explicit and actionable data on the causes of biodiversity loss that can be conveyed to company decision-makers in a format that they can use easily and efficiently. [ABSTRACT FROM AUTHOR]

This document provides summaries of several doctoral dissertations in the field of economic history. The first dissertation focuses on Chinese immigration and exclusion in the United States, challenging previous assumptions and highlighting the impact of anti-Chinese laws. The second dissertation explores expanding financial participation in the early 20th century, examining employee stock ownership programs and the impact of the Liberty Loan Program during World War I. The third dissertation examines disparities in access to opportunity in the United States, particularly focusing on the intergenerational effects of anti-Black institutions. These dissertations contribute valuable data, methods, and findings to their respective fields of study. Another dissertation explores various aspects of inequality in America, focusing on racial and gender disparities in access to opportunity. Lastly, a dissertation focuses on the drivers of labor market inequalities throughout economic history, examining intergenerational mobility and the impact of the feudal system. [Extracted from the article]

Environmental markets are a rapidly emerging tool to mobilize private funding to incentivize landholders to undertake more sustainable land management. How units of biodiversity in these markets are measured and subsequently traded creates key challenges ecologically and economically because it determines whether environmental markets can deliver net gains in biodiversity and efficiently lower the costs of conservation. We developed and tested a metric for such markets based on the well‐established principle of irreplaceability from systematic conservation planning. Irreplaceability as a metric avoids the limitations of like‐for‐like trading and allows one to capture the multidimensional nature of ecosystems (e.g., habitats, species, ecosystem functioning) and simultaneously achieve cost‐effective, land‐manager‐led investments in conservation. Using an integrated ecological modeling approach, we tested whether using irreplaceability as a metric is more ecologically and economically beneficial than the simpler biodiversity offset metrics typically used in net gain and no‐net‐loss policies. Using irreplaceability ensured no net loss, or even net gain, of biodiversity depending on the targets chosen. Other metrics did not provide the same assurances and, depending on the flexibility with which biodiversity targets can be achieved, and how they overlap with development pressure, were less efficient. Irreplaceability reduced the costs of offsetting to developers and the costs of ecological restoration to society. Integrating economic data and systematic conservation planning approaches would therefore assure land managers they were being fairly rewarded for the opportunity costs of conservation and transparently incentivize the most ecologically and economically efficient investments in nature recovery. [ABSTRACT FROM AUTHOR]

The book "Cost-Inclusive Evaluation: Planning It, Doing It, Using It" by Nadini Persaud and Brian Yates provides a comprehensive guide to cost analysis in evaluation. The book covers foundational economic knowledge, various cost evaluation methods, and processes. It offers actionable procedures and processes, making it a useful resource for practitioners and students. The authors present two primary approaches to cost analysis: a financial analysis approach and an economics-based approach. The book also discusses the importance of ethics and equity in cost analysis. Overall, "Cost-Inclusive Evaluation" is a valuable reference guide for evaluators and students alike. [Extracted from the article]

The literature has witnessed an upsurge of interest in model selection in diverse fields and optimization applications. Despite the substantial progress, model selection remains a significant challenge when covariates are highly correlated, particularly within economic and financial datasets that exhibit cross‐sectional and serial dependency. In this paper, we introduce a novel methodology named factor augmented regularized model selection with weak factors (WeakFARM) for generalized linear models in the presence of correlated covariates with weak latent factor structure. By identifying weak latent factors and idiosyncratic components and employing them as predictors, WeakFARM converts the challenge from model selection with highly correlated covariates to that with weakly correlated ones. Furthermore, we develop a variable screening method based on the proposed WeakFARM method. Comprehensive theoretical guarantees including estimation consistency, model selection consistency and sure screening property are also provided. We demonstrate the effectiveness of our approach by extensive simulation studies and a real data application in economic forecasting. [ABSTRACT FROM AUTHOR]

I discuss a framework to allow dynamic sparsity in time-varying parameter regression models. The conditional variances of the innovations of time-varying parameters are time varying and equal to zero adaptively via thresholding. The resulting model allows the dynamics of the time-varying parameters to mix over different frequencies of parameter changes in a data driven way and permits great flexibility while achieving model parsimony. A convenient strategy is discussed to infer if each coefficient is static or dynamic and, if dynamic, how frequent the parameter change is. An MCMC scheme is developed for model estimation. The performance of the proposed approach is illustrated in studies of both simulated and real economic data. [ABSTRACT FROM AUTHOR]

In this article, we focus on both the bright and dark sides of the organisational social capital of Russian manufacturing enterprises and its influence on their economic trajectories after the 2007–2008 global financial crisis. The analysis is based on a sociological survey of the CEOs and employees of 300 companies and statistics on their economic performance in the period 2001–2014. We found that only trust in the CEO helped companies to survive, while other characteristics did not. The relation between an enterprises' social capital and its economic performance after 2008 is counterintuitive: companies wherein the CEO enjoyed a high level of trust but where cooperation between managers and employees was generally low showed significant average annual sales growth. [ABSTRACT FROM AUTHOR]

Support for EU membership has long been an important topic of study. Individual-level research shows that winners of globalisation, including the higher educated, express greater EU support as they profit from economic and cultural conditions resulting from European integration. Recent context-focused work suggests that individuals in 'left-behind' places are less supportive of EU integration because of their home regions' weaker long-term economic conditions. This paper brings these two research strands into conversation with each other by examining how the relationship between EU support and individual-level education is contingent on subnational economic conditions. Using harmonised Eurobarometer data from almost 750,000 respondents spanning 2004–2019, combined with subnational economic data from 201 European regions, we find no evidence that subnational economic conditions influence the relationship between EU support and respondents' education level. At the same time, when cross-sectionally comparing long-term differences between regions, we find that EU support is positively related to regional GDP per capita (though unrelated to regional unemployment), among both the higher and lower educated, and especially in the post-Great Recession period. Longitudinally, EU support is positively related to declining regional unemployment, both among the higher and lower educated, but not to increasing regional GDP per capita. [ABSTRACT FROM AUTHOR]

In this paper, we study asymptotic model change detection in multivariate linear regression by using the Kolmogorov–Smirnov function of the partial sum process of recursive residuals. We approximate the rejection region and also the power function of the test by establishing a functional central limit theorem for the sequence of the partial sum processes of the recursive residuals of the observations. When the assumed model is true, the limit process is given by the standard multivariate Brownian motion which does not depend on the regression functions. However, when the assumed model is not true (some models change), the limit process is represented by a vector of deterministic trend plus the standard multivariate Brownian motion. The finite sample size rejection region and the power of the test are investigated by means of Monte Carlo simulation. The simulation study shows evidence that the proposed test is consistent in the sense that it attains the power larger than the size of the test when the hypothesis is not true. We also demonstrate the application of the proposed test method to Indonesian economic growth data in which we test the adequacy of three-variate low-order polynomial model. The test result shows that the growth of the Indonesian economy is neither simultaneously constant nor linear. The test has successfully detect the appearance of a change in the model which is mainly caused by the COVID-19 pandemic in 2020. [ABSTRACT FROM AUTHOR]

Motivation: Anecdotal evidence suggests that local non-farm opportunities slow down rural–urban migration. However, there is hardly any empirical evidence on the relation between household non-farm income and migration. Understanding this relation is essential for rural development strategies, sustainable urban development, and policies that influence domestic migrations. Purpose: We examine whether households' decision to send a member to urban areas for employment depends on the extent of their non-farm income and, in turn, on local non-farm opportunities. We also study how this impact is dependent on household income, education of the household head, and land holdings. Methods and approach: Households' decision to migrate or to choose non-farm employment is endogenous, likely to be influenced by unobserved characteristics. We construct local measures of non-farm opportunities at the Union (lowest administrative) level using Economic Census data. Local opportunities are exogenous to household decisions and are used to instrument household-level non-farm income. Findings: Regression results using instrumental variables show that a greater share of non-farm income reduces the probability that members of the household will migrate to other districts. This effect is most pronounced for semi-rural areas such as municipalities. We find similar results when we use areas with small and medium enterprise (SME) clusters to instrument household-level non-farm income. We also observe substantial heterogeneity of impact: a higher share of non-farm income deters migration more for income-rich, land-poor and educated households. Policy implications: Our results suggest that creating non-farm opportunities curbs migration to urban areas. Developing rural growth centres, secondary towns, may reduce migration to larger cities and reduce poverty. [ABSTRACT FROM AUTHOR]

It has been determined on the basis of the analysis of official statistical data, the changes in the employment structure in the Sharur administrative district during the last 10 years (2009-2019), and the factors influencing the formation of the population's employment in the article. In the Sharur administrative district, the number dynamics, gender and age composition of the population, settlement of the able-bodied population in villages and cities, changes of the economically active population over the years, as well as the distribution of the population by economic activities have been analyzed. The reasons why the economically active population works more in various fields of agriculture were analyzed, and negative and positive factors affecting the structure of employment in the region were noted. Statistical data are shown and compared with tables and charts. The differences in the working population according to employment status were analyzed according to the statistical data of 2009 and 2019. The changes in the wage-earning and self-employed population over the years, the gender composition, and the settlement of the population working in these fields were recorded and compared on the basis of the data recorded in the tables. [ABSTRACT FROM AUTHOR]

This paper provides a systematic and critical review of the economics literature on data as an economic good and draws lessons for data governance. We conclude that focusing on data as an economic good in governance efforts is hardwired to only result in more data production and cannot deliver other societal goals contrary to what is often claimed in the literature and policy. Data governance is often a red herring which distracts from other digital problems. The governance of digital society cannot rely exclusively on data-centric economic models. We review the literatures and the underlying empirical and political claims concerning data commons. While commons thinking is useful to frame digital problems in terms of ecologies, it has important limitations. We propose a political-ecological approach to governing the digital society, defined by ecological thinking about governance problems and the awareness of the political nature of framing the problems and mapping their ecological makeup. [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]

This paper proposes a novel framework for modelling the spread of financial crises in complex networks, combining financial data, Extreme Value Theory and an epidemiological transmission model. We accommodate two key aspects of contagion modelling: fundamentals-based contagion, where the transmission is due to direct financial linkages, and pure contagion, where a crisis might trigger additional crises due to global effects. We use stock price, geographical location and economic sector data for a set of 398 companies to construct multiplex networks of four layers, on which a susceptible-infected-recovered transmission model is defined, in order to model the spread of financial shocks between companies by accounting for their interconnected nature. By utilizing stock price data for the 2008 and 2020 financial crises, we investigate and assess the effectiveness of our model in forecasting the propagation of financial shocks through the network, where a shock is detected by measuring stock price volatility. The results suggest that the proposed framework is effective in predicting the spread of financial crises. Our findings demonstrate the significance of each layer of the multiplex network structure, which differentiates between various transmission pathways, for predicting the number of affected companies, as well as for company-, sector- or location-specific predictions. [ABSTRACT FROM AUTHOR]

Model-based health economic evaluations of ischemic stroke are in need of cost- and utility estimates related to relevant outcome measures. This study aims to describe societal cost- and utility estimates per modified Rankin Scale (mRS)-score at different time points within 2 years post stroke. Included patients had a stroke between 3 months and 2.5 years ago. mRS and EQ-5D-5L were scored during a telephone interview. Based on the interview date, records were categorized into a time point: 3 months (3M; 3-6 months), 1 year (Y1; 6-18 months), or 2 years (Y2; 18-30 months). Patients completed a questionnaire on healthcare utilization and productivity losses in the previous 3 months. Initial stroke hospitalization costs were assessed. Mean costs and utilities per mRS and time point were derived with multiple imputation nested in bootstrapping. Cost at 3 months post stroke were estimated separately for endovascular treatment (EVT)-/non-EVT-patients. 1106 patients were included from 18 Dutch centers. At each time point, higher mRS-scores were associated with increasing average costs and decreasing average utility. Mean societal costs at 3M ranged from €11 943 (mRS 1, no EVT) to €55 957 (mRS 5, no EVT). For Y1, mean costs in the previous 3 months ranged from €885 (mRS 0) to €23 215 (mRS 5), and from €1655 (mRS 0) to €22 904 (mRS 5) for Y2. Mean utilities ranged from 0.07 to 0.96, depending on mRS and time point. The mRS-score is a major determinant of costs and utilities at different post-stroke time points. Our estimates may be used to inform future model-based health economic evaluations. • Model-based health economic evaluations of acute ischemic stroke are in need of cost- and utility estimates related to relevant outcome measures, such as the modified Rankin Scale. • Using prospectively collected data, this cross-sectional study provides cost and utility estimates per modified Rankin Scale score at 3 months, 1 year, and 2 years post stroke. [ABSTRACT FROM AUTHOR]

The article focuses on the escalating defense costs in the U.S. over several decades, which have outpaced overall economic inflation. It examines how public choice theory can explain the incentives within the military-industrial complex (MIC) that drive defense spending increases, highlighting how political institutions incentivize politicians, bureaucrats, and contractors to pursue policies that benefit them personally, leading to higher costs for national security provision.

Objective: To examine the relationship between the level of state funding for Home‐ and Community‐Based Services (HCBS) and state overall and dimension‐specific performances in Long‐Term Services and Supports (LTSS). Data Sources and Study Setting: We employed state‐level secondary data from the Medicaid LTSS Annual Expenditures Reports, the American Association of Retired Persons (AARP) State Scorecards, the U.S. Census, and Federal Reserve Economic data, spanning the timeframe of 2010–2020. Study Design: Overall state LTSS rankings, along with dimension‐specific rankings, were modeled separately against state Medicaid spending on HCBS relative to total Medicaid spending on LTSS. All models were adjusted for state covariates, secular trend, and state fixed effects. Data Collection/Extraction Methods: The study sample included all 50 states and the District of Columbia. However, California, Delaware, Illinois, and Virginia were excluded from FY2019 due to missing data on Medicaid HCBS expenditures. Principal Findings: Every 10 percentage‐point increase in the proportion of Medicaid LTSS spending to HCBS demonstrated 2.05 points improvement (95% confidence interval [CI]: −3.88 to 0.22, p = 0.03) in rankings for state overall LTSS system performance, 2.92 points improvement (95% CI: −4.87 to 0.98, p < 0.01) in rankings for the Choice of Setting and Provider dimension, as well as 1.73 points (95% CI: −3.14 to 0.32, p = 0.02) ranking improvement in the dimension of Effective Transitions. Conclusions: Our study suggested promising effects of increased state funding for HCBS on LTSS performance. [ABSTRACT FROM AUTHOR]

This study examines the effects of educational attainment and household income on wealth accumulation in Vietnam using various Vietnam Household Living Standards Surveys (VHLSSs) in 2008, 2018 and 2020. We use the threshold regression to separate households into different groups depending on their assets. We find that owning a university degree is optimal to maximize wealth accumulation for households in 2008. However, post-graduate qualifications are needed for wealth accumulation in 2018 and 2020. For those household heads without a university degree, owning advanced training certificates provides the best opportunity to accumulate household wealth in all three surveys. Besides, our empirical results confirm that household income played no role in wealth accommodation in 2008. Interestingly, household income positively contributes to wealth accumulation for households with assets above the threshold of VND 634.40 m in 2020. However, the effect is negative for households with assets below the threshold of VND 440.25 m in 2018. We also find that households with widowed or divorced household heads are associated with lower wealth accumulation. Another interesting finding is that living in urban areas reduces wealth accumulation for households whose assets are below VND 440.25 m in 2018. These findings largely remain unchanged when Lewbel's (Lewbel, Journal of Business and Economic Statistics 30:67–80, 2012) instrument variable estimator is used to ensure the robustness of the empirical results. [ABSTRACT FROM AUTHOR]

The purpose is to cut the costs of Supply Chain enterprises in Ice-Snow Tourism (IST) and improve the intelligence and automation of Supply Chain Management (SCM). First, the spatial-temporal characteristics of economic data of the IST Supply Chain are analyzed based on the Internet of Things (IoT). Second, the annual Online Public Attention (OPA) data to IST in domestic cities and regions are collected. The quarterly concentration index and Gini coefficient are used to analyze their spatial and temporal characteristics. Then, the weighted fusion algorithm used for the Supply Chain scenario modeling is improved to solve data redundancy and improve information accuracy. Finally, the framework of the IST-oriented Supply Chain scenario ontology model is proposed. The experimental results show that Internet users give much attention to IST from 2011 to 2021. OPA to IST increased first and decreased and peaked in 2016. The final fusion value of the proposed data fusion algorithm is 20.0221, and that of the adaptive Weighted Average Method (WAM) is 20.0724. Thus, the proposed algorithm outperforms the adaptive WAM. The traditional scenario-based ontology model takes people as the center. In contrast, the Supply Chain scenario-based ontology model centers around product state and scenario. Therefore, the proposed Supply Chain scenario-based ontology model is entirely new. The proposed scenario-based ontology model using polymorphic IoT lays the foundation for developing an intelligent and automatic SCM. It has great practical significance in realizing efficient tourism industry management and SCM. [ABSTRACT FROM AUTHOR]

We propose a new model averaging approach to investigate segment regression models with multiple threshold variables and multiple structural breaks. We first fit a series of models, each with a single threshold variable and multiple breaks over its domain, using a two‐stage change point detection method. Then these models are combined together to produce a weighted ensemble through a frequentist model averaging approach. Consequently, our segment regression model averaging (SRMA) method may help identify complicated subgroups in a heterogeneous study population. A crucial step is to determine the optimal weights in the model averaging, and we follow the familiar non‐concave penalty estimation approach. We provide theoretical support for SRMA by establishing the consistency of individual fitted models and estimated weights. Numerical studies are carried out to assess the performance in low‐ and high‐dimensional settings, and comparisons are made between our proposed method and a wide range of existing alternative subgroup estimation methods. Two real economic data examples are analyzed to illustrate our methodology. [ABSTRACT FROM AUTHOR]

Utilizing expanded data analysis, this study aims to determine how China's macroeconomic performance affects the global economy. Employing the Global Vector Auto Regression (GVAR) model, we investigate the impact of China's macroeconomic variables on the economies of its selected trading partners throughout the world. For 2000Q1–2019Q4, developed countries (the US, Hong Kong), developing Europe countries (Russian Federation and Poland), and two developing Asia countries (Vietnam and India) show that Chinese macroeconomic performance has a greater impact than middle east and central Asia, Sub-Saharan Africa, and Western hemisphere. The Chinese GDP shock hits emerging Europe harder because of its extensive Russian production network. Results reveal that Chinese depreciation impacts developing Europe and Asia, while the Chinese stock market shock hits advanced economies more. Advanced and developing Asian economies are greatly affected by TV shock. Chinese FDI affects the Middle East and Central Asia, because of the China-Pakistan Economic Corridor. China trading partners use macroeconomic statistics to assess their economic priorities, industrial strengths, and weaknesses to create national interest plans. Currency adjustment, trade policies, domestic stimulus, and market diversification can minimize negative effects or capitalize on possibilities from China's macroeconomic conditions. [ABSTRACT FROM AUTHOR]

• There is a simultaneous equations model to systematically study the impact of servitization of manufacturing on the upgrading of power status in the global value network. • The servitization of manufacturing will directly promote the international competitiveness of China's manufacturing based on the domestic value added of exports, and the improvement of international competitiveness will interact with the centralization of network power status. • Compared with the non-commercial sectors, the servitization of the commercial sectors has a more obvious impact on the power status of the global value network. • China's manufacturing enterprises should strengthen their own service-oriented transformation to improve the core competitiveness, especially in the forms of digital servitization and financial servitization. In the context of the great changes of the global value network pattern, the servitization of manufacturing has become an important strategy for the transformation and upgrading of multinational enterprises. China's manufacturing industry has been in the core area of the global value network, creating a lot of export value, but the quality of development needs to be improved. Especially, the servitization level and structure of China's manufacturing have a great space for development. The servitization level affects the international competitiveness of manufacturing enterprises, and their power status in the global value network depends on their international competitiveness to a large extent. With value-added trade data, inter-country input-output tables and China's industrial economic data, there is a simultaneous equations model in this paper to systematically study the impact of servitization of manufacturing on the upgrading of power status in the global value network. The empirical results show that the servitization level of the manufacturing significantly improves the competitiveness of the export value-added, and then affects its core position in the global network. This is a transmission mechanism with a complete mediating effect. Heterogeneity studies show that both commercial and non-commercial servitization have positive impacts on the status of China's manufacturing in the global value network, but the difference is significant. The distance between service and production, and the development stage of service dominance are the main reasons for the difference. China's manufacturing enterprises should optimize the allocation structure of production factors and strengthen their own service-oriented transformation to improve the core competitiveness, especially in the forms of digital servitization and financial servitization. And the government should innovate public service supply to carry out economic governance. [ABSTRACT FROM AUTHOR]

In the context of the debate on cryptocurrencies as the 'digital gold', this study explores the nexus between the Bitcoin and US oil returns by employing a rich set of parametric and non-parametric approaches. We examine the dependence structure of the US oil market and Bitcoin through Clayton copulas, normal copulas, and Gumbel copulas. Copulas help us to test the volatility of these dependence structures through left-tailed, right-tailed or normal distributions. We collected daily data from 5 February 2014 to 24 January 2019 on Bitcoin prices and oil prices. The data on bitcoin prices were extracted from coinmarketcap.com. The US oil prices were collected from the Federal Reserve Economic Data source. Maximum pseudo-likelihood estimation was applied to the dataset and showed that the US oil returns and Bitcoin are highly vulnerable to tail risks. The multiplier bootstrap-based goodness-of-fit test as well as Kendal plots also suggest left-tail dependence, and this adds to the robustness of the results. The stationary bootstrap test for the partial cross-quantilogram indicates which quantile in the left tail has a statistically significant relationship between Bitcoin and US oil returns. The study has crucial implications in terms of portfolio diversification using cryptocurrencies and oil-based hedging instruments. [ABSTRACT FROM AUTHOR]

"Vietnam: Navigating a Rapidly Changing Economy, Society, and Political Order" is a collection of essays edited by Börje Ljunggren and Dwight H. Perkins that provides a comprehensive overview of Vietnam's economic, social, and political landscape. The book focuses on Vietnam's economic development, foreign relations, institutional reform, poverty reduction, and challenges in achieving its ambitious goals. It also examines the country's political system, civil society, education, health, and family dynamics. The authors highlight the need for further reforms and a stronger domestic private sector to unlock Vietnam's full potential. While the book offers valuable insights, more research is needed to address the future challenges and opportunities facing Vietnam. [Extracted from the article]

Wavelets analysis has become a powerful mathematical tool to decompose a series and to provide its frequent and temporal features. Despite the fact that many researchers have begun testing this approach on financial and economic data to detect and treat outliers, it still not widely applied for longitudinal data. Therefore, this article proposes two algorithms for improving the accuracy of outlier detection in longitudinal data using Wavelets Decomposition, namely, Wavelets Decomposition for Outliers Detection and Handling across Subjects (WDODHAS), and Wavelets Decomposition for Outliers Detection and Handling within Subjects (WDODHWIS). The results show that Wavelets Decomposition is capable of detecting and handling outliers without erasing them, as well as highlighting hypothetical scenarios when these observations cannot be handled. [ABSTRACT FROM AUTHOR]

We present a methodology to determine optimal financial parameters in shale-gas production, combining numerical simulation of decline curves and stochastic modeling of the gas price. The mathematical model of gas production considers free gas in the pore and the gas adsorbed in kerogen. The dependence of gas production on petrophysical parameters and stimulated permeability is quantified by solving the model equations in a 3D geometry representing a typical fractured shale well. We use Monte Carlo simulation to characterize the statistical properties of various common financial indicators of the investment in shale-gas. The analysis combines many realizations of the physical model, which explores the variability of porosity, induced permeability, and fracture geometry, with thousands of realizations of gas price trajectories. The evolution of gas prices is modeled using the bootstrapping statistical resampling technique to obtain a probability density function of the initial price, the drift, and the volatility of a geometric Brownian motion for the time evolution of gas price. We analyze the Net Present Value (NPV), Internal Rate of Return (IRR), and Discounted Payback Period (DPP) indicators. By computing the probability density function of each indicator, we characterize the statistical percentile of each value of the indicator. Alternatively, we can infer the value of the indicator for a given statistical percentile. By mapping these parametric combinations for different indicators, we can determine the parameters that maximize or minimize each of them. We show that, to achieve a profitable investment in shale-gas with high certainty, it is necessary to place the wells in extremely good locations in terms of geological parameters (porosity) and to have exceptional fracturing technology (geometry) and fracture permeability. These high demands in terms of petrophysical properties and hydrofracture engineering may explain the industry observation of "sweet spots", that is, specific areas within shale-gas plays that tend to yield more profitable wells and where many operators concentrate their production. We shed light on the rational origin of this phenomenon: while shale formations are abundant, areas prone to having a multi-parameter combination that renders the well profitable are less common. [ABSTRACT FROM AUTHOR]

Background: Economic incentives can improve clinical outcomes among in-care people living with HIV (PLHIV), but evidence is limited for their effectiveness among out-of-care PLHIV or those at risk of disengagement. We propose a type 1 hybrid effectiveness-implementation study to advance global knowledge about the use of economic incentives to strengthen the continuity of HIV care and accelerate global goals for HIV epidemic control. Methods: The Rudi Kundini, Pamoja Kundini study will evaluate two implementation models of an economic incentive strategy for supporting two groups of PLHIV in Tanzania. Phase 1 of the study consists of a two-arm, cluster randomized trial across 32 health facilities to assess the effectiveness of a home visit plus one-time economic incentive on the proportion of out-of-care PLHIV with viral load suppression (< 1000 copies/ml) 6 months after enrollment (n = 640). Phase 2 is an individual 1:1 randomized controlled trial designed to determine the effectiveness of a short-term counseling and economic incentive program offered to in-care PLHIV who are predicted through machine learning to be at risk of disengaging from care on the outcome of viral load suppression at 12 months (n = 692). The program includes up to three incentives conditional upon visit attendance coupled with adapted counselling sessions for this population of PLHIV. Consistent with a hybrid effectiveness-implementation study design, phase 3 is a mixed methods evaluation to explore barriers and facilitators to strategy implementation in phases 1 and 2. Results will be used to guide optimization and scale-up of the incentive strategies, if effective, to the larger population of Tanzanian PLHIV who struggle with continuity of HIV care. Discussion: Innovative strategies that recognize the dynamic process of lifelong retention in HIV care are urgently needed. Strategies such as conditional economic incentives are a simple and effective method for improving many health outcomes, including those on the HIV continuum. If coupled with other supportive services such as home visits (phase 1) or with tailored counselling (phase 2), economic incentives have the potential to strengthen engagement among the subpopulation of PLHIV who struggle with retention in care and could help to close the gap towards reaching global "95–95-95" goals for ending the AIDS epidemic. Trial registration: Phase 1: ClinicalTrials.gov, NCT05248100, registered 2/21/2022. Phase 2: ClinicalTrials.gov, NCT05373095, registered 5/13/2022. [ABSTRACT FROM AUTHOR]

The Russian federal and consolidated government budgets accrue large revenues directly from oil and natural gas, mostly from royalties and export fees. We use Russian Input-Output tables and other economic statistics to estimate how much revenue flowed to these budgets indirectly due to government spending of direct revenues in 2016–21. Although the addition of indirect revenues to the federal budget is modest (8–10 Russian rubles per 100 rubles spent), the corresponding increase in consolidated budget is quite sizable (26–30 rubles per 100 rubles spent). Given the high degree of political and economic centralization in Russia, the impact on the consolidated budget is more important than the impact on the federal budget. In addition, we speculate about the changing role of oil and natural gas budget revenues in the current environment and the near future. [ABSTRACT FROM AUTHOR]

Generalizability of trial-based cost-effectiveness estimates to real-world target populations is important for decision making. In the context of independent aggregate time-to-event baseline and relative effects data, complex hazards can make modeling of data for use in economic evaluation challenging. Our article provides an overview of methods that can be used to apply trial-derived relative treatment effects to external real-world baselines when faced with complex hazards and follows with a motivating example. Approaches for applying trial-derived relative effects to real-world baselines are presented in the context of complex hazards. Appropriate methods are applied in a cost-effectiveness analysis using data from a previously published study assessing the real-world cost-effectiveness of a treatment for carcinoma of the head and neck as a motivating example. Lack of common hazards between the trial and target real-world population, a complex baseline hazard function, and nonproportional relative effects made the use of flexible models necessary to adequately estimate survival. Assuming common distributions between trial and real-world reference survival substantially affected survival and cost-effectiveness estimates. Modeling time-dependent vs proportional relative effects affected estimates to a lesser extent, dependent on assumptions used in cost-effectiveness modeling. Appropriately capturing reference treatment survival when attempting to generalize trial-derived relative treatment effects to real-world target populations can have important impacts on cost-effectiveness estimates. A balance between model complexity and adequacy for decision making should be considered where multiple data sources with complex hazards are being evaluated. • Generalizability is an important consideration for decision making. • Approaches used for applying trial-derived treatment effects to real-world target population baseline risks can substantially affect cost-effectiveness estimates. • Model complexity in light of requirements for informed decision making should be considered when generalizing cost-effectiveness estimates. [ABSTRACT FROM AUTHOR]

In the transition towards a more sustainable energy system, hydrogen is seen as the key low-emission energy source. However, the limited H 2 volumetric density hinders its transportation. To overcome this issue, liquid organic hydrogen carriers (LOHCs), molecules that can be hydrogenated and, upon arrival, dehydrogenated for H 2 release, have been proposed as hydrogen transport media. Considering toluene and dibenzyltoluene as representative carriers, this work offers a systematic methodology for the analysis and the comparison of LOHCs, in view of identifying cost-drivers of the overall value-chain. A detailed Aspen Plus process simulation is provided for hydrogenation and dehydrogenation sections. Simulation results are used as input data for the economic assessment. The process economics reveals that dehydrogenation is the most impactful cost-item, together with the carrier initial loading, the latter related to the LOHC transport distance. The choice of the most suitable molecule as H 2 carrier, ultimately, is a trade-off between its hydrogenation enthalpy and cost. • Toluene and dibenzyltoluene technical assessment as H 2 carrier. • Hydrogen destination to H 2 valley. • Hydrogenation and dehydrogenation process simulation in Aspen Plus V11®. • Most impactful cost-items: hydro/dehydrogenation and carrier initial loading. • Key parameters for the H 2 carrier selection: hydrogenation enthalpy and cost. [ABSTRACT FROM AUTHOR]

In regional development studies, GDP serves as an important indicator for evaluating the developing levels of a region. However, due to statistical methods and possible human-induced interfering factors, GDP is also a commonly criticized indicator for less accurately assessing regional economic development in a dynamic environment, especially during a globalized era. Moreover, common data collection approaches are often challenging to obtain in real-time, and the assessments are prone to inaccuracies. This is especially true in economically underdeveloped regions where data are often less frequently or accurately collected. In recent years, Nighttime Light (NTL) data have emerged as a crucial supplementary data source for regional economic development evaluation and analysis. We adapt this approach and attempt to integrate multiple sources of spatial data to provide a new perspective and more effective tools for economic development evaluation. In our current study, we explore the integration of OpenStreetMap (OSM) data and NTL data in regional studies, and apply a Geographically and Temporally Weighted Regression model (GTWR) for modeling and evaluating regional economic development. Our results suggest that: (1) when using OSM data as a single data source for economic development evaluation, the adjusted R2 value is 0.889. When using NTL data as a single data source for economic development evaluation, the adjusted R2 value is 0.911. However, the fitting performance of OSM data with GDP shows a gradual improvement over time, while the fitting performance of NTL data exhibits a gradual decline starting from the year 2014; (2) Among the economic evaluation models, the GTWR model demonstrates the highest accuracy with an AICc value of 49,112.71, which is 2750.94 lower than the ordinary least squares (OLS) model; (3) The joint modeling of OSM data with NTL data yields an adjusted R2 value of 0.956, which is higher than using either one of them alone. Moreover, this joint modeling approach demonstrates excellent fitting performance, particularly in economically underdeveloped regions, providing a potential alternative for development evaluation in data-poor regions. [ABSTRACT FROM AUTHOR]

With the ongoing sovereign debt and biodiversity crises in many emerging economies, applications of debt-for-nature swaps as a dual solution for sovereign debt and nature conservation have been re-emerging. We analyze how debt-for-nature swaps (DNS) can be scaled to protect biodiversity priority areas and reduce debt burden. We build a dataset for biodiversity conservation and debt restructuring in 67 countries at risk of sovereign debt distress and show that they hold over 22% of global biodiversity priority areas, 82.96% of which are unprotected. Furthermore, we show that for 35 of the 67 countries, using conservative cost estimates, 100% of unprotected biodiversity priority areas could be protected for a fraction of debt; for the remaining countries, applying DNS would allow the protection of 11–13% of currently unprotected biodiversity priority areas. By applying interdisciplinary research combining fundamental biodiversity and economic data and methods merging, the research contributes methodologically and practically to the understanding of debt-for-nature swaps for emerging economies. [ABSTRACT FROM AUTHOR]

The present study aimed to develop a methodology to estimate jute (Corchorus sp.) cultivated areas in the Indian subcontinent using sentinel-1A Synthetic aperture radar (SAR) C band dual polarization data. We developed a hierarchical decision tree (DT) model to estimate jute area and compared it with other supervised and unsupervised methods. The proposed DT model showed improved overall accuracy of 82–83% (Kappa coefficient, k = 0.78–0.79) over supervised or unsupervised methods and validated against Directorate of Economics and Statistics (DES) data, Government of India during 2016–2019. The VV backscatter record improved accuracy over VH backscatter. [ABSTRACT FROM AUTHOR]

Since the mid-2000s, democratization has slowed, stopped, and even reversed across the world. At the same time, societies have become more oriented toward free markets as measured by indexes of economic freedom. Relying on a panel sample of 141 developed and developing countries between 2006 and 2017, this paper is the first to investigate whether the two phenomena are related by employing economic freedom data. It finds that there is no net-negative relationship between aggregate economic freedom and democracy in this time-period. Instead, mixed findings of both an overall positive and overall neutral (but not negative) association are uncovered in between-country and within-country analyses, respectively. In between-country analyses, using the disaggregated index shows that the legal system/property rights component drives most of the positive relationship between aggregate economic freedom and democracy in the developed world. The same between-country analyses in the developing world show that freedom of international trade is positively associated with democracy, while modest regulation has a negative relationship. However, additionally controlling for omitted variable bias using country-fixed effects, the paper does not find evidence for either a positive or negative relationship between subsequent changes in levels of economic freedom and democracy. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is one of the most common cancers worldwide. Comprehensive data on the economic burden of CRC at a populationlevel is critical in informing policymaking, but such data are currently limited in China. Methods: From a societal perspective, the economic burden of CRC in 2019 was estimated, including direct medical and nonmedical expenditure, disability, and premature-death-related indirect expenditure. Data on disease burden was taken from the GBD 2019 and analyzed using a prevalence-based approach. The perperson direct expenditure and work loss days were from a multicenter study; the premature-death-related expenditure was estimated using a human capital approach. Projections were conducted in different simulated scenarios. All expenditure data were in Chinese Yuan (CNY) and discounted to 2019. Results: In 2019, the estimated overall economic burden of CRC in China was CNY170.5 billion (0.189% of the local GDP). The direct expenditure was CNY106.4 billion (62.4% of the total economic burden), 91.4% of which was a direct medical expenditure. The indirect expenditure was CNY64.1 billion, of which 63.7% was related to premature death. The predicted burden would reach CNY560.0 billion in 2030 given constant trends for disease burden; however, it would be alternatively reduced to

With the promotion and development of oversized data technology, many data analysis platforms based on super large data storage and computing frameworks have emerged in the industry. While the platforms with oversized economic data analysis combined with machine learning models are still relatively lacking. And oversized data also brings a new problem, that is the security of economic development. It is an important and difficult task to analyse and detect risks from oversized economic data. Based on machine learning, data analysis, economic market and other multidisciplinary fields, this paper proposes a machine learning method, which is a genetic algorithm (GA) based feature selection method: FSGA. This method abstracts every possible feature selection result into an individual in GA, generates a population through genetic operation, and measures the merits of the individual through fitness. In addition, this paper has conducted multitudinous simulation experiments on the GA‐based FSGA method and the traditional LSTM data analysis method respectively. The accuracy rate and other indicators are obtained by comparing the training. The experimental results show that the GA‐based FSGA machine learning method has higher prediction accuracy when analysing oversized economic data. And it is practical to accelerate the development of digital economy. [ABSTRACT FROM AUTHOR]

Soil acidity is one of the major crop production constraints in the highlands of Ethiopia. Liming is becoming a common practice to amend soil acidity, but its effects on soil properties, crop yield, and farm income are not well studied. In this study, an on-farm liming experiment was conducted for two consecutive years (2020-2021) on acidic Nitisols (pH < 5.5) in Southern Ethiopia. The experiment consisted of six liming rates (control, 2.74, 4.11, 5.48, 6.85, and 8.22 t·ha−1) laid out in a randomized complete block design with three replications. Soil, agronomic, and economic data were collected in 2020 and 2021 cropping seasons and analyzed. The application of lime in the ranges of 2.74–8.22 t·ha−1 increased soil pH by 0.46–1.25 units and reduced exchangeable acidity by 2.02–3.17 units. Higher lime rates of 6.85–8.22 t·ha−1 increased soil pH sharply from 5.22 to 5.99 and 6.46, respectively, but such a rise in soil pH was not proportionally reflected in the yield increment. Higher available phosphorus contents of 7.16 and 6.01 mg·kg−1 were measured at the liming rates of 4.11 and 5.48 t·ha−1, respectively. Combined over the two years, 5.45 t·ha−1 lime application yielded the highest barley total biomass of 19,199 kg·ha−1 and a grain yield of 4,328 kg·ha−1, which are 46% and 30% higher than those of the control, respectively. It also yielded the highest marginal rate of return of 477% and a gross margin of 192,857.3 ETB1·ha−1, which is 53% higher than the control. Based on our results, 5.45 t·ha−1 of lime appears to have the optimal rate for economically viable barley production in the study area or similar environments. [ABSTRACT FROM AUTHOR]

Population displacement is one of the most common consequences of disasters, and it can profoundly affect communities and territories. However, gaining an accurate measure of the size of displacement in the days and weeks following a major disaster can be extremely difficult. This study uses aggregated Call Detail Records as an inexpensive and efficient technique to measure post-disaster displacement in four Italian regions affected by repeated earthquakes in 2016–2017. By comparing post-disaster mobile phone count with a forecast computed before the earthquake hit, we can compute an index of change in the presence of mobile phones (MPE). This measure, obtained thanks to advanced analytical techniques, provides a reliable indication of the effect of the earthquake in terms of immediate and medium-term displacement. We test this measure against census data and in combination with other datasets. Looking into available data on economic activities and requests for financial support to rebuild damaged buildings, we can explain MPE and identify significant factors affecting population displacement. It is possible to apply this innovative methodology to other disaster scenarios and use it by policymakers who want to understand the determinants of population displacement. [ABSTRACT FROM AUTHOR]

Between 2010 and 2019, five Eurozone governments in economic difficulty received assistance from international lenders on condition that certain policies specified in the Memoranda of Understanding (MoU) were implemented. To what extent were those conditions implemented? After conditionality, to what extent have governments rolled back changes pursued under external constraint? Do we find variation across governments regarding implementation and reversals, and if so, why?. This paper presents a database allowing the answers to those questions, the Data Base on EcoNomic Adjustment Policies (ENAP) database. We codified all policies and reforms included in the MoU's, and whether those were subsequently fully or partially implemented. We also codified all decisions taken by bailed out countries since the beginning of the financial crisis and verified whether those had been kept or fully or partially reversed until December 2019. For each condition or policy, a series of explanatory variables were coded: policy sector, type of reform, timing and type of reversal, origin of reversal and number of veto players. The ENAP shows that in all countries, MoU's were largely implemented and are resilient. [ABSTRACT FROM AUTHOR]

China's export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China's macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China's financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China's macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China's and the United States' economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model's sampling results are good. China's pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China's pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market. [ABSTRACT FROM AUTHOR]

Trial-based economic evaluations are increasingly being conducted to support healthcare decision-making. When analysing trial-based economic evaluation data, different methodological challenges may be encountered, including (i) missing data, (ii) correlated costs and effects, (iii) baseline imbalances and (iv) skewness of costs and/or effects. Despite the broad range of methods available to account for these methodological challenges in effectiveness studies, they may not always be directly applicable in trial-based economic evaluations where costs and effects are analysed jointly, and more than one methodological challenge typically needs to be addressed simultaneously. The use of inappropriate methods can bias results and conclusions regarding the cost-effectiveness of healthcare interventions. Eventually, such low-quality evidence can hamper healthcare decision-making, which may in turn result in a waste of already scarce healthcare resources. Therefore, this tutorial aims to provide step-by-step guidance on how to combine appropriate statistical methods for handling the abovementioned methodological challenges using a ready-to-use R script. The theoretical background of the described methods is provided, and their application is illustrated using a simulated trial-based economic evaluation. [ABSTRACT FROM AUTHOR]

A brief overview of the evolution of the robust estimation methods is presented, in particular, the main concepts of m-estimation methods and their advantages when working with data containing statistical outliers. The comparison of the results of the methods of M-estimates by P. J. Huber, F. Hampel, and the method of biquadratic weights by J. Tukey was conducted on real economic data of the regions of Ukraine. The empirical confirmation of the efficiency of using M-estimates is obtained. The comparison of all the generated models indicates a significant shift in parameter estimates in case the approach of "naive" removal of statistical outliers is applied. Based on the constructed robust models, it is possible to more accurately compare the regions of the country in terms of the efficiency of the labor and capital factors, as well as determine the priority of investments that will stimulate the economic development of the region to a greater extent accordingly. [ABSTRACT FROM AUTHOR]