Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
This study presents a comprehensive Bayesian hierarchical analysis of the U.S. homeowners’ insurance market from 2018 to 2022. By integrating federal insurance metrics with American Community Survey (ACS) income data, we model the drivers of premiums across more than 25,000 ZIP codes. Our findings reveal that policy cancellations and temporal trends are the dominant drivers of premium variation, while geographic and socioeconomic factors play secondary roles. Comparative regularization analysis shows that the Bayesian model with Lasso priors exhibits strong shrinkage, effectively reducing variables such as ZIP Code and Income to near-zero coefficients, whereas the Ridge model retains all predictors with moderate shrinkage to offer a more balanced view of the feature space. The Ridge and Pitman-Yor models achieved the highest predictive accuracy, explaining approximately 7% of variance, compared with 4.8% for the Dirichlet Process Mixture model. The Bayesian model with Lasso priors was used primarily for variable selection rather than predictive accuracy, and its R2 is therefore not directly compared here. Notably, both the Dirichlet Process Mixture (DPM) and Pitman-Yor models identified only a single unified cluster, indicating that insurance risk exists on a continuous spectrum rather than in distinct, isolated risk pools. This finding was further confirmed by the Normal Random Intercepts Model (NREM), which showed minimal variance explained by decile groupings. Across all models, cancellation rates, particularly non-payment and other cancellations, and year consistently emerge as the most influential predictors of homeowners’ insurance premiums, though the magnitude of these effects varies according to the specific regularization approach employed. These results suggest that market stability and policy retention are more critical to understanding premium behavior than traditional geographic or income-based risk segmentation....
This paper examines how the composition of digital trade liberalization shapes trade adjustment patterns in a low-tariff environment. While digital technologies are often treated as a unified category, different components may generate distinct economic effects and policy implications. Using Vietnam as a case study, the analysis distinguishes between information technologies (ITs), which are embedded in production processes, and communication technologies (CTs), which primarily reduce coordination costs. A partial equilibrium simulation is conducted using the WITS-SMART model based on 2023 trade and tariff data. The results show that tariff elimination in IT products leads to a substantially stronger import response and a larger reduction in tariff revenue compared to CT. However, the overall magnitude of these effects remains modest, reflecting Vietnam’s already low tariff structure. The findings highlight the importance of accounting for heterogeneity within digital technologies when assessing trade liberalization in emerging economies....
This study aims to develop a theoretical model explaining the influence of internal control practices on the financial performance of public enterprises in Côte d’Ivoire. The research adopts a constructivist epistemological stance combined with an abductive reasoning approach within a qualitative exploratory design. An extensive literature review was conducted, followed by ten (10) semi-structured interviews with internal audit and control experts. The qualitative results, analyzed through thematic coding, made it possible to identify key internal control dimensions and formulate four research hypotheses adapted to the Ivorian public sector context. The findings highlight the central role of internal control procedures, decision-making quality, control activities, and fraud risk management in improving financial performance....
Wałbrzych Subregion was based on the coal industry until 1991 when the last mine was closed. The area still faced the consequences of unfinished transformation: a low level of social-economic development, infrastructure degradation and a small endogenic potential insufficient to support longterm development. Negative effects of transformation can be observed in social and economic indicators. Just Transition Mechanism of European Union leads toward new possibilities for small local units with post-coal legacy. The aim of this paper is to analyse undertaken action towards new local development activators in Wałbrzyski Subregion based on Just Transition Mechanism. Poland originally drafted Territorial Just Transition Plans for seven subregions: Eastern Wielkopolska, Upper Silesia, Lublin Voivodeship, Łódź Voivodeship (the Bełchatów region), Western Małopolska, the Zgorzelec and Wałbrzych regions (both Lower Silesia). However, only five plans were formally submitted to the European Commission: Eastern Wielkopolska, Upper Silesia, Łódź (for Bełchatów), Lower Silesia (for Wałbrzych) and Western Małopolska. The main research methods were statistical methods and comparative methods. The undertaken research consisted of three stages. To evaluate the social and economic situation, Eurostat and Local Data Bank data were used. In further steps, the analysis of both Territorial Just Transition Plan for Lower Silesia and Social Plan for Just Transition for Wałbrzyski subregion was the basis study. In some areas, the Wałbrzyski subregion was compared to other Coal Subregions accepted for financing from Just Transition Mechanism of the European Union. The last step was the analysis of ongoing projects as well as the ones submitted for further financing from the European Fund for Lower Silesia 2021-2027 and interviews with local authorities....
The economic growth rate is a core indicator for measuring the pace of regional economic growth, reflecting dynamic economic changes over a specific period and serving as a critical foundation for macroeconomic analysis and policy formulation. Based on the 2009-2025 economic statistical data of counties (districts) in Henan Province, China, this paper first applies the logistic economic growth regression model to calculate the economic growth rate of each research unit, then analyzes the overall spatial pattern of economic growth rates, identifies the distribution of economic growth hot and cold spots, examines the radiation characteristics of economic growth through semivariogram analysis, and further explores the spatial distribution features of economic growth rates via Kriging interpolation analysis. The research results show that there are significant disparities in the county-level economic growth rate, with the coefficient of variation (ratio of standard deviation to mean) standing at 0.44. Geographically, northeastern Henan demonstrates relatively higher economic growth rates, and significant positive correlation exists between economic growth rate and economic aggregate. In terms of spatial agglomeration, the economic growth rate forms two major hot spot agglomerations centered on the urban built-up areas of Zhengzhou and Luoyang, as well as two major cold spot agglomerations—one centered on Anyang’s urban built-up area, and the other distributed zonally along the boundary of China’s second and third topographic steps. Regions with relatively high economic growth rates exert an outward radiation effect with a linear decreasing trend, with a radiation radius of approximately 254.10 km, and no significant correlation is observed between radiation intensity and topographic features. Specifically, interpolation analysis reveals that high-speed, medium-speed, and low-speed economic growth regions in the province account for 34.56%, 56.60%, and 8.84% of the total administrative area, respectively....
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