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This paper studies how private equity creates value and its consequences for consumer welfare in the insurance industry, where PE investments grew tenfold following the financial crisis. PE firms add value through regulatory and tax arbitrage that increases profits relative to their non-PE counterparts. Crucially, the impact on consumer welfare is nuanced: in the short run, consumers benefit from more favorably priced products. But the arbitrage strategy also exposes them to more risk, as annual expected losses scaled by capital buffers rise by 50 percentage points. This creates the possibility of consumer harm in the event of a downturn. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Purpose: To report the results of a 2020 international survey of the most commonly used materials and techniques for the fabrication of extraoral maxillofacial prosthetics and to assess the use of 3D technology.Materials and Methods: A 43-question survey was administered via Qualtrics to the members of the American Academy of Maxillofacial Prosthetics, the International Anaplastology Association, and the International Society of Maxillofacial Rehabilitation. The use of current 3D technology in the fabrication of maxillofacial prostheses, as well as barriers to care such as patient cost and insurance coverage, were also assessed.Results: A total of 134 respondents (13%) completed the survey; 96 (73%) reported that they were currently fabricating maxillofacial prostheses and were thus included in the survey. The majority of respondents reported currently using Silastic MDX4-4210 BioMedical Grade Elastomer (DuPont) with either Silastic Medical Adhesive Silicone Type A or Silastic MDX4-4210 crosslinker. Incorporating the use of current CAD/CAM technology was reported by a majority of respondents (58%; 45/77); however, a small cohort (33%, 15/45) reported they were "still experimenting how best to use technology." A total of 39 clinicians (49%) reported that facial prosthetics were most often paid for by the patient (fee for service), with the fee ranging from $10 to $5,500 USD.Conclusion: The results of this survey indicate that current CAD/CAM technology has been adopted and incorporated into the design and fabrication of maxillofacial prostheses. More research is needed to advance CAD/CAM technology as well as the physical and mechanical properties of materials in maxillofacial prosthetics and anaplastology clinical practice. [ABSTRACT FROM AUTHOR]

This study investigates whether directors' and officers' liability insurance (D&O insurance) misleads creditors' lending decisions by examining its effect on corporate debt maturity structure. We find that purchasing D&O insurance leads to increased corporate debt maturity, and this effect is more pronounced for firms with weaker corporate governance. These results suggest that creditors may view D&O insurance as an external monitoring tool that helps improve corporate governance. However, D&O insurance induces higher firm risk, but cannot help decrease agency costs or improve firm performance, that is, it results in more severe managerial opportunism. Our findings suggest that D&O insurance, to some extent, misguides creditors' lending decisions. [ABSTRACT FROM AUTHOR]

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer 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.)

Research on personal adornments depends on the reliable characterisation of materials to trace provenance and model complex social networks. However, many analytical techniques require the transfer of materials from the museum to the laboratory, involving high insurance costs and limiting the number of items that can be analysed, making the process of empirical data collection a complicated, expensive and time-consuming routine. In this study, we compiled the largest geochemical dataset of Iberian personal adornments (n = 1243 samples) by coupling X-ray fluorescence compositional data with their respective X-ray diffraction mineral labels. This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. As a proof of concept, we developed a multiclass model and evaluated its performance on two assemblages from different Portuguese sites with current mineralogical characterisation: Cova das Lapas (n = 15 samples) and Gruta da Marmota (n = 10 samples). Our results showed that decisión-tres based classifiers outperformed other classification logics given the discriminative importance of some chemical elements in determining the mineral phase, which fits particularly well with the decision-making process of this type of model. The comparison of results between the different validation sets and the proof-of-concept has highlighted the risk of using synthetic data to handle imbalance and the main limitation of the framework: its restrictive class system. We conclude that the presented approach can successfully assist in the mineral classification workflow when specific analyses are not available, saving time and allowing a transparent and straightforward assessment of model predictions. Furthermore, we propose a workflow for the interpretation of predictions using the model outputs as compound responses enabling an uncertainty reduction approach currently used by our team. The Python-based framework is packaged in a public repository and includes all the necessary resources for its reusability without the need for any installation. [ABSTRACT FROM AUTHOR]

This study aims to analyze the problem of developing mutanaqisah musyarakah contracts in property financing by reviewing the benefits, opportunities, costs, and risks involved in mutanaqisah musyarakah contracts. This research uses a mix method between qualitative and quantitative with the ANP BOCR approach with the help of superdecision software. This research consists of four stages of research including pre-research, data collection, data processing and interpretation of results. The results of this study indicate that the ANP BOCR analysis regarding the development of property financing using the musyarakah mutanaqisah contract which is the top priority in the BOCR criteria aspect is Benefit with a Normalize value of 0.35644, followed by the risk aspect in second place with a value of 0.32573, followed by the opportunity aspect with a value of 0.19358 and in the last rank is the cost aspect with a value of 0.12426. The top priority in alternative strategies in developing property financing using the musyarakah mutanaqisah contract is the strategy that banks must work with honest developers. The next strategy is a socialization strategy, namely the bank conducts socialization to the public so that people understand more about the mutanaqisah musyarakah contract. In the third rank is to develop competitive and innovative products. Furthermore, the last rank is a promotion strategy in the form of banners, banners and billboards. Based on the calculation of the benefit cluster, each cluster has the same value, it means that the three benefit subcriteria have the same priority. Furthermore, in the opportunity cluster, the top priority is the development of property investment, followed by product innovation and needs according to the financing contract. Then in the cost cluster, the priority of the cost cluster is notary fees, followed by insurance costs in second place and administrative costs in third place. And in the risk cluster, the priority is credit risk, followed by market risk and compliance risk. [ABSTRACT FROM AUTHOR]

This study examines design principles for how to display financial information about a consumer loan that supports locating a value (e.g., What is the annual percentage rate (APR) for an 18-month loan?), comparing two values (e.g., Is the APR for a 24-month load greater than for an 18-month loan?), or subtracting two values (e.g., What is the difference in the APR for a 24-month load and an 18-month loan?). One hundred and thirty-three college students carried out these three types of tasks at a banking site that displayed information as a list (e.g., listing the value for interest rate, monthly payment, insurance cost, APR, amount financed, and total cost for a given duration ranging from 6 to 48 months) or as one of three types of tables or matrices (containing values for each of six variables across one axis and each of eight loan durations across the other). Accuracy and response time on each of the three types of tasks was tallied for each of the four groups. Consistent with the principle of distraction minimization, the list was the best display for locating a value. Consistent with the principle of conjoint alignment, a vertical matrix (with loan duration across columns) or horizontal matrix (with loan duration across rows) was the best for comparing two values, but an interactive matrix was less effective. Consistent with the principle of canonical form, a vertical matrix (with loan duration across columns) was the best for subtracting two values. Overall, the best information display depended on the type of task, consistent with cognitive theories of information processing. [ABSTRACT FROM AUTHOR]

Broader adoption of battery electric vehicles (BEVs) and reductions in household car ownership are key to meeting climate goals. Yet, BEV market shares remain low in most countries, and car sharing systems tend to appeal to a limited demographic. In this research we examine a novel ownership option that allows households more flexibility in car ownership: short-term vehicle subscriptions that provide users with exclusive access to a vehicle for 6–12 months and incorporate soft costs such as insurance and maintenance. We investigate whether subscriptions can appeal to distinct segments of the population and whether preferences for subscriptions compared to purchases differ between fuel types. Among other factors, we consider the role of drivers' self-identity as technology enthusiasts and environmentalists. Data are collected through a discrete choice experiment with 1,567 individuals in three US states. An integrated latent class and latent variable choice model indicates the presence of three distinct classes: one motivated by preference for ownership, one motivated by a combination of enthusiasm for new technology and care for the environment, and one motivated by cost concerns. While the subscription option is never appealing to the ownership-oriented class, we find a preference for subscribing to a BEV rather than purchasing it among the class that is both technology-oriented and environmentally conscious. Furthermore, while the cost-oriented class would be averse to subscribing to a conventional vehicle, it is not averse to a BEV or hybrid vehicle subscription. This suggests that subscription models could allow consumers who are unwilling to commit to purchasing a BEV to gather experience with one. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a novel pricing model for delivery insurance in a food delivery company in Latin America, with the aim of reducing the high costs associated with the premium paid to the insurer. To achieve this goal, a thorough analysis was conducted to estimate the probability of losses based on delivery routes, transportation modes, and delivery drivers’ profiles. A large amount of data was collected and used as a database, and various statistical models and machine learning techniques were employed to construct a comprehensive risk profile and perform risk classification. Based on the risk classification and the estimated probability associated with it, a new pricing model for delivery insurance was developed using advanced mathematical algorithms and machine learning techniques. This new pricing model took into account the pattern of loss occurrence and high and low-risk behaviors, resulting in a significant reduction of insurance costs for both the contracting company and the insurer. The proposed pricing model also allowed for greater flexibility in insurance contracting, making it more accessible and appealing to delivery drivers. The use of estimated loss probabilities and a risk score for the pricing of delivery insurance proved to be a highly effective and efficient alternative for reducing the high costs associated with insurance, while also improving the profitability and competitiveness of the food delivery company in Latin America.

In this paper, we propose a novel pricing model for delivery insurance in a food delivery company in Latin America, with the aim of reducing the high costs associated with the premium paid to the insurer. To achieve this goal, a thorough analysis was conducted to estimate the probability of losses based on delivery routes, transportation modes, and delivery drivers' profiles. A large amount of data was collected and used as a database, and various statistical models and machine learning techniques were employed to construct a comprehensive risk profile and perform risk classification. Based on the risk classification and the estimated probability associated with it, a new pricing model for delivery insurance was developed using advanced mathematical algorithms and machine learning techniques. This new pricing model took into account the pattern of loss occurrence and high and low-risk behaviors, resulting in a significant reduction of insurance costs for both the contracting company and the insurer. The proposed pricing model also allowed for greater flexibility in insurance contracting, making it more accessible and appealing to delivery drivers. The use of estimated loss probabilities and a risk score for the pricing of delivery insurance proved to be a highly effective and efficient alternative for reducing the high costs associated with insurance, while also improving the profitability and competitiveness of the food delivery company in Latin America. [ABSTRACT FROM AUTHOR]

This study presents moral cost as a novel behavioral constraint on firm resource adjustment, specifically layoff decisions that can cause severe harm to employees. Revising the prevailing negative view of managers as purely self-interested, we propose that managers care about their employees and incur moral cost from layoffs. We leverage expansions in unemployment insurance as a quasi-natural experiment that reduces economic hardship for laid-off workers and, in turn, the moral cost of layoffs to managers. We find that these expansions license larger layoffs. The effects are stronger for chief executive officers (CEOs) with stronger prosocial preferences who dismiss fewer workers despite low performance, such as non-Republican, internally promoted, small town, or family firm CEOs, and weaker for CEOs who lack the discretion to avoid moral cost due to shareholder or financial pressures. Our findings suggest that the role of moral cost is substantial but also highly heterogeneous and readily suppressed by external pressures. Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2022.16734. [ABSTRACT FROM AUTHOR]

This study explores the insurance pricing domain in the motor insurance industry, focusing on the creation of "technical models" which are essentially obtained after combining the frequency model (the expected number of claims per unit of exposure) and the severity model (the expected amount per claim). Technical models are designed to predict the loss costs (the product of frequency and severity, i.e., the expected claim amount per unit of exposure) and this is a main factor that is taken into account for pricing insurance policies. Other factors for pricing include the company expenses, investments, reinsurance, underwriting, and other regulatory restrictions. Different machine learning methodologies, including the Generalised Linear Model (GLM), Gradient Boosting Machine (GBM), Artificial Neural Networks (ANN), and a unique hybrid model that combines GLM and ANN, were explored for creating the technical models. This study was conducted on the French Motor Third Party Liability datasets, "freMTPL2freq" and "freMTPL2sev" included in the R package CASdatasets. After building the aforementioned models, they were evaluated and it was observed that the hybrid model which combines GLM and ANN outperformed all other models. ANN also demonstrated better predictions closely aligning with the performance of the hybrid model. The better performance of neural network models points to the need for actuarial science and the insurance industry to look beyond traditional modelling methodologies like GLM. [ABSTRACT FROM AUTHOR]

A "seat belt defense" allows a defendant in a third‐party auto liability event to present evidence of seat belt nonuse to reduce a plaintiff's recovery. When successfully applied, the defense has a direct effect on reducing liability payouts by insurers for accidents. Our analysis reveals that seat belt defense laws are associated with a 10% reduction in auto claims costs, which equates to a reduction in loss costs of approximately $14 per insured vehicle. We find little evidence that the defense reduces accident frequency and no evidence that the defense affects seat belt usage. The lack of a deterrent effect on driving behavior suggests that the availability of the defense is not salient to drivers. The reduction in loss costs supports the notion that defense lawyers are aware of the defense and make use of it. The resulting effect of lowering insurance payouts may alleviate increasing auto insurance costs. [ABSTRACT FROM AUTHOR]

Theoretical arguments regarding the effect of corporate social responsibility (CSR) on firm liability risk are abundant; however, empirical evidence about this relationship is scarce. We investigate the relationship between CSR and the personal liability risk of a firm's directors and officers. We argue that companies with better CSR performance represent a better underwriting risk for directors' and officers' (D&O) insurance providers and, therefore, have a lower cost of insurance. Our results show that firms with better CSR performance are more likely to purchase D&O insurance and have a lower premium-to-coverage ratio, known as the insurance rate-on-line. We also show that this risk-reduction effect is stronger for firms that operate in a high-risk environment and have higher sales growth. These results provide evidence that CSR can be used as a risk management tool to mitigate liability risk and suggest which firms benefit most from this effect. [ABSTRACT FROM AUTHOR]

Fertility preservation (FP) is the use of a specific medical intervention to protect the fertility of individuals whose disease or disease treatment may lead to infertility. These medical interventions include the cryopreservation of oocytes, embryos, ovarian tissue, sperm, and testicular tissue; oocyte and embryo cryopreservation are the most widely used interventions in the United States. Although guidelines recommend FP prior to undergoing gonadotoxic treatments, cost barriers are high. For example, the average cost of an oocyte cryopreservation cycle in the United States exceeds $10,000. High cost and lack of insurance coverage are two of the most cited reasons explaining the low Reproductive Endocrinology and Infertility (REI) referral rates and limited FP utilization. Broadening insurance mandates for FP prior to gonadotoxic treatments could improve utilization and provide cancer survivors with improved quality of life post treatment. [ABSTRACT FROM AUTHOR]

This study investigates the impact of an active workstation on heating and cooling energy consumption in commercial buildings. The improved performance and health of office workers are well-known and supported by several surveys and studies. However, the preferred and revised indoor air temperature for different activities of the employees, such as standing and walking, directly impacts the energy consumption of the building. Therefore, this study systematically quantifies the impact of indoor environments on building energy consumption using a simulation tool. Based on literature reviews with multidisciplinary fields, revised setpoint temperature, metabolic rate, and required outdoor air rate due to the level of active workstation (standing and walking) are applied to EnergyPlus reference building models. The impact on heating and cooling energy consumption is analyzed by considering several representative cities in the United States. Their average sensitivity was found to be 7.28% lower and 14.28% higher for heating and cooling energy, respectively. The total increment of $13,887 represents approximately 0.16% of the total profit of the corporation. This increment can be justified given the benefits to workers' health, which include lower insurance costs and improved performance. This suggests that it is economically desirable to deploy active workstations widely. [ABSTRACT FROM AUTHOR]

Introduction: The increasing number of gabapentinoid (pregabalin and gabapentin) harms, including deaths observed across countries is concerning to health‐care professionals and policy makers. However, it is unclear if the public shares these concerns. This study aimed to describe posts related to gabapentinoids, conduct a content analysis to identify common themes and describe adverse events or symptoms. Methods: Keywords of 'pregabalin' or 'Lyrica' or 'gabapentin' or 'Neurontin' were used to search for related tweets posted by people in the community between 8 March and 7 May 2021. Eligible tweets included a keyword in the post. We extracted de‐identified data which included descriptive data of the total number of posts over time; and data on individual tweets including date, number of re‐tweets and post content. Data were exported separately for pregabalin‐ and gabapentin‐related tweets. A 20% random sample was used for the thematic analysis. Results: There were 2931 pregabalin‐related tweets and 2736 gabapentin‐related tweets. Thematic analysis revealed three themes (sharing positive experiences and benefits of taking gabapentinoids, people voicing their negative experiences, and people seeking opinions and sharing information). Positive experiences of gabapentinoids were related to sharing stories and giving advice. This was contrasted to negative experiences including ineffectiveness, withdrawals, side effects and frustration related to cost and insurance coverage. Brain fog was the most common adverse symptom reported. Gabapentinoid‐related deaths were only mentioned in three tweets. Discussion: The increasing public health concern of gabapentinoid‐related deaths was not translated to Twitter discussions. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: The healthcare insurance industry faces a significant challenge predicting individuals’ insurance costs, which are based on complex parameters such as age and physical characteristics. Insurance companies categorize policyholders into highrisk and low-risk groups to manage risks and avoid potential losses. However, the accurate estimation of costs for each individual can be a daunting task. By leveraging data science and machine learning techniques, insurance companies can improve their cost estimation accuracy and better manage risks. This approach can help insurance companies to provide more accurate insurance coverage and pricing for individuals leading to higher customer satisfaction and lower financial losses. METHODS: To address this challenge, a data science and machine learning-based approach that uses ensemble learning to predict high-risk and low-risk individuals is used. The method involves several steps including data preprocessing, feature engineering, and cross-validation to evaluate the model’s performance. The first step involves preprocessing the data by cleaning it, handling missing values, and encoding categorical variables. The second step generates new features using feature engineering techniques such as scaling, normalization, and dimensionality reduction. Next, ensemble learning is used to combine multiple regression methods such as logistic regression, neural networks, support vector machines, random forests, LightGBM, and XGBoost. By combining these methods, the aim is to leverage their strengths and minimize their weaknesses to achieve better prediction accuracy. Finally, the model’s performance is evaluated using cross-validation techniques such as k-fold cross-validation. These techniques help to validate the model’s accuracy and prevent overfitting. FINDINGS: The proposed approach achieves an AUC of 0.73 demonstrating its effectiveness in predicting high-risk and low-risk individuals. CONCLUSION: In conclusion, the healthcare insurance industry can benefit greatly from data science and machine learning-based approaches. By accurately predicting high-risk and low-risk individuals, insurance companies can better manage risks and provide more accurate coverage and pricing for their customers. This can lead to the improvement of customer satisfaction and the reduction of financial losses for insurance companies. [ABSTRACT FROM AUTHOR]

Purpose – This study investigated the impact of corporate ownership structure on agency costs in the insurance industry. Design/methodology/approach – The study sample included 23 insurance companies listed on the Amman Stock Exchange (ASE) from 2010 to 2019. Panel regression was used to account for the firm- and time-specific unobservable variables and system-GMM estimation was used to address endogeneity concerns. Findings – The results show that managerial ownership positively (negatively) affects selling, general and administrative (SG&A) expenses (assets turnover), implying that unmonitored managers engage in activities that serve their own interests rather than those of shareholders. The largest shareholder’s ownership has no impact on agency costs, implying that the ownership of the largest shareholder is irrelevant. However, as the wedge between the percentage of capital owned by the largest shareholders and managers increases, SG&A expenses (efficiency ratio) decrease (increases), indicating that the existence of large non-management shareholders reduces agency costs. After accounting for the endogeneity problem, the impact of ownership structure on agency costs measured by asset turnover remains robust. Originality/value – To the best of the authors’ knowledge, this study is the first to provide unique evidence and useful insights into the determinants of agency costs from a frontier market in the Middle East and North Africa (MENA), with a focus on the insurance sector. Additionally, this study uses a new measure of separation between ownership and control by calculating the wedge between managers’ and large shareholders’ ownership. [ABSTRACT FROM AUTHOR]

Copyright of World Food Policy is the property of Wiley-Blackwell 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.)

Doctors have a duty to provide trustworthy and ethical medical care, and professional self-governments, such as chambers of physicians, play a crucial role in ensuring this responsibility. Economically, professional responsibility involves insurance costs, financial sanctions, increased healthcare expenses, and a decrease in patient trust. Managerially, it includes risk management, policy development, quality monitoring, and fostering a culture of safety. The self-government contributes by establishing standards, conducting inspections, and addressing complaints. Effective management of professional responsibility is essential for providing safe and high-quality healthcare. Mandatory membership in the self-government is determined by the supervision it exercises, ensuring that all practitioners are subject to oversight and comply with professional ethics. The self-government operates independently within its defined legal tasks. The article explores the organization and the role of professional self-government of doctors in upholding their professional responsibility. It examines economic, managerial, and legal aspects, addressing questions about monitoring professional practice, mandatory membership, ethical formulation, autonomy vs. state authority, and the connection between self-government and professional responsibility. The economic, managerial, and legal aspects emphasized underscore the significance of a well-functioning self-government system for the benefit of doctors and society at large. Brief description of the state of knowledge. The review was based on data obtained from articles published in research databases and law documents. [ABSTRACT FROM AUTHOR]

Container shipping is the backbone of the global supply chain, and its security is directly linked to the global economy. Disruptions in container transportation could have severe consequences, such as an increase in transport and insurance costs and damage to the environment and cargo. The first aim of this study is to identify, assess, and prioritize the security risks associated with maritime container transportation using the Delphi technique and to draw a risk map accordingly. The second aim is to identify the strengths, weaknesses, opportunities, and threats to using SWOT analysis from a security perspective. Maritime container transportation between Türkiye and the Far East serves as a case study for this purpose. As a conclusion of the first part of the study, the risk of cyber-attacks has one of the highest probability factors scored first, war and warlike conditions having the highest impact factor scored second, and piracy and armed robbery scored third in general. In the second part, the SWOT factors are determined and prioritized using the Analytic Hierarchy Process. Strengths scored the highest among the main SWOT factors, which indicates that it is more prominent than other factors. Weaknesses, opportunities, and threats to. The overall conclusion drawn is that security risk assessment has become essential given recent technological changes, such as the increasing risk of cyber-attacks on electronic navigational aids, and geopolitics, such as tensions in the Middle East and the South China Sea. [ABSTRACT FROM AUTHOR]

Purpose: To determine the cost-effectiveness and efficacy of camrelizumab when combined with radiotherapy in the management of advanced/metastatic esophageal squamous cell carcinoma (ESCC). Methods: A retrospective analysis was carried out on clinical information of 115 patients with advanced/metastatic ESCC admitted to Huai'an Cancer Hospital between February 2017 and January 2021. Based on therapeutic modality, patients were distributed into control group (CG, 55 cases, cisplatin + paclitaxel chemotherapy) and study group (SG, 60 cases, camrelizumab + radiotherapy regimen). Clinical efficacy, adverse effects, cost indicators (total cost of treatment, out-of-pocket costs, medical insurance cost), serum tumor marker levels (CEA, CYFRA21-1, SCC-Ag), immune function (IgA, IgG, IgM), and quality of life were compared between the two groups. Results: The objective remission rate (ORR) in SG (80 %) was significantly higher than in CG (61.8 %) (p < 0.05). Serum levels of CEA, CYFRA21-1, SCC-Ag in both groups and symptom scores decreased, while serum IgA, IgG, IgM, function, and overall quality of life score increased, but the improvement was greater in SG after 6 weeks of treatment (p < 0.05). Study group had significantly higher out-of-pocket expenses, health insurance costs, total costs, and cost/effectiveness than CG (p < 0.05). Conclusion: Camrelizumab combined with radiotherapy in the treatment of advanced/metastatic ESCC is efficacious, improves immune function, reduces serum tumor marker levels, and prolongs the survival time of patients. Clinical studies using larger population should be carried out to validate the findings of this study. [ABSTRACT FROM AUTHOR]

This article studies the model proposed by Laudagé et al. (Insur Math Econ 88:77–92, 2019) and examines whether the combination between a Generalized Linear Model (GLM) and a Generalized Pareto Distribution (GPD) is valid for modeling claims severity in a practical framework. For this, we consider a real fire insurance dataset and fit the proposed model to these data. In this modeling, the threshold is of great importance since it separates the data into two parts and represents the point from which the observations become extremes. Therefore, in order to guarantee the correct choice of this threshold, one extra method is adopted in addition to that used by Laudagé et al. (2019). Furthermore, we build on the authors' results and extend them by fitting the attritional data to three well-known distributions. The results of this study show that the GLM-GPD combination outperforms the benchmark model (classical GLM) in terms of predictive power. In addition, the application of an excess of loss reinsurance treaty to these two models proves that it is more interesting for an insurer to adopt a GLM-GPD combination so as not to underestimate the risk and go bankrupt. This justifies that the combined modeling is reasonably good to describe insurance claim costs. [ABSTRACT FROM AUTHOR]

Cyber security importance has escalated globally, driven by its pivotal role in shaping daily life, encompassing both personal and non-personal aspects. Cyber security breach probability functions play a crucial role in comprehending how cyber security investments affect vulnerability to cyber attacks. These functions employ mathematical models to guide decision making in cyber risk management. Thus, studying and improving them is useful in this context. In particular, using these models, this article explores the effectiveness of an integrated risk management strategy that merges insurance and security investments, aiming to minimize overall security expenses. Within this strategy, security investments contribute to reducing the insurance premium. This research investigates the optimal investment for this blended approach under total insurance coverage. When the integrated risk management strategy combining insurance and security investments is deemed the optimal choice, this paper reveals that the insurance premium tends to be the dominant component in the overall security expense in the majority of cases. This implies that the cost of insurance outweighs the cost of security investments. [ABSTRACT FROM AUTHOR]

This article examines whether the capitalization–amortization or the direct-expensing method for insurance acquisition costs and commissions better reflects the economic substance. We find that both the currently capitalized–amortized acquisition costs and the as-if asset balance of expensed commissions are positively associated with the market value of equity, and are closely related to the level and volatility of subsequent insurance premiums. Results also show that when explaining the market value of equity, the insurance commission is significantly negative if capitalized and amortized, but is insignificant if directly expensed. The authors are the first to examine how the insurance acquisition costs and commissions are related to subsequent economic benefits, and our results are consistent with IFRS 17 under which the acquisition costs shall not be immediately expensed. [ABSTRACT FROM AUTHOR]

Unlike some other high-income counties, Canada does not provide universal prescription drug coverage. The various extent of coverage may left some Canadians vulnerable to cost-related non-adherence (CRNA) to medications. Using data from the 2015 national cycle of the Canadian Community Health Survey, we examine the impact of having private and public drug coverage on mitigating the risk of CRNA with a logit model and a Heckman selection model. CRNA was only observed in respondents who had prescriptions to fill, and respondents did not randomly make decisions on whether to get a prescription. This results in a classic sample selection problem. We found a higher estimated probability of reporting CRNA for uninsured respondents from the Heckman selection model than from the logit model. Respondents with government coverage only had a slightly higher probability of reporting CRNA relative to respondents with private coverage. These findings suggest that, without accounting for sample selection, the risk of not having drug insurance coverage is likely to be underestimated. Moreover, despite covering a less healthy cohort of respondents, the government insurance plans reduce risk of CRNA to a comparable level with private insurance. [ABSTRACT FROM AUTHOR]

The United States tort reform movement has capped noneconomic damage awards in many jurisdictions, thereby preventing the most injured plaintiffs from being fully compensated for their suffering. While litigants have asserted numerous state constitutional challenges to these tort recovery limits, with varying degrees of success, aggrieved plaintiffs have underutilized the Fifth Amendment's Takings Clause. This Article advocates that judicial reduction of a jury's noneconomic damage calculation after the court has informed the successful plaintiff of the full verdict is a regulatory taking in violation of the federal Takings Clause, as incorporated against the states through the Fourteenth Amendment. A Takings Clause violation requires a government taking of private property for public use without just compensation. A noneconomic damage award of which an injured plaintiff has been informed is a vested property interest; a trial judge reduces that award based on a statute; this is a regulatory, rather than a physical, taking under the Penn Central ad-hoc, three-factor standard; the taking is uncompensated because the plaintiff does not receive an equivalent of the full noneconomic damage verdict; and the taking is for public use because it is intended to reduce liability insurance premiums for the general public and encourage business investment. Finding enforcement of noneconomic damage caps to be an impermissible regulatory taking is supported by the Fifth Amendment's historical roots and the Supreme Court's Takings cases since the Founding. Moreover, ensuring that the most injured members of society, who are damaged because of a tortfeasor's actions, are adequately compensated and are not required by states to carry the burden of lower insurance costs and business investment for a whole jurisdiction is sound public policy. [ABSTRACT FROM AUTHOR]

The designation of enterprise zones is a place-based policy that seeks to revitalize economically blighted areas. The literature on place-based policies has found mixed results regarding their effects on local payroll employment. This paper examines the causal effects of five of New Jersey's Urban Enterprise Zones (UEZs) on local payroll employment: Bayonne, Gloucester City, New Brunswick, Roselle Borough, and The Wildwoods (Wildwood City, Wildwood Crest, North Wildwood, and West Wildwood). All were designated as UEZs by the state in the 2000s, and none have been previously evaluated in the academic literature. The program offers reduced local sales tax, tax credits for newly hired employees, subsidized unemployment insurance costs, worker training assistance, and tax-free purchases on capital equipment and facilities. A synthetic control approach is used with the industrial composition of local firms and poverty rate as the covariate group and no impact of UEZ status on local employment in the treatment periods of the five areas is found. These results suggest that enterprise zones may not be effective job creators for treated areas, particularly for those zones that were added long after the program's inception. [ABSTRACT FROM AUTHOR]

There are several index insurance methodologies. Most of them rely on linear piece-wise methods. Recently, there has been studies promoting the potential of data-driven methodologies in construction index insurance models due to their ability to capture intricate non-linear structures. However, these types of frameworks have mainly been implemented in high-income countries due to the large amounts of data and high-frequency requirements. This paper adapts a data-driven methodology based on high-frequency satellite-based climate indices to explain flood risk and agricultural losses in the Antioquia area (Colombia). We used flood records as a proxy of crop losses, while satellite data comprises run-off, soil moisture, and precipitation variables. We analyse the period between 3 June 2000 and 31 December 2021. We used a logistic regression model as a reference point to assess the performance of a deep neural network. The results show that a neural network performs better than traditional logistic regression models for the available loss event data on the selected performance metrics. Additionally, we obtained a utility measure to derive the costs associated for both parts involved including the policyholder and the insurance provider. When using neural networks, costs associated with the policyholder are lower for the majority of the range of cut-off values. This approach contributes to the future construction of weather insurance indexes for the region where a decrease in the base risk would be expected, thus, resulting in a reduction in insurance costs. [ABSTRACT FROM AUTHOR]

To characterize stone-related financial burden among adults with nephrolithiasis through validated questionnaires for financial toxicity. We performed a cross-sectional survey of adults with history of nephrolithiasis at an outpatient clinic. The survey contained a measure of stone-related financial toxicity (COST score), and assessed demographics, stone event history, and burden of overall, ancillary, preventative, and insurance costs. A COST score ≤21 was classified as high financial toxicity. Multivariable logistic regression was used to evaluate associations with demographic variables and stone event history. One hundred patients completed the survey (median age 57, IQR, 48-64). The median COST score was 30 (IQR, 23-37), and 19% reported high financial toxicity. Insurance status, household income, stone surgeries during lifetime and within the last 3 years were associated with financial toxicity on univariable analysis (P <.05). Burden of costs was significantly higher in all categories within the high financial toxicity cohort (each P <.05). On multivariable logistic regression, only income >$75,000 remained significant within the second model (OR: 0.22, 95% CI, 0.05-0.77, P =.02), however, this relationship did not persist on the final model. When asked whether providers should be mindful of their financial situation prior to making treatment recommendations, 39% responded "quite a bit" or "very much." One in 5 patients seeking care for nephrolithiasis meet criteria for high financial toxicity. Financial toxicity was associated with household income, insurance, education, and frequency of stone events. Thirty-nine percent reported that their provider should be mindful of their financial situation when making recommendations. [ABSTRACT FROM AUTHOR]