Significant Factors of Aviation Insurance and Risk Management Strategy: An Empirical Study of Taiwanese Airline Carriers

Aviation insurance premiums have become a heavy burden for the airline industry since September 11, 2001. Although the industry must constantly balance its operations between profitability and safety, the reality is that airlines are in the business of making money. Therefore, their ability to reduce cost and manage risk is a key factor for success. Unlike past research, which used subjective judgment methods, this study applied quantitative historical data (1999–2000) and gray relation analysis to identify the primary factors influencing ratemaking for aviation insurance premiums. An empirical study of six airlines in Taiwan was conducted to determine these factors and to analyze the management strategies used to deal with them. Results showed that the loss experience and performance of individual airlines were the key elements associated with aviation insurance premiums paid by each airline. By identifying and understanding the primary factors influencing ratemaking for aviation insurance, airlines will better understand their relative operational strengths and weaknesses, and further help top management identify areas for further improvement. Knowledge of these factors combined with effective risk management strategies, may result in lower premiums and operating costs for airline companies.     

On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection

Wavelet thresholding of spectra has to be handled with care when the spectra are the predictors of a regression problem. Indeed, a blind thresholding of the signal followed by a regression method often leads to deteriorated predictions. The scope of this article is to show that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are selected to optimize the prediction of a given target of interest. This approach can be seen as a joint thresholding designed for a predictive purpose. The method is illustrated on a real world problem where metabolomic data are linked to poison ingestion. This example proves the usefulness of wavelet expansion and the good behavior of sparse and regularized methods. A comparison study is performed between the two-steps approach (wavelet thresholding and regression) and the one-step approach (selection of wavelet coefficients with a sparse regression). The comparison includes two types of wavelet bases, various thresholding methods, and various regression methods and is evaluated by calculating prediction performances. Information about the location of the most important features on the spectra was also obtained and used to identify the most relevant metabolites involved in the mice poisoning.     

The effect of fast and slow decisions on risk taking

We experimentally compare fast and slow decisions in a series of experiments on financial risk taking in three countries involving over 1700 subjects. To manipulate fast and slow decisions, subjects were randomly allocated to responding within 7 seconds (time pressure) or waiting for at least 7 or 20 seconds (time delay) before responding. To control for different effects of time pressure and time delay on measurement noise, we estimate separate parameters for noise and risk preferences within a random utility framework. We find that time pressure increases risk aversion for gains and risk taking for losses compared to time delay, implying that time pressure increases the reflection effect of Prospect Theory. The results for gains are weaker and less robust than the results for losses. We find no significant difference between time pressure and time delay for loss aversion (tested in only one of the experiments). Time delay also leads to less measurement noise than time pressure and unconstrained decisions, and appears to be an effective way of decreasing noise in experiments.     

Private equity regulation: a comparative analysis

This article examines the existing contractual arrangements and industry standards in private equity. It shows that investors are, in principle, capable of structuring their particular investments according to their own preferences, there are a range of governance problems and risks that could be potentially hazardous for some classes of investors. We examine the circumstances where existing industry codes and legal tools can be used to address the problems that arise in relation to private equity and buyout activity.     

Activity patterns and temporal niche partitioning of dogs and medium-sized wild mammals in urban parks of Xalapa,Mexico

Urban Ecosystems - Free-ranging domestic dogs are the world most common exotic carnivore species that could negatively interact with the native wildlife as predators, competitors, and disease...     

Housing hardship and energy insecurity among native-born and immigrant low-income families with children in the United States

The costs for rent and utilities account for the largest share of living expenses, yet these two critical dimensions of material hardship have seldom been examined concurrently in population-based studies. This paper employs multivariate statistical analysis using American Community Survey data to demonstrate the relative risk ratio of low-income renter-occupied households with children experiencing ‘rent burden', ‘energy insecurity', or a ‘double burden’ as opposed to no burden. Findings suggest that low-income households are more likely to experience these economic hardships in general but that specific groups are disproportionately burdened in different ways. For instance, whereas immigrants are more likely to experience rental burden, they are less likely to experience energy insecurity and are also spared from the double burden. In contrast, native-born African Americans are more likely than all other groups to experience the double burden. These results may be driven by the housing stock available to certain groups due to racial residential segregation, decisions regarding the quality of housing low-income householders are able to afford, as well as home-country values, such as modest living and energy conservation practices, among immigrant families. This paper also points to important policy gaps in safety net benefits related to housing and energy targeting low-income households.     

The “Silent Dog” Method: Analyzing the Impact of Self-Generated Rules When Teaching Different Computer Chains to Boys with Autism

The purpose of the study was to extend the literature on verbal self-regulation by using the “silent dog” method to evaluate the role of verbal regulation over nonverbal behavior in 2 individuals with autism. Participants were required to talk-aloud while performing functional computer tasks.Then the effects of distracters with increasing demands on target behavior were evaluated as well as whether self-talk emitted by Participant 1 could be used to alter Participant 2''s performance. Results suggest that participants'' tasks seemed to be under control of self-instructions, and the rules generated from Participants 1''s self-talk were effective in teaching computer skills to Participant 2. The silent dog method was useful in evaluating the possible role of self-generated rules in teaching computer skills to participants with autism.     

Pandemic Recovery Analysis Using the Dynamic Inoperability Input-Output Model

Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input-output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input-output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input-output model^{( 1 , 2 )} and the dynamic inoperability input-output model (DIIM).^{( 3 )} These models introduced sector inoperability, or the inability to satisfy as-planned production levels, into input-output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce-explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.     

RAILROADS AND GROWTH IN PRUSSIA

We study the effect of railroad access on urban population growth. Using GIS techniques, we match triennial population data for roughly 1,000 cities in 19th‐century Prussia to georeferenced maps of the German railroad network. We find positive short‐ and long‐term effects of having a station on urban growth for different periods during 1840–1871. Causal effects of (potentially endogenous) railroad access on city growth are identified using propensity score matching, instrumental variables, and fixed‐effects estimation techniques. Our instrument identifies exogenous variation in railroad access by constructing straight‐line corridors between nodes. Counterfactual models using pre‐railroad growth yield no evidence to support the hypothesis that railroads appeared as a consequence of a previous growth spurt.     

Urban ecosystem Services in Latin America: mismatch between global concepts and regional realities?

Urban Ecosystems - Latin America and the Caribbean (LAC) is one of the most urbanized and biologically diverse regions in the world but is often characterized by weak environmental governance and...