WEATHER-BASED ADVERSE SELECTION AND THE U.S. CROP INSURANCE PROGRAM: THE PRIVATE INSURANCE COMPANY PERSPECTIVE

Surprisingly, investigations of adverse selection have focused only on farmers. Conversely, this article investigates if insurance companies, not farmers, can generate excess rents from adverse selection activities. Currently political forces fashioning crop insurance as the cornerstone of U.S. agricultural policy make our analysis particularly topical. Focusing on El Nino/La Nina and winter wheat in Texas, we simulate out-of-sample reinsurance decisions during the 1978 through 1997 crop years while reflecting the realities imposed by the risk-sharing arrangement between the insurance companies and the federal government. The simulations indicate that economically and statistically significant excess rents may be garnered by insurance companies through weather-based adverse selection.Risk and Uncertainty,

"Suicide and Life Insurance"

In this paper, we investigate the nexus between life insurance and suicide behavior using OECD cross-country data from 1980 to 2002. Through semiparametric instrumental variable regressions with fixed effects, we find that for the majority of observations, there exists a positive relationship between suicide rate and life insurance density (premium per capita). Since life insurance policies pay death benefits even in suicide cases after the suicide exemption period, the presence of adverse selection and moral hazard suggests an incentive effect that leads to this positive relationship. The novelty of our analysis lies in the use of cross-country variations in the length of the suicide exemption period in life insurance policies as the identifying instrument for life insurance density. Our results provide compelling evidence suggesting the existence of adverse selection and moral hazards in life insurance markets in OECD countries.

Risk modeling concepts relating to the design and rating of agricultural insurance contracts

The authors identify the key issues and concerns that arise in the design and rating of crop yield insurance plans, with a particular emphasis on production risk modeling. The authors show how the availability of data shapes the insurance scheme and the ratemaking procedures. Relying on the U.S. experience and recent developments in statistics and econometrics, they review risk modeling concepts and provide technical guidelines in the development of crop insurance plans. Finally, they show how these risk modeling techniques can be extended to price risk in order to develop crop revenue insurance schemes.Health Economics&Finance,Insurance Law,Environmental Economics&Policies,Insurance&Risk Mitigation,Labor Policies,Insurance&Risk Mitigation,Crops&Crop Management Systems,Health Economics&Finance,Insurance Law,Environmental Economics&Policies

Panel Data Sample Selection Model: an Application to Employee Choice of Health Plan Type and Medical Cost Estimation

This paper utilizes a nonparametric panel data sample selection model to correct selection bias in the analysis of longitudinal medical claims data. Selection bias in the health economics data is a common problem and many health economists have used Heckman type selection models in cross-sectional analyses. Since longitudinal data structure is common in health economics data, especially medical claims data, the correction of selection bias in the longitudinal sense is especially valuable for health economics related researches. The complicated modeling and extensive computer programming needs, however, resulted to only a few health economics researches in this direction. This paper suggests a relatively simple estimation framework to correct sample selection bias in longitudinal data. An example of health care utilization of PPO type plan holders in an employee pool is also provided as follows: in the first step, a random effect panel data probit model was used to estimate each employee’s choice between HMO type plans and PPO type plans; in the second step, a nonparametric fixed effect panel data selection model, using the estimates from the first step, was used to estimate the medical expenditures of PPO plan holders (similar to Kyriazidou, Econometrica 1997). Since the second step estimation can be expressed as a weighted least squares regression, this framework is simple to use, but among others, this nonparametric framework is robust from any parametric misspecification and free from a controversial health econometric problem called retransformation in two part model (Manning, Journal of Health Economics 1998; Mullahy, Journal of Health Economics 1998; Ai and Norton, Journal of Health Economics 2000). There are some interesting results from this example, but among others, the selection bias influenced significantly on the Age effect of medical expenditures. Since there were more young employees in the HMO plan holders, the Age effect of PPO plan holders was almost doubled after considering for selection biasSample Selection Model; Panel Data

Modelling beyond Regression Functions: an Application of Multimodal Regression to Speed-Flow Data

An enormous amount of publications deals with smoothing in the sense of nonparametric regression. However, nearly all of the literature treats the case where predictors and response are related in the form of a function y=m(x)+noise. In many situations this simple functional model does not capture adequately the essential relation between predictor and response. We show by means of speed-flow diagrams, that a more general setting may be required, allowing for multifunctions instead of only functions. It turns out that in this case the conditional modes are more appropriate for the estimation of the underlying relation than the commonly used mean or the median. Estimation is achieved using a conditional mean-shift procedure, which is adapted to the present situation

Does Pre-trade Transparency Affect Market Quality in the Tokyo Stock Exchange?

This paper presents an examination of the relation between pre-trade transparency and market quality in the Tokyo Stock Exchange (TSE). Mixed evidence related to this relation has been reported worldwide. We analyzed this relation using a discrete change of disclosure policy in the 2000s. A positive relation pertains between pre-trade transparency and market quality. This result implies that the change of disclosure policy on the TSE might be effective for market quality improvement to some extent.Pre-trade transparency; Market quality; Quote Disclosure

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction