On the Implications of the Internet for Insurance Markets and Institutions

By most accounts, the Internet and related advances in information technology significantly affect financial services in general and insurance markets and institutions in particular. Coupled with other important trends such as globalization and regulatory reform, these changes are forcing far‐reaching changes upon the insurance industry and making it more competitive. This article focuses specifically on the implications of the Internet for insurance markets and institutions. The conventional wisdom that the Internet constitutes a sufficient condition for the disintermediation of traditional insurance distribution networks is called into question. To the extent that the Internet reduces transaction costs, it will create opportunities for new intermediaries as well as for existing ones. It will also influence product design, in some cases making it economically attractive to unbundle and repackage various forms of coverage. By removing entry barriers and reducing insurance costs, the Internet will also provide a private market solution to a major insurance regulatory concern—enhancing insurance affordability and availability.     

Parallel sourcing and supplier performance in the Japanese automobile industry

Japanese auto makers are reported to enjoy high supplier performance through long-term relationships, specific investments, and sole sourcing. Quality management consultants in the U.S. have been strongly advocating adoption of these practices. But economic and management theorists would predict that the combination of a high level of relationship-specific investments and sole sourcing will lead to problems with supplier performance. In fact the Japanese auto makers use a hybrid form of organization we term parallel sourcing. We present a transaction costs model that shows how parallel sourcing provides incentives for supplier performance associated with multiple sourcing while preserving claimed benefits of sole sourcing.     

A model of fractional cointegration, and tests for cointegration using the bootstrap

The paper proposes a framework for modelling cointegration in fractionally integrated processes, and considers methods for testing the existence of cointegrating relationships using the parametric bootstrap. In these procedures, ARFIMA models are fitted to the data, and the estimates used to simulate the null hypothesis of non-cointegration in a vector autoregressive modelling framework. The simulations are used to estimate p-values for alternative regression-based test statistics, including the F goodness-of-fit statistic, the Durbin–Watson statistic and estimates of the residual d. The bootstrap distributions are economical to compute, being conditioned on the actual sample values of all but the dependent variable in the regression. The procedures are easily adapted to test stronger null hypotheses, such as statistical independence. The tests are not in general asymptotically pivotal, but implemented by the bootstrap, are shown to be consistent against alternatives with both stationary and nonstationary cointegrating residuals. As an example, the tests are applied to the series for UK consumption and disposable income. The power properties of the tests are studied by simulations of artificial cointegrating relationships based on the sample data. The F test performs better in these experiments than the residual-based tests, although the Durbin–Watson in turn dominates the test based on the residual d.     

Ambient Taxes Under m-Dimensional Choice Sets, Heterogeneous Expectations, and Risk-Aversion

We consider the design of ambient taxes forrisk-neutral and risk-averse polluters whenpolluters and the regulatory agency haveasymmetric information about environmentalrelationships and probabilities associatedwith random events. Unlike prior work, we showthat under these conditions, optimal ambienttaxes must be firm-specific, and accompaniedby additional incentives to influencepolluters' choices of abatement techniques.     

中国投资者进军美国市场将获新途径

中国股市从不久前的辉煌之巅急转直下，而与此同时，这个全球第一人口大国的投资者们又将获得新的渠道进军包括美国在内的海外股市。     

Age-test Markov chains for drug testing and detection

Random urinalysis strategies stratified by time since the last test are characterized with a set of Markov chain models. The probability of a person being tested depends on the amount of time since the person's last test. The Nuclear Regulatory Commission (NRC) has proposed a two strata drug testing strategy based on time since last test. The proposal included a high testing rate for people not yet tested in a given time period and a low testing rate for people testing negative in a given time period. Southern California Edison has implemented a variation of the NRC proposal. These strategies can be modeled within a Markov chain framework. Time to detection is calculated as a function of testing probabilities and drug usage levels. Drug user gaming strategies are discussed with illustrations. These models are implemented as part of a U.S. Navy drug policy analysis system.     

Estimating Willingness to Pay for Improved Water Quality in the Presence of Item Nonresponse Bias

This article deals with the problem of item nonresponse in contingent valuation surveys using a payment-card method, by applying a grouped-data sample-selection estimation technique that is capable of imputing the missing values conditional upon a respondent's decision to answer a willingness-to-pay question. The advantage of the technique lies in its ability to utilize all of the information in the sample, permitting a more efficient estimation in the presence of item nonresponse bias. The major determinants of willingness to pay appear to be household income, number of children, education, perception of existing water quality, and identification with environmental issues.