Product pricing and solvency capital requirements for long-term care insurance

This paper presents a comprehensive assessment of premiums, reserves and solvency capital requirements (SCRs) for long-term care (LTC) insurance policies using Activities of Daily Living and US data. We compare stand-alone policies, whole life insurance policies with LTC benefit riders (LTC insurance combined with whole life insurance), life care annuities (LTC insurance combined with annuities) and shared LTC insurance in terms of net premium cost and SCRs. Net premiums and best-estimate reserves for base LTC insurance policies are determined using Thiele’s differential equation. Product features such as the elimination period and the maximum benefit period are compared using a simulation-based model. We show how a maximum benefit period can reduce costs and risks for LTC insurance products. SCRs for longevity risk and disability risk are based on the Solvency II standard formula. We quantify the extent to which whole life insurance policies with LTC benefit riders and life care annuities provide lower SCRs than stand-alone LTC insurance policies.     

Interpreting the results of studies using latent variable models to assess data quality: an empirical example using confirmatory factor analysis

Latent variable models (LVMs) offer one route to examine the quality of data collected in surveys. The possibility exists that individuals equivalent in their true level of a construct or variable being measured are unlikely to have equivalent observed responses as a function of an extraneous variable, e.g., group membership. This potential is labeled here as differential item functioning (DIF). Survey methods generally considers measurement bias to be estimators that do no not accurately reflect true values. DIF may be thought of as differential measurement bias, i.e., measurement bias conditional on group membership. As a function of group membership, the degree, amount, or type of measurement bias changes. DIF has the potential to negatively affect the quality of data. LVMs, e.g., confirmatory factor analysis (CFA), offer one tool to assess DIF. However, few published examples exist in the survey research field and training in the interpretation of these models is lacking. The purpose of the current paper is to describe CFA sufficiently for interpretive purposes and demonstrate an empirical application of CFA to assess survey data quality to provide further interpretive guidance. References are provided for analysts wishing to conduct analyses of this type.     

A model of person‐pay interaction: how executives vary in their responses to compensation arrangements

A wealth of research indicates that both executive characteristics and incentive compensation affect organizational outcomes, but the literatures within these two domains have followed distinct, separate paths. Our paper provides a framework for integrating these two perspectives. We introduce a new model that specifies how executive characteristics and incentives operate in tandem to influence strategic decisions and firm performance. We then illustrate our model by portraying how executive characteristics interact with a specific type of pay instrument—stock options—to affect executive behaviors and organizational outcomes. Focusing on three individual‐level attributes (executive motives and drives, cognitive frame, and self‐confidence), we develop propositions detailing how executives will vary in their risk‐taking behaviors in response to stock options. We further argue that stock options will amplify the implications of executive ability, such that option‐heavy incentive schemes will increase the performance of talented executives but worsen the performance of low‐ability executives. Our framework and propositions are meant to provide a starting point for future theorizing and empirical testing of the interactive effects of executive characteristics and incentive compensation on strategic decisions and organizational performance. Copyright © 2010 John Wiley & Sons, Ltd.     

Value in business and industrial marketing: Past,present, and future

This article offers an overview of research on the ‘value’ that businesses and industrial marketers analyze, create, and deliver. First, value literature (up to and around 2005 and post 2005) is discussed. This review highlights the changes in our notions of value and helps to identify seven areas for consideration that should drive future research: value proposition, relationship form, capabilities management, value metrics, temporal horizon, innovation imperative, and tactical focus. Following this depiction of value analysis, value creation, and value delivery, we present a process model for value orchestration in business and industrial marketing. Three activities collectively form the basis of value orchestration, namely structuring activities, bundling activities, and leveraging of resources.     

Fair welfare maximization

We consider the general problem of finding fair constrained resource allocations. As a criterion for fairness we propose an inequality index, termed “fairness ratio,” the maximization of which produces Lorenz-undominated, Pareto-optimal allocations. The fairness ratio does not depend on the choice of any particular social welfare function, and hence it can be used for an a priori evaluation of any given feasible resource allocation. The fairness ratio for an allocation provides a bound on the discrepancy between this allocation and any other feasible allocation with respect to a large class of social welfare functions. We provide a simple representation of the fairness ratio as well as a general method that can be used to directly determine optimal fair allocations. For general convex environments, we provide a fundamental lower bound for the optimal fairness ratio and show that as the population size increases, the optimal fairness ratio decreases at most logarithmically in what we call the “inhomogeneity” of the problem. Our method yields a unique and “balanced” fair optimum for an important class of problems with linear budget constraints.     

Machine tools and mass production in the armaments boom: Germany and the United States, 1929–44

This article anatomizes the ‘productivity race’ between Nazi Germany and the US over the period from the Great Depression to the Second World War in the metalworking industry. We present novel data that allow us to account for both the quantity of installed machine tools and their technological type. Hitherto, comparison of productive technologies has been limited to case studies and well‐worn narratives about US mass production and European‐style flexible specialization. Our data show that the two countries in fact employed similar types of machines combined in different ratios. Furthermore, neither country was locked in a rigid technological paradigm. By 1945 Germany had converged on the US both in terms of capital‐intensity and the specific technologies employed. Capital investment made a greater contribution to output growth in Germany, whereas US growth was capital‐saving. Total factor productivity growth made a substantial contribution to the armaments boom in both countries. But it was US industry, spared the war's most disruptive effects, that was in a position to take fullest advantage of the opportunities for wartime productivity growth. This adds a new element to familiar explanations for Germany's rapid catch‐up after 1945.     

REAL OPTIONS: STATE OF THE PRACTICE

In the mid‐1980s, financial economists began building option‐based models to value corporate investments in real assets, laying the foundation for an extensive academic literature in this area. The 1990s saw several books, numerous conferences, and many articles aimed at corporate practitioners, who began to experiment with these techniques. Now, as we approach the end of 2001, the real options approach to valuing real investments has established a solid, albeit limited, foothold in the corporate world. Based on their recent interviews with 39 individuals from 34 companies in seven different industries, the authors of this article attempt to answer the question, “How is real options being practiced, and what impact is it having in the corporate setting?” The article identifies three main corporate uses of real options—as a strategic way of thinking, an analytical valuation tool, and an organization‐wide process for evaluating, monitoring, and managing capital investments. For example, in some companies, real options is used as an input into an M&A process in which rigorous numerical analysis plays only a small role. In such cases, real options contributes as a qualitative way of thinking, with little formality either in terms of analytical rigor or organizational procedure. In other firms, real options is used in a commodity trading environment where options are clearly specified in contracts and simply need to be valued. In this case, real options functions as an analytical tool, though generally only in specialized areas of the firm and not on an organization‐wide basis. In still other companies, real options is used in a technology or R&D context where the firm's success is driven by identifying and managing potential sources of flexibility. In such cases, real options functions as an organization‐wide process with both a broad conceptual and analytical core. The companies that have shown the greatest interest in real options generally operate in industries where large investments with uncertain returns are commonplace, such as oil and gas, and life sciences. Major applications include the evaluation of exploration and production investments in oil and gas firms, generation plant investments in power firms, R&D portfolios in pharmaceutical and biotech firms, and technology investment portfolios in high‐tech firms. While the approaches to implementation are quite varied, there appears to be a common path to the successful adoption of real options. The key steps of the adoption process are: (1) conducting pilot projects; (2) getting buy‐in from senior‐level and rank‐and‐file managers; (3) codifying real options through expert working groups, specialist training, and customization; and (4) institutionalizing and integrating real options firm‐wide. After citing best practices for each of these four steps, the authors close by predicting that a “network” effect and acceptance by Wall Street will serve as catalysts for more widespread corporate use of real options.     

II. Allgemeine und angewandte Statistik

Ohne Zusammenfassung     

Discretionary monetary policy and the zero lower bound on nominal interest rates

Ignoring the existence of the zero lower bound on nominal interest rates one considerably understates the value of monetary commitment in New Keynesian models. A stochastic forward-looking model with an occasionally binding lower bound, calibrated to the U.S. economy, suggests that low values for the natural rate of interest lead to sizeable output losses and deflation under discretionary monetary policy. The fall in output and deflation are much larger than in the case with policy commitment and do not show up at all if the model abstracts from the existence of the lower bound. The welfare losses of discretionary policy increase even further when inflation is partly determined by lagged inflation in the Phillips curve. These results emerge because private sector expectations and the discretionary policy response to these expectations reinforce each other and cause the lower bound to be reached much earlier than under commitment.