Slack free MEA and RDM with comprehensive efficiency measures

This paper extends two directional distance function models, the Multi-directional Efficiency Analysis (MEA) Model and the Range Directional Model (RDM), in order to account for any type of technical inefficiency, i.e. both directional and non-directional inefficiencies. We first focus on the variable returns to scale (VRS) case, because both VRS-MEA and RDM are translation invariant models, which mean that both models are able to deal with negative data. Our main result is the definition of a new comprehensive efficiency measure which is units invariant and translation invariant and covers both models. Secondly, we introduce the RDM model under constant returns to scale (CRS) together with a new comprehensive efficiency measure.     

Understanding Cumulative Risk Perception from Judgments and Choices: An Application to Flood Risks

Catastrophic events, such as floods, earthquakes, hurricanes, and tsunamis, are rare, yet the cumulative risk of each event occurring at least once over an extended time period can be substantial. In this work, we assess the perception of cumulative flood risks, how those perceptions affect the choice of insurance, and whether perceptions and choices are influenced by cumulative risk information. We find that participants' cumulative risk judgments are well represented by a bimodal distribution, with a group that severely underestimates the risk and a group that moderately overestimates it. Individuals who underestimate cumulative risks make more risk‐seeking choices compared to those who overestimate cumulative risks. Providing explicit cumulative risk information for relevant time periods, as opposed to annual probabilities, is an inexpensive and effective way to improve both the perception of cumulative risk and the choices people make to protect against that risk.     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Physician strives to create lean,clean health care machine. Studies of manufacturing processes may one day help make your practice more efficient

Elisabeth Hager, MD, MMM, CPE, is teaming up with scientists and industrialists to teach physicians how to apply principles of lean, total-quality manufacturing to their practices. She believes innovation and efficiencies can help doctors resurrect their profession's image and their control over it--and perhaps even reinvent American health care.     

Pluralism in Organizations: Learning from Unconventional Forms of Organizations

The bureaucratic organization is still regarded as the conventional organizational form, but is ill‐suited to an increasingly pluralistic world. Research on the variety of organizational forms has increased dramatically over the past three decades and offers the potential to understand better how pluralism is manifested and managed within organizations. However, this research remains fragmented. The purpose of this paper is to review and synthesize research on unconventional organizations to explore how organizations resolve or attenuate the tensions related to pluralism. Drawing from research in leading management journals, it covers seven distinct literatures: ‘referent organization’, ‘temporary organization’, ‘pluralistic organization’, ‘meta‐organization’, ‘bridging organization’, ‘hybrid organization’ and ‘field‐configuring event’. For each literature, the authors trace the genealogy of the key concepts and review their distinct insights regarding organizational pluralism. They then synthesize and discuss their collective contributions and conclude with avenues of research for pluralism in organizations.     

Determinants of Organizational Flexibility: A Study in an Emerging Economy

This paper examines the processes of organizational adaptation and competitiveness of firms in an emerging economy. The study is set in the Argentinian context of the 1990s when a combination of economic and political change triggered a massive change in the competitive context of indigenous firms. Two highly flexible firms and two less‐flexible firms are studied from the pharmaceutical and edible oil industries and longitudinal data are supplied to explore the determinants of organizational flexibility in those organizations.     

Risk Perception in a Developing Country: The Case of Chile

In this work we characterize risk perception in Chile, based on the psychometric paradigm, exploring the difference between perceived social and personal risk. For this purpose, we conducted a survey including 54 hazards, 16 risk attributes, and 3 risk constructs. The survey, divided into four parts, was administered to 508 residents of Santiago, Chile. Using factor analysis, three main factors, which accounted for 80% of the sample's variance, were identified: factor 1, commonly called "Dread Risk" in the literature, explained 37% of variance; factor 2, "Unknown Risk," explained 28%; and factor 3, which we called "Personal Effect," explained 15% of the variance. On average, individuals perceived themselves as less exposed to risk and with more control and knowledge about them than the general population. OLS regression models were used to test the association of perceived risk with the three main factors. For social risk, factor 1 had the greatest explanatory power, while factor 2 had a negative sign. For personal risk, only factors 2 and 3 were significant, with factor 3 having the greatest explanatory power. Risk denial (defined as the difference between perceived personal and social risk) was associated with factors 1 and 2 only, with factor 2 having a negative sign. The difference between desired and actual regulation levels proved positive for all hazards, thus indicating that Chileans are dissatisfied with the current regulation level for all the hazards analyzed. The comparison of data at the aggregate and at the individual subject's level suggests that while the aggregate analysis overestimates the magnitude of the correlations it still reflects the tendency of the individual responses.     

Panel regression models for measuring multidimensional poverty dynamics

This work concerns the study of poverty dynamics and the analysis of the influencing socio-demographic factors. A fuzzy and multidimensional approach has been chosen in order to define two different poverty measures. A panel regression model has been estimated and particular attention has been paid to the treatment of the unobservable heterogeneity among longitudinal units. The specified model combines autoregression with variance components. The empirical analysis has been conducted using the data set of the British Household Panel Survey (BHPS) from 1991 to 1997. This work was co-financed by Murst funds for the projects “Occupazione e disoccupazione in Italia: misura e analisi dei comportamenti”. The paper is the result of the common work of all the authors; in particular G. Betti has written Sects. 2,5.1 and 5.3.1; A. D’Agostino has written sections 4, 5.2 and 5.4; L. Neri has written Sects. 1, 3, 5.3.2 and 6.     

Extreme value and cluster analysis of European daily temperature series

Time series of daily mean temperature obtained from the European Climate Assessment data set is analyzed with respect to their extremal properties. A time-series clustering approach which combines Bayesian methodology, extreme value theory and classification techniques is adopted for the analysis of the regional variability of temperature extremes. The daily mean temperature records are clustered on the basis of their corresponding predictive distributions for 25-, 50- and 100-year return values. The results of the cluster analysis show a clear distinction between the highest altitude stations, for which the return values are lowest, and the remaining stations. Furthermore, a clear distinction is also found between the northernmost stations in Scandinavia and the stations in central and southern Europe. This spatial structure of the return period distributions for 25-, 50- and 100-years seems to be consistent with projected changes in the variability of temperature extremes over Europe pointing to a different behavior in central Europe than in northern Europe and the Mediterranean area, possibly related to the effect of soil moisture and land-atmosphere coupling.