A general class of poverty measures

Measurement of poverty in the spirit of Sen essentially consists of two steps, namely (1) the identification of the poor and (2) the aggregation of the income distribution of the poor into an indicator called poverty measure. We contribute to the second step from a mathematical and statistical point of view by defining a class of poverty measures and investigating its properties including sensitivity with respect to changes in the income distribution. Various poverty orderings are defined and their relation to stochastic dominance is investigated. Finally estimation of the class of poverty measures from individual and grouped data is considered.     

Merging nonprofit organizations: Analysis of a case study

This article presents a case study of merging among nonprofit organizations that provide social and community services. The study sheds light on the dynamics and problems of merging nonprofit organizations, whose espoused ideology focuses on promoting the well-being of their clients. The author describes and analyzes a merger in metropolitan Jerusalem, Israel, and highlights the potential problems that may ultimately undermine its success. This merger is the outcome of an ongoing power and political struggle. The analysis focuses on the organizational ideologies and culture as well as the strategies and structure of the merging organizations. In addition, the author deals with the underlying motives for merging, the driving and restraining forces, and the implications of merging nonprofit community services organizations with other social and human services organizations.     

Nonprofit and for-profit home care in Israel: clients' assessments

Clients' assessments of differences between nonprofit and for-profit organizations delivering home care services to Israel's frail elderly were studied. No significant differences were found between nonprofit and for-profit providers with respect to organizational efficiency; speed of placement and replacement of home care workers; responsiveness to clients' complaints; and supervision of care plans, schedules, and service delivery. Systematic differences were found, however, in clients' perceptions of workers employed by nonprofit versus for-profit service providers. The differences related to the home care workers' adaptation to clients' needs and wants; how well workers delivered services; and how satisfied clients were with the services received. Indications are that the relatively high efficiency of nonprofit organizations can largely be attributed to the performance of their home care workers.     

Capital structure decisions in family firms: empirical evidence from a bank-based economy

This paper analyzes the question if and how founding families influence the capital structure decision of their firms. By using a unique, partially hand-collected panel dataset of 660 listed German companies (5,135 firm years) over the period 1995–2006, we come up with the following results: German family firms have significantly lower leverage ratios than non-family firms. With respect to the question how families influence the capital structure of their firms, we can show that the family impact is mostly driven via management involvement. In this context, we also detect that the presence of a founder CEO has a strong negative effect on the leverage ratio. Our results prove to be stable against a battery of robustness tests, including the influence of other types of blockholders and the firms’ life cycle. Moreover, we use a propensity-score based matching estimator to alleviate concerns of reverse causality. Overall, our study suggests a strong, negative and causal relationship between family firm characteristics (especially family management) and the level of leverage.     

Performance Evaluation of Foreign Subsidiaries: A Review of the Literature and a Contingency Framework

During recent years, the literature on the international dimension of business has grown. In particular, the literature on headquarters–subsidiary relationships has expanded. This paper aims to review the literature on performance evaluation and elaborate an integrative contingency framework. This contingency framework helps to clarify the concept of performance evaluation and to classify existing contributions. Furthermore, the framework serves as a tool to identify potential influencing factors on performance evaluation at corporate and at subsidiary level. By reviewing existing studies, the paper analyses the impact of these influencing factors on performance evaluation and reveals mostly contradictory results or no significant impact at all. Based on the findings, more research outside the classical tradition of contingency approaches is suggested.     

CUSUM control schemes for Gaussian processes

A CUSUM control scheme for detecting a change point in a Gaussian process is derived. An upper and a lower bound for the distribution of the run length and for its moments is given. In a Monte Carlo study the average run length (ARL) of this chart is compared with the ARL of two other CUSUM procedures which are based on approximations to the sequential probability ratio, and, moreover, with EWMA schemes for autocorrelated data. Results on the optimal choice of the reference value are presented. Furthermore it is investigated how these charts behave if the model parameters are estimated.     

Spatial robust small area estimation

The accuracy of recent applications in small area statistics in many cases highly depends on the assumed properties of the underlying models and the availability of micro information. In finite population sampling, small sample sizes may increase the sensitivity of the modeling with respect to single units. In these cases, area-specific sample sizes tend to be small such that normal assumptions, even of area means, seem to be violated. Hence, applying robust estimation methods is expected to yield more reliable results. In general, two robust small area methods are applied, the robust EBLUP and the M-quantile method. Additionally, the use of adequate auxiliary information may further increase the accuracy of the estimates. In prediction based approaches where information is needed on universe level, in general, only few variables are available which can be used for modeling. In addition to variables from the dataset, in many cases further information may be available, e.g. geographical information which could indicate spatial dependencies between neighboring areas. This spatial information can be included in the modeling using spatially correlated area effects. Within the paper the classical robust EBLUP is extended to cover spatial area effects via a simultaneous autoregressive model. The performance of the different estimators are compared in a model-based simulation study.