Using the lessons learned from US and English law to create a regulatory framework for charities in evolving democracies

The evolving democracies of Eastern Europe and the former USSR republics are keenly interested in establishing charities. They have little law on this issue because their former governments did not encourage the development of charities. This article compares US and English regulation of charities and proposes a regulatory framework based on what the authors believe are the best attributes of both. Although the framework is based on the common law system, it can be adapted to the civil law systems of the Eastern European nations and the former USSR republics. The article includes such issues as developing a body of law, tax relief and creating a regulatory agency.Mr Hopkins is a lawyer with Powers, Pyles & Sutter, 1275 Pennsylvania Ave., N.W., Washington, D.C. 20007, and author ofThe Law of Tax-Exempt Organizations (1992a).Ms Moore is also a lawyer, 5908 N. 35th St., Arlington, Virginia 22207.The authors would like to thank Mssrs Kersi Shroff and Stephen F. Clarke, Senior Legal Specialists, American-British Law Division, Library of Congress, and Mr. William H. Morris, an attorney at Steptoe & Johnson, Washington, D.C., for their kind assistance during the researching of this article.     

Creating a reference to a complex emergency situation using time series methods: war in Guinea-Bissau 1998-1999

Impacts of complex emergencies or relief interventions have often been evaluated by absolute mortality compared to international standardized mortality rates. A better evaluation would be to compare with local baseline mortality of the affected populations. A projection of population-based survival data into time of emergency or intervention based on information from before the emergency may create a local baseline reference. We find a log-transformed Gaussian time series model where standard errors of the estimated rates are included in the variance to have the best forecasting capacity. However, if time-at-risk during the forecasted period is known then forecasting might be done using a Poisson time series model with overdispersion. Whatever, the standard error of the estimated rates must be included in the variance of the model either in an additive form in a Gaussian model or in a multiplicative form by overdispersion in a Poisson model. Data on which the forecasting is based must be modelled carefully concerning not only calendar-time trends but also periods with excessive frequency of events (epidemics) and seasonal variations to eliminate residual autocorrelation and to make a proper reference for comparison, reflecting changes over time during the emergency. Hence, when modelled properly it is possible to predict a reference to an emergency-affected population based on local conditions. We predicted childhood mortality during the war in Guinea-Bissau 1998-1999. We found an increased mortality in the first half-year of the war and a mortality corresponding to the expected one in the last half-year of the war.     

Community involvement in land regularization for informal settlements in Tanzania: A strategy for enhancing security of tenure in residential neighborhoods

This paper documents and analyzes how landholders managed to uplift status of their neighborhood from hazard land as designated by the 1978 master plan to a regularized residential settlement through land regularization in Dar es Salaam city. Specifically, explores policy framework governing land regularization and how the local community explored the opportunities it offers. Documents the local community planning and land regularization processes undertaken focusing on land use planning, drainage construction, and cadastral survey and discuses how financial resources were raised, trust was built as well as factors which sustained community involvement towards meeting their interests of securing tenure. The paper also, draws challenges facing land regularization policy and recommends areas for further interventions commensurate with the human dimension challenges in securing tenure.Underlying community involvement, those aspects of community, which have been connected to the idea of social capital namely existence of committed leadership in land development matters, embracing mechanism for participatory decision making process and educational background to local leaders were particularly important in determining success for the case. Others include economic ability to contribute, high proportion of landholder settlers, land conflict task force formulation, local consensus to solve commonly felt problems, existence of strong community organization, and unwritten norms put in place to regulate individual behavior in building construction. Weak legal recognition of informal settlement, lengthy and bureaucratic procedures in planning and approval of regularization plans, weak knowledge on land management matters, short-term title deeds with low financial betterments, political popularity are identified critical challenges.Some of recommendations put forward include formalization of the grass-roots role in decision-making, decentralizing some of land development control functions to sub-ward leaders and training the same in basic land management matters. Others include definition of norms; by-laws and government facilitation of informal land parceling. Besides community support should be sought so as to create partnership in the promotion of security of tenure in informal areas. The study concludes that, unless land development activities ongoing in informal settlements are closely monitored and regulated as the settlement grow, it will be too costly socially and economically to retrofit once the settlement have identified.     

Sektion Frauen- und Geschlechterforschung

Ohne Zusammenfassung     

Low dose risk estimation via simultaneous statistical inferences

Summary.  The paper develops and studies simultaneous confidence bounds that are useful for making low dose inferences in quantitative risk analysis. Application is intended for risk assessment studies where human, animal or ecological data are used to set safe low dose levels of a toxic agent, but where study information is limited to high dose levels of the agent. Methods are derived for estimating simultaneous, one-sided, upper confidence limits on risk for end points measured on a continuous scale. From the simultaneous confidence bounds, lower confidence limits on the dose that is associated with a particular risk (often referred to as a bench-mark dose ) are calculated. An important feature of the simultaneous construction is that any inferences that are based on inverting the simultaneous confidence bounds apply automatically to inverse bounds on the bench-mark dose.     

Divergence-based estimation and testing with misclassified data

The well-known chi-squared goodness-of-fit test for a multinomial distribution is generally biased when the observations are subject to misclassification. In Pardo and Zografos (2000) the problem was considered using a double sampling scheme and ø-divergence test statistics. A new problem appears if the null hypothesis is not simple because it is necessary to give estimators for the unknown parameters. In this paper the minimum ø-divergence estimators are considered and some of their properties are established. The proposed ø-divergence test statistics are obtained by calculating ø-divergences between probability density functions and by replacing parameters by their minimum ø-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an example.     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.