Providing Spatial Data for Secondary Analysis: Issues and Current Practices Relating to Confidentiality

Spatially explicit data pose a series of opportunities and challenges for all the actors involved in providing data for long-term preservation and secondary analysis—the data producer, the data archive, and the data user. We report on opportunities and challenges for each of the three players, and then turn to a summary of current thinking about how best to prepare, archive, disseminate, and make use of social science data that have spatially explicit identification. The core issue that runs through the paper is the risk of the disclosure of the identity of respondents. If we know where they live, where they work, or where they own property, it is possible to find out who they are. Those involved in collecting, archiving, and using data need to be aware of the risks of disclosure and become familiar with best practices to avoid disclosures that will be harmful to respondents.

A COMPARATIVE ANALYSIS OF THE PREDICTIVE ABILITY OF ADAPTIVE FORECASTING,RE-ESTIMATION,AND RE-IDENTIFICATION USING BOX-JENKINS TIME-SERIES ANALYSIS ON QUARTERLY EARNINGS DATA

This paper assesses the predictive ability of the Box-Jenkins methodology when utilized in an on-going setting. Three procedures are utilized to update the original forecasts generated from the Box-Jenkins models: adaptive forecasting, re-estimation, and re-identification. The results indicate that constant monitoring of the structure and parameters of the time-series models are necessary through time. It appears that adaptive forecasting techniques are insufficient to update BJ time-series models when used in conjunction with quarterly earnings data. Re-estimation is recommended as each new observation becomes available. Re-identification procedures are recommended on a less frequent basis.     

Policy relevant heterogeneity in the value of statistical life: New evidence from panel data quantile regressions

We examine differences in the value of statistical life (VSL) across potential wage levels in panel data using quantile regressions with intercept heterogeneity. Latent heterogeneity is econometrically important and affects the estimated VSL. Our findings indicate that a reasonable average cost per expected life saved cut-off for health and safety regulations is 7 million to7 million to 8 million per life saved, but the VSL varies considerably across the labor force. Our results reconcile the previous discrepancies between hedonic VSL estimates and the values implied by theories linked to the coefficient of relative risk aversion. Because the VSL varies elastically with income, regulatory agencies should regularly update the VSL used in benefit assessments, increasing the VSL proportionally with changes in income over time.     

'I made some mistakes . . . but I love them dearly' the views of parents of children in informal kinship care

In-depth semi-structured interviews with 30 parents of children living with relatives in informal kinship care arrangements revealed the parents' views of the reasons for the informal kinship care arrangements, quality of their relationships with the children and their caregivers, their current and future roles in their children's lives, feelings experienced when with and away from the children, positive and negative aspects of kinship care, future goals and dreams for their children, and their assessments of their own strengths and challenges. Results of these interviews suggest several implications for social work practice and research.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

Risk,Media, and Stigma at Rocky Flats

Public responses to nuclear technologies are often strongly negative. Events, such as accidents or evidence of unsafe conditions at nuclear facilities, receive extensive and dramatic coverage by the news media. These news stories affect public perceptions of nuclear risks and the geographic areas near nuclear facilities. One result of these perceptions, avoidance behavior, is a form of technological stigma that leads to losses in property values near nuclear facilities. The social amplification of risk is a conceptual framework that attempts to explain how stigma is created through media transmission of information about hazardous places and public perceptions and decisions. This paper examines stigma associated with the U.S. Department of Energy's Rocky Flats facility, a major production plant in the nation's nuclear weapons complex, located near Denver, Colorado. This study, based upon newspaper analyses and a survey of Denver area residents, finds that the social amplification theory provides a reasonable framework for understanding the events and public responses that took place in regard to Rocky Flats during a 6-year period, beginning with an FBI raid of the facility in 1989.     

Principles for Conduct of Pest Risk Analyses: Report of an Expert Workshop

The North American Free Trade Agreement (NAFTA) and the General Agreement on Tariffs and Trade (GATT) have focused attention on risk assessment of potential insect, weed, and animal pests and diseases of livestock. These risks have traditionally been addressed through quarantine protocols ranging from limits on the geographical areas from which a product may originate, postharvest disinfestation procedures like fumigation, and inspections at points of export and import, to outright bans. To ensure that plant and animal protection measures are not used as nontariff trade barriers, GATT and NAFTA require pest risk analysis (PRA) to support quarantine decisions. The increased emphasis on PRA has spurred multiple efforts at the national and international level to design frameworks for the conduct of these analyses. As approaches to pest risk analysis proliferate, and the importance of the analyses grows, concerns have arisen about the scientific and technical conduct of pest risk analysis. In January of 1997, the Harvard Center for Risk Analysis (HCRA) held an invitation-only workshop in Washington, D.C. to bring experts in risk analysis and pest characterization together to develop general principles for pest risk analysis. Workshop participants examined current frameworks for PRA, discussed strengths and weaknesses of the approaches, and formulated principles, based on years of experience with risk analysis in other setting and knowledge of the issues specific to analysis of pests. The principles developed highlight the both the similarities of pest risk analysis to other forms of risk analysis, and its unique attributes.     

On perfect simulation for some mixtures of distributions

This paper studies the implementation of the coupling from the past (CFTP) method of Propp and Wilson (1996) in the set-up of two and three component mixtures with known components and unknown weights. We show that monotonicity structures can be exhibited in both cases, but that CFTP can still be costly for three component mixtures. We conclude with a simulation experiment exhibiting an almost perfect sampling scheme where we only consider a subset of the exhaustive set of starting values.