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.

EMPIRICAL UTILITY FUNCTIONS AND RANDOM DEVICES: AN EXPERIMENT

The primary purpose of the present experiment was to examine the effects on the empirical derivation of executive's utility functions caused by the use of random device analogues. The results indicate that utility functions obtained using a random device analogue imply more risk aversion than when these functions are determined by simulating actual decision situations. Furthermore, there is also evidence that the bias is directly related to the monetary amounts involved.     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

A SIMULATION MODEL OF PATRONAGE BEHAVIOR WITHIN SHOPPING CENTERS

This paper presents a micro-analytic store choice model which eliminates the assumptions of homogeneity of stores and homogeneity of shoppers found in earlier models. The model simulates individual shopping trips within a shopping center and was successful in predicting the stores chosen by a sample of shopping center patrons. Two earlier models were also tested, but the proposed model performs better than either of the simpler models. Besides its predictive value, the model suggests a number of strategic implications for retailers and shopping center developers and provides a methodology that may be useful in other areas of behavioral research.     

AN OPERATIONAL APPROACH TO THE EVALUATION OF LEASES BY HOSPITALS*

The evaluation of the lease versus purchase alternative by not-for-profit hospitals presents a problem that stems from the interaction between investment and financial decisions. This paper provides an analytical framework that resolves this issue by neutralizing the impact of the lease contract on the hospital's financial structure and debt capacity. The formulation incorporates the special operating characteristics of not-for-profit hospitals: namely, they are not subject to federal income tax, and part of their revenue is generated through cost-based reimbursement.     

THE REAL AND ILLUSORY VIRTUES OF ENTROPY-BASED MEASURES FOR BUSINESS AND ECONOMIC ANALYSIS

The entropy measure H=−σp_i log p_i is being used with increasing frequency in the analysis of business and economic data. It is, however, simply another measure of dispersion which can be related to the moments of the probability function. Its virtues stem from its decomposition and interpretative properties. This paper surveys the uses to which the measure has been put in the literature, and discusses whether its use has been appropriate and innovative.     

THE DETAIL SCHEDULING OF A PART-TIME WORK FORCE: A CASE STUDY OF TELLER STAFFING

This paper presents a comparison of two heuristic methods for assigning a part-time work force to meet varying daily work loads. The authors use actual operating data from Purdue National Bank, Lafayette, Indiana, to make teller staffing decisions. The performance criteria for comparison of the two heuristics is the minimum number of workers employed, minimum number of interbranch transfers, and computer time necessary to attain a solution.     

ON THE DESIGN OF HIERARCHICAL PRODUCTION PLANNING SYSTEMS

To provide effective managerial support for decisions related to production planning and scheduling processes, it is useful to partition the set of decisions into a hierarchical framework. In the resulting system, higher level decisions impose constraints on lower level actions, and lower level decisions provide the necessary feed-back to reevaluate higher level actions. The purpose of this paper is to suggest optimum procedures to deal with the resulting subproblems and to analyze the interaction mechanisms among the different hierarchical levels. Computational results are given.     

Pooling designs associated with unitary space and ratio efficiency comparison

Let mathbbF^(2n+d)_q²mathbb{F}^{(2nu+delta)}_{q^{2}} be a (2ν+δ)-dimensional unitary space of mathbbF_q²mathbb{F}_{q^{2}} , where δ=0 or 1. In this paper we construct a family of inclusion matrices associated with subspaces of mathbbF^(2n+d)_q²mathbb{F}^{(2nu+delta)}_{q^{2}} , and exhibit its disjunct property. Moreover, we compare the ratio efficiency of this construction with others, and find it smaller under some conditions.