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.

Three faces of international inequality

Recent studies of international inequality have focused mostly on the trend in international income inequality. This article extends the analysis of international inequality to also include inequalities in education and health. Analyses of time-series data for more than 100 countries show that international income inequality declined from 1980 to 2003 as several large, poor Asian countries outpaced many Western countries in national income growth. By contrast, international health inequality followed a U-shaped trend, falling in the 1980s before rising in the 1990s. The turnaround in health inequality coincides with a trend of declining life expectancy in sub-Saharan Africa. International educational inequality experienced the sharpest recent decline, spurred by the global expansion of formal schooling. These findings confirm that there is more to international inequality than income inequality alone and suggest that patterns of inequality in the current era of globalization are likely more complex than many leading theories suggest.     

APPLICATIONS OF SPECTRAL ANALYSIS

The basic concepts and application of spectral analysis are explained. Stationary time series and autocorrelation are first defined. Autocorrelation is related to the familiar concepts of variance and covariance. The use of autocorrelation analysis is explained in estimating the interdependent relationship of a time series over discrete time lags. In order to measure the behavior of the time series using autocorrelation, it would be necessary to examine a very large number of autocorrelation lags. Alternatively, the technique of Fourier analysis can be used to transform the autocorrelation function of the time series into a continuous function, termed a spectrum. The spectrum has a one to one correspondence to the autocorrelation for the time series and has the advantage of representing all possible autocorrelations over the discrete time lags. The spectrum can then be examined as a measure of the behavior of the time series. Spectral analysis indicates the reliability of the analysis of autocorrelated variables when familiar statistical techniques such as sample means and variances are used. The application of spectral analysis to management science problems in three general areas is illustrated: (1) inventory demand, (2) transportation simulation, and (3) stock market price behavior. Spectral analysis was used to detect cycles and trends in the data. Analyses were focused on the spectrum which provides a measure of the relative contribution of cycles in a band of frequencies to the total variance of the data.     

CONFLICT RESOLUTION IN INVESTMENT DECISIONS: IMPLEMENTATION OF GOAL PROGRAMMING METHODOLOGY FOR DUAL-PURPOSE FUNDS*

Goal programming (GP) is designed to resolve allocation problems with conflicting goals. Both goals and constraints are incorporated in the allocational decision, and the objective function is stated in a way that, upon solution, yields a result “as close as possible” to the priority-weighted goals. The present paper applies GP methodology to the investment decision of dual-purpose funds (DPFs), that are required by law to pursue allocational decisions with potentially conflicting objectives. It provides an empirical demonstration that DPF managers could have improved their investment selection and subsequent performance by the use of GP methodology. Finally the paper stresses the importance of sensitivity analysis to improve both the goal-ranking and target-selection aspects of the methodology and provides a limited but illuminating empirical demonstration of post-optimality analysis.     

DETERMINING THE APPROPRIATE DISCOUNT RATES IN PURE CAPITAL RATIONING

This paper proposes an interpretation of the pure capital rationing problem as it is faced by many managers in decentralized firms in which budgets serve as the principal means of control. It is argued that the appropriate objective for situations such as these is the maximization of either undiscounted earnings over the planning horizon or total value of the investments at the horizon. When either objective function is used in conjunction with the frequently encountered linear programming models for the capital rationing problem, shadow prices result which give rise to discount rates that will reproduce the optimal solution using discounted cash flow as a criterion. These results are then used as a means for clarifying several confusing and misleading statements that have appeared in the literature.     

AUTOCORRELATED CASH FLOWS AND THE SELECTION OF A PORTFOLIO OF CAPITAL ASSETS *

Capital budgeting models for analyzing real assets typically are based on a set of restrictive assumptions that influence financial managers' decisions and may prevent optimization of the firm's objectives. This research examines the common restrictive assumption that cash flows are intertemporally independent by first developing an economic state and simulation model based on a Markov process for including autocorrelated cash flows in the capital budgeting decision process and then demonstrating why managers should include autocorrelated cash flows in capital budgeting models by empirically testing the impact of assuming intertemporally independent cash flows on capital budgeting decisions. The results indicate that ignoring autocorrelated cash flows seriously limits the ability of capital budgeting models to provide optimal investment decisions. The model also is very attractive for practical application because it can be implemented with a minimum number of estimates and provides the set of input data required by a number of capital budgeting models. A discussion of the implementation of the model is included.     

DYNAMIC OPERATING RULES FOR MOTEL RESERVATIONS

When customers call a motel or a hotel to reserve a particular room (large, small, suites, single-bed, double-bed, etc.) decisions have to be made concerning the allocation of the available rooms. This paper presents a model for determining the maximum number of specific bookings that should be made for each type of room. The objective is to maximize the expected contribution to profit per rental day. The model considers the distribution of requests for reservations over time as well as cancellations. Also included are the effects of late (unbooked) arrivals and overbookings.     

Impact of an Overt Response System on Staff Performance and Maintenance of Computer-Based Instruction

Interactive Computer Based Instruction (CBI) systems involve teaching strategies to facilitate greater response opportunities during training in an effort to improve learner performance. The current study investigated the effect of online staff training videos with and without overt response requirements on posttest and maintenance test scores across six block-randomized instructional modules for four employees of a university located in the southeastern region of the United States. The overt response consisted of multiple-choice questions administered throughout select teaching modules using an alternating treatment design. Findings suggest that learning occurred as a result of the CBI training; however, the effectiveness of the overt response system varied across individuals, with no discernible differentiation between learning gains associated with response requirements. Other areas of research for expanding the efficacy of CBI and overt response systems are highlighted.     

A surplus process involving a compound Poisson counting process and applications

Abstract

In this paper, we introduce a surplus process involving a compound Poisson counting process, which is a generalization of the classical ruin model where the claim-counting process is a homogeneous Poisson process. The incentive is to model batch arrival of claims using a counting process that is based on a compound distribution. This reduces the difficulty of modeling claim amounts and is consistent with industrial data. Recursive formula, some properties and relevant main ruin theory results are provided. Further, we consider applications involving zero-truncated negative binomial and zero-truncated binomial batch arrivals when the claim amounts follow exponential or Erlang distribution.