Communicating future water needs to an at-risk population: lessons learned following defeat of the Applewhite Dam and Reservoir Project in San Antonio,Texas

San Antonio, Texas, the seventh largest city in the United States, has experienced steady population growth, since the “boom” of the 1960s. Projected water shortages due to this growth were realized as early as the 1970s by city leaders and south-central Texas regional development decision makers. To reduce dependence on the already over-taxed, Edwards aquifer, a solution, the Applewhite Dam and Reservoir Project, was developed with wide acceptance by federal, state, and city leaders who regarded the project as a necessary measure for regional growth and development. However, opposition by taxpayer and environmental groups led to referendums of 1991 and 1994 in which voters blocked construction of the dam and reservoir leaving the city with limited options for water provision. This case study investigated the factors which led to a clear mismatch in communication between decision makers—those who were aware of the actual and quantifiable risk to the region in terms of reduced water supplies—and the general public, a population that did not have complete and/or adequate knowledge of their actual risk regarding future water shortages, nor, of solutions being developed, such as the Applewhite project. The findings from this case study indicate that when municipal leadership fails to adequately communicate risk regarding resource shortage to an affected public, as well as, openly planned solutions, that voters are likely to underestimate future impacts of water shortages, heed last-minute opposition, and reject long-standing, publicly proposed projects. The intent of this research is not to support either side in the Applewhite controversy, but to shed perspective on the process of adequately and effectively communicating future water needs to an at-risk population. Decision makers in cities across the United States who are faced with solving problems of limited resources needed by a large populace may be informed by the results of this research.     

Cohort Succession in the US Housing Market: New Houses, the Baby Boom, and Income Stratification

Federal housing policy in the US across the postwar period supported the construction of new houses more than public provision or renovation of older structures as a means of ensuring a sufficient supply of quality dwellings. Understanding trends in new housing in particular is thus crucial to understanding the housing regime. Following Myers (Housing demography: Linking demographic structure and housing markets. University of Wisconsin Press, 1990; Housing Studies, 14, 473–490,1999), this paper conceptualizes historical change in the housing stock within a demographic framework as the movement of cohorts of households through cohorts of housing stock. Recent evidence suggests that a new cohort of houses arose in the 1980s and 1990s (larger with more amenities than past vintages), and that buyers of those new houses were increasingly affluent. In this paper, I link the succession to a new cohort of houses to household cohort succession and examine the increasing affluence of new house buyers by age and cohort, focusing especially on the entry of the Baby Boom generation exactly when the new cohort of houses arrived. I use US Census microdata for 1960–2000 to develop a cohort longitudinal dataset, and analyze historical change in stratification in new house ownership. I find significant shifts between cohorts in income inequality among new house buyers, with implications for the capacity of the housing regime to meet the future needs of an increasingly diverse population.

A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil

A heteroscedastic regression based on the odd log-logistic Marshall–Olkin normal (OLLMON) distribution is defined by extending previous models. Some structural properties of this distribution are presented. The estimation of the parameters is addressed by maximum likelihood. For different parameter settings, sample sizes and some scenarios, various simulations investigate the performance of the heteroscedastic OLLMON regression. We use residual analysis to detect influential observations and to check the model assumptions. The new regression explains the mass loss of different wood species in civil construction in Brazil.     

THE EFFECT OF RETAIL PROMOTIONAL ACTIVITIES ON SALES*

This article reports the formulation and results of a multiple equation econometric model to relate retail sales by brand and package size to retail promotional variables for a branded, frequently purchased grocery product. Primary emphasis is placed on the formulation of the model as opposed to the results obtained from its use.     

OPTIMAL SELECTION OF MATCHED PAIRS FROM LARGE DATA BASES

This paper considers the problem of selecting matched pairs of observations for the reduction of bias in statistical hypothesis testing. A Euclidean distance function is suggested for measuring the similarity between paired observations. The matching process is then formulated initially as an assignment problem. Alternative formulations of the problem that would reduce computational difficulty are considered.     

SENSITIVITY ANALYSIS WITH INTERACTION EFFECTS

The purpose of this paper is to extend the sensitivity analysis methodology as it applies to linear programming. The methodology developed herein is concerned with those changes in a technological coefficient that cause simultaneous changes in the relevant cost coefficients of the objective function. By proceeding from the definitions of feasibility and optimality, the authors develop formulas for determining bounds on the extent to which a technological coefficient, which has a predetermined effect on the corresponding cost coefficient, can vary without changing the optimal set of activities. After the formulas are developed, their use is demonstrated on a product-mix problem. This technique is of interest because changes in the rate at which an input is utilized can be expected to change the cost per unit of a resulting product. This interdependence should be recognized whenever a sensitivity analysis is performed on a technological coefficient of either a cost minimization or a contribution margin maximization problem.     

ON PREDICTING COMPUTATIONAL TIME OF A BRANCH AND BOUND ALGORITHM FOR THE ASSIGNMENT OF FACILITIES

Experience with branch and bound algorithms indicates that computational time is a function of not only the size of the problem, but also the nature of the input data. This paper formulates statistically-based variables which describe certain characteristics of the input data and experimentally evaluates their ability to predict computational time for one branch and bound algorithm, the relative location of facilities or “plant layout” problem. Results suggest that the described experimental procedure may be useful for an a priori assessment of the computational difficulty of specific branch and bound problems.