Behind closed doors: Sex bias at professional and managerial levels

U.S. companies have made important strides in combating sex discrimination in the workplace over the last two decades, but more subtle forms of sex bias still exist, often in decisions and behaviors that occur behind closed doors. This paper focuses on sex bias at professional and managerial levels. It explores sources of sex bias in the informal culture, selection and recruitment, task assignment, performance appraisal, promotion, and salary allocation, and then suggests action steps to help reduce sex bias in each of these areas.     

Ratings and confirmation

We present a formalism which makes explicit and precise the confirming effect of multiple observers and repeated trials on composite ratings, considering the relevant subjective inputs.     

The economics of urban yard space: An “implicit-market” model for housing attributes

A theoretical housing model is developed embodying the approach of the recent hedonic price literature. Instead of focusing on a scalar “housing service” consumption measure, the model portrays housing as a commodity with two attributes: floor space and yard space. Developers react to a consumer bid-rent function, which relates dwelling rent to floor space, yard space, and location, in choosing the profit-maximizing characteristics of their housing complexes. The spatial behavior of the developer's choice variables is investigated (an interesting question is whether yards are larger farther from the urban center), and a comparative static analysis of the housing market equilibrium is presented.     

MSE-improvement of the least squares estimator by dropping variables

It is well known that dropping variables in regression analysis decreases the variance of the least squares (LS) estimator of the remaining parameters. However, after elimination estimates of these parameters are biased, if the full model is correct. In his recent paper, Boscher (1991) showed that the LS-estimator in the special case of a mean shift model (cf. Cook and Weisberg, 1982) which assumes no “outliers” can be considered in the framework of a linear regression model where some variables are deleted. He derived conditions under which this estimator outperforms the LS-estimator of the full model in terms of the mean squared error (MSE)-matrix criterion. We demonstrate that this approach can be extended to the general set-up of dropping variables. Necessary and sufficient conditions for the MSE-matrix superiority of the LS-estimator in the reduced model over that in the full model are derived. We also provide a uniformly most powerful F-statistic for testing the MSE-improvement.