CLASH OF CAREER AND FAMILY: FERTILITY DECISIONS AFTER JOB DISPLACEMENT

In this paper we investigate how career considerations may affect fertility decisions in the presence of a temporary employment shock. We compare the birth rates of women displaced by a plant closure with those of women unaffected by job loss after establishing the pre‐displacement comparability of these groups. Our results reveal that job displacement reduces average fertility by 5%–10%, and that these effects are largely explained by the response of women in more skilled occupations. We offer an explanation of our results based on career interruptions of women.     

Addressing self-selection effects in evaluations of mutual help groups and professional mental health services: An introduction to two-stage sample selection models

Because random assignment to conditions is often neither possible nor desirable in longitudinal evaluations of mutual help organizations, the influence of self-selection effects must be assessed in order to accurately interpret outcome data. One approach to adjusting for self-selection effects is to control for covariates that predict outcome using statistical procedures such as analysis of covariance (ANCOVA), partial correlations, and hierarchical regression. This approach has considerable power, but is less useful when an evaluator is interested in directly modeling the process of entry into a program and incorporating information on the factors affecting self-selection into estimation of program effects. Two-stage sample selection models are designed to address such situations. These models rely on regression procedures in which program participation is modeled in an initial equation, which yields a sample selection correction factor. The correction factor is included with participation in a second equation that predicts outcome. This two-stage procedure allows the evaluator to interpret the observed effects of a professional service or a self-help group in the context of the magnitude and direction of selection effects. We compare and contrast the covariate control and sample selection models in a longitudinal study of the effects of participation in Alcoholics Anonymous on drinking behavior.     

Root-n-consistent Estimation in Partial Linear Models with Long-memory Errors

We consider estimation of β in the semiparametric regression model y ( i ) - x ^T( i )β + f ( i / n ) + ε( i ) where x ( i ) = g ( i )/ n ) + e ( i , f and g are unknown smooth functions and the processes ε( i ) and e ( i ) are stationary with short- or long-range dependence. For the case of i.i.d. errors, Speckman (1988) proposed a √ n –consistent estimator of β. In this paper it is shown that, under suitable regularity conditions, this estimator is asymptotically unbiased and √ n –consistent even if the errors exhibit long-range dependence. The orders of the finite sample bias and of the required bandwidth depend on the long-memory parameters. Simulations and a data example illustrate the method     

Adaptation over parametric families of symmetric linear estimators

This paper treats an abstract parametric family of symmetric linear estimators for the mean vector of a standard linear model. The estimator in this family that has smallest estimated quadratic risk is shown to attain, asymptotically, the smallest risk achievable over all candidate estimators in the family. The asymptotic analysis is carried out under a strong Gauss–Markov form of the linear model in which the dimension of the regression space tends to infinity. Leading examples to which the results apply include: (a) penalized least squares fits constrained by multiple, weighted, quadratic penalties; and (b) running, symmetrically weighted, means. In both instances, the weights define a parameter vector whose natural domain is a continuum.     

Arbeit und Leben in Organisationen 2008

Mitteilungen

Arbeit und Leben in Organisationen 2008 Anlage und Ergebnisse der Studie     

Weighted Median of the Data in Solving Least Absolute Deviations Problems

We consider the weighted median problem for a given set of data and analyze its main properties. As an illustration, an efficient method for searching for a weighted Least Absolute Deviations (LAD)-line is given, which is used as the basis for solving various linear and nonlinear LAD-problems occurring in applications. Our method is illustrated by an example of hourly natural gas consumption forecast.