Intertemporal choice under timing risk: An experimental approach

This paper investigates how individuals evaluate delayed outcomes with risky realization times. Under the discounted expected utility (DEU) model, such evaluations depend only on intertemporal preferences. We obtain several testable hypotheses using the DEU model as a benchmark and test these hypotheses in three experiments. In general, our results show that the DEU model is a poor predictor of intertemporal choice behavior under timing risk. We found that individuals are averse to timing risk and that they evaluate timing lotteries in a rank-dependent fashion. The main driver of timing risk aversion is nothing but probabilistic risk aversion that stems from the nonlinear treatment of probabilities.     

Accuracy of the College Majors Finder for Three Majors

The Self-Directed Search was administered to 68 university seniors who were majoring in Chemistry/Chemical Engineering, Elementary Education, and Office Administration. Their resulting three-letter summary codes were compared with three-letter college major codes obtained from the College Majors Finder. A high degree of agreement between the two sets of codes was found for each of the three majors. Effective methods for matching people to jobs have held the interest and occupied much time for career counselors and vocational theorists since the inception of the trait-factor approach to career counseling. More recently, Holland (1985a) broadened and clarified matching procedures through the development of his typological theory of vocational choice. Just as personality types and occupational environments have been organized using Holland's framework to foster an understanding of occupational choice, Holland's theory can also be used to explain other types of choices, such as choice of a college major.     

Values and Vegetarianism: An Exploratory Analysis1

The American diet has changed substantially over the last few decades. These changes provide a test for social theory and have substantial effects on the demand for various food products, thus impacting U.S. agriculture. In this paper a rapidly growing dietary choice—vegetarianism—is explored. Prevalence of various forms of vegetarianism is estimated in a representative sample of a suburban population and factors that influence choice of a vegetarian diet are examined. About 7 percent of the sample report they are vegetarians. The prevalence of vegetarianism is not influenced by age, gender, or education, but individuals holding traditional values are less likely to be vegetarian than others. There also is some evidence mat individuals holding altruistic values are more likely than others to be vegetarians.     

Employment growth in high-tech new ventures

We specify and estimate a model of the early employment growth of technology-driven new ventures. Our estimated model is derived from labor demand theory. A data set of 100 German firms is used to conduct this analysis. The study’s findings support the relevance of the theory as applied to these nascent firms. Employment growth is significantly enhanced in ventures exhibiting high profits, experienced founders, formal information processes, and outsourced product distribution. Our findings also confirm aspects of Penrose’s theory of firm growth suggesting that transactions costs associated with larger founder teams may inhibit employment growth.     

Analytical approximations for iterated bootstrap confidence intervals

Standard algorithms for the construction of iterated bootstrap confidence intervals are computationally very demanding, requiring nested levels of bootstrap resampling. We propose an alternative approach to constructing double bootstrap confidence intervals that involves replacing the inner level of resampling by an analytical approximation. This approximation is based on saddlepoint methods and a tail probability approximation of DiCiccio and Martin (1991). Our technique significantly reduces the computational expense of iterated bootstrap calculations. A formal algorithm for the construction of our approximate iterated bootstrap confidence intervals is presented, and some crucial practical issues arising in its implementation are discussed. Our procedure is illustrated in the case of constructing confidence intervals for ratios of means using both real and simulated data. We repeat an experiment of Schenker (1985) involving the construction of bootstrap confidence intervals for a variance and demonstrate that our technique makes feasible the construction of accurate bootstrap confidence intervals in that context. Finally, we investigate the use of our technique in a more complex setting, that of constructing confidence intervals for a correlation coefficient.     

Exposure Reconstruction for the TCDD-Exposed NIOSH Cohort Using a Concentration- and Age-Dependent Model of Elimination

Recent studies demonstrating a concentration dependence of elimination of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) suggest that previous estimates of exposure for occupationally exposed cohorts may have underestimated actual exposure, resulting in a potential overestimate of the carcinogenic potency of TCDD in humans based on the mortality data for these cohorts. Using a database on U.S. chemical manufacturing workers potentially exposed to TCDD compiled by the National Institute for Occupational Safety and Health (NIOSH), we evaluated the impact of using a concentration- and age-dependent elimination model (CADM) (Aylward et al., 2005) on estimates of serum lipid area under the curve (AUC) for the NIOSH cohort. These data were used previously by Steenland et al. (2001) in combination with a first-order elimination model with an 8.7-year half-life to estimate cumulative serum lipid concentration (equivalent to AUC) for these workers for use in cancer dose-response assessment. Serum lipid TCDD measurements taken in 1988 for a subset of the cohort were combined with the NIOSH job exposure matrix and work histories to estimate dose rates per unit of exposure score. We evaluated the effect of choices in regression model (regression on untransformed vs. ln-transformed data and inclusion of a nonzero regression intercept) as well as the impact of choices of elimination models and parameters on estimated AUCs for the cohort. Central estimates for dose rate parameters derived from the serum-sampled subcohort were applied with the elimination models to time-specific exposure scores for the entire cohort to generate AUC estimates for all cohort members. Use of the CADM resulted in improved model fits to the serum sampling data compared to the first-order models. Dose rates varied by a factor of 50 among different combinations of elimination model, parameter sets, and regression models. Use of a CADM results in increases of up to five-fold in AUC estimates for the more highly exposed members of the cohort compared to estimates obtained using the first-order model with 8.7-year half-life. This degree of variation in the AUC estimates for this cohort would affect substantially the cancer potency estimates derived from the mortality data from this cohort. Such variability and uncertainty in the reconstructed serum lipid AUC estimates for this cohort, depending on elimination model, parameter set, and regression model, have not been described previously and are critical components in evaluating the dose-response data from the occupationally exposed populations.     

Beheimatungsprozesse begleiten — ein Werkstattbericht

Supporting processes of belonging in supervision and coachingThe contemporary employee has to cope with frequent changes. Not only does he lose his familiar environment, he also has to find ways to familiarize himself with new places and to develop a fresh sense of belonging. This process can be understood and facilitated by interpreting the German concept of “Heimat“ as something we have to create ourselves. First experiences demonstrate the usefulness of this approach in coaching processes.     

Expected sample moments of concomitants of selected order statistics

In this paper, the task of determining expected values of sample moments, where the sample members have been selected based on noisy information, is considered. This task is a recurring problem in the theory of evolution strategies. Exact expressions for expected values of sums of products of concomitants of selected order statistics are derived. Then, using Edgeworth and Cornish-Fisher approximations, explicit results that depend on coefficients that can be determined numerically are obtained. While the results are exact only for normal populations, it is shown experimentally that including skewness and kurtosis in the calculations can yield greatly improved results for other distributions.