RURAL POVERTY, URBAN POVERTY, AND PSYCHOLOGICAL WELL-BEING

Data from the National Survey of Families and Households are used to compare the psychological well-being of the rural and urban poor. Overall, the urban poor are higher in perceived health than the rural poor, although no differences are apparent in happiness or depression. Significant interactions are present between rural/urban poverty and sex, race, and family status. The psychological well-being of poor African Americans is higher in rural than urban areas, whereas the well-being of poor whites is higher in urban than rural areas. This trend is especially pronounced for depression among males. In addition, single men without children have especially high depression scores in rural areas, whereas married women without children have especially low depression scores in urban areas. The results are interpreted in terms of the environmental quality of inner-city neighborhoods and attitudes toward poverty in urban and rural communities.     

Parent‐Adolescent Involvement: The Relative Influence of Parent Gender and Residence

The 1995 wave of the Add Health study is used to investigate the relative influence of parent gender and residence on patterns of parental involvement with adolescents. Adolescent reports (N =17,330) of shared activities, shared communication, and relationship quality with both biological parents are utilized. A multidimensional scaling analysis reveals that parent gender explains most of the variance in parent‐adolescent involvement, with residential status playing a secondary yet a fundamental role in accounting for these patterns. Resident mothers who do not live with adolescents’ biological fathers engage in the broadest range of activities with their children. Unpartnered resident fathers display patterns of parenting that are as similar to mothers as they are to other fathers.     

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.     

Immigrants' Language Skills and Visa Category1

This article is concerned with the determinants of English language proficiency among immigrants in a longitudinal survey for Australia. It focuses on both visa category and variables derived from an economic model of the determinants of destination‐language proficiency among immigrants. Skills‐tested and economic immigrants have the greatest proficiency shortly after immigration, followed by family‐based visa recipients, with refugees having the lowest proficiency. Other variables the same, these differences disappear by 3.5 years after immigration for speaking skills; and although they diminish, they persist longer for reading and writing skills. The variables generated from the model of destination‐language proficiency (such as schooling and age at migration) are, in part, predictions of visa category, but they are more important statistically for explaining proficiency.     

Intuitive Toxicology: Expert and Lay Judgments of Chemical Risks

Human beings have always been intuitive toxicologists, relying on their senses of sight, taste, and smell to detect harmful or unsafe food, water, and air. As we have come to recognize that our senses are not adequate to assess the dangers inherent in exposure to a chemical substance, we have created the sciences of toxicology and risk assessment to perform this function. Yet despite this great effort to overcome the limitations of intuitive toxicology, it has become evident that even our best scientific methods still depend heavily on extrapolations and judgments in order to infer human health risks from animal data. Many observers have acknowledged the inherent subjectivity in the assessment of chemical risks and have indicated a need to examine the subjective or intuitive elements of expert and lay risk judgments. We have begun such an examination by surveying members of the Society of Toxicology and the lay public about basic toxicological concepts, assumptions, and interpretations. Our results demonstrate large differences between toxicologists and laypeople, as well as differences between toxicologists working in industry, academia, and government. In addition, we find that toxicologists are sharply divided in their opinions about the ability to predict a chemical's effect on human health on the basis of animal studies. We argue that these results place the problems of risk communication in a new light. Although the survey identifies misconceptions that experts should clarify for the public, it also suggests that controversies over chemical risks may be fueled as much by limitations of the science of risk assessment and disagreements among experts as by public misconceptions.     

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.     

Russki adventures

This case study, by Paul Beamish, was a prize-winner in the EFMD 1992 Annual Case Writing Competition, in the section entitled, ‘Entrepreneurship’. It concerns a daring new business venture in the Russian Caucasus Mountains.In mid-1991, Guy Crevasse and Andrei Kakov, the two major partners in Russki Adventures contemplated their next move. They had spent the last year and a half exploring the possibility of starting a helipcopter skiing operation in Russia.Their plan was to bring clients from Europe, North America, and Japan to the Caucasus Mountains to ski the vast areas of secluded mountain terrain made accessible by the use of helicopters and the recent business opportunities offered by ‘glasnost’.Three options for proceeding were being considered. The first was to proceed with the venture on their own, in the Caucasus Mountains area that had been made available to them by a Soviet government agency. The second was to accept the offer of partnership with Extreme Dreams, a French tour operator that had begun operations in the Caucasus region during the 1991 season. The final option was to wait, save their money and not proceed with the venture at this time.The data presented in the case includes information on: the company founders the helicopter skiing industry the Russian environment proposed market positioning of the business the potential alliance partners forecast financial analysis, under several scenarios.     

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.