THE ROLE OF LABOR AND MARRIAGE MARKETS,PREFERENCE HETEROGENEITY,AND THE WELFARE SYSTEM IN THE LIFE CYCLE DECISIONS OF BLACK,HISPANIC, AND WHITE WOMEN*

Using data from the NLSY79, we structurally estimate a dynamic model of the life cycle decisions of young women. The women make sequential joint decisions about school attendance, work, marriage, fertility, and welfare participation. We use the model to perform counterfactual simulations designed to shed light on three questions: (1) How much of observed minority–majority differences in behavior can be attributed to differences in labor market opportunities, marriage market opportunities, and preference heterogeneity? (2) How does the welfare system interact with these factors to augment those differences? (3) How can new cohorts that grow up under the new welfare system (Temporary Aid for Needy Families) be expected to behave compared to older cohorts?     

Accounting for Gender in a Study of the Motivation-Involvement Relationship

ABSTRACT

The effects of gender on involvement in high-risk recreation have received limited research attention despite mounting evidence suggesting the learned interactions between people and places likely vary for men and women. The purpose of this study was to provide insights into how gender influenced the motivation-involvement relationship among whitewater recreationists on a Wild and Scenic River in California. Our results revealed the motivations of Risk, Escape, Learning, and Achievement/Stimulation positively influenced involvement in rafting activities. Although gender did not influence all dimensions of involvement, we found that identity expression varied between subgroups. Specifically, men were more likely to ascribe meaning to rafting than women because this activity allowed them to affirm and express their individual character. The implications emanating from this study advance theoretical understanding of the factors that influence enduring involvement and inform natural resource management decisions about maintaining the desired benefits of activities sought by nature-based recreationists.     

Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks

Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. ( 2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of “new age” classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that “new age” classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).     

Financial competence,overconfidence, and trusting investments: Results from an experiment

Financial transactions sometimes occur in an environment where third-party enforcement is lacking. Behavioral explanations typically allude to the social preferences, where an individual’s utility is directly affected by another’s outcome, as the driver of the trusting investments and reciprocal returns. We hypothesize that, in part, these decisions are determined by an individual’s financial literacy and overconfidence in one’s knowledge. Experimental evidence is coupled with an innovative financial literacy assessment, which measures general competence, numeracy skills, and overconfidence in one’s knowledge. Results indicate that overconfidence is a significant determinant of behavior. Specifically, overconfident individuals make larger contributions in the investment game. We also document that there is an escalated effect in overconfident individuals who are also exhibit risk loving preferences.     

An integrative model of e-learning use: Leveraging theory to understand and increase usage

Employees are increasingly using technology to access content for learning, and theory development has been outpaced by practice. Drawing on a well validated theory of behavior change (the Transtheoretical Model of Change), as well as theories on technology acceptance and employee development, this paper offers an integrative model of factors that influence employee use of e-learning as well as practical recommendations for how use might be increased. Recommendations for future research on e-learning are also offered.