Workaholics and Dropouts in Organizations

This paper reports the results of experiments designed to test the theory of the optimal composition of prizes in contests. In the aggregate the behavior of subjects is consistent with that predicted by the theory, but we find that such aggregate results mask an unexpected compositional effect on the individual level. Whereas theory predicts that subject efforts are continuous and increasing functions of ability, the actual efforts of our laboratory subjects bifurcate. Low‐ability workers drop out and exert little or no effort, and high‐ability workers try too hard. This bifurcation, which is masked by aggregation, can be explained by assuming loss aversion on the part of the subjects. (JEL: C92, D44, D72, D82, J31)     

Knowledge Life Cycle,Knowledge Inventory,and Knowledge Acquisition Strategies*

For a knowledge‐ and skill‐centric organization, the process of knowledge management encompasses three important and closely related elements: (i) task assignments, (ii) knowledge acquisition through training, and (iii) maintaining a proper level of knowledge inventory among the existing workforce. Trade‐off on choices between profit maximization in the short run and agility and flexibility in the long term is a vexing problem in knowledge management. In this study, we examine the effects of different training strategies on short‐term operational efficiency and long‐term workforce flexibility. We address our research objective by developing a computational model for task and training assignment in a dynamic knowledge environment consisting of multiple distinct knowledge dimensions. Overall, we find that organizational slack is an important variable in determining the effectiveness of training strategies. Training strategies focused on the most recent skills are found to be the preferred option in most of the considered scenarios. Interestingly, increased efficiencies in training can actually create preference conflict between employees and the firm. Our findings indicate that firms facing longer knowledge life cycles, higher slack in workforce capacity, and better training efficiencies actually face more difficult challenges in knowledge management.     

Is Psychological Well-Being Linked to the Consumption of Fruit and Vegetables?

Little is known about the influence of people’s diet on their psychological well-being. This study provides evidence of a link between the consumption of fruit and vegetables and high well-being. In cross-sectional data, happiness and mental health rise in an approximately dose–response way with the number of daily portions of fruit and vegetables. Well-being peaks at approximately 7 portions per day. We document this relationship in three data sets, covering approximately 80,000 randomly selected British individuals, and for seven measures of well-being (life satisfaction, WEMWBS mental well-being, GHQ mental disorders, self-reported health, happiness, nervousness, and feeling low). The pattern is robust to adjustment for a large number of other demographic, social and economic variables. Reverse causality and problems of confounding remain possible. We discuss the strengths and weaknesses of our analysis, how government policy-makers might wish to react to it, and what kinds of further research—especially randomized trials—would be valuable.     

Happy People Have Children: Choice and Self-Selection into Parenthood

There is mixed evidence in the existing literature on whether children are associated with greater subjective well-being, with the correlation depending on which countries and populations are considered. We here provide a systematic analysis of this question based on three different datasets: two cross-national and one national panel. We show that the association between children and subjective well-being is positive only in developed countries, and for those who become parents after the age of 30 and who have higher income. We also provide evidence of a positive selection into parenthood, whereby happier individuals are more likely to have children.     

Through Civil War, Food Crisis and Drought: Trends in Fertility and Nuptiality in Post-Soviet Tajikistan

This article has two objectives. First, it aims to complement and extend existing research on post-socialist demographic change, which has thus far tended to focus on Central and Eastern Europe. It does this by describing the nature of post-Soviet trends in nuptiality and fertility in Tajikistan, the republic with the highest rate of population growth during the Soviet period. It finds evidence for a decrease in period fertility after independence: initially, through a decline at higher orders; then, through a significant decrease in the rate of first births, associated with a dramatic decrease in the rate of first union formation since the mid-1990s. Second, it aims to contribute to the demography of conflict and of food crisis. Most clearly, it finds strong evidence for a decrease in nuptiality and fertility associated with the 1995 food crisis.     

Comparing Possibly Misspecified Forecasts

Abstract

Recent work has emphasized the importance of evaluating estimates of a statistical functional (such as a conditional mean, quantile, or distribution) using a loss function that is consistent for the functional of interest, of which there is an infinite number. If forecasters all use correctly specified models free from estimation error, and if the information sets of competing forecasters are nested, then the ranking induced by a single consistent loss function is sufficient for the ranking by any consistent loss function. This article shows, via analytical results and realistic simulation-based analyses, that the presence of misspecified models, parameter estimation error, or nonnested information sets, leads generally to sensitivity to the choice of (consistent) loss function. Thus, rather than merely specifying the target functional, which narrows the set of relevant loss functions only to the class of loss functions consistent for that functional, forecast consumers or survey designers should specify the single specific loss function that will be used to evaluate forecasts. An application to survey forecasts of U.S. inflation illustrates the results.     

Assessing Statistical Results: Magnitude,Precision, and Model Uncertainty

ABSTRACT

Evaluating the importance and the strength of empirical evidence requires asking three questions: First, what are the practical implications of the findings? Second, how precise are the estimates? Confidence intervals provide an intuitive way to communicate precision. Although nontechnical audiences often misinterpret confidence intervals (CIs), I argue that the result is less dangerous than the misunderstandings that arise from hypothesis tests. Third, is the model correctly specified? The validity of point estimates and CIs depends on the soundness of the underlying model.