Bargaining over waiting time in ultimatum game experiments

While preference-based explanations play an increasing role in economics and sociology, the accurate measurement of social preferences deserves more attention. Most laboratory experiments measure social preferences by studying the division of “a cake that nobody had to bake” (Güth and Kliemt, 2003). This article reports results of the first ultimatum game experiment with bargaining over waiting time. The experiment was created to avoid effects of windfall gains. In contrast to donated money, time is not endowed by the experimenter and implies a natural loss to subjects. This allows for a better measurement of the inherent conflict in the ultimatum game. We implemented three anonymity conditions; one baseline condition, one condition with anonymity among subjects and one double-blind condition in which the experimenter did not know the division of waiting time. While we expected to observe less other-regarding behavior in ultimatum game bargaining over time, our experimental results rather confirm previous ultimatum game experiments, in which people bargained over money. The modal offer was half of the waiting time and only one offer was rejected. Interestingly, anonymity did not change the results significantly. In conclusion, our experiment confirms other-regarding behavior in the ultimatum game.     

A comparative study of occupancy and patient care quality in four different types of intensive care units in a children's hospital

This paper reports a comparative study of occupancy and patient care quality in four types of intensive care units in a children's hospital,: an Infant Care Center (ICC), a Medical/Surgical (Med/Surg) unit, a Neonatal Intensive Care Unit (NICU), and a Pediatric Intensive Care Unit (PICU), each featuring a mix of multi-bed and private room (PR) patient care environments. The project is prompted by interest by the project sponsor in a pre-occupancy analysis, before the units are upgraded to exclusive PR designs. Methods comprised, for each unit: (1) observations of ergonomic design features; (2) task activity analyses of job performance of selected staff; and (3) use of a survey to collect perceptions by unit nursing and house staff (HS) of indicators of occupancy and patient care quality. Conclusions: (1) the five most common task activities are interaction with patients, charting, and interaction with equipment, co-workers and family members; (2) job satisfaction, patient care, work environment, job, patient care team interaction, and general occupancy quality rankings by ICC and/or NICU respondents are significantly higher than those by other staff respondents; and (3) ergonomic design shortcomings noted are excess noise, problems with equipment, and work environment, job-related health, and patient care quality issues.     

How do people differentiate between jobs: and how do they define a good job?

Employed individuals from a range of jobs (n=18) were interviewed using a repertory grid technique, to explore the criteria they used to distinguish between different jobs. The concepts of 'a good job' and 'a job good for health' were also discussed. Interactions with others and the job itself were the most commonly used criteria and were also the most common features of a 'good job'. Pay and security were mentioned frequently but were less important when comparing jobs and when defining a 'good job'. Physical activity was rarely associated by interviewees with a 'good job' but was frequently associated with a 'job good for health'. A comprehensive definition of a 'good job' needs to take all these factors into account.     

Family structure and child outcomes: a high definition, wide angle “snapshot”

Using data from the National Survey of America??s Families (NSAF), this research investigates the relationships between a highly defined set of family structures and a broad set of child outcomes at a particular point in time in a child??s life. A detailed classification of family structures is constructed that clarifies key differences among various types of diverse families, and facilitates equivalencies testing and pairwise comparisons across nontraditional family structures. The NSAF contains a large number of observations for less common, but growing, family structures such as single-father families, grandparent-headed households and cohabiters, which makes such detailed analyses feasible and allows further stratification by child age, gender and race. The data also contains information on child behavioral, educational and physical health outcomes, as well as extensive household characteristics, economic resources and parental behaviors and inputs. Results suggest that differences across nontraditional family structures are particularly prominent for child health outcomes and that the gender of the resident parent is empirically important, more so than the presence of a cohabiting or married step-parent. Children in single father families have lesser access to health care yet enjoy better health outcomes than those in other families, even after controlling for economic resources (and inputs). In contrast, few differences are found between grandparent-headed families and other non-parent families. While we explore alternative explanations for these results, our cross-sectional data and complex set of family structure variables preclude isolating causal relationships; instead, our analyses yield empirically important distinctions that point to promising avenues for future research.     

A cross-border perspective on migration: beyond the assimilation/transnationalism debate

The transnational perspective emerged in the early 1990s as an alternative to assimilation theory, gaining instant and wide influence. But curiously, the intellectual confrontation between these two perspectives was averted, as scholars concluded that persistent homeland engagement was fully compatible with hostland integration. This paper seeks to pick up that challenge. I demonstrate how a cross-border perspective, encompassing places of origin and destination and the flows of people, ideas, and resources between them, highlights the ways in which population movements across state borders create tensions on both receiving and sending sides. In the process, I will show how looking across borders paradoxically highlights the centrality of the territorial boundary, as it simultaneously underscores the importance of dissimilation – the social and political separation of immigrants from the people they have left behind – yet also the ways in which non-citizen status and foreign origins simultaneously hamper immigrants’ ability to gain acceptance in receiving states while furnishing sending states with opportunities to reconnect with nationals abroad.     

The Use of Simulation to Reduce the Domain of “Black Swans” with Application to Hurricane Impacts to Power Systems

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts.     

Improving the detection of unusual observations in high‐dimensional settings

Multivariate control charts are used to monitor stochastic processes for changes and unusual observations. Hotelling's T² statistic is calculated for each new observation and an out‐of‐control signal is issued if it goes beyond the control limits. However, this classical approach becomes unreliable as the number of variables p approaches the number of observations n, and impossible when p exceeds n. In this paper, we devise an improvement to the monitoring procedure in high‐dimensional settings. We regularise the covariance matrix to estimate the baseline parameter and incorporate a leave‐one‐out re‐sampling approach to estimate the empirical distribution of future observations. An extensive simulation study demonstrates that the new method outperforms the classical Hotelling T² approach in power, and maintains appropriate false positive rates. We demonstrate the utility of the method using a set of quality control samples collected to monitor a gas chromatography–mass spectrometry apparatus over a period of 67 days.     

Empirical Prediction Intervals for County Population Forecasts

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.