Building a Values‐Informed Mental Model for New Orleans Climate Risk Management

Individuals use values to frame their beliefs and simplify their understanding when confronted with complex and uncertain situations. The high complexity and deep uncertainty involved in climate risk management (CRM) lead to individuals’ values likely being coupled to and contributing to their understanding of specific climate risk factors and management strategies. Most mental model approaches, however, which are commonly used to inform our understanding of people's beliefs, ignore values. In response, we developed a “Values‐informed Mental Model” research approach, or ViMM, to elicit individuals’ values alongside their beliefs and determine which values people use to understand and assess specific climate risk factors and CRM strategies. Our results show that participants consistently used one of three values to frame their understanding of risk factors and CRM strategies in New Orleans: (1) fostering a healthy economy, wealth, and job creation, (2) protecting and promoting healthy ecosystems and biodiversity, and (3) preserving New Orleans’ unique culture, traditions, and historically significant neighborhoods. While the first value frame is common in analyses of CRM strategies, the latter two are often ignored, despite their mirroring commonly accepted pillars of sustainability. Other values like distributive justice and fairness were prioritized differently depending on the risk factor or strategy being discussed. These results suggest that the ViMM method could be a critical first step in CRM decision‐support processes and may encourage adoption of CRM strategies more in line with stakeholders’ values.     

Stock investors’ preference for short-term vs. long-term bonuses

Bonuses in the finance sector may be based on too short time intervals for environmental and social factors to be taken into account in investment decisions. We report two experiments to investigate whether investors prefer short-term to long-term bonuses. In Experiment 1 employing 27 undergraduates, preferences were measured for four short-term certain bonuses, evenly distributed across a time interval, and one certain long-term bonus at the end of the time interval. A majority chose the short-term bonuses, and in order for the long-term bonus to be equally preferred it had to be about 40% higher than the four added short-term bonuses. Experiment 2 employing another 36 undergraduates introduced outcome uncertainty that more accurately reflects the choices stock investors face. The participants again choose between a long-term bonus and four distributed short-term bonuses. It was shown that uncertainty made more participants prefer the long-term bonus to the added short-term bonuses than when the outcome was certain. A smaller increase of the long-term bonus of about 20% was now required to make it equally attractive as the four added short-term bonuses.     

Taken in by the numbers game: the globalization of a religious ‘illusio’ and ‘doxa’ in nineteenth-century evangelical missions to India

While the Bourdieusian concepts of capital and relational configurations of positions and position-takings have recently been fruitfully employed to theorize global fields, this paper argues that the concepts of illusio and doxa are especially conducive to analyzing the globalization of a field as an expansion into and transformation of formerly independent national arenas. In deploying the concept of illusio as a (quasi-libidinous) investment in the game, globalization is here first and foremost framed as a process in which more and more actors in various contexts ‘succumb’ to specific field logics that orient them to transnational structures. By emphasizing the concept of doxa as the taken-for-granted fundamentals of a field, the paper furthermore calls attention to the mechanisms that help forge and globalize the tacit presuppositions and shared ontologies on which such logics build. Focusing on a field of global Christian missions that emerged during the nineteenth century, the paper illustrates how its competitive logic of proselytization was introduced into the religious arena of India. It highlights how Western notions of religion gained ground among local traditions and pulled them into a game in which numerical relations of adherents mattered.     

Polynomial algorithms for canonical forms of orientations

We introduce canonical forms that represent certain equivalence classes of totally cyclic and acyclic orientations of graphs and present a polynomial algorithms for their constructions. The forms are used in new formulas evaluating tension and flow polynomials on graphs.     

Complementary person-supervisor fit: An investigation of supplies-values (S-V) fit,leader-member exchange (LMX) and work outcomes

By applying the supplies-values (S-V) fit approach from the complementary person-environment (P-E) fit literature to the leader-employee perspective, and drawing upon social exchange theory, we examine how fulfillment of different work values is related to Leader-Member Exchange (LMX) and work outcomes. First, polynomial regression analyses combined with response surface analysis of data collected at two time points (N = 316) showed that LMX (Time 2) was higher the more the leader fulfills the employee's work values (Time 1). Second, LMX (Time 2) was higher when leader supplies (Time 1) and employee work values (Time 1) were both high than when both were low. Third, analyses of data from a sub-sample of matched leader-employee dyads (N = 140), showed that LMX (Time 2) played a mediating role on the relation between S-V fit (Time 1) and work outcomes (Time 2). Specifically, we found eight out of 10 relationships between S-V fit (Time 1) and leader-rated task performance and OCB (Time 2) to be fully mediated by LMX (Time 2). LMX (Time 2) partially mediated the relation between S-V fit (Time 1) and job satisfaction (Time 2) as only two out of five relationships were fully mediated.     

Treatment Evaluation in the Presence of Sample Selection

Sample selection and attrition are inherent in a range of treatment evaluation problems such as the estimation of the returns to schooling or training. Conventional estimators tackling selection bias typically rely on restrictive functional form assumptions that are unlikely to hold in reality. This paper shows identification of average and quantile treatment effects in the presence of the double selection problem into (i) a selective subpopulation (e.g., working—selection on unobservables) and (ii) a binary treatment (e.g., training—selection on observables) based on weighting observations by the inverse of a nested propensity score that characterizes either selection probability. Weighting estimators based on parametric propensity score models are applied to female labor market data to estimate the returns to education.     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.