Penalized Pseudolikelihood Inference in Spatial Interaction Models with Covariates

Given spatially located observed random variables ( x , z = {( x _i , z _i )} _i , we propose a new method for non-parametric estimation of the potential functions of a Markov random field p ( x | z ), based on a roughness penalty approach. The new estimator maximizes the penalized log-pseudolikelihood function and is a natural cubic spline. The calculations involved do not rely on Monte Carlo simulation. We suggest the use of B-splines to stabilize the numerical procedure. An application in Bayesian image reconstruction is described.     

The differential impact of friendship on cooperative and competitive coordination

Theory and Decision - Friendship is commonly assumed to reduce strategic uncertainty and enhance tacit coordination. However, this assumption has never been tested across two opposite poles of...     

Legalism and creativity: tax non-compliance in the eyes of the economic elite

This paper analyzes elite tax compliance from a sociological perspective in order to examine how different tax behaviors are justified or how they embody different taxpayers’ subjectivities. Drawing from the case of Chile and using a qualitative approach based on 32 interviews conducted with members of the economic elite, it is argued that forms of non-compliance -such as evasion and avoidance- are grounded in different ‘repertoires of evaluation’ while denoting a plurality of distinctions and criteria of evaluation which relate to the legal culture in which the tax system operates. Findings show that legalism -a literal conception of the rules- does not always favor tax compliance and that it may even go against the purpose of tax collection when a creative use of the law prevails. Furthermore, legalism and creativity allow for the main justifications for challenging tax payment as well as the perceptions and beliefs that underlie the everyday relationship between taxpayers and the state to be understood.     

Asylum Migration in OECD Countries: In Search of Lost Well-Being

Social Indicators Research - Alongside the economic determinants and unobserved structural forces that drive migration flows, asylum migration faces additional natural and man-made hazards, which...     

Exploring Employee Perceptions towards Smart Working during the COVID-19 Pandemic: a Comparative Analysis of Two Italian Public Research Organizations

Public Organization Review - Of all the socio-economic changes caused by the Covid-19 pandemic, the disruption to workforce organizations will probably leave the largest indelible mark. The way...     

On two random search problems

The paper is concerned with static search on a finite set. An unknown subset of cardinality k of the finite set is to be found by testing its subsets. We investigate two problems: in the first, the number of common elements of the tested and the unknown subset is given; in the second, only the information whether the tested and the unknown subset are disjoint or not is given. Both problems correspond to problems on false coins. If the unknown subset is taken from the family of k-element sets with uniform distribution, we determine the minimum of the lengths of the strategies that find the unknown element with small error probability. The strategies are constructed by probabilistic means.     

Optimal Method for Realizing Two-Sided Inferences About a Linear Combination of Two Proportions

In order to reach the inference about a linear combination of two independent binomial proportions, various procedures exist (Wald's classic method, the exact, approximate, or maximized score methods, and the Newcombe-Zou method). This article defines and evaluates 25 different methods of inference, and selects the ones with the best behavior. In general terms, the optimal method is the classic Wald method applied to the data to which z ² _α/2/4 successes and z ² _α/2/4 failures are added (≈1 if α = 5%) if no sample proportion has a value of 0 or 1 (otherwise the added increase may be different).

Supplemental materials are available for this article. Go to the publisher's online edition of Communications in Statistics - Simulation and Computation to view the free supplemental file.     

Advancing careers through ‘merit’: a rationalized-sensemaking narrative in hierarchical organizations

ABSTRACT

This contribution provides an in-context exploration of how middle-managers make sense of their career progress, and particularly focuses on ‘merit’ to understand how careers are driven in a hierarchical organization. The study exposes ‘merit’ as a fragmented and individualized construction that links back to the participants’ broader life ambitions and identity footprint. It also shows a tendency for maintaining trust in ‘merit’ above other circumstantial and opportunity factors, even in face of events which undermine the application of the merit-based principle. ‘Merit’ is hence portrayed to be a rationalized narrative in careers’ trajectory; a marker used by participants to make sense of events in a coherent manner, consequently experiencing self-efficacy and reducing uncertainty. The findings add complexity to the ‘meritocracy’ debate, calling for new critiques which address its underlying inequality dimension while also contemplating the individual psychological purpose driving ‘merit’ beliefs.     

Imputation of repeatedly observed multinomial variables in longitudinal surveys

It is now a standard practice to replace missing data in longitudinal surveys with imputed values, but there is still much uncertainty about the best approach to adopt. Using data from a real survey, we compared different strategies combining multiple imputation and the chained equations method, the two main objectives being (1) to explore the impact of the explanatory variables in the chained regression equations and (2) to study the effect of imputation on causality between successive waves of the survey. Results were very stable from one simulation to another, and no systematic bias did appear. The critical points of the method lied in the proper choice of covariates and in the respect of the temporal relation between variables.