The "Positivity Bias" inEvaluations of Public Figures:Evidence Against Instrument Artifacts

The "positivity bias" is a term used to describe the consistentfavorable evaluation of public figures found in surveys overthe past 40 years. This paper explored several possible artifactualexplanations for this bias,focusing on the survey instrumentitself. Two experiments varied the labeling and ordering ofscale endpoints, the affective value of the initial contextevaluated, and the presence or absence of a prestigious jobtitle associated with the nameof the public figure. None ofthe variations produced significantly different levels of positivitythan the standard control condition used in each experiment.RichardR. Lau is a graduate student in social psychology at the Universityof California, Los Angeles. David O. Sears is Professor of Psychologyand Political Science at UCLA. Richard Centers is Professorof Psychology at UCLA. This research was supported in part byGrant #SOC73-09153 A03 from the National Science Foundationto David O. Sears. The authors wish to expess their thanks toMark Williams, who did most of the coding and checking involvedin the survey.     

How Autonomous are Public Corporations in Hong Kong? The Case of the Airport Authority

This article examines the degree of autonomy of Hong Kong’s Airport Authority (AA). Traditionally, airport management in Hong Kong was the responsibility of the government’s Civil Aviation Department. The AA was established in 1995 to manage the new international airport at Chek Lap Kok which opened in 1998. It was expected to run as a commercial enterprise but, because of its strategic importance, there was concern that it should not become an independent empire detached from government control. As a hybrid type of organization, the AA has moved steadily towards full commercialization. The government has also put its privatization on the agenda. Its transformation may have some implications for the redefinition of the future autonomy boundaries of public corporations in Hong Kong.

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Generalised Rank Regression Estimator with Standard Error Adjusted Lasso

One of the standard variable selection procedures in multiple linear regression is to use a penalisation technique in least‐squares (LS) analysis. In this setting, many different types of penalties have been introduced to achieve variable selection. It is well known that LS analysis is sensitive to outliers, and consequently outliers can present serious problems for the classical variable selection procedures. Since rank‐based procedures have desirable robustness properties compared to LS procedures, we propose a rank‐based adaptive lasso‐type penalised regression estimator and a corresponding variable selection procedure for linear regression models. The proposed estimator and variable selection procedure are robust against outliers in both response and predictor space. Furthermore, since rank regression can yield unstable estimators in the presence of multicollinearity, in order to provide inference that is robust against multicollinearity, we adjust the penalty term in the adaptive lasso function by incorporating the standard errors of the rank estimator. The theoretical properties of the proposed procedures are established and their performances are investigated by means of simulations. Finally, the estimator and variable selection procedure are applied to the Plasma Beta‐Carotene Level data set.     

Improving Risk Management: From Lame Excuses to Principled Practice

The three classic pillars of risk analysis are risk assessment (how big is the risk and how sure can we be?), risk management (what shall we do about it?), and risk communication (what shall we say about it, to whom, when, and how?). We propose two complements as important parts of these three bases: risk attribution (who or what addressable conditions actually caused an accident or loss?) and learning from experience about risk reduction (what works, and how well?). Failures in complex systems usually evoke blame, often with insufficient attention to root causes of failure, including some aspects of the situation, design decisions, or social norms and culture. Focusing on blame, however, can inhibit effective learning, instead eliciting excuses to deflect attention and perceived culpability. Productive understanding of what went wrong, and how to do better, thus requires moving past recrimination and excuses. This article identifies common blame‐shifting “lame excuses” for poor risk management. These generally contribute little to effective improvements and may leave real risks and preventable causes unaddressed. We propose principles from risk and decision sciences and organizational design to improve results. These start with organizational leadership. More specifically, they include: deliberate testing and learning—especially from near‐misses and accident precursors; careful causal analysis of accidents; risk quantification; candid expression of uncertainties about costs and benefits of risk‐reduction options; optimization of tradeoffs between gathering additional information and immediate action; promotion of safety culture; and mindful allocation of people, responsibilities, and resources to reduce risks. We propose that these principles provide sound foundations for improving successful risk management.     

Global liberalization on homosexuality: Explaining the African gap

We are trapped in two divergent worlds when it comes to global views on homosexuality. There is the liberal world epitomized by Spain and other nations, where homosexuality is increasingly accepted; gays and lesbians are claiming their human rights; and laws are changing to codify that transformation. The second is the extremely anti-gay world symbolized by Africa, the Middle East and parts of Asia, where attitudes are favorable to criminalization. This research explains the “African Gap” in attitudes toward homosexuality in a comparative analysis of six African nations and Argentina and Canada, South and North America's most liberal nations on gay rights. Using Pew's 2015 Spring Global Attitudes Survey data, we find that the major variables have essentially similar effects on opinion in any context. Africa's distinction is explained by its comparatively higher levels of factors such as religion, morality dogma, and low socioeconomic status that generally retard support for homosexuality, at the same time of lower levels of factors such as education, urbanization, and personal liberty that increase gay support. Africa's extreme anti-gay outlook is mutable. Two social forces will facilitate this softening: expansion of liberalizing agents such as education and urbanization, and repositioning away from “traditionalism” toward modernism emphasizing individualism, civil rights, and personal liberties.     

Practical procedures to deal with common support problems in matching estimation

This paper assesses the performance of common estimators adjusting for differences in covariates, such as matching and regression, when faced with the so-called common support problems. It also shows how different procedures suggested in the literature affect the properties of such estimators. Based on an empirical Monte Carlo simulation design, a lack of common support is found to increase the root-mean-squared error of all investigated parametric and semiparametric estimators. Dropping observations that are off support usually improves their performance, although the magnitude of the improvement depends on the particular method used.