Comparative Advantage, Learning, and Sectoral Wage Determination

We develop a model in which a worker's skills determine the worker's current wage and sector. Both the market and the worker are initially uncertain about some of the worker's skills. Endogenous wage changes and sector mobility occur as labor-market participants learn about these unobserved skills. We show how the model can be estimated using non-linear instrumental-variables techniques. We then apply our methodology to study the wages and allocation of workers across occupations and across industries. For both occupations and industries, we find that high-wage sectors employ high-skill workers and offer high returns to workers' skills. Estimates of these sectoral wage differences that do not account for sector-specific returns are therefore misleading. We also suggest further applications of our theory and methodology.

Comparative Advantage, Learning, and Sectoral Wage Determination

We develop a model in which a worker's skills determine the worker's current wage and sector. Both the market and the worker are initially uncertain about some of the worker's skills. Endogenous wage changes and sector mobility occur as labor-market participants learn about these unobserved skills. We show how the model can be estimated using non-linear instrumental-variables techniques. We then apply our methodology to study the wages and allocation of workers across occupations and across industries. For both occupations and industries, we find that high-wage sectors employ high-skill workers and offer high returns to workers' skills. Estimates of these sectoral wage differences that do not account for sector-specific returns are therefore misleading. We also suggest further applications of our theory and methodology. Dans cet article, nous cherchons à développer un modèle par lequel le salaire d'un travailleur est fonction de ses qualifications. Le marché ainsi que le travailleur sont au préalable dans l'incertitude quant à certaines de ces qualifications. L'endogénéité à la fois des changements de salaire et des décisions de changements du secteur d'affiliation résulte du processus d'apprentissage relié aux qualifications du travailleur. Nous montrons ensuite comment le modèle peut être estimé par les méthodes des variables instrumentales non-linéaires. Nous appliquons notre méthodologie à l'étude des salaires et de l'allocation des travailleurs aux différentes occupations et industries. Nous trouvons que les secteurs à salaires élevés emploient des travailleurs ayant davantage de qualifications et que ces secteurs rémunèrent ces qualifications à un taux supérieur relativement aux secteurs à faibles salaires. Les estimés des rendements associés aux qualifications qui ne tiennent pas compte du fait que les rendements diffèrent d'un secteur à un autre sont par conséquent erronés. Nous proposons enfin d'autres applications possibles de notre méthodologie.comparative advantage, learning, non-linear instrumental variables, avantages comparés, apprentissage, variables instrumentales

Independent component analysis of interictal fMRI in focal epilepsy: comparison with general linear model-based EEG-correlated fMRI

The general linear model (GLM) has been used to analyze simultaneous EEG–fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of independent components analysis (ICA) and EEG-based GLM analyses of fMRI data from eight patients with focal epilepsy. Spatial ICA was used to extract independent components (IC) which were automatically classified as either BOLD-related, motion artefacts, EPI-susceptibility artefacts, large blood vessels, noise at high spatial or temporal frequency. The classifier reduced the number of candidate IC by 78%, with an average of 16 BOLD-related IC. Concordance between the ICA and GLM-derived results was assessed based on spatio-temporal criteria. In each patient, one of the IC satisfied the criteria to correspond to IED-based GLM result. The remaining IC were consistent with BOLD patterns of spontaneous brain activity and may include epileptic activity that was not evident on the scalp EEG. In conclusion, ICA of fMRI is capable of revealing areas of epileptic activity in patients with focal epilepsy and may be useful for the analysis of EEG–fMRI data in which abnormalities are not apparent on scalp EEG

Conceptual models for describing virtual worlds

A conceptual model of a virtual world is a high-level representation of how the objects behave and how they are related to each other. The conceptual models identify the most essential elements of the reality to be simulated. This is the first and a very important step in the process of designing a virtual world. Afterwards, specific and complex models can be implemented and inserted into these conceptual models. This paper provides an overview of existing conceptual models used to design virtual worlds. A number of existing frameworks and architecture for describing virtual worlds are classified into six kinds of conceptual models: unstructured, graphic-oriented, network-oriented, object-oriented, environment-oriented and relational graph-oriented representations. The advantages and issues regarding virtual world design, management, reusability and interoperability are discussed

How Perpetrator Identity (Sometimes) Influences Media Framing Attacks as “Terrorism” or “Mental Illness”

Do media frame attacks with Muslim perpetrators as “terrorism” and attacks with White perpetrators as the result of “mental illness”? Despite public speculation and limited academic work with relatively small subsets of cases, there have been no systematic analyses of potential biases in how media frame terrorism. We addressed this gap by examining the text of print news coverage of all terrorist attacks in the United States between 2006 and 2015. Controlling for fatalities, affiliation with a group, and existing mental illness, the odds that an article references terrorism are approximately five times greater for a Muslim versus a non-Muslim perpetrator. In contrast, the odds that an article references mental illness do not significantly differ between White and non-White perpetrators. Results partially confirm public speculation and are robust against numerous alternative explanations. Differences in media framing can influence public (mis)perceptions of violence and threats, and ultimately harm counterterrorism policy

When Data Do Not Matter: Exploring Public Perceptions of Terrorism

Public perceptions of terrorism are out of line with reality. How can perceptions be changed? Using a 4 × 2 experimental design with a national sample of U.S. adults, we examine how source of information and details provided impact views of terrorism. Sources, details, and individual-level factors—Islamophobia, trust in media, and trust in science—impact perceived accuracy of terrorism data. Many people updated their views on terrorism after reading factual information, yet only trust in science was related with this change. In short, people can be persuaded by factual information on terrorism, but it is less clear why they change beliefs

Why Do Some Terrorist Attacks Receive More Media Attention Than Others?

Terrorist attacks often dominate news coverage as reporters seek to provide the public with information. Yet, not all incidents receive equal attention. Why do some terrorist attacks receive more media coverage than others? We argue that perpetrator religion is the largest predictor of news coverage, while target type, being arrested, and fatalities will also impact coverage. We examined news coverage from LexisNexis Academic and CNN.com for all terrorist attacks in the United States between 2006 and 2015 (N=136). Controlling for target type, fatalities, and being arrested, attacks by Muslim perpetrators received, on average, 357% more coverage than other attacks. Our results are robust against a number of counterarguments. The disparities in news coverage of attacks based on the perpetrator’s religion may explain why members of the public tend to fear the “Muslim terrorist” while ignoring other threats. More representative coverage could help to bring public perception in line with reality