Heterogeneous Returns to Training: An Analysis with German Data Using Local Instrumental Variables

Empirical work on the wage impact of training has noted that unobserved heterogeneity of training participants should play a role. The expected return to training, which partly depends on unobservable characteristics, is likely to be a crucial criterion in the decision to take part in training or not. We try to account for this fact by using recent advances in estimating returns to schooling, which allow for selection on unobservables, and apply it to estimating the impact of training on earnings. Allowing heterogeneity to be unobserved by the econometrician, but assuming that individuals may act upon this heterogeneity, completely changes the interpretation and properties of commonly used estimators. Our results based on local instrumental variables suggest that traditional estimates of the wage impact of training overestimate this effect. --Vocational training,treatment effects,semiparametric estimation,Local instrumental variables

Heterogeneous Returns to Training : An Analysis with German Data Using Local Instrumental Variables

Empirical work on the wage impact of training has noted that unobserved heterogeneity of training participants should play a role. The expected return to training, which partly depends on unobservable characteristics, is likely to be a crucial criterion in the decision to take part in training or not. We try to account for this fact by using recent advances in estimating returns to schooling, which allow for selection on unobservables, and apply it to estimating the impact of training on earnings. Allowing heterogeneity to be unobserved by the econometrician, but assuming that individuals may act upon this heterogeneity, completely changes the interpretation and properties of commonly used estimators. Our results based on local instrumental variables suggest that traditional estimates of the wage impact of training overestimate this effect

What Captures Gaze in Visual Design - Insights from Cognitive Psychology

Visual information is vital for user behaviour and thus of utmost importance to design. Consequently, tracking and interpreting gaze data has been the target of increasing amounts of research in design science. This research is in part facilitated by new methods, such as eye-tracking, becoming more readily available. Visual attention is the principle mechanism that governs where we direct our gaze. Understanding the factors that influence how attention is directed is therefore necessary for understanding user intentions and gaze patterns. In this paper, we provide an overview of the characteristics and factors that have been experimentally shown to capture attention, as well as those factors that modulate the capture and direction of attention. We do so by drawing on the large body of evidence provided by cognitive psychology, as we believe this research area could potentially provide a source of untapped potential for design research and practice

Data-driven engineering design research: Opportunities using open data

Engineering Design research relies on quantitative and qualitative data to describe design-related phenomena and prescribe improvements for design practice. Given data availability, privacy requirements and other constraints, most empirical data used in Engineering Design research can bedescribed as “closed”. Keeping such data closed is in many cases necessary and justifiable. However, this closedness also hinders replicability, and thus, may limit our possibilities to test the validity and reliability of research results in the field. This paper discusses implications and applications of using the already available and continuously growing body of open data sources to create opportunities for research in Engineering Design. Insights are illustrated by an examination of two examples: a study of open source software repositories and an analysis of open business registries in the cleantech industry. We conclude with a discussion about the limitations, challenges and risks of using open data in Engineering Design research and practice