Early Life Health and Adult Earnings: Evidence from a Large Sample of Siblings and Twins

We study the relationship between early life health and adult earnings using a unique dataset that covers almost the entire population of Swedish males born between 1950 and 1970. The health information is obtained from medical examinations during the mandatory military enlistment tests at age 18, which we have further linked to register data on adult earnings. We find that most types of major diagnoses have long-run effects on future earnings with the largest effects resulting from mental conditions. Including sibling fixed effects or twin-pair fixed effects reduces the magnitudes of the estimates, although remaining substantial.earnings, health, specific conditions, siblings, twins

Does Early Life Health Predict Schooling Within Twin Pairs?

A large number of studies in labor economics estimate the returns to schooling using data on monozygotic twins, under the assumption that educational attainment is random within twin pairs. This exogeneity assumption has been commonly questioned, however, but there is to date little evidence on the topic. Using a large dataset of twins, including comprehensive information on their health status at the age of 18 and later educational attainment, we investigate whether educational attainment is related to early health status within monozygotic twin pairs. In general, we obtain no indication of this being so. As a result, we find little evidence that early health differences between twins would bias the estimates of the returns to schooling available in the literature.twins, twin-fixed effects, schooling, returns to schooling, ability bias, health

A standardization approach to Virtual Commissioning strategies in complex production environments

The ongoing industrial revolution puts high demands on the component manufacturers and suppliers to meet the tough requirements set by the development industries to follow the technological advancement of highly digitalized factories with more future-oriented applications as Virtual Commissioning for cyber-physical systems. This paper provides a production system lifecycle assessment regarding the technical specification strategies using Virtual Commissioning for implementation and integration of new systems or plants and its predicted future challenges. With the use of standards and a common language practice between a purchaser/contractor procurement situation and across the different technical disciplines internally and externally, the implementation strategies is reiterated to achieve a new sustainable business model. The paper investigates different types of production systems and how a defined classification framework of different levels of Virtual Commissioning can connect the implementation requirements to a desired solution. This strategy includes aspects of standardization, communication, process lifecycle, and predicted cost parameters

An evolutionary developmental approach to cultural evolution

Evolutionary developmental theories in biology see the processes and organization of organisms as crucial for understanding the dynamic behavior of organic evolution. Darwinian forces are seen as necessary but not sufficient for explaining observed evolutionary patterns. We here propose that the same arguments apply with even greater force to culture vis-à-vis cultural evolution. In order not to argue entirely in the abstract, we demonstrate the proposed approach by combining a set of different models into a provisional synthetic theory, and by applying this theory to a number of short case studies. What emerges is a set of concepts and models that allow us to consider entirely new types of explanations for the evolution of cultures. For example we see how feedback relations - both within societies and between societies and their ecological environment - have the power to shape evolutionary history in profound ways. The ambition here is not to produce a definite statement on what such a theory should look like but rather to propose a starting point along with an argumentation and demonstration of its potential

White supremacists anonymous: how digital media emotionally energize far-right movements

Digital media platforms have been implicated in the recent rise of far-right extremism. This study proposes that these platforms afford emotional processes that lie at the core of far-right movements. Drawing on Randall Collins’ interactional framework and the literature on cultural trauma, we investigate the emotional processes triggered by traumatic experiences within far-right online communities. As a case, we examine how the white supremacist community Stormfront responded to the 2008 election of Barack Obama, by analyzing the complete datasets of discussion on the forum through a combination of computational methods and qualitative analysis. Our findings suggest that the community functioned as a “emotional refuge”, where members collectively interpreted and transformed their emotional reactions, thereby shaping an emotionally energized collective with a focused target of collective action

A framework concept for data visualization and structuring in a complex production process

This paper provides a concept study for a visual interface framework together with the software Sequence Planner for implementation on a complex industrial process for extracting process information in an efficient way and how to make use of a lot of data to visualize it in a standardized human machine interface for different user perspectives. The concept is tested and validated on a smaller simulation of a paint booth with several interconnected and supporting control systems to prove the functionality and usefulness in this kind of production system.The paper presents the resulting five abstraction levels in the framework concept, from a production top view down to the signal exchange between the different resources in one production cell, together with additional features. The simulation proves the setup with Sequence Planner and the visual interface to work by extract and present process data from a running sequence

Slope and generalization properties of neural networks

Neural networks are very successful tools in for example advanced classification. From a statistical point of view, fitting a neural network may be seen as a kind of regression, where we seek a function from the input space to a space of classification probabilities that follows the "general" shape of the data, but avoids overfitting by avoiding memorization of individual data points. In statistics, this can be done by controlling the geometric complexity of the regression function. We propose to do something similar when fitting neural networks by controlling the slope of the network. After defining the slope and discussing some of its theoretical properties, we go on to show empirically in examples, using ReLU networks, that the distribution of the slope of a well-trained neural network classifier is generally independent of the width of the layers in a fully connected network, and that the mean of the distribution only has a weak dependence on the model architecture in general. The slope is of similar size throughout the relevant volume, and varies smoothly. It also behaves as predicted in rescaling examples. We discuss possible applications of the slope concept, such as using it as a part of the loss function or stopping criterion during network training, or ranking data sets in terms of their complexity

Exact spectral norm regularization for neural networks

We pursue a line of research that seeks to regularize the spectral norm of the Jacobian of the input-output mapping for deep neural networks. While previous work rely on upper bounding techniques, we provide a scheme that targets the exactspectral norm. We showcase that our algorithm achieves an improved generalization performance compared to previous spectral regularization techniques while simultaneously maintaining a strong safeguard against natural and adversarialnoise. Moreover, we further explore some previous reasoning concerning the strong adversarial protection that Jacobian regularization provides and show that it can be misleading

Fama-Frenchs trefaktormodell och CAPM

I den här uppsatsen testas Fama-Frenchs trefaktormodell och ”Capital Asset Pricing Model” för att jämföra vilken av modellerna som bäst förklarar avkastningen på OMX-Stockholmsbörsen. Aktiedata från ett urval av Large cap och Small cap aktier analyseras under åren 2010-2015. Hur väl modellerna fungerar avgörs med en regressionsanalys, där riskpremierna RMRF, SMB och HML studeras. RMRF blev genomgående positivt signifikant medan varken SMB eller HML var särskilt relevanta för att förklara avkastningen. Resultatet tyder på att trefaktormodellen förklarar avkastningen i lika hög utsträckning som CAPM men eftersom trefaktormodellen använder ytterligare två variabler skulle modellen kunna betraktas som överflödig. Slutsatsen blir att en passiv investeringsstrategi, likt CAPM, är att föredra framför en aktiv investeringsstrategi som utgår ifrån Fama-Frenchs trefaktormodell