Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Partial Identification of Economic Mobility: With an Application to the United States

Abstract

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong assumptions, and often require data from more than two periods. In this article, we investigate what can be learned about economic mobility as measured via transition matrices while formally accounting for measurement error in a reasonably transparent manner. To do so, we develop a nonparametric partial identification approach to bound transition probabilities under various assumptions on the measurement error and mobility processes. This approach is applied to panel data from the United States to explore short-run mobility before and after the Great Recession.     

Radical Distrust: Are Economic Policy Attitudes Tempered by Social Trust?

Social Indicators Research - Debates about the appropriate role of markets and governments are often shaped by sharply contrasting opinions. Based on individual data from the World Values Survey...     

Decision-making: from neuroscience to neuroeconomics—an overview

Theory and Decision -     

Book Review: Restless Nation: Starting Over in America. By James M. Jasper. Chicago: University of Chicago Press, 2000. 296 pages,hardcover, ISBN: 0-226-39478-6

Qualitative Sociology -     

Nichts genaues weiss man nicht!?

What do voters really know about party platforms and how do they perceive the contents? Are there any relationships between party election platforms and electoral behavior? Despite of much research on parties, there are hardly any answers to these questions. If political parties devise programmes in order to influence political attitudes or electoral behavior, it will be necessary that these programmes are read by people. But it seems to be unclear if and how people do so. This article shows clearly that voters don’t know much about party manifestoes. Still, programmes are more important for voters than many people believe. Programmes are also an important factor for electoral behavior. But there is still a lack of data to get evident results.     

The Relationship Between Sense of Coherence and Career Thoughts

The authors examined the impact of sense of coherence on the career thought processes of a sample of college students. A. Antonovsky (1987) defined sense of coherence as the global orientation that the world is comprehensible, manageable, and meaningful. Study participants completed the Sense of Coherence Scale (A. Antonovsky, 1987) and the Career Thoughts Inventory (CTI; J. P. Sampson, G. W. Peterson, J. G. Lenz, R. C. Reardon, & D. E. Saunders, 1996). Multivariate tests indicated a medium relationship between sense of coherence and CTI Total and subscale scores; sense of coherence accounted for 14% of the variance. Univariate tests also indicated a medium relationship between sense of coherence and each subscale.     

TOOLS OR TOYS? THE IMPACT OF HIGH TECHNOLOGY ON SCHOLARLY PRODUCTIVITY

We examine the impact of communication technology on scholarly productivity by considering patterns of coauthored economics articles. Using articles in three major economics journals from 1970–79 and 1992–96, we find (1) sharp growth from distant coauthorships (authors not in the same metropolitan area prior to publication), as the theory predicts, and, contrary to theory, (2) lower productivity of distant than close-coauthored works and no decline in their relative disadvantage. These findings are reconciled by noting that high technology has aspects of a consumer good. The relative productivity of solo-authored articles has decreased, perhaps explaining the secular increase in coauthorship.     

Media Publicity and the Voluntary Sector: The Case of Nonprofit Organizations in New York City

Media publicity is an important resource for contemporary voluntary associations, but very little is actually known about the resources and organizational characteristics that are most important for getting media attention. To address this question, we collected and analyzed data on the organizational attributes and news publicity of 739 nonprofit organizations in New York City. We find that an organization's income, paid staff, membership size, and library resources are significantly related to getting media publicity, whereas the number of chapter affiliations is inversely related to publicity. Association type is also a significant factor that influences an organization's ability to get publicity. We discuss the implications that these findings have for current debates about advocacy and civic engagement in the nonprofit sector.