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.     

A new generalized Weibull distribution in income economic inequality curves

Researchers have been developing various extensions and modified forms of the Weibull distribution to enhance its capability for modeling and fitting different data sets. In this note, we investigate the potential usefulness of the new modification to the standard Weibull distribution called odd Weibull distribution in income economic inequality studies. Some mathematical and statistical properties of this model are proposed. We obtain explicit expressions for the first incomplete moment, quantile function, Lorenz and Zenga curves and related inequality indices. In addition to the well-known stochastic order based on Lorenz curve, the stochastic order based on Zenga curve is considered. Since the new generalized Weibull distribution seems to be suitable to model wealth, financial, actuarial and especially income distributions, these findings are fundamental in the understanding of how parameter values are related to inequality. Also, the estimation of parameters by maximum likelihood and moment methods is discussed. Finally, this distribution has been fitted to United States and Austrian income data sets and has been found to fit remarkably well in compare with the other widely used income models.     

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...     

Nested exposure case-control sampling: a sampling scheme to analyze rare time-dependent exposures

For large cohort studies with rare outcomes, the nested case-control design only requires data collection of small subsets of the individuals at risk. These are typically randomly sampled at the observed event times and a weighted, stratified analysis takes over the role of the full cohort analysis. Motivated by observational studies on the impact of hospital-acquired infection on hospital stay outcome, we are interested in situations, where not necessarily the outcome is rare, but time-dependent exposure such as the occurrence of an adverse event or disease progression is. Using the counting process formulation of general nested case-control designs, we propose three sampling schemes where not all commonly observed outcomes need to be included in the analysis. Rather, inclusion probabilities may be time-dependent and may even depend on the past sampling and exposure history. A bootstrap analysis of a full cohort data set from hospital epidemiology allows us to investigate the practical utility of the proposed sampling schemes in comparison to a full cohort analysis and a too simple application of the nested case-control design, if the outcome is not rare.

     

The context of employment discrimination: interpreting the findings of a field experiment

Although field experiments have documented the contemporary relevance of discrimination in employment, theories developed to explain the dynamics of differential treatment cannot account for differences across organizational and institutional contexts. In this article, I address this shortcoming by presenting the main empirical findings from a multi‐method research project, in which a field experiment of ethnic discrimination in the Norwegian labour market was complemented with forty‐two in‐depth interviews with employers who were observed in the first stage of the study. While the experimental data support earlier findings in documenting that ethnic discrimination indeed takes place, the qualitative material suggests that theorizing in the field experiment literature have been too concerned with individual and intra‐psychic explanations. Discriminatory outcomes in employment processes seems to be more dependent on contextual factors such as the number of applications received, whether requirements are specified, and the degree to which recruitment procedures are formalized. I argue that different contexts of employment provide different opportunity structures for discrimination, a finding with important theoretical and methodological implications.     

Managing Migration: The Role of Economic Instruments

This paper reviews economic policies and instruments available to the developed countries to reduce unwanted migration from developing countries, not all of which is irregular migration. Countries generally welcome legal immigrants and visitors, try to make it unnecessary for people to become refugees and asylum seekers, and try to discourage, detect, and remove irregular foreigners. There are three major themes: 1. There are as many reasons for migration as there are migrants, and the distinction between migrants motivated by economic and non–economic considerations is often blurred. Perhaps the best analogy is to a river – what begins as one channel that can be managed with a dam can become a series of rivulets forming a delta, making migration far more difficult to manage. 2. The keys to reducing unwanted migration lie mostly in emigration countries, but trade and investment fostered by immigration countries can accelerate economic and job growth in both emigration and immigration countries, and make trading in goods a substitute for economically motivated migration. Trade and economic integration had the effect of slowing emigration from Europe to the Americas, between southern Europe and northern Europe, and in Asian Tiger countries such as South Korea and Malaysia. However, the process of moving toward freer trade and economic integration can also increase migration in the short term, producing a migration hump, and requiring cooperation between emigration and immigration destinations so that the threat of more migration does not slow economic integration and growth. 3. Aid, intervention, and remittances can help reduce unwanted migration, but experience shows that there are no assurances that such aid, intervention, and remittances would, in fact, lead migrants to stay at home. The better use of remittances to promote development, which at US$65 billion in 1999 exceeded the US$56 billion in official development assistance (ODA), is a promising area for cooperation between migrants and their areas of origin, as well as emigration and immigration countries. There are two ways that differences between countries can be narrowed: migration alone in a world without free trade, or migration and trade in an open economy. Migration will eventually diminish in both cases, but there is an important difference between reducing migration pressures in a closed or open world economy. In a closed economy, economic differences can narrow as wages fall in the immigration country, a sure recipe for an anti–immigrant backlash. By contrast, in an open economy, economic differences can be narrowed as wages rise faster in the emigration country. Areas for additional research and exploration of policy options include: (1) how to phase in freer trade, investment, and economic integration to minimize unwanted migration; (2) strategies to increase the job–creating impacts of remittances, perhaps by using aid to match remittances that are invested in job–creating ways.