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.     

Responsibilisation,social work and inclusive social security in Finland

ABSTRACT

In this article responsibilisation in social work is studied by analysing two Finnish state-level policy documents (called final report and research report) which concern a current activation initiative called inclusive social security (ISS). It is asked how social workers and clients are constructed as responsible subjects in these documents. Responsibilisation refers to the advanced liberal mode of governmentality, which aims to strengthen citizens’ abilities to self-governance through various techniques that include the intertwined elements of surveillance and empowerment. It is demonstrated that the policy documents construct the social workers’ and the clients’ responsibilities partly in different ways. The final report leads activation to be based on shared responsibility and social work to be more community-based, whereas the research report strengthens more individual-based responsibility of clients and social workers. For the clients, the interpretation of ISS based on shared responsibility would probably be less stigmatising and paternalistic than the one based on individual responsibilities, i.e. approaching long-term unemployed citizens as being personally ‘at risk’ and thus a justified target group of individualised techniques for activation. For social workers and clients, future activation appears to be a wide mix of different techniques, moral expectations and possible ways of being a responsible subject.     

Professional blinders? The novel as an eye-opener in organizational analysis

ABSTRACT

Scholarly textbooks often follow a logic where suitable empirical cases are selected to illustrate the theoretical and analytical points that we as scholars want to make. But what would happen if we would do the opposite: build a textbook on a novel written by a novelist for such purpose and let the theories explain the actions and emotions of fictional characters? In this article, we share and reflect upon our experiences of co-authoring a textbook in organization theory together with a professional novelist. We argue that the novel can function as an eye-opener in organizational analysis, forcing us to look beyond more static and rationalistic perspectives on organizations as well as the stereotypes of such. We build and relate our experiences to the growing literature about using fiction in scholarly work and discuss the potential of such genre-bending work when we bring in flesh and blood into the analyses.     

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.     

The identification of logistic regression models with errors in the variables

The simple logistic regression model with normal measurement error and normal regressor is shown to be identifiable without any extra information about the measurement error. The multiple logistic regression model with more than one regressor variable measured with error is not identifiable. If the covariance matrix of the measurement error is known up to a scalar factor, the model is identified. Further we discuss why in spite of the identifiability the models cannot be estimated in a reasonable way without extra information about the measurement error.