Antwort zur Diskussion um ?Mehr Wirtschaftsstatistik in der Statistikausbildung für Volks– und Betriebswirte“

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Alcohol and drug abuse among young adults who grew up in substitute care — Findings from a Swedish national cohort study

To what extent substitute long term care modifies intergenerational transmission of substance abuse has rarely been investigated. Using register data, we followed a national cohort born 1973–1985 consisting of 1012 national adoptees, 2408 former children from long term foster care, 348/846 environmental siblings of adoptees/foster children, and 952,935 majority population peers, from their 15th birthday to age 27–35. Using Cox regression, we calculated hazard ratios (HR) for hospital care and criminality associated with illicit drug/alcohol abuse, with adjustments for socio-demographic indicators of caring families, and substance abuse in caring and birth parents.     

Strategic sustainability and financial performance: exploring abnormal returns

The ongoing empirical debate about whether SRI is associated, if anything, with subpar or surpassing financial performance is characterized by a somewhat indistinct focus and the infeasibility of tapping the full potential of existing models. By indistinct focus, we mean an analysis based on an aggregation of a myriad of SRI factors that potentially affect a firm’s financial performance. The inability of taking full advantage of existing models is reflected by the fact that studies with European data have not been able to comprehensively account for systematic risk tilts. This paper presents a portfolio analysis that overcomes these issues by analyzing a distinct selection of small and innovative firms. We argue that both their strategic implementation of Corporate Social Responsibility and the general growth in socially responsible investments (SRI) lend themselves to an explanation for positive abnormal returns of this portfolio. We account for the idiosyncratic investment style of SRI by introducing a comprehensive pan-European risk-adjusted portfolio analysis based on the Carhart four-factor model. A novel propensity score matching method in conjunction with the estimation of structural models completes the conventional robustness checks in the literature.     

A continuous-time Markov chain approach with the analytic likelihood in studies of behavioral changes

Abstract

We develop an analytic likelihood approach for a four-state CTMC by solving the backwards Kolmogorov differential equations, reducing this bias in transition rate estimates. A simulation study is performed to assess the performance of this new method and confirms that it achieves good coverage probabilities with low bias and standard errors. Finally, we analyzed data from Project SUCCESS to estimate the study each participant’s transitions among behavioral stage, consisting of risky drinking and possible ineffective using of contraception, which comprise the primary endpoint of risk of an alcohol-exposed pregnancy.     

Is poverty multidimensional? A comparison of income and asset based measures in five Southern African countries.

This paper contests the major emphasis placed on the multidimensional nature of poverty measurement. Instead, it argues that poverty pictures created by different measures and at different units of analysis tend to converge. This argument is derived from a comparison of poverty pictures created using income and asset-based measures at the national and household level in five South African Development Community countries. Although multidimensional measures have value in illuminating subtle differences, the findings indicate a single dimension of poverty that runs throughout all the measures and levels of analysis. However, despite the single poverty picture provided by different measures, the abandonment of these different measures is not supported. Multidimensional measures provide insight into particular elements of poverty that is useful and relevant to poverty interventions.     

Cognitive and Motivational Biases in Decision and Risk Analysis

Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research.     

Alte Herren,junge Projekte und Praxisbezug

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