Unemployment and economic security for young adults in Finland,Norway and Sweden: From unemployment protection to poverty relief

In this study, we investigated if there has been a displacement in the type and coverage of welfare services available for young unemployed adults in Finland, Norway and Sweden over the last two decades. This question is important because a number of studies have argued that the generous unemployment benefits and extensive labour market intervention found in the Nordic welfare states shield young people from the most severe consequences of economic inactivity. In this article, we instead show that during this period, less generous means‐tested unemployment and social assistance benefits have become the most important form of income protection for young people. In evidence, earnings‐related unemployment benefits now cover only 10 per cent of unemployed Swedes and Finns and 45 per cent of unemployed Norwegians aged 24 years or younger. This development marks a significant change in our understanding of unemployment protection for young people in Nordic countries.     

On the Distribution of Matrix Quadratic Forms

A characterization of the distribution of the multivariate quadratic form given by X A X′, where X is a p × n normally distributed matrix and A is an n × n symmetric real matrix, is presented. We show that the distribution of the quadratic form is the same as the distribution of a weighted sum of non central Wishart distributed matrices. This is applied to derive the distribution of the sample covariance between the rows of X when the expectation is the same for every column and is estimated with the regular mean.     

Spatial robust small area estimation

The accuracy of recent applications in small area statistics in many cases highly depends on the assumed properties of the underlying models and the availability of micro information. In finite population sampling, small sample sizes may increase the sensitivity of the modeling with respect to single units. In these cases, area-specific sample sizes tend to be small such that normal assumptions, even of area means, seem to be violated. Hence, applying robust estimation methods is expected to yield more reliable results. In general, two robust small area methods are applied, the robust EBLUP and the M-quantile method. Additionally, the use of adequate auxiliary information may further increase the accuracy of the estimates. In prediction based approaches where information is needed on universe level, in general, only few variables are available which can be used for modeling. In addition to variables from the dataset, in many cases further information may be available, e.g. geographical information which could indicate spatial dependencies between neighboring areas. This spatial information can be included in the modeling using spatially correlated area effects. Within the paper the classical robust EBLUP is extended to cover spatial area effects via a simultaneous autoregressive model. The performance of the different estimators are compared in a model-based simulation study.     

Optimal Viterbi Bayesian predictive classification for data from finite alphabets

A family of Viterbi Bayesian predictive classifiers has been recently popularized for speech recognition applications with continuous acoustic signals modeled by finite mixture densities embedded in a hidden Markov framework. Here we generalize such classifiers to sequentially observed data from multiple finite alphabets and derive the optimal predictive classifier under exchangeability of the emitted symbols. We demonstrate that the optimal predictive classifier which learns from unlabelled test items improves considerably upon marginal maximum a posteriori rule in the presence of sparse training data. It is shown that the learning process saturates when the amount of test data tends to infinity, such that no further gain in classification accuracy is possible upon arrival of new test items in the long run.     

Have I seen you before? Principles of Bayesian predictive classification revisited

A general inductive Bayesian classification framework is considered using a simultaneous predictive distribution for test items. We introduce a principle of generative supervised and semi-supervised classification based on marginalizing the joint posterior distribution of labels for all test items. The simultaneous and marginalized classifiers arise under different loss functions, while both acknowledge jointly all uncertainty about the labels of test items and the generating probability measures of the classes. We illustrate for data from multiple finite alphabets that such classifiers achieve higher correct classification rates than a standard marginal predictive classifier which labels all test items independently, when training data are sparse. In the supervised case for multiple finite alphabets the simultaneous and the marginal classifiers are proven to become equal under generalized exchangeability when the amount of training data increases. Hence, the marginal classifier can be interpreted as an asymptotic approximation to the simultaneous classifier for finite sets of training data. It is also shown that such convergence is not guaranteed in the semi-supervised setting, where the marginal classifier does not provide a consistent approximation.     

Effects of labeled child benefits on family savings

Contrary to standard microeconomic principles, it is by now well understood that income is not fungible. For example, the label of a government transfer can induce individuals to make expenditure decisions that are skewed towards the label. In this paper, I show that child benefits are disproportionately used for savings assignable to children. I exploit a policy reform in a difference-in-differences approach to estimate the effect of child benefits on savings. The results suggest a significant positive effect on long-term savings and weak evidence for effects on child-assignable consumption. I conclude that labeling effects should be considered carefully by policy makers, if not for nudging individuals, then to avoid affecting decisions unintentionally.     

Supported Employment as a Paradigm Shift and a Cause of Legitimation Crisis

Supported employment in its original form represents a new service paradigm, where services are organised as individualised supports aimed at securing the presence of disabled people in their local communities. Using Finland as an example, this article analyses difficulties that arise when a new service paradigm is introduced that is incompatible with the old forms of service. In this case, the accession of Finland to the European Union brought with it an active employment policy of a marginalised workforce. Along with this development, funding for supported employment projects threatened to produce a legitimation crisis in old service organisations. The crisis was managed in two ways. First, the concept of supported employment was redefined to more accurately fit into old forms of service. Secondly the project activity was ceremonialised and its goals were made ambiguous.     

Superiority comparisons of homogeneous linear estimators

In the paper homogeneous linear estimators of the parameter vector of the general linear model are compared in terms of their MSE matrices. A necessary and sufficient condition for the difference of two MSE matrices to be positive definite is obtained and its practical existence discussed. The non-negative definiteness of the difference also receives attention, and conditions for this case are discussed. The absence of any conditions of the above type is taken into consideration as well.     

The dynamics of online news discussions: effects of news articles and reader comments on users’ involvement,willingness to participate,and the civility of their contributions*

This study investigates when and why news website visitors write civil or uncivil comments in response to news articles or related user comments. In an experiment, we manipulated the news value of news articles and the presence of ‘deliberative’ or ‘detrimental’ elements of comments to compare their impact on participants’ involvement, willingness to comment, and the comments they posted. News factors and comment characteristics increased participants’ willingness to comment via cognitive and affective involvement. Cognitive involvement made it less likely and affective involvement more likely that participants wrote uncivil comments. Additionally, involvement with previous comments increased participants’ willingness to comment on articles and vice versa. Results imply that news articles and comments dynamically shape the discussion value of online news.