Einstellungen zur sozialen Mindestsicherung

Within the debate about the future of the welfare state a guaranteed social minimum income is often discussed as an alternative to the current social security system. One objection to such a proposal argues that a social minimum income lacks social acceptance and normative recognition. Starting from the thesis that the standard instruments of survey research have little to offer for giving a detailed account of the attitudes towards a social minimum, we deploy the more complex method of Factorial Survey Design. The study sample consists of 121 employed persons. Within the study, the focus is on the criteria used and the differentiations made when people are asked to evaluate a just minimum income. It can be shown that a uniform and flat-rate social minimum possesses less legitimacy than a transfer system that differentiates entitlements according to the beneficiaries relation to the employment sphere and certain need criteria. In addition, we find support for the idea of low income subsidies as suggested by the negative income concept. Though the results give evidence of the normative attraction of a social minimum, it is also apparent that the social judgments of the respondents are influenced by in the normative principles of the existing social security arrangements.     

Time-Series Evidence on the Nonlinearity Hypothesis for Public Spending

Barro-type endogenous growth models propose a nonmonotonic relationship between productive public spending and growth. Under this so-called nonlinearity hypothesis the size and direction of growth effects due to an increase in public spending depend on the share of public spending in GDP. Employing German time-series data we examine the validity of the nonlinearity hypothesis. We estimate growth effects by using models whose coefficients are allowed to vary with the share of public spending in GDP. Our results support the hypothesis for public consumption but not for public investment data. (JEL H54 , E62 , C22 )     

Make or buy? That’s the question!

The author discusses the question if and in which HR processes, tools or programs the implementation of an internal coach pool makes sense or where companies should continue to buy this service from the external market. For this purpose the article introduces a definition of internal Coaching. The central concept of this outline is a blueprint of an offering of internal or external coaching. The author presents a generic model for the analyzing the risks of such a project, which increases the probability of a decision which leads in the right direction. The author concludes that internal coaching makes sense in specific settings e.g. in order to ensure the ROI of learning opportunities. If this however in every situation has to be called “Coaching” should be discussed in the context of another essay.     

Bayesian inference for generalized additive mixed models based on Markov random field priors

Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with fixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.     

Derivation of the theoretical autocovariance and autocorrelation function of autoregessive moving average processes

Closed form expressions for the theoretical autocovariance and autocorrelation function of mixed autoregressive moving average processes are presented. The results provide insight into the construction of autocovariances and autocorrelatians and are useful in theoretical analysis, model identification as well as in implementing maximum likelihood estimation algorithms.