Policies to Create and Destroy Human Capital in Europe

Trends in skill bias and greater turbulence in modern labor markets put wages and employment prospects of unskilled workers under pressure. Weak incentives to utilize and maintain skills over the life-cycle become manifest with the ageing of the population. Reinvention of human capital policies is required to avoid increasing welfare state dependency among the unskilled and to reduce inefficiencies in human capital formation. Policy makers should acknowledge strong dynamic complementarities in skill formation. Investments in the human capital of children should expand relative to investment in older workers. There is no trade-off between equity and efficiency at early ages of human development but there is a substantial trade-off at later ages. Later remediation of skill deficits acquired in early years is often ineffective. Active labor market and training policies should therefore be reformulated. Skill formation is impaired when the returns to skill formation are low due to low skill use and insufficient skill maintenance later on in life. High marginal tax rates and generous benefit systems reduce labor force participation rates and hours worked and thereby lower the utilization rate of human capital. Tax-benefit systems should be reconsidered as they increasingly redistribute resources from outsiders to insiders in labor markets which is both distortionary and inequitable. Early retirement and pension schemes should be made actuarially fairer as they entail strong incentives to retire early and human capital is thus written off too quickly.family policy, (non)cognitive skills, returns to education, inequality, dynamic complementarity, training, retirement, labor supply, human capital, skill formation, training policy, active labor market policy, tax, pension, benefit systems, welfare state

On-line nonparametric regression to learn state-dependent disturbances

A combination of recursive least squares and weighted least squares is made which can adapt its structure such that a relation between in- and output can he approximated, even when the structure of this relation is unknown beforehand.\ud This method can adapt its structure on-line while it preserves information offered by previous samples, making it applicable in a control setting. This method has been tested with compntergenerated data, and it b used in a simulation to learn the non-linear state-dependent effects, both with good success

Phase correction for Learning Feedforward Control

Intelligent mechatronics makes it possible to compensate for effects that are difficult to compensate for by construction or by linear control, by including some intelligence into the system. The compensation of state dependent effects, e.g. friction, cogging and mass deviation, can be realised by learning feedforward control. This method identifies these disturbing effects as function of their states and compensates for these, before they introduce an error. Because the effects are learnt as function of their states, this method can be used for non-repetitive motions. The learning of state dependent effects relies on the update signal that is used. In previous work, the feedback control signal was used as an error measure between the approximation and the true state dependent effect. If the effects introduce a signal that contains frequencies near the bandwidth, the phase shift between this signal and the feedback signal might seriously degenerate the performance of the approximation. The use of phase correction overcomes this problem. This is validated by a set of simulations and experiments that show the necessity of the phase corrected scheme

On Using a Support Vector Machine in Learning Feed-Forward Control

For mechatronic motion systems, the performance increases significantly if, besides feedback control, also feed-forward control is used. This feed-forward part should contain the (stable part of the) inverse of the plant. This inverse is difficult to obtain if non-linear dynamics are present. To overcome this problem, learning feed-forward control can be applied. The properties of the learning mechanism are of importance in this setting. In the paper, a support vector machine is proposed as the learning mechanism. It is shown that this mechanism has several advantages over other learning techniques when applied to learning feed-forward control. The method is tested with simulation

Pruning Error Minimization in Least Squares Support Vector Machines

The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an /spl epsi/-insensitive cost function, meaning that errors smaller than /spl epsi/ remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsparse solution is obtained. The sparseness is imposed by pruning, i.e., recursively solving the approximation problem and subsequently omitting data that has a small error in the previous pass. However, omitting data with a small approximation error in the previous pass does not reliably predict what the error will be after the sample has been omitted. In this paper, a procedure is introduced that selects from a data set the training sample that will introduce the smallest approximation error when it will be omitted. It is shown that this pruning scheme outperforms the standard one

Psychometric Properties of Questionnaires on Functional Health Status in Oropharyngeal Dysphagia: A Systematic Literature Review

Introduction. Questionnaires on Functional Health Status (FHS) are part of the assessment of oropharyngeal dysphagia. Objective. To conduct a systematic review of the literature on the psychometric properties of English-language FHS questionnaires in adults with oropharyngeal dysphagia. Methods. A systematic search was performed using the electronic databases Pubmed and Embase. The psychometric properties of the questionnaires were determined based on the COSMIN taxonomy of measurement properties and definitions for health-related patient-reported outcomes and the COSMIN checklist using preset psychometric criteria. Results. Three questionnaires were included: the Eating Assessment Tool (EAT-10), the Swallowing Outcome after Laryngectomy (SOAL), and the Self-report Symptom Inventory. The Sydney Swallow Questionnaire (SSQ) proved to be identical to the Modified Self-report Symptom Inventory. All FHS questionnaires obtained poor overall methodological quality scores for most measurement properties. Conclusions. The retrieved FHS questionnaires need psychometric reevaluation; if the overall methodological quality shows satisfactory improvement on most measurement properties, the use of the questionnaires in daily clinic and research can be justified. However, in case of insufficient validity and/or reliability scores, new FHS questionnaires need to be developed using and reporting on preestablished psychometric criteria as recommended in literature

The asymptotic structure of nearly unstable non-negative integer-valued AR(1) models

This paper considers non-negative integer-valued autoregressive processes where the autoregression parameter is close to unity. We consider the asymptotics of this `near unit root' situation. The local asymptotic structure of the likelihood ratios of the model is obtained, showing that the limit experiment is Poissonian. To illustrate the statistical consequences we discuss efficient estimation of the autoregression parameter and efficient testing for a unit root.Comment: Published in at http://dx.doi.org/10.3150/08-BEJ153 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm