Time Aggregation and State Dependence in Welfare Receipt

Dynamic discrete-choice models have been an important tool in studies of state dependence in benefit receipt. An assumption of such models is that benefit receipt sequences follow a conditional Markov process. This property has implications for how estimated period-to-period benefit transition probabilities should relate when receipt processes are aggregated over time. This paper assesses whether the conditional Markov property holds in welfare benefit receipt dynamics using high-quality monthly data from Norwegian administrative records. We find that the standard conditional Markov model is seriously misspecified. Estimated state dependence is affected substantially by the chosen time unit of analysis, with the average treatment effect of past benefit receipt increasing with the level of aggregation. The model can be improved considerably by permitting richer types of benefit dynamics: Allowing for differences between the processes for entries and persistence we find important disparities especially in terms of the effects of permanent unobserved characteristics. Extending the model further, we obtain strong evidence for duration and occurrence dependence in benefit receipt. Based on our preferred model, the month-to-month persistence probability in benefit receipt for a first-time entrant is 37 percentage points higher than the entry rate of an individual without previous benefit receipt. Over a 12-month period, the average treatment effect is about 5 percentage points.Research Council of Norway (194339) INET grant INO1200010, Institute for New Economic Thinking, Oxford Martin SchoolpublishedVersio

Comput Stat On the discovery of events in EEG data utilizing information fusion

Abstract One way to tackle brain computer interfaces is to consider event related potentials in electroencephalography, like the well established P300 phenomenon. In this paper a multiple classifier approach to discover these events in the bioelectrical signal and with them whether or not a subject has recognized a particular pattern, is employed. Dealing with noisy data as well as heavily imbalanced target class distributions are among the difficulties encountered. Our approach utilizes partitions of electrodes to create robust and meaningful individual classifiers, which are then subsequently combined using decision fusion. Furthermore, a classifier selection approach using genetic algorithms is evaluated and used for optimization. The proposed approach utilizing information fusion shows promising results (over 0.8 area under the ROC curve)

Die Wege junger Erwachsener aus dem Arbeitslosengeld-II-Bezug durch eine Arbeitsmarktintegration

The study explores routes off benefits through labour market integration for young adults in Germany. Policies for young people are focused on a rapid integration into employment or training to prevent long-term benefits dependency. The causes of long-term benefits receipt can be related to poor labour market opportunities. But in political and public discourse, long-term benefits dependency is most widely regarded as the consequence of young adults' low labour supply. The article examines the labour market transitions of 650 beneficiaries aged 18 to 24. The analysis combines survey data on beneficiaries in Germany and longitudinal register data for 2005 to 2007. Though most of the young adults surveyed enter employment or vocational training, a high percentage continues to receive benefits. Long-term benefits receipt is related to low levels of qualifications and young parenthood; there is no evidence for young people resigning themselves to benefits receipt