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The generous Nordic model of welfare is commonly viewed as an exceptional success, in terms of both equality and economic growth. However, it recently became evident that subgroups of the population with weak labour market attachment and high welfare dependency, such as lone mothers, were vastly overrepresented among the poor. This prompted a workfare reform of the Norwegian welfare system for lone mothers: activity requirements were brought in, time limits imposed and benefit levels raised. To evaluate the reform we introduce an estimator that, unlike the much used difference-in-difference approach, accounts for the fact that policy changes are typically phased in gradually rather than coming into full effect immediately. The results were striking: the workfare reform has not only led to increased earnings and educational attainment but also reduced poverty.

The aim of this paper is to assess the quality of the ranking of institutions obtained with multilevel techniques in presence of different model misspecifications and data structures. Through a Monte Carlo simulation study, we find that it is quite hard to obtain a reliable ranking of the whole effectiveness distribution while, under various experimental conditions, it is possible to identify institutions with extreme performances. Ranking quality increases with increasing intra class correlation coefficient and/or overall sample size. Furthermore, multilevel models where the between and within cluster components of first-level covariates are distinguished perform significantly better than both multilevel models where the two effects are set to be equal and the fixed effect models.