The introduction scheme in the municipalities
A comparison of the municipalities' results
This project is the first attempt in Norway to compute weighted results of the introductory programme in Norwegian municipalities. The analysis contributes to our knowledge on what affects the results of the programme.
The analysis focuses on all refugees that have a right and a duty to complete the introductory programme. The population is collected from The National Introductory Register and consists of 20,332 persons. Among these, 90 per cent were granted a residence permit in Norway in the period 2007–2011, the rest in 2006 or earlier. The majority of the refugees come from Eritrea, Somalia, Afghanistan and Iraq.
We measure the duration from when a residence permit was granted to the time the refugee become a student or enter the labour market. Employment is defined as at least 10 working hours per week, three months in a row. Because many refugees may find work or education before entering the introductory programme, this analysis measures the effect of the municipalities’ integration work on a more general basis than just the effect of the programme.
By using a Cox regression model, we find an expected duration for each refugee in the population, given the refugee’s characteristics and the conditions in the municipality where the person is settled. By aggregating the individual results up to municipality level, a municipality’s results can be compared to what the same municipality – with its refugees and conditions – is expected to achieve. This comparison is the basis for a benchmarking indicator, which in turn can be used to create a municipality ranking list.
The data shows a great variation among the municipalities. Among the 82 municipalities for which a benchmarking indicator is calculated, we find a maximum difference of 22 months when we look at the mean duration from residence permit to employment or education.
The expected durations for the refugees in the population show that there are great differences among groups in the population. For example we find that women need more time from residence permit to employment or education than men do. But it also turns out that such differences only to a small degree can explain the variation among the municipalities. After correcting for refugee characteristics and municipality conditions, the ‘best’ municipalities are still the best, and the ‘worst’ municipalities still the worst.
Other variables may explain more of the variation, especially variables that tell us something about local conditions in the municipalities. Unfortunately these types of variables can be hard to define, and the data foundation is rather weak.
The analysis provides a starting point in investigating differences among the municipalities, conditions which explain local variations, and what characterizes the ‘most efficient’ municipalities. This will contribute towards identifying solutions that may be applied in other municipalities.