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Labour market integration of immigrants: estimating local authority effects

Abstract

We propose a method for computing indicators of the relative success of local authorities at integrating new immigrants in the labour market, taking account of differences in characteristics of immigrants and local labour markets. The indicator for integration success is based on mean duration from date of residence permit to start of an employment spell. We apply this method to a Danish administrative dataset. Correcting for differences in immigrant characteristics and local labour market conditions has an important impact on the estimated ranking of local authorities. We investigate how robust the ranking is with respect to changes in the specification of criteria for integration success and controls.

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Notes

  1. 1.

    The commuting area of a given municipality is defined from a restriction on commuting costs: if typical commuting costs from the municipality to other municipalities are below a certain level, these other municipalities are part of the commuting area. The cost restriction applied for this analysis corresponds to a typical commuting distance of about 50 km each way. However, when ferry transportation or crossing a bridge with toll is needed, costs are higher and the maximum distance shorter.

  2. 2.

    Due to our treatment of persons who move (a person who moves from one municipality to another appears with two spells, etc.), this number is higher than the actual number of people in the population; 6% of the males and 8% of the females move during the sample period.

  3. 3.

    These are Iraq, Afghanistan, Turkey, Somalia, Thailand, Serbia-Montenegro, Pakistan, Iran, Russia, Lebanon, China, USA, Morocco, Bosnia, Yugoslavia, Vietnam, Poland, Sri Lanka, Syria, Rumania, Ghana, Brazil, India, Philippines, Lithuania, Ukraine, FYR of Macedonia.

  4. 4.

    These groups are ‘Other Asian countries’, ‘Other South and Central American countries’, ‘Other African countries’ and ‘Other countries’.

  5. 5.

    In 1999, 21.8% gained their residence permit; 26.4% gained it in 2000; 29.9% in 2001 and 17.4% in 2002. Thus, only 4.5% are from 2003 (reference category), but this is because only immigrants who arrived in the first 4 months of 2003 are included in the analysis due to the restrictive success criterion of at least 6 months of employment.

  6. 6.

    In our model, favourable labour market conditions are characterised by a low unemployment rate and a large share of workplaces. An index of labour market conditions for each municipality was calculated using estimated coefficients of these two variables as weights. This index was calculated for males and females separately, and an overall index for each municipality was calculated as a weighted average of the gender-specific indices. The 10% of the municipalities with the largest value of this overall index are those with the most favourable conditions. Using the weighted average of unemployment rate and share of work places for these municipalities, we simulated the effect of ‘favourable labour market conditions’ for all immigrants in the dataset and calculated the restricted mean duration.

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Acknowledgements

We are grateful for helpful comments from three anonymous referees and the editor-in-chief Klaus F. Zimmermann. The Danish Social Science Research Council is acknowledged with gratitude for support.

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Correspondence to Eskil Heinesen.

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Responsible editor: Klaus F. Zimmermann

Appendix

Appendix

Variance of local authority effects

The variance of the Kaplan-Meier restricted mean estimate is

$$Var\left( {\hat E_{KM,t_{\max } } \left( T \right)} \right) = \sum\limits_{t_i \leqslant t_{\max } } {\left( {\int\limits_{t_i }^{t_{\max } } {\hat S_{KM} \left( t \right)dt} } \right)} ^2 \frac{{\Delta N\left( {t_i } \right)}}{{Y\left( {t_i } \right)\left( {Y\left( {t_i } \right) - \Delta N\left( {t_i } \right)} \right)}}$$

where t i , i = 1,...,n i , are discrete durations between 0 and t max (in our application months, i.e. t i  = 0, 1, 2, ... months), \(\hat S_{KM} \left( t \right)\) is the estimated Kaplan-Meier survival function, Y(t i ) is the number of spells ‘at risk’ (i.e. the number of spells not completed or right-censored at duration t i ), and ΔN(t i ) is the number of spells which are completed in the time interval after t i (i.e. in [t i , t i  + 1]); see Klein and Moeschberger (2003, chapter 4).

The variance of the restricted mean of the survival function of the estimated model, \(\hat E_{t_{\max } } \left( {T\left| x \right.} \right)\) , is estimated as follows. Given the estimates of the parameters and their covariance matrix, we draw randomly 500 parameter vectors. For each of these, we calculate for each spell its expected survival curve (given the model), then the average survival curve of all spells in each municipality and from this, the restricted mean duration of each municipality. Thus, for each municipality, we have 500 different values of the restricted mean duration. From these, we calculate for each municipality the variance \(Var\left[ {\hat E_{t_{\max } } \left( {T\left| x \right.} \right)} \right]\).

The variance of the estimated ‘residuals’ \(R_{t_{\max } } \) is estimated as the sum of the variances of the restricted mean durations of the Kaplan-Meier and model-based survival functions, respectively:

$$Var\left( {\hat R_{t_{\max } } } \right) = Var\left[ {\hat E_{KM,t_{\max } } \left( T \right)} \right] + Var\left[ {\hat E_{t_{\max } } \left( {T\left| x \right.} \right)} \right]$$

Thus, it is assumed that the covariance is equal to zero. Because it is probably positive, our variance estimate is likely to be ‘conservative’.

Descriptive statistics and estimation results for women

Table A1 shows descriptive statistics and estimation results for females.

Table A1 Descriptive statistics for controls, and estimation results; females

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Husted, L., Heinesen, E. & Andersen, S.H. Labour market integration of immigrants: estimating local authority effects. J Popul Econ 22, 909 (2009). https://doi.org/10.1007/s00148-008-0185-8

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Keywords

  • Employment of immigrants
  • Local authority effects
  • Duration analysis

JEL Classification

  • C41
  • H11
  • J61