Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

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Appendix Tables and Figures

Table A.1:

Cohort effects

article image
Note: The dependent variable is the probability of being employed. Robust standard errors are clustered at the regional level.
Table A.2:

Quality of employment

article image
Note: The dependent variable is the probability of being employed. Robust standard errors are clustered at the regional level.
Figure A.1:
Figure A.1:
Figure A.1:
Figure A.1:
Figure A.1:
Figure A.1:
Figure A.1:
Figure A.1:

Simulation of Employment Paths for Migrants from Individual Host Countries

Citation: IMF Working Papers 2018, 232; 10.5089/9781484381168.001.A999

Figure A.2:
Figure A.2:
Figure A.2:
Figure A.2:

Effects of Initial Conditions, Alternative Measures

Citation: IMF Working Papers 2018, 232; 10.5089/9781484381168.001.A999

1

We are grateful to Eurostat for providing us with micro data from the Labor Force Survey. The results and conclusions are ours and not those for Eurostat, The European Commission, or any of the national authorities whose data have been used. We thank Arnout Baeyens, Craig Beaumont, Helge Berger, Dilyana Dimova, Florence Jaumotte, Davide Malacrino, Celine Piton, Anna Raggl, Antonio Spilimbergo, and Petia Topalova for helpful comments and suggestions. The paper has also benefited from comments by seminar participants at the Swedish Ministry of Finance, the IMF’s European Department, the 2017 World Bank-IMF Annual Meetings’ Analytical Corner, and the Offices of Executive Directors for Austria, Belgium, and the United Kingdom at the IMF. All remaining errors are our own.

2

For the importance of achieving a successful integration of migrants in Europe against the background of the demographic challenges, see Cuaresma, Huber, Oberdabernig, and Raggl (2015) and Cuaresma, Huber, and Raggl (2015).

3

While we do not attempt to identify refugees in our dataset (Cortes, 2004), we provide a special focus on Middle East and North Africa, which is the region from which most asylum seekers originated during the recent refugee crisis in Europe.

4

Data for Germany are in EU LFS but were not made available for this research. Data for Ireland and Italy is available as of 2010.

5

EUR-NA also includes Australia and New Zealand.

6

Recent work by Liebig and Tronstad (2018) suggests a role for effective labor and education policies to mitigate this. We thank Celine Piton and Arnout Baeyens for this comment.

7

Assigning a value of 0 to the variable Ysm (year since migration in equation 1) to natives implies that natives are used as a “control group” for those migrants who just migrated. This could introduce some bias in the estimated coefficients that translates in an overstatement of the initial estimated difference in terms of labor market outcomes between natives and migrants. The option to assign a very large value to Ysm for natives leads to perfect correlation of Ysm with migrants M. Another option that allows for variability in natives’ Ysm could arguably be to use their age as it measures the time they spent in the host country, but this variable would also not be suitable as a control.

8

We expect this result to be affected by the presence of children in the household, which could be particularly relevant for women. While the number of children is in principle included in the LFS, it is not available for most countries in our sample across years.

9

While in principle cohort quality can change within a decade, we group cohorts in such a way to have a manageable number of dummy variables.

10

We thank Davide Malacrino and Anna Raggl for suggesting examining network effects.

11

For France, Ireland, Portugal, and the UK, these data are either not available or missing for the majority of years considered.

13

We are grateful to Anna Raggl for this suggestion.

The Labor Market Integration of Migrants in Europe: New Evidence from Micro Data
Author: Giang Ho and Rima Turk-Ariss