1. Project Proposal Elina Dayanova Gregor Vulturius Youth unemployment and residential location

2. Outline About the research Current studies review Methodology description Results anticipated

3. About the research Aim of the research: To evaluate the influence of residential location on youth unemployment probability Object of the research: individuals at the age of 16 to 30 years in Russia and Germany Subject of the research: influence of residential location on youth unemployment probability

4. Tasks Task 1: To distinguish regional characteristics impact on youth probability of unemployment Task 2: To distinguish education impact on youth probability of unemployment

5. Current studies review Holloway S. R., “The Role of Residential Location in Conditioning the Effect of Metropolitan Economic Structure on Male Youth Employment”, 2004 The Professional Geographer, Vol. 50 (1), p. 31 – 45 R. Breen, “Explaining Cross-national Variation in Youth Unemployment”, 2005 European Sociological Review, Vol. 21(2), p.125-134 Kieselbach, T., Beelmann, G., Erwien, B., Stitzel, A., Traiser, U. (1998): Youth Unemployment and Social Exclusion in Germany. In: Kieselbach, T. [Ed]: Youth Unemployment and Social Exclusion. A Comparison of Six European Countries. Psychology of Social Inequality, vol. 10. Pages 131-174 Lawless, Martin, Hardy (1998): Unemployment and Social Exclusion. Landscapes of Labour Inequality. Moulaert, Rodriguez, Swyngedouw (2005): The Globalized City. Economic Restructuring and Social Polarization in European Cities.

6. Methodology description Russia Longitudinal Monitoring Survey [www.cpc.unc.edu/rlms] Cross-sectional repeated sample with split-panel is used. Data on households and individuals is included separately. Amount of sampling: 4000 of households. Periods of realization: 1994, 1995, 1996, 1998, 2000-2007. Socio-economic Panel (SOEP) www.diw.de/deutsch/soep/ Cross-sectional repeated sample with split-panel is used. Data on households and individuals is included separately. Amount of sampling 11000 households with more than 20000 individuals starting from 1984

7. Methodology description Methods in use: Multinominal regression Dependent variable: Labour Market Status Independent variables: Sex Age Residential Location: Region (Russia: 7 Federal Districts Germany: 16 Federal Lands), Urban vs. Rural, Regional characteristics (average rate of unemplozment and average wage) Education (School, Vacational Training, Higher Education, Postgraduate Studies) Income (Personal Income, Spouse Income, other family members income) Work Experience (worked or not)

8. Results anticipated Youth who live in regions with high average unemployment rates are less likely to have a job Youth who live in regions with high average wage level are more likely to have a job Youth who live in the capital of the country and in regional centers in Russia as well as in Germany are more likely to have a job Youth who live in urban areas in Russia as well as in Germany are more likely to have a job than those who live in rural areas Youth with higher education level are more likely to have a job than those who have lower education level even in regions with high average level of unemployment and low average wage level

9. Results delimitations The results could be improved by including additional factors in the model, such as social ties, informal practices, etc.

10. Thank you for your attention!