Project Proposal

1
Youth unemployment and residential location

• Project Proposal
• Elina Dayanova Gregor Vulturius

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!