1. Arjen Edzes, Marten Middeldorp en Jouke van Dijk University of Groningen, Department of Economic Geography, PO Box 800, 9700 AV Groningen Atlanta, NARSC, 16-11-2013 Do urban environments stimulate successful career paths? The case of school-leavers

2. | 2 Content 1.Introduction/background/motivation 2.Conceptualization of career paths 3.(Mico)Data and operationalization 3.The Case of School-leavers: descriptives and estimations 4.Conclusions

3. | 3 Introduction/background/motivation Two-year research program on ‘Dynamics on the labor market’ / Consortium of University of Groningen, Platform31, Municipalities of Amsterdam, Rotterdam, The Hague, Eindhoven, Emmen, Almelo and Provence of Groningen 1.What are the career paths of school-leavers, unemployed and working people and what are determinants of success? 2.What is the regional labour dynamics in terms of in-, through and outflow patterns in firms, branches, industries and regions and how does this relate to unemployment en job opportunities on the one hand and economic growth potential on the other hand? 3.What are the implications of successful individual career paths on the one hand and urban and regional dynamics on the other hand for a place-based labour market policy?

4. | 4 Introduction/background/motivation

5. | 5 Relevancy, theory and literature / different perspectives (1) 1.Literature (mainly sociological) on career paths focusses on cross-country differences in relation to educational systems, institutions and labor market regulation (De Lange, Gesthuizen & Wolbers, 2012; Corrales-Herrero et al., 2012; Quintini & Manfredi, 2009; Brzinsky-Fay, 2007) No connections with regional differences (within country) in labor markets and urbanity 2.Literature on the relation between skills in cities, including sorting, migration patterns, externalities and spill-overs (Edzes, Hamersma and Van Dijk, 2013; Combes, Duranton and Gobillon, 2012; Moretti, 2012; Venhorst, 2012; Niebuhr, Granato, Haas and Hamann, 2012) Analysis on the aggregate level, no connections with individual career paths 3.Labor market research is not conclusive on individual and aggregated effects of flexibility and mobility (Human Capital theory vs. segmented labor markets).

6. | 6 Research question: 1.Do urban environments stimulate successful career paths? 2.Central elements: 1.How to define and operationalize career paths? 2.How to define success? 3.Establishing links between characteristics of careers and level of urbanity and relation with success

7. | 7 Conceptualization career paths: state versus transitions How to capture the complexity of (individual and aggregated) transitions and labour relations on the labor market? 1.Career paths = sequence of social-economic statuses and transitions a)Number and direction of transitions – measure of volatility (incl. job-job) b)Duration and lengths of periods – measure of stability 2.What is success? a)Employment at end of period b)Wage at end of period

8. How to measure transitions? Starting point: socioeconomic statuses | 8 Conceptualisation – questions to answer Job #2 Job #1 Job #3 Job #4 Employee BenefitsStudent Employee BenefitsStudent Join with job data, create new statuses

9. | 9 Microdata (2006-2010) 1.Census data of Dutch inhabitants on individual characteristics (gender, age, ethnicity, household, location). We select everyone between15-65 years 2.Main Social-Economic Status: employee, director-owner, entrepreneur, recipient sickness, unemployment or disability benefit, pension, student, no income – with start and end date 3.Job(characteristics): start and end date, type, sector, business 4.Business(characteristics): size, sector, location Definition School-leavers: Every person on 1 Oct 2006 that is 15-30 years old who had in the previous nine months‘education’ as their main social-economic status, but did not in the three months after 1 October 2006

10. | 10 Microdata (2006-2010): main choices 1.Location: place of residence in October 2006 2.Transformation of start and end date of socio-economic statuses in monthly statuses 3.In case a persons hold more than one job, the one with the highest income for that month is selected 4.Wage = earned month salary in december 2010 (corrected for hours worked) 5.Cut off extreme wages (12 times higher or one fifth of the average of a person of comparable education)

11. Original – Statistics NetherlandsIn our study Employee Director/shareholderEmployee Entrepreneur Rest category: income Unemployment benefitBenefit Social assistance benefitBenefit Rest category: benefitBenefit Sickness/disability benefitBenefit Pension Student – incomeStudent Student – no incomeStudent Rest category: no incomeRest Microdata in detail (social-economic status)

12. The case of school-leavers - descriptives N% Total80.359100 GenderMale Female 39.715 40.644 49,4 50,6 Age 15-19 20-24 25-30 35.012 34.532 10.815 43,6 43,0 13,5 EducationLow (without start qualification) Middle High Unknown 24.421 33.671 17.327 5.120 30,2 41,9 21,6 6,4 Etnicity Native Immigrant, first generation Immigrant, second generation 60.761 7.705 11.893 75,6 9,6 14,8 Household Single Couple without children Couple with children Single parent with children Child living at home Unknown 11.580 8.706 1.665 478 54.057 3.873 14,4 10,8 2,1 0,6 67,3 4,8

13. The case of school-leavers - descriptives N% Regions (NUTS-3) Groningen Friesland Drenthe Overijssel Flevoland Gelderland Utrecht Noord-Holland Zuid-Holland Zeeland Noord-Brabant Limburg Unknown 3.492 3.157 1.975 5.218 1.863 9.306 6.322 13.098 17.278 1.625 10.784 4.981 1.260 4,3 3,9 2,5 6,5 2,3 11,6 7,9 16,3 21,5 2,0 13,4 6,2 1,6 Level of Urbanity Very Strong Urban Strong Urban Moderate Urban Low Urban Not Urban Unknown 19.813 22.965 13.880 15.072 7.369 1.260 24,7 28,6 17,3 18,8 9,2 1,6

14. The case of school-leavers - career start NEmployee, one job Employee, multi jobs EntrepreneurBenefitsStudentOther Total80.35958,8 %8,5 %1,0 %3,5 %-28,1 % GenderMale Female 39.715 40.644 59,6 % 58,1 % 6,6 % 10,3 % 1,4 % 0,7 % 2,9 % 4,0 % -29,5 % 26,9 % Age 15-19 20-24 25-30 35.012 34.532 10.815 54,6 % 63,2 % 58,6 % 6,9 % 10,7 % 6,5 % 0,3 % 1,3 % 2,5 % 2,6 % 4,0 % 4,2 % - 35,5 % 20,8 % 28,2 % EducationLow (without start qualification) Middle High Unknown 24.421 33.671 17.327 5.120 53,8 % 65,4 % 61,3 % 31,1 % 5,5 % 10,4 % 10,3 % 3,1 % 0,5 % 1,1 % 2,0 % 0,5 % 6,1 % 2,5 % 1,7 % 3,1 % -34,1 % 20,6 % 24,8 % 62,2 % Etnicity Native Immigrant, first generation Immigrant, second generation 60.761 7.705 11.893 62,6 % 39,2 % 52,2 % 9,3 % 4,9 % 6,3 % 1,1 % 0,9 % 1,1 % 2,9 % 6,7 % 3,9 % - 24,1 % 48,3 % 36,6 % Household Single Couple without children Couple with children Single parent with children Child living at home Unknown 11.580 8.706 1.665 478 54.057 3.873 56,5 % 63,5 % 43,7 % 31,6 % 60,8 % 37,5 % 8,8 % 9,2 % 4,5 % 3,3 % 8,7 % 4,5 % 2,1 % 1,9 % 1,7 % 0,2 % 0,7 % 0,6 % 5,7 % 2,8 % 9,5 % 35,4 % 2,5 % 4,6 % -27,0 % 22,7 % 40,6 % 29,4 % 27,1 % 53,0 %

16. Employment Employment, multiple jobs Entrepreneur Benefit Student Rest Unknown/missing Employment Employment, multiple jobs Entrepreneur Benefit Student Rest Unknown/missing All Low educated Middle educated High educated

17. Degree of urbanizationTotal Very strong urban Strong urban Moderate urban Low urbanNot urban Nl Total number of transitions (incl. job-job) 5,45,75,65,5 5,04,8 Total number of transitions (excl. job-job) 2,32,52,32,2 2,01,9 Total number of job-job transitions 2,52,42,62,5 2,4 > with change in industry,8,9,8,7 > with change in region COROP,1 > with change in industry and region,3 > only change in company,2,3,2 > rest of job-job transitions 1,0 1,1 Longest period in employment 30,027,829,830,3 32,233,5 Longest period in entrepreneurship 1,21,51,0 1,21,3 Longest period in benefit 3,4 3,63,5 3,32,9 Longest period as a student 4,64,84,74,8 4,23,8 Longest period rest 4,65,54,44,2 3,73,5 Months until first employment 2,42,62,32,1 2,01,9 Career characteristics: The case of school-leavers

18. Level of educationTotalLowMeHigh Nl Total number of transitions (incl. job-job) 5,46,75,24,1 Total number of transitions (excl. job-job) 2,33,11,91,5 Totaal number of job-job transitions 2,52,62,42,5 > with change in industry,81,0,8,7 > with change in region COROP,06,05,07 > with change in industry and region,3,5 > only change in company,2 > rest of job-job transitions 1,0 1,1 Longest period in employment 30,023,632,038,8 Longest period in entrepreneurship 1,2,81,21,9 Longest period in benefit 3,45,81,8,9 Longest period as a student 4,65,06,3,6 Longest period rest 4,65,93,43,5 Months until first employment 2,43,31,6 Career characteristics: The case of school-leavers

19. LogitTotalLowMiddleHigh Exp(B) Total number of transitions (excl. job-job),979***,974***,984*,987** Job-job, with change in industry 1,020**1,034***1,0181,042 Job-job, with change in region (NUTS-3) 1,023,9891,160*,883 Job-job, with change in industry and region 1,006,998,9911,059 Job-job, only change in company,965**,971,9841,039 Job-job, rest of job-job transitions 1,051***1,046*** 1,076*** Longest period in employment 1,089***1,088***1,085***1,086*** Longest period in benefit,921***,930***,901***,912*** Longest period as a student,953***,977***,931***,957*** Very strong Urban,910***,942,882**,819** Strong urban,9611,010,939,787** Modest urban Ref. Low urban 1,0191,027,981,867 Not urban 1,0101,048,935,866 R2 (Nagelkerke),638,573,669,439 Dependent variable = Three months work at end of period Controlled for gender, age, household, etnicity

20. OLSTotalLowMiddleHigh Std. Beta Total number of transitions (excl. job-job) -,044***-,028***-,050***-,035*** Job-job, with change in industry -,030***-,018***-,028***-,030*** Job-job, with change in region (NUTS-3),005*,005,003,007 Job-job, with change in industry and region,009***,006,008*-,001 Job-job, only change in company -,021***-,003-,028***-,029*** Job-job, rest of job-job transitions,008***,028***,017***-,022*** Longest period in employment,088***,121***,064***,094*** Longest period in benefit -,077***-,061***-,060***-,063*** Longest period as a student -,013***-,011-,012**-,001 Very strong Urban,024***-,008,018***,035*** Strong urban,009**,001,012**,004 Modest urban Low urban,005,007,011*-,014* Not urban,009***,018***,006-,008 Adj. R2,60,59,56 Dependent variable = Wage (log) Controlled for gender, age, etnicity, household and region (NUTS-2)

21. | 21 Conclusion 1.Career paths are more volatile in urban regions, especially transitions between socio-economic statuses, not in job-job transitions. In contrast, the average longest period in employment is shorter, and in school and benefit dependency is longer. 2.The same conclusion holds for low educated 3.Volatile careers have an adverse effect on success 4.In urban regions is the success in terms of employment at the end of the period lower, certainly for higher educated. In contrast, the wage is higher.

22. Thank you for your attention