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

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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

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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?

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Introduction/background/motivation

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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).

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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

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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

How to measure transitions?

•Starting point: socioeconomic statuses

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Conceptualisation – questions to answer

Job #2

Job #1

Job #3

Job #4

Employee Employee EmployeeBenefitsStudent

Employee Employee EmployeeBenefitsStudent

• Join with job data, create new statuses

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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

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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)

Original – Statistics Netherlands In our study

Employee Employee

Director/shareholder Employee

Entrepreneur Entrepreneur

Rest category: income Rest category: income

Unemployment benefit Benefit

Social assistance benefit Benefit

Rest category: benefit Benefit

Sickness/disability benefit Benefit

Pension Pension

Student – income Student

Student – no income Student

Rest category: no income Rest

Microdata in detail (social-economic status)

The case of school-leavers - descriptivesN %

Total 80.359 100

Gender MaleFemale

39.71540.644

49,450,6

Age 15-1920-2425-30

35.01234.53210.815

43,643,013,5

Education Low (without start qualification)MiddleHighUnknown

24.42133.67117.3275.120

30,241,921,66,4

Etnicity NativeImmigrant, first generationImmigrant, second generation

60.7617.70511.893

75,69,614,8

Household SingleCouple without childrenCouple with childrenSingle parent with childrenChild living at homeUnknown

11.5808.7061.66547854.0573.873

14,410,82,10,667,34,8

The case of school-leavers - descriptivesN %

Regions (NUTS-3) GroningenFrieslandDrentheOverijsselFlevolandGelderlandUtrechtNoord-HollandZuid-HollandZeelandNoord-BrabantLimburgUnknown

3.4923.1571.9755.2181.8639.3066.32213.09817.2781.62510.7844.9811.260

4,33,92,56,52,311,67,916,321,52,013,46,21,6

Level of Urbanity Very Strong UrbanStrong UrbanModerate UrbanLow UrbanNot UrbanUnknown

19.81322.96513.88015.0727.3691.260

24,728,617,318,89,21,6

The case of school-leavers - career startN Employee,

one jobEmployee,multi jobs

Entrepreneur Benefits Student Other

Total 80.359 58,8 % 8,5 % 1,0 % 3,5 % - 28,1 %

Gender MaleFemale

39.71540.644

59,6 %58,1 %

6,6 %10,3 %

1,4 %0,7 %

2,9 %4,0 %

- 29,5 %26,9 %

Age 15-1920-2425-30

35.01234.53210.815

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 %

Education Low (without start qualification)MiddleHighUnknown

24.42133.67117.3275.120

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 NativeImmigrant, first generationImmigrant, second generation

60.7617.70511.893

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 SingleCouple without childrenCouple with childrenSingle parent with childrenChild living at homeUnknown

11.5808.7061.66547854.0573.873

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 %

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

Degree of urbanization Total Very strong urban Strong urban Moderate

urban Low urban Not urban

Nl Nl Nl Nl Total number of transitions (incl. job-job) 5,4 5,7 5,6 5,5 5,0 4,8Total number of transitions (excl. job-job) 2,3 2,5 2,3 2,2 2,0 1,9Total number of job-job transitions 2,5 2,4 2,6 2,5 2,4 2,4 > with change in industry ,8 ,8 ,9 ,8 ,7 ,7 > with change in region COROP ,1 ,1 ,1 ,1 ,1 ,1 > with change in industry and region ,3 ,3 ,3 ,3 ,3 ,3 > only change in company ,2 ,2 ,3 ,3 ,2 ,2 > rest of job-job transitions 1,0 1,0 1,0 1,0 1,0 1,1 Longest period in employment 30,0 27,8 29,8 30,3 32,2 33,5Longest period in entrepreneurship 1,2 1,5 1,0 1,0 1,2 1,3Longest period in benefit 3,4 3,4 3,6 3,5 3,3 2,9Longest period as a student 4,6 4,8 4,7 4,8 4,2 3,8Longest period rest 4,6 5,5 4,4 4,2 3,7 3,5 Months until first employment 2,4 2,6 2,3 2,1 2,0 1,9

Career characteristics: The case of school-leavers

Level of education Total Low Me High Nl Nl Nl NlTotal number of transitions (incl. job-job) 5,4 6,7 5,2 4,1Total number of transitions (excl. job-job) 2,3 3,1 1,9 1,5Totaal number of job-job transitions 2,5 2,6 2,4 2,5 > with change in industry ,8 1,0 ,8 ,7 > with change in region COROP ,06 ,05 ,05 ,07 > with change in industry and region ,3 ,3 ,3 ,5 > only change in company ,2 ,2 ,2 ,2 > rest of job-job transitions 1,0 1,0 1,1 1,1 Longest period in employment 30,0 23,6 32,0 38,8Longest period in entrepreneurship 1,2 ,8 1,2 1,9Longest period in benefit 3,4 5,8 1,8 ,9Longest period as a student 4,6 5,0 6,3 ,6Longest period rest 4,6 5,9 3,4 3,5 Months until first employment 2,4 3,3 1,6 1,6

Career characteristics: The case of school-leavers

Logit Total Low Middle High Exp(B) Exp(B) Exp(B) Exp(B)

Total number of transitions (excl. job-job) ,979*** ,974*** ,984* ,987**Job-job, with change in industry 1,020** 1,034*** 1,018 1,042Job-job, with change in region (NUTS-3) 1,023 ,989 1,160* ,883Job-job, with change in industry and region 1,006 ,998 ,991 1,059Job-job, only change in company ,965** ,971 ,984 1,039Job-job, rest of job-job transitions 1,051*** 1,046*** 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 ,961 1,010 ,939 ,787**Modest urban Ref. Ref. Ref. Ref.Low urban 1,019 1,027 ,981 ,867Not urban 1,010 1,048 ,935 ,866

R2 (Nagelkerke) ,638 ,573 ,669 ,439

Dependent variable = Three months work at end of period

Controlled for gender, age, household, etnicity

OLS Total Low Middle High Std. Beta Std. Beta Std. Beta 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 ,007Job-job, with change in industry and region ,009*** ,006 ,008* -,001Job-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** ,004Modest urbanLow urban ,005 ,007 ,011* -,014*Not urban ,009*** ,018*** ,006 -,008

Adj. R2 ,60 ,60 ,59 ,56

Dependent variable = Wage (log)

Controlled for gender, age, etnicity, household and region (NUTS-2)

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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.

Thank you for your attention