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LABOR MOBILITY AND URBAN DENSITY Martin Andersson (JIBS, KTH) and Per Thulin (KTH)

LABOR MOBILITY AND URBAN DENSITY Martin Andersson (JIBS, KTH) and Per Thulin (KTH) DIME workshop on ” Regional innovation and growth: Theory, empirics and policy analysis ” Pecs, Hungary. Background and Motivation. Significant evidence of an “Urban Productivity Premium”

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LABOR MOBILITY AND URBAN DENSITY Martin Andersson (JIBS, KTH) and Per Thulin (KTH)

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  1. LABOR MOBILITY AND URBAN DENSITY Martin Andersson (JIBS, KTH) and Per Thulin (KTH) DIME workshop on ”Regional innovation and growth: Theory, empirics and policy analysis” Pecs, Hungary

  2. Background and Motivation • Significant evidence of an “Urban Productivity Premium” • Workers earn more in densely populated areas • Glaeser and Maré (2001), Wheeler (2006), Yankow (2006), Ciccone and Hall (1996), Ciccone (2002), Rice et al. (2006), Brülhart and Mathys (2008) • agglomeration economies • productivity advantages brought about by agglomeration • While there is evidence of productivity advantages of dense areas, evidence on the nature and sources of agglomeration economies is limited (Rosenthal and Strange 2004)

  3. Background and Motivation • This paper => Labor mobility • Often cited, but seldom measured and tested source of agglomeration economies • Related to 2 out of 3 families of micro-foundations • ‘Families’ of micro-foundations: (Duranton and Puga 2004): • (1) Sharing – indivisible investments, variety • (2) Learning – technology, formation of human capital, information flows • (3) Matching – firms and their inputs • Labor mobiility => related to matching and learning Main question: Does density spur labor mobility?

  4. The bulk of papers on agglomeration economies Current study

  5. How does density facilitate mobility? (1) • employees have high accessibility to a large set of potential employers, and vice versa. • reduced costs associated with labor market transactions • potential to change of job without change of residence • Cheaper search => more “job-hopping” in search for a match of high quality (2) • density may improve accessibility to information about potential jobs and employers. • Granovetter (1995): personal networks are an important source of information about job opportunities. • Such networks may be assumed to be wider and grow faster in regions with higher density of people (Fenney and Kohlhase 2008).

  6. Why is labor mobility a source of agglomeration economies? Learning • Labor mobility a vehicle for knowledge flows (Almeida and Kogut 1999, Oettl and Agrawal 2008, Agrawal et al. 2006) • Oettl and Agrawal (2008): • knowledge flows that are generated by labor mobility but cannot be priced on the market. • Social networks • Literature on Human Capital Externalities (Moretti 2004, Lucas 1988, Rauch 1993)

  7. Why is labor mobility a source of agglomeration economies? The matching argument • Quality of the match between employer and employee is uncertain and can only be evaluated ex post. • match quality is an ‘experience good’ (Farber 1994, Topel and Ward 1992) • Models of match quality and the thickness of local labor markets (Helsley and Strange 1990, Kim 1987, 1990) • expected quality of a match between employer and employee higher in thick markets • when more employers and employees are attracted to an agglomeration, the expected match quality increase for all actors in the agglomeration.

  8. Why is labor mobility a source of agglomeration economies? Note • Effective matching: higher average quality of match may imply lower mobility in dense regions • Bleakley and Lin (2007): • average labor mobility is lower in denser regions. • subsample of young individuals => labor mobility is higher in more densely populated areas. • density and search costs: less time and less costly to find new relevant jobs in denser areas with thicker labor markets • workers are more likely to search for new jobs in denser regions and they do so early in their careers to maximize their work life income. • Our “conclusion”: density spurs mobility => relevant to split sample by age cohorts

  9. DATA • Matched employer-employee dataset 1987-2005 • All employed individuals in private firms • About 1,4 million individuals per year • Mobility indicator: • employed individuals that switch employer (firm) between two years • a job switcher is defined as an individual who is employed both year t-1 and t and is working in a new firm and a new establishment year t as compared to t-1.

  10. General mobility in Sweden

  11. General mobility in Sweden

  12. General mobility in Sweden

  13. General mobility in Sweden

  14. DATA • Prime interest on the influence of density on the rate of intra-regional inter-firm labor mobility • intra-regional job switching accounts for about 90 % of all job switching • 2 measures of density: • Employment per square kilometer: • accessibility measure which accounts for the quality of transport infrastructure through travel-time distance decay extraregional intraregional

  15. DATA • Regions • 72 functional regions in Sweden • identified based on commuting patterns

  16. Mobility across regions

  17. Does density spur mobility? • Selection issue: • Higher mobility may be due to that inhabitants and firms in dense regions are a ‘select’ group (education, sector, gender, experience, etc.) • Need to control for employer and employee characteristics • Probit model Individual characteristics Regional characteristics Firm characteristics

  18. Variables of interest: density Controls • Individual characteristics • Age (and age squared) • Sex • Tenure • Education (7 categories) • Firm characteristics • Size (dummy variables) • Declining employment • Productivity (value-added per employee) • Region, time and sector dummies and Stockholm dummy

  19. We include density, density squared and the accessibility variables • We split the sample according to: • Age of individual (< 30 and 30 +) • Education categories • long university education (at least 3 years) • PhD • Other • Clustered standard errors at the individual level • Time period: 1997-2005

  20. Conclusions • Labor mobility is positively associated with density • Result is robust to across age cohorts and education categories • Higher mobility appears as a likely source of agglomeration economies (matching, learning)

  21. Conclusions • Labor mobility inherently influenced by commuting time distances • Improved transportation infrastructure => regional enlargement and increased density (choice set of employers increases for a given place of residence) => productivity effects (matching and learning) • Rice et al. (2006): positive relationship between ‘economic mass’ and productivity among regions in the UK. • doubling of the population in areas within short time distances to a region, such that accessibility to economic mass increase, is associated with 3.5 percent higher productivity in the region. • positive effect ceases to be important when travel time distances increase over 80 minutes. • around 60 minutes critical time distance span for commuting (Johansson et al. 2002) • effects are reduced when the time distances exceed those that normally define a functional region, i.e. a local labor market region. • suggest that the labor market is an important part of agglomeration economies. • provides some understanding of distance decay • transportation infrastructure investments and productivity

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