1. Hearing About A Job: Networks, Information, and Labor Market Segregation Katherine Stovel and Christine Fountain University of Washington March 02 2006

3. Labor Market Segregation Reskin, Hargens, and Hirsch 2004

4. Classical explanations for segregation “Demand-side”  Employer preferences for- or against- particular groups of workers (blacks, women, immigrants)  Includes idea of labor queues “Supply-side”  Difference in skill level between groups  Difference in preferences of workers for types of jobs

5. But how do workers and jobs find each other? The seeking/recruiting/matching process…  Costly or scarce information  Structured channels of information flow  Sorting, sifting, and exerting influence

6. Networks and Job Matching Important features of networks: Structure: Clustered or expansive Granovetter and many others Composition: integrated or segregated Baron and Podolny, Heckathorn, others

7. A more comprehensive view of where segregation comes from Is a taste for discrimination necessary? Are there other conditions that can generate segregation in labor markets?  Skill distributions  Network structures

8. Our approach: Stochastic Dynamic Agent-Based Models Laboratory for exploring theoretical principles Specify simple rules at micro level, examine macro-level patterns Used in studies of collective action, production of order, neighborhood segregation, diffusion, …

9. Selected recent examples of applying formal models to labor market hiring  Calvo-Armengol and Jackson AER 2004  Rubineau and Fernandez unpublished ms 2006  Tassier JMS 2005, unpublished ms 2005  Fountain and Stovel unpublished ms 2005

10. Big Decision in Agent-Based Models: How to spend complexity chips?

11. Priorities in our artificial labor market  Agents and jobs are heterogeneous  Agents linked by (dynamic) network ties  Information flows through networks  Network structure is variable  Preference regimes are variable

12. Our Framework Workers skills, attributes Workers and Jobs Matched Calculate utilities from regime and LM conditions Save Data Jobs attractiveness Managers attributes 100 iterations

13. Employer Preference Regimes Baseline  All preferences = 0 Discriminatory preferences  Employers prefer workers who share their attribute Skill-based preferences  Employers prefer workers with higher skill Referral preferences  Employers prefer workers referred by a current employee

14. Structures of Heterogeneity Workers have 2 characteristics:  Attribute [0,1] ………………………think race, sex  Skill Points [0-100] ………………...think education Managers have 1 characteristic:  Attractiveness [0-100] ……………..think working conditions Association between skill and attribute? Association between attribute and network ties?

15. Association between skill and attribute 2002 CPS Demographic Supplement

16. Operationalizing association between skill and attribute Simulated Distributions = 50, s.d. = 10 = 50 = 50, s.d. = 10 = 30 = 70

17. P ij = probability of a tie between i and j Hij = indicator matrix containing 1 if attribute (i) == attribute (j) θ = tunable parameter governing in-group bias θ = 0 out-group bias θ =.5random graph θ = 1 in-group bias p =small underlying probability of a tie p =.00001 Simulated Distributions Operationalizing association between attribute and network ties

18. Levels of network homophily Simulated Networks N = 220, = 5 θ =.5θ =.9θ = 1

19. Employer Preferences Baseline Discrimination Skill-based  Outcomes:  Segregation (Index of Dissimilarity)  Level of Homophily Population Characteristics Correlation between skill and attribute Experimental Conditions Network Characteristics Level of Homophily Static or Dynamic X X

20. Primary Outcome: Index of Dissimilarity Low Segregation High Segregation

21. Simulated Data Full information: Comparing Employer Preference Regimes

22. Simulated Data

23. Network restricted information Simulated Data θ Baseline Model Index of Dissimilarity

24. Network restricted information Simulated Data θ Discrimination Index of Dissimilarity

25. Network restricted information Simulated Data θ Skill Preference Index of Dissimilarity

26. Network restricted information Simulated Data θ Skill Preference Index of Dissimilarity

27. Network restricted information Simulated Data θ Skill Preference Index of Dissimilarity

28. Network restricted information Simulated Data θ Skill Preference Index of Dissimilarity

29. What produces firm-level segregation? Summary of results from static network models:  Discriminatory preferences  Skill preference + group-difference in skill  Segregated nets

30. Where do segregated networks come from? Where:θ tunable parameter governing in-group bias α tunable parameter governing clustering Φ tunable parameter governing co-worker bias and P ij = probability of a tie between i and j 5% of ties re-wired at each iteration P ij = f(θ, α, Φ)

31. Incorporating a preference for ties to co-workers into a dynamic network model GSS Data

32. Building Network Segregation Simulated Data

33. Building Network Segregation Simulated Data

34. Building Network Segregation Simulated Data

35. Building Network Segregation Simulated Data

36. Building Network Segregation Simulated Data

37. Building Network Segregation Reducing Network Segregation Simulated Data

38. Building Network Segregation Reducing Network Segregation Simulated Data

39. Building Network Segregation Reducing Network Segregation Simulated Data

40. Summary Several mechanisms are sufficient to produce segregation in labor markets When networks are segregated and job- relevant information flows through nets, labor market segregation is substantial Workplace practices can increase network segregation

41. Implications: Networks and the New Economy

42. Future Directions Integrate other features of model  Institutional variation  Influence model (referrals) Examine mixed regimes Refocus data collection efforts at pre-hire phase

43. Acknowledgements Research Team:  Yen-Sheng Chiang  Stephanie Lee  Anshuman Shukla  Jim Moody  Peter Hoff Funding  National Science Foundation (SES- 0351934, Stovel, PI)  Center for Statistics and the Social Sciences, University of Washington