1. Job Displacement and the Duration of Joblessness: The Role of Spatial Mismatch
Fredrik Andersson, U.S. Office of the Controller of the Currency
John Haltiwanger, University of Maryland and U.S. Census Bureau
Mark Kutzbach, U.S. Census Bureau, Center for Economic Studies
Henry Pollakowski, Harvard University
Daniel Weinberg, DHW Consulting and U.S. Census Bureau (retired)
C2ER Webinar September 21, 2016
Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau or the Department of the Treasury. These results have been reviewed to protect confidentiality.

2. Outline Motivation – What is Spatial Mismatch? Contributions
Research Summary
Methodology
Data
Results
Summary of Findings
Data Availability

3. Origin of “Spatial Mismatch”
Jobs moving out of inner city to suburbs
Kain, John F “Housing Segregation, Negro Employment, and Metropolitan Decentralization.” Quarterly Journal of Economics 82:1, pp
Figure from Kain, 1968

4. Spatial Mismatch Literature
Initial question
Do job decentralization and residential restrictions contribute to high levels of unemployment for inner city blacks? (Kain 1968)
Later applications to youth
Does teen employment depend on job density near parent’s home? (Ihlanfelt 1990, O'Regan and Quigley 1996, Raphael 1998)
Criticism
Selection concern: Unemployment rate may be the result of selection of those with less propensity to be employed into less accessible locations (Glaeser, 1996)
Public policy implications
Enterprise Zones in job poor locations (Neumark and Kolko 2010)
Transportation links to job rich areas (Holzer et al. 2003)
Placement into residences in job rich areas (Katz et al. 2001)

5. Job Search and Mismatch
Longitudinal outcome helps to deal with endogeneity concerns
However,
job search may still be endogenous or a choice, and
studies have been for small or spatially limited samples
Study
Finding
Study Area
Data
Spatial scale
Sample size
Rogers, 1997
+ 2 SD Access:
- 10 weeks duration
Pittsburgh
Pennsylvania UI claims records
ZIP Code
1,791
Dawkins et al., 2005
+1 SD Access:
-9% search duration
United States
Panel Study of Income Dynamics (PSID)
1,098
Johnson, 2006
+2 SD Access:
3 times prob. find job in 6 m.
Atlanta, Boston, LA, Detroit
Multi-City Study of Urban Inequality
Census tract
1,205

6. Contributions
Estimate effect of job accessibility on search duration for a sample of job separators from “Mass Displacement Events”
Involuntary searchers, using pre-displacement residence
Jacobson, LaLonde, and Sullivan (1993), for this analysis:
Worker has 1+ years of tenure at employer
Employer with >25 workers
Employer loses 30% or more of jobs in 1 year
Large sample size: ~247,000 lower earning job seekers
Longitudinal Employer-Household Dynamics (LEHD)
9 metropolitan areas in “Great Lakes” region, 6 years
Disaggregated by: industry, race/ethnicity, sex, age
Job accessibility measure
Job opportunities and competing searchers
Auto or transit travel time as measure of proximity

7. Decline in Expected Duration of Joblessness from an Increase in Job Accessibility from 25th to 75th percentile of access

8. Modeling approach Tobit specification ln 𝐷 𝑖 =𝛼 𝐴 𝑖𝑗𝑡 + 𝑥 𝑖𝑗𝑡 𝛽+ 𝜀 𝑖
Di : Duration of search for person i
quarters to find new job (Censored after 2 years)
Aijt: Job accessibility for
individual i (probability of auto/transit use)
in neighborhood j (Census tract of residence)
at time t (year of job loss)
Xijt: vector of control variables
i: Demographic and employment history
jt: Metropolitan/Year effects, Quarter effects
Cluster standard errors by census tract

9. Job Accessibility Index
Index calculated on scale of [-2,2] as:
𝐴 𝑖𝑗𝑡 = ( 𝐽𝑂 𝑖𝑗𝑡 − 𝐶𝑆 𝑖𝑗𝑡 ) 1 2 ∙ 𝐽𝑂 𝑖𝑗𝑡 + 𝐶𝑆 𝑖𝑗𝑡
JOijt: Job Opportunities – how many lower earning jobs are nearby?
CSijt: Competing Searchers – how many others might be looking for those jobs

10. Illustration of Job Opportunities and Competing Searchers
Goal is to measure the thickness of the local labor market
If others live closer to a job than I do, I will face competition from them
Jobs in Tracts within 20 minutes of tract j
Searchers in Tracts within 20 minutes of jobs in tract k

11. Job accessibility measure
Aggregate Stock of jobs and potential job seekers
Job opportunities in tracts k = 1 to K
𝐽𝑂 𝑖𝑗𝑡 = 𝑘=1 𝐾 𝑀 𝐿 𝑘𝑡 exp 𝜃∙max 0, 𝑑 𝑗𝑘 −𝜏
Competing searchers for tract k jobs, residing in tracts l = 1 to L
𝐶𝑆 𝑖𝑗𝑡 = 1 𝐽𝑂 𝑖𝑗𝑡 𝑘=1 𝐾 𝑀 𝐽𝑂 𝑖𝑗𝑘𝑡 𝑙=1 𝐿 𝑀 𝑊 𝑙𝑡 exp 𝜃∙max 0, 𝑑 𝑙𝑘 −𝜏
Job accessibility: (-2 to 2 scale)
𝐴 𝑖𝑗𝑡 = 𝐽𝑂 𝑖𝑗𝑡 − 𝐶𝑆 𝑖𝑗𝑡 ∙ 𝐽𝑂 𝑖𝑗𝑡 + 𝐶𝑆 𝑖𝑗𝑡

12. Job accessibility measure
Discount with negative exponential impedance function with beginning travel time threshold of 10 minutes
Job opportunities in tracts k = 1 to K
𝐽𝑂 𝑖𝑗𝑡 = 𝑘=1 𝐾 𝑀 𝐿 𝑘𝑡 exp 𝜃∙max 0, 𝑑 𝑗𝑘 −𝜏
Competing searchers for tract k jobs, residing in tracts l = 1 to L
𝐶𝑆 𝑖𝑗𝑡 = 1 𝐽𝑂 𝑖𝑗𝑡 𝑘=1 𝐾 𝑀 𝐽𝑂 𝑖𝑗𝑘𝑡 𝑙=1 𝐿 𝑀 𝑊 𝑙𝑡 exp 𝜃∙max 0, 𝑑 𝑙𝑘 −𝜏
Job accessibility: (-2 to 2 scale)
𝐴 𝑖𝑗𝑡 = 𝐽𝑂 𝑖𝑗𝑡 − 𝐶𝑆 𝑖𝑗𝑡 ∙ 𝐽𝑂 𝑖𝑗𝑡 + 𝐶𝑆 𝑖𝑗𝑡

13. Job accessibility measure
Job opportunities in tracts k = 1 to K
𝐽𝑂 𝑖𝑗𝑡 = 𝑘=1 𝐾 𝑀 𝐿 𝑘𝑡 exp 𝜃∙max 0, 𝑑 𝑗𝑘 −𝜏
Competing searchers for tract k jobs, residing in tracts l = 1 to L
𝐶𝑆 𝑖𝑗𝑡 = 1 𝐽𝑂 𝑖𝑗𝑡 𝑘=1 𝐾 𝑀 𝐽𝑂 𝑖𝑗𝑘𝑡 𝑙=1 𝐿 𝑀 𝑊 𝑙𝑡 exp 𝜃∙max 0, 𝑑 𝑙𝑘 −𝜏
Job accessibility: (-2 to 2 scale)
𝐴 𝑖𝑗𝑡 = 𝐽𝑂 𝑖𝑗𝑡 − 𝐶𝑆 𝑖𝑗𝑡 ∙ 𝐽𝑂 𝑖𝑗𝑡 + 𝐶𝑆 𝑖𝑗𝑡
Normalize imbalance of jobs and competing searchers
(invariant to city size)

14. Job opportunities in Orange County, CA
LODES in OnTheMap web tool
Workplace job counts at Census Block level
Darker shading indicates greater density

15. Competing searchers by place of residence
Residence distribution more disperced than employment

16. Travel times from a selected Census Tract
Metropolitan Planning Organizations (MPOs) model travel time in order to plan infrastructure investments
Estimate travel time during morning peak travel period from each origin to each destination
Automobile
Transit
Orange County, CA not in study, shown here for illustration only
Source:
Southern California Assoc. Gov’ts (SCAG)

17. Data Integration Longitudinal Employer-Household Dynamics
Mass displacement events
Worker earnings histories (e.g. tenure, job finding)
Employer characteristics (e.g. industry, location)
LEHD Origin Destination Employment Statistics (LODES)
Composite Person Record
Place-of-Residence (census tract)
2000 Census
Demographic and household characteristics
Neighborhood characteristics
Metropolitan Planning Organizations
Automobile and transit travel time between tracts

18. Local Employment Dynamics (LED) Partnership
State partners provide quarterly:
2000 Census:
Demographics
Employer account information
(ES-202 or QCEW):
~7 million employers
Unemployment Insurance (UI) Wage Records:
~130 million jobs
Personmatch
Federal administrative data: Place-of-residence
Numident (demo.)
State EIN match
Quarterly Workforce Indicators
County*Quarter*Industry*Demographic
LEHD Infrastructure Files
LEHD Origin Destination Employment Statistics
Workplace*Residence*Year*Characteristics

19. Estimation Sample Metropolitan Areas Sample construction
Minneapolis-St. Paul, MN; Milwaukee, WI; Chicago, IL; Indianapolis, IN; Detroit, MI; Columbus, OH; Cleveland, OH; Pittsburgh, PA; and Buffalo, NY
Sample construction
6.7 million separated workers
2.4 million earnings < $40,000 a year
247,000 separated from mass displacement

20. Distribution of Job Accessibility
Range of [-2,2]

21. Job Search Outcomes Higher earning new jobs require longer search
Less job finding (>75%) for low access workers
Quarters of job search
Any new job(s)
Single new job, earnings > 75% old job
Single new job, earnings > 90% old job
Same quarter
31.70%
22.30%
18.90%
1-2 quarters
30.20%
24.70%
22.60%
3-4 quarters
10.50%
9.50%
9.00%
5-8 quarters
6.90%
8.50%
9.10%
9+ quarters
20.80%
35.00%
40.50%
Quarters of job search
Job accessibility < -0.5
Job accessibility > 0.5
Same quarter
21.60%
23.70%
1-2 quarters
24.20%
24.90%
3-4 quarters
9.70%
9.50%
5-8 quarters
8.70%
8.20%
9+ quarters
35.80%
33.70%

22. Job Access by Group Central cities actually have better access Sample
Sample Percent
Median
All
100.0
0.021
White non-Hispanic
67.8
-0.020
Black non-Hispanic
18.6
0.183
Hispanic
9.4
-0.093
Other race non-Hispanic
4.3
0.263
Central city
 35.9
0.259
Remainder of central county
 28.0
0.085
Outside central county
 36.0
-0.265

23. Effect of Job Accessibility
Finding a single new job with earnings > 90% old job 
Tobit
Ordinary Least Squares
Employer Fixed Effects
Job accessibility parameter estimate
-0.091***
***
-0.019***
(0.012)
(0.0048)
(0.0064)
Observations
247,000
Controls
Yes
Number of employer/year fixed effects
None
42,000

24. Effects by Earnings Outcome and Race/Ethnicity Subsample

25. Summary of Results
Broad-based effect of job accessibility on job search duration
More important for finding a high earning job
Subgroups
Larger point estimates for blacks than whites
Female and age job seekers more affected
Goods producing as well as Education and Public Administration industries more affected

26. The Economist, Review of this Study
The geography of joblessness: The difficulty people have in getting to jobs makes unemployment unnecessarily high (Oct 25th 2014)

27. Data Availability For job accessibility measure:
LEHD Origin Destination Employment Statistics
OnTheMap web tool
Metropolitan Planning Organizations
Federal Statistical Research Data Centers
Paper

28. Thank you Mark Kutzbach Senior Economist Center for Economic Studies