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.

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

Origin of “Spatial Mismatch” Jobs moving out of inner city to suburbs Kain, John F. 1968. “Housing Segregation, Negro Employment, and Metropolitan Decentralization.” Quarterly Journal of Economics 82:1, pp. 32-59. Figure from Kain, 1968

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)

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

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

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

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

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

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

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 1 2 ∙ 𝐽𝑂 𝑖𝑗𝑡 + 𝐶𝑆 𝑖𝑗𝑡

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 1 2 ∙ 𝐽𝑂 𝑖𝑗𝑡 + 𝐶𝑆 𝑖𝑗𝑡

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 1 2 ∙ 𝐽𝑂 𝑖𝑗𝑡 + 𝐶𝑆 𝑖𝑗𝑡 Normalize imbalance of jobs and competing searchers (invariant to city size)

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

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

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)

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

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

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 2000-2005 2.4 million earnings < $40,000 a year 247,000 separated from mass displacement

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

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%

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

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*** -0.0383*** -0.019***   (0.012) (0.0048) (0.0064) Observations 247,000 Controls Yes Number of employer/year fixed effects None 42,000

Effects by Earnings Outcome and Race/Ethnicity Subsample

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 55-64 job seekers more affected Goods producing as well as Education and Public Administration industries more affected

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) http://www.economist.com/news/finance-and-economics/21627628-difficulty-people-have-getting-jobs-makes-unemployment-unnecessarily

Data Availability For job accessibility measure: LEHD Origin Destination Employment Statistics http://lehd.ces.census.gov/data/ OnTheMap web tool http://onthemap.ces.census.gov/ Metropolitan Planning Organizations https://www.planning.dot.gov/mpo.asp Federal Statistical Research Data Centers http://www.census.gov/fsrdc Paper https://ideas.repec.org/p/cen/wpaper/11-30r.html

Thank you Mark Kutzbach Senior Economist Center for Economic Studies mark.j.kutzbach@census.gov