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The Rising Residential Concentration of Joblessness in Urban America: 1980 to 2000. Christopher H. Wheeler* Federal Reserve Bank of St. Louis July 2007. *The views expressed herein do not represent the official positions of the Federal Reserve Bank of St. Louis or the Federal Reserve System.
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The Rising Residential Concentration of Joblessness in Urban America:1980 to 2000 Christopher H. Wheeler* Federal Reserve Bank of St. Louis July 2007 *The views expressed herein do not represent the official positions of the Federal Reserve Bank of St. Louis or the Federal Reserve System.
Unemployment • Basic economic indicator • Tends to signify whether times are good (expansion) or bad (recession) • Not only varies with the business cycle • At any time, it varies from place to place
Variation Within Cities • Neighborhoods also tend to vary in terms of the unemployment rates of their residents • Some have virtually no unemployment • Some have extremely high rates
Some Comments • Not surprising: individuals sort residentially along many dimensions -Income, wealth, race • Decades of research has examined these patterns • Few studies have looked at differences in neighborhood-level unemployment
Q: Why should we care about unemployment differences? A: The characteristics of our neighborhoods, including the unemployment rate, tend to influence the labor market outcomes we experience.
Some Evidence • Peer Effects Case and Katz (1991) show that the prevalence of certain behaviors (e.g. school attendance, employment status) promote similar behavior within neighborhoods
Some Evidence • Social Networks Granovetter (1995) finds that workers locate jobs primarily through personal contacts, many of whom live nearby
Some Evidence • Localized Spillovers Topa (2001) finds evidence that high levels of unemployment in a neighborhood tend to make unemployment more likely in adjacent neighborhoods
Implication • Individuals in neighborhoods with a high incidence of unemployment may find it extremely difficult to find work • Few networks • Negative peer effects • Negative views held by employers about individuals from certain neighborhoods
An Additional Concern ▪ The extent to which unemployment is concentrated residentially has risen in recent decades
Evidence, 1980-2000 • 361 U.S. metropolitan areas • Approximately 166,000 block groups • Data from Decennial U.S. Census • Compiled by GeoLytics – consistent geographic definitions across all years
Evidence, 1980-2000 • Unemployment rate of median unemployedworker’s neighborhood 1980: 7.5% (U.S. rate = 6.9%) 2000: 7.9% (U.S. rate = 5.9%)
Evidence, 1980-2000 • 90th percentile of distribution of block group unemployment 1980: 11% (U.S. rate = 6.9%) 2000: 12.5% (U.S. rate = 5.9%) • 10th percentile of distribution of block group unemployment 1980: 3.7% (U.S. rate = 6.9%) 2000: 1.3% (U.S. rate = 5.9%)
Figure 1: Neighborhood Unemployment Percentiles 0.14 0.12 0.1 p90 0.08 p50 p10 0.06 0.04 0.02 0 1980 1990 2000 Year
Evidence, 1980-2000 • Difference between 90th percentile and the 10th percentile 1980: 7.3 percentage points 2000: 11.2 percentage points
Alternative Measure of Unemployment Concentration • Index of Dissimilarity • Ranges between 0 and 1 • Commonly interpreted as fraction of unemployed that must be relocated across neighborhoods for the unemployed to be evenly distributed across a metro area
Evidence, 1980-2000 • Index of Dissimilarity - Average across 361 metro areas 1980: 0.18 1990: 0.27 2000: 0.31
Correlates of Neighborhood Unemployment • Exercise: attempt to identify some features of high- and low- unemployment neighborhoods • Characteristics: income, demographics, commuting time, educational attainment, industry of employment
Analytical Method • Regression of block group unemployment rate on block group characteristics • Pooled sample: 1980, 1990, 2000 • Account for time trends • Account for metropolitan area-specific effects
Theories of Rising Unemployment Concentration (1) Urban decentralization (sprawl) (2) Changing industrial structure and unionization (3) Rising segregation by income and education
Urban Decentralization • As people and jobs spread out from city centers to suburban areas, some are left without access to work • Spatial Mismatch • Some reasons: transportation, networking, negative stereotypes
Preliminary Evidence ♦Statistical analysis shows some indication that longer commutes (30 + minutes) are positively associated with unemployment
Industrial Structure and Unionization • Makeup of U.S. economy has changed • Decline in manufacturing, rise in services • Drop of union activity • Employment opportunities for some have decreased more than for others • If these people live in different neighborhoods, residential differences in unemployment may be the result of these economic shifts
Preliminary Evidence ♦Statistical analysis shows some indication that high-unemployment neighborhoods tend to have more manufacturing workers
Segregation by Income and Education • Rising unemployment concentration may reflect changes in where people with different levels of education and income live • College-educated have done very well in last 30 years • They could be moving into ‘exclusive’ areas
Preliminary Evidence ♦Statistical analysis shows strong association between unemployment and both income and education
Analysis of Unemployment Concentration • Regression of concentration in a metro area on its basic characteristics: • Population density • Industrial composition and union activity • Measures of income and education segregation • Other: demographics, overall unemployment, year- and metro area-specific effects
