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ARE TRAVEL TIMES AND DISTANCES TO WORK GREATER FOR RESIDENTS OF POOR URBAN NEIGHBORHOODS?

ARE TRAVEL TIMES AND DISTANCES TO WORK GREATER FOR RESIDENTS OF POOR URBAN NEIGHBORHOODS?. Asad J. Khattak Virginie Amerlynck Roberto G. Quercia Department of City and Regional Planning University of North Carolina at Chapel Hill. Value.

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ARE TRAVEL TIMES AND DISTANCES TO WORK GREATER FOR RESIDENTS OF POOR URBAN NEIGHBORHOODS?

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  1. ARE TRAVEL TIMES AND DISTANCES TO WORK GREATER FOR RESIDENTS OF POOR URBAN NEIGHBORHOODS? Asad J. Khattak Virginie Amerlynck Roberto G. Quercia Department of City and Regional Planning University of North Carolina at Chapel Hill

  2. Value US central cities have pockets of concentrated poverty: • Mismatch between residences & work for low-income urban residents • Low-skills jobs in suburbs--but limited housing choice in suburbs • Longer commute distances & times • Mismatch implications greater for African-American/minority urban residents

  3. Literature Mixed results: • Little difference in minority & white travel times in urban areas • Some studies support spatial mismatch hypothesis--others do not • Effect of individual, household & travel characteristics explored

  4. Gaps in existing studies • Assessment of spatial mismatch from national policy perspective needed • Employment probability ignored--sample selectivity • Effect of neighborhood factors unexplored

  5. The 1995 NPTS • Telephone survey conducted over 14-month period • N = 42,033 households & 95,360 persons • Stratification and weighting techniques yield representative sample • Oversampling, e.g., large cities, census blocks near transit • Weights needed for estimation

  6. NPTS Features Advantages: • Reported preferences • Long sampling time frame • Large, representative data set • Individual & nbd. characteristics surveyed • Allows correction for sample selectivity Disadvantages: • Unavailable data, e.g., occ. characteristics & personal income • Self-reported commute time--errors

  7. Methodology: Model & Data Two-step weighted least squares regression • Dependent variables: • Employed or unemployed--probit sample-selection • Reported travel time & distance--least squares • Independent variables: Individual, household, travel & nbd. characteristics Whole NPTS data set used: • N=95,360 for probit, N=45,963 for least squares

  8. Methodology - Model & Results • Sample-selection emp. model (probit) • Statistically significant • More: White, educated, male, suburban • Travel time & distance (least squares) model • Model significant • Yields estimators and marginal effects • Results of models with and without selectivity similar

  9. Selected Results

  10. More Selected Results

  11. Conclusions & Implications • Residents of low-income urban neighborhoods commute • significantly longer distances • significantly longer times • At $50K nbd. inc., urban & suburban commutes equal • Additional commute time & distance not large • 3 min. • 1.5 miles • Spatial mismatch more of regional issue

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