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Suburban Sub-centers and employment density in metropolitan Chicago

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Suburban Sub-centers and employment density in metropolitan Chicago Daniel P. McMillen (Tulane U) John F. McDonald (U of Illinois) Journal of Urban Eco, 1998 – PowerPoint PPT presentation

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Title: Suburban Sub-centers and employment density in metropolitan Chicago


1
Suburban Sub-centers and employment density in
metropolitan Chicago
  • Daniel P. McMillen (Tulane U)
  • John F. McDonald (U of Illinois)
  • Journal of Urban Eco, 1998

2
Introduction (1)
  • A location well served by highways, rail lines
    and other transportation network may attract many
    firms even when the firms have no interest in
    locating near one another ---- suburban
    sub-center may form near transportation centers.
    Accessibility to these sub-centers lead to
    employment concentration. This implies that scale
    economies -- agglomeration (transportation saving
    cost) can generate concentration of employment at
    certain locations within an urban area.

3
Scale Economies - transportation network
  • Transportation network

4
Theoretic Framework
  • Bid-rent function is used to see effects of
    suburban sub-center on employment density. A
    bid-rent function represents the maximum amount a
    firm or an individual will pay for a unit of
    land. In standard mono-centric model, the
    bid-rent function is a simple function of
    distance from the city center -- because all
    economic activities is assumed to take place
    there. Here, sub-urban employment is influenced
    by access to expressway interchanges and other
    features of the transportation network. (cont)

5
Theoretic Framework (2)
  • Such accessibility measures are represented by
    the vector A, sub-center access measures by
    vector S and idiosyncratic characteristics
    (clear, level land, swampy) by C, this affects
    construction cost.
  • Hypothesis
  • Employment probability in the sub-centers
  • Their impact on employment density

6
Methodology
  • Non-residential lnR1 ?1X ?1 X (A,S,C)
  • Household lnR2 ?2X ?2
  • Net employment density ln(E/Le) ?ln R1 ?1
    ??1X ?1 ??1
  • Gross employment lnD ?Z ? ------ (1)
  • Employment density is a function of the same
    variables that determine land rents.
  • Employment density increases when non-residential
    land rent increases

7
Methodology (2)
  • Employment density increases when non-residential
    land rent increases
  • Employment density decreases when residential
    land rent increases
  • Prob (I1) Prob ( ?Z ? gt 0 ) ------- (2)
  • This equation determines whether there is some
    employment in a zone or not.
  • Correlation between ? and ? implies employment
    density functions are subject to selection bias.

8
Estimation Procedure
  • Two-stage method a) probit/logit b) OLS
  • Maximum-likelihood estimation
  • E (lnD/I1) ? Z ??u ? (? Z) / ? (? Z)
  • Northeastern Illinois Planning Commission data
  • Sub-center identification A set of nearby tracts
    that each have at least 10 employee/acre in
    either 1980 or 1990 and together have an average
    over the 2 sample years of at least 10,000
    employees ---20 sub-centers

9
Estimation Results
  • Expect Increasing distance from a suburban
    employment sub-center lowers non-residential bid
    rents if scale economies exist --- lower
    employment density -- negative coefficient on
    distance ---- lower employment

10
Estimation Results (2)
11
Estimation Results (3)
12
Conclusion
  • Correlation between errors of employment density
    and employment probability exists ---OLS
    estimates are subject to selection bias -- either
    two-stage method or maximum likelihood is
    appropriate.
  • Transportation facilities are subject to
    economies of scale. Firms will cluster near
    transportation facilities even if there are no
    direct benefits of locating near one another. The
    empirical results show that the measures of
    access to the transportation system are highly
    statistically significant determinants of both
    employment probability and employment density.

13
Thank you
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