AgroIndustry Location: Theory And Test

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Agro-Industry Location Theory And Test

• Draft of work in progress
• by
• Maureen Kilkenny and Scott Coleman1
• 1 Kilkenny is an Associate Professor in the
  Dept. of Resource Economics, University of
  Nevada, Reno. Scott Coleman recently earned an
  MS in Economics at Iowa State University. The
  empirical work in this paper was conducted by
  Scott Coleman in partial fulfillment of his
  graduate degree requirements.
• The research was supported initially by
  co-operative agreement 43-3AEM-8-80122 between
  Kilkenny while she was at Iowa State University
  and the Economic Research Service of the USDA,
  and later by an assistantship provided to Scott
  Coleman by Prof. Dermot Hayes at Iowa State
  University. The views expressed herein are
  solely those of the authors. They thank Gerry
  Schluter, Jacques Thisse, and Wolfgang Kliemann.
  Please do not quote without permission from
  kilkenny_at_unr.edu 775 784-6785.

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• Q Why do industries in which plants compete with
  each other for inputs,
• but not customers, neither fully disperse nor
  concentrate spatially?
• A Transport costs for shipping good
  establishments that
• employ ubiquitously supplied inputs
• whose opportunity cost declines with distance
  from the market center
• are strictly convex in distance.
• the Exclusion property/Endpoint optimality
  doesnt hold
• ? new model of agroindustrial location
• ? both the site and the extensive margins of an
  establishment's input market area are endogenous
• solution requires evaluating an integral with
  respect to endogenous limits
• first direct empirical test of establishment
  location theory
• unified theoretical rationale for concentration
  or co-location (in cities or urban counties), or
  dispersion (city, urban, or rural)
• transport costs alone can give rise to all
  three patterns

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What do we already know?

• Canonical Location Theory
• Lederer and Hurter (1986), Hurter and Martinich
  (1989) Facility Location and the Theory of
  Production
• Kilkenny and Thisse (1999)
• Choose single site with respect to von Thunen
  inputs (island) Hsu (1997)
• Normative (math programming) Optimal agriculture
  processing plant location
• across a set of material source and market
  sink point locations on a network
• the exclusion property (endpoint
  optimality) is implicit
• (1) math programming problems to determine the
  optimal scales, numbers, and locations of
  processing plants
• King and Logan, 1964 Polopolos, 1965 Fuller,
  Randolph and Klingman, 1976
• von Oppen and Scott, 1976 Kilmer, Spreen, and
  Tilley 1983
• Dunham, Sexton, and Song, 1995 Kawaguchi,
  Suzuki, and Kaiser, 1997
• (2) Social planner's problem where the objective
  is to maximize producer plus consumer surplus
• Takayama and Judge, 1964
• (3) points regions, given distances are between
  geographic centers
• Apland and Andersson, 1996
• Positive (econometrics) hypothesis testing
• Barkley and Henry (1998) locational Gini
  coefficient is 0.15 (dispersed)

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Agroindustry location

• a new general model
• industry point source inputs
• agroindustry ubiquitous inputs
• Q1. Where does the first establishment optimally
  locate?
• In the middle of input supply region
• And these central and accessible places became
  cities!
• Q2. Where do subsequent plants optimally locate?
• (i) at the same market site
• concentrate
• metro or urban counties
• (ii) within the same supply region
• co-locate
• urban counties
• (iii) patronize a new supply region or serve a
  new market
• disperse
• metro, urban or rural counties

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0
N
locations m (mile), f (farm), s
(establishments site)
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• Q1. Where would the first plant locate?
• opportunity cost P , purchase price Pc
• Farmers at f transport corn to a processor at s
• input transport cost rate t per unit product
  and distance
• Pc - tf-s P
• Processor at s fixed cost of plant K,
• marginal cost c (q IO1, QN)
• input price Pc,
• output transport cost rate T,
• mill price PM Ts-m PD
• Farmer-consumers at m each demand one unit at the
  delivered price, PD.
• Profits at site s PDN K - cN - PcN -

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p tx
p tx - 2tf
t(f - x)
t(x - f)
t(f - x)
p - tx
-d
d N/2
2d N
Figure 2. Input price schedules with respect to
distance
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p tx
p tx - 2tf
t(f - x)
t(x - f)
t(f - x)
p - tx
-d
d N/2
2d N
three domains concentrated,
co-located dispersed
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Figure 3. Costs (PROFITS) are strictly convex
(CONCAVE) in distance
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the empirical model
X aZ ßW ?
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Figure 5. Establishing unique
plant pairs
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