ECN 501 Grantham University Week 7 Managerial Economics Essay

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Managerial Economics – Week 7 Assignment
Please analyze and contrast the Introduction and Literature Review of the articles below.  A synthesis discussing the economic impact of Aerotropoli on economies should be submitted. All Articles can be found in EBSCO.
Flores-Fillol, R., Garcia-López, M.-Á., & Nicolini, R. (2016). Organization of Land Surrounding Airports: The Case of the Aerotropolis. Land Economics, 92(1), 57–81. https://doi.org/10.3368/le.92.1.57
 
LIOU, J. J. H., Chao-Che HSU, Chun-Sheng Joseph LI, Gudiel PINEDA, P. J., & Gin-Weng CHANG. (2018). Developing a Successful Aerotropolis by Using a Hybrid Model under Information Uncertainty. Technological & Economic Development of Economy, 24(3), 1080–1103. https://doi.org/10.3846/20294913.2017.1289484
Wium D, Coetzee M. Africa’s first aerotropolis in Ekurhuleni – will it foster economic growth? Civil Engineering (10212000). 2014;22(1):32-34. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=94962116&site=ehost-live&authtype=uid&user=grantham&password=research.

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Organization of Land Surrounding Airports: The Case
of the Aerotropolis
Ricardo Flores-Fillol, Miquel-A?ngel Garcia-Lo?pez, and
Rosella Nicolini
ABSTRACT. We analyze the conditions driving the
organization of the territory near airports by studying
the distribution of economic activities. We consider
how commercial firms, service operators, and consumers compete for land. The theoretical setting identifies an aerotropolis (airport city) as a land equilibrium outcome characterized by the following spatial
sequence: services area, commercial area, residential
area. Using data on the distribution of establishments
in the United States, we analyze the existence and
determinants of aeropolitan configurations. Estimations performed with parametric methods detect some
interesting dynamic patterns affecting the density and
distribution of activities around selected U.S. airports. (JEL R12, R15)
I. INTRODUCTION
Logistics are becoming an increasingly important issue because firms are in search of
flexibility. Speed and agility are already as important as price and quality in the strategy of
firms that adopt the just-in-time good-supplying system. Firms choose their location to enhance their accessibility to markets. No longer
are logistics seen as costs to be minimized,
but as value-added activities in firms’ supply
chain that need to be optimized.
Consequently, fast delivery is a key element (see Leinbach and Bowen 2004 for empirical evidence). In this context, airports are
seen (especially by e-tailers) as a new kind of
central business district (CBD) with enough
capacity to leverage air commerce into high
profits. In that spirit, Kasarda (2000) introduces for the first time the idea of an aerotropolis (airport city), namely, a large industrial
area characterized by a high concentration of
Land Economics • February 2016 • 92 (1): 57–81
ISSN 0023-7639; E-ISSN 1543-8325
? 2016 by the Board of Regents of the
University of Wisconsin System
logistic facilities and commercial activities
near specific airports.1 Arend, Bruns, and
McCurry (2004) suggest that aerotropolises
may extend up to 32 kilometers (20 miles),
including a number of activities and infrastructures such as retail and distribution centers, light industrial parks, office and research
parks, districts zoned for specific purposes,
foreign trade zones, entertainment and conference facilities, and even residential development that contributes substantially to the
competitiveness of firms in the area.2 Furthermore, empirical evidence also emphasizes
that aerotropolises are also an important
source of employment for the local territory;
as we describe extensively in the subsection
“Selected Airports: Some Descriptive Features,” they are able to account for about 10%
to 30% of the employment in a region.
Our paper analyzes the conditions driving
the organization of the territory near airports
1 This type of land organization started becoming more
and more visible at the beginning of the 2000s. Kasarda and
Lindsay (2011) document this rise and refine the definition
of aerotropolis suggested by Kasarda (2000).
2 The formation of an aerotropolis is an expression of
the self-organizing dynamics of a territory. Another example
of this dynamics has been the creation of edge cities in the
1980s. Edge cities are defined as highly dense business and
residential centers containing every city function but offering a low-density automobile-oriented land configuration.
This is the case of the areas around Route 128 in Massachusetts and the city of Irvine in southern California (Garreau 1991).
The authors are, respectively, associate professor, Departament d’Economia and CREIP, Universitat Rovira
i Virgili, Reus, Spain; associate professor, Department
of Applied Economics, Universitat Auto?noma de Barcelona, and, economist, Institut d’Economia de Barcelona, Barcelona, Spain; and associate professor,
Department of Applied Economics, Universitat Auto?noma de Barcelona, Bellaterra, Spain.
58
Land Economics
by studying the distribution of economic activities. More specifically, we study the existence and determinants of aeropolitan configurations.
The importance of airports as economic
fuel for their surrounding areas has been underlined in the urban literature. McDonald and
McMillen (2000) discuss the centripetal force
of Chicago’s O’Hare International Airport for
industrial and commercial activities, and Cohen and Morrison Paul (2003) evaluate the
spillovers entailed by own-state airport infrastructure as a device for lowering manufacturing costs. By contrast, rather than focusing
on the economic impact of airports, our analysis deals with the spatial organization of activities near airports and its evolution over
time.
Starting from the setting of the location of
divisible activities developed by von Thu?nen
(1826), various models have tried to explain
the configuration of the space in which households commute to the CBD and form urban
agglomerations around it.3 As pointed out by
Fujita and Thisse (2013), the novelty of von
Thu?nen’s work is that he introduced the notion of a bid-rent function: land is not homogeneous and is assigned to the highest bidder.4
A piece of land at a particular location can be
associated with a commodity whose price is
not fixed by market supply and demand. Extending the von Thu?nian agricultural model to
an urban context, Alonso (1964) suggests the
rent each agent can bid at each location is explained by the savings in transportation costs
with respect to a more distant site. Hence, land
gives rise to a spatial heterogeneity, and
agents stop bidding for the most distant land
since no further savings can be enjoyed.5
In that spirit, we consider how commercial
firms, service operators, and consumers compete for land. We define service operators as
3 See Fujita and Thisse (2013) for a complete overview
of this literature.
4 All agents compete for land with an auction mechanism and pay a rent to an absentee landlord once the land
auction takes place. Therefore they become actual land owners after the land bidding process.
5 Empirical evidence has been presented by Muto
(2006).
February 2016
all firms developing activities associated with
the use of an airport. Service operators provide a number of complementary services to
commercial firms (e.g., freighter docks,
bonded warehouse, mechanical handling, refrigerated storage, fresh meat inspection, mortuary, animal quarantine, livestock handling,
health officials, security for valuables, decompression chamber, express/courier center, and
equipment for dangerous and radioactive
goods and large or heavy cargo).6
Our theoretical setting is simple. A group
of service operators supplies a range of services near an airport, and commercial firms
need to settle close enough to enjoy them. The
spatial concentration of these services in the
proximity of an airport allows firms to benefit
from an easy access to many facilities. The
two types of firms compete with consumers,
who also aim at settling close to the airport.
The theoretical setting models land competition across agents and analyzes the formation
of aerotropolises. An aerotropolis requires a
nondegenerated land equilibrium in which
both service operators and commercial firms
assign a higher value than consumers to land
plots located in the proximity of the airport,
and it is characterized by the following spatial
sequence: services area, commercial area,
residential area (as introduced by Kasarda
2000). Among other things, the land demand
elasticity of service operators, commercial
firms, and consumers determines the level of
their respective bid rent functions (i.e., their
willingness to pay).
The second part of the study proposes an
empirical application to identify the presence
of aeropolitan areas in the United States. Kasarda and Lindsay (2011) propose a qualitative identification by detailing the distinguishing features of four cases in which
aerotropolises could be observed. Our contribution consists of suggesting a quantitative
(econometric) strategy to detect the existence
of these spatial structures. In particular, we are
interested in assessing the conditions that
6 See www.azworldairports.com for important nonaviation services provided by the major worldwide airports.
92(1)
Flores-Fillol, Garcia-Lo?pez, and Nicolini: Land Surrounding Airports: Aerotropolis
make an airport a true center of attraction (i.e.,
a new CBD), which gives rise to the formation
of aerotropolises’ structures. Information on
firms’ distribution in the United States is collected to bring our model to the data: we collect part of the county business patterns data
released by the U.S. Census Bureau, and we
focus on the years 2000 and 2010. First, we
extract the data concerning firms’ spatial distribution (by ZIP code), limiting our interest
to a finite number of North American Industry
Classification System (NAICS) codes to identify the groups of commercial and service activities. Then we select some representative
U.S. airports (Memphis, Louisville, Los Angeles, and Newark) to study the land organization in their surroundings. Our approach
owes to McMillen (2004a) the idea to think
of airports as a source of formation of subcenters that could compete with the city center
for the local land organization for productive
activities. Estimations performed with parametric methods detect a few interesting evolutionary patterns of the density distribution
of the two groups of activities for our sample
of airports. The distance from/to the airport
mostly drives the land organization, but the
creation of an aeropolitan configuration is associated with the existence of some specific
conditions. The degree of attractiveness of the
airport (measured by the density gradient) is
dynamic over time and is stronger in the proximity of the more cargo-oriented airports. We
find evidence for classifying Memphis as a
reinforcing aerotropolis, Louisville as a growing aerotropolis, Los Angeles as a declining
aerotropolis, and Newark as an aborted aerotropolis.
II. THE THEORETICAL MODEL
We introduce a simple theoretical framework to guide the interpretation of the results
of our empirical exercise. Our model builds
on the von Thu?nian orthodox framework described by Fujita and Thisse (2013). Space is
represented by the real line X = ( ? ?,?) with
the CBD lying at the origin. The CBD is an
exogenous fixed point that corresponds to the
59
airport terminals.7 We adopt a broad definition
of CBD, so that it comprises the airport terminals and the surrounding space up to the
noise contour line (so that the severe-noise
area does not affect the agents’ location
choice).8 We define any spatial distance from/
to it as x ? X , with x > 0.
In the wake of the housing problem framework discussed by Glaeser (2008), we model
the case of heterogeneous agents. We consider
three types of agents competing for land who
aim at settling as close as possible to the CBD:
(1) a continuum of identical service operators
with density n a(x) ? 0 at x ? X ; (2) a continuum of identical firms with density n i(x) ? 0
at x ? X ; and (3) a continuum of consumers
with density n c(x) ? 0 at x ? X , where the subscripts “a,” “i,” and “c” denote service operators, commercial firms, and consumers, respectively.
As in the classical tradition, an absentee
landlord is assumed. Land is finite and the total area occupied by service operators, firms,
and consumers at each x ? X is fixed and normalized to 1 (as done by Cavailhe?s et al.
2004), that is,
na(x)Sa(x) + ni(x)Si(x) + nc(x)Sc(x) = 1,
[1]
where S a(x), S i(x), and S c(x) stand for the sizes
of land plots, and n a(x)S a(x), n i(x)S i(x), and
n c(x)S c(x) denote the total amount of land being used by each type of agent at a location
7 The force driving land competition (and ultimately,
land assignment and land specialization) is the attraction exerted by the CBD. Introducing explicit agglomeration economies in our model would reinforce the attractiveness of
land plots located in the proximity of airports. However, this
would complicate unnecessarily the analysis without providing any additional insight.
8 Alternatively, we could simply argue that in accordance to the findings in the literature, the existing severenoise area around airports is relatively small and is becoming less and less important (see McMillen 2004b; Federal
Aviation Administration 2005). This is due to the recent dramatic gains in aircraft quietness, as reported by both
McMillen (2004b) and Brueckner and Girvin (2008). Therefore, for the sake of simplicity, the severe-noise area around
the airport terminals is not formally included in the analysis
(since this would imply assuming an inverted U-shape bidrent function for consumers-workers, which would complicate the analysis substantially without providing any additional insight).
60
Land Economics
February 2016
x ? X . The equilibrium is computed by considering any point in the available space at which
each agent exhausts her income and maximizes
her profits or utility. There is competition for
land because each type of agent nurtures a particular interest in settling as close as possible
to the CBD. More precisely, service operators
need to be close to the airport terminals to provide commercial firms with a full range of services; commercial firms also want to be close
to the terminals to have their merchandise delivered as fast as possible; finally, consumers
are also attracted by land plots around the airport because we assume a complete information setting in which consumers know that the
firms that can hire them are searching for settling as close as possible to the CBD. Any location entails some rental costs and some transportation costs (which increase with distance).
costs.10 Thus, consumers-workers’ budget
constraint is given by
Consumers-workers
L(x) ? = 1 ? ?
We consider the existence of a group of
consumers-workers (they supply labor to service operators). Their utility function relies on
the consumption of two goods: leisure and
land.9 Their source of revenue is the wage
they earn for the time they devote to labor.
Each consumer-worker’s available time at
x ? X is fixed and normalized to 1. Therefore,
H(x) = 1 ? L(x), where L(x) stands for labor
and H(x) denotes leisure. More precisely, leisure and land consumption report utility to
consumer-workers in the following way (a? la
Cavailhe?s et al. 2004):
U=
[1 ? L(x)] ? Sc(x)1 ? ?
? ? (1 ? ?)1 ? ?
,
Rc(x)Sc(x) + tx ? wL(x),
where the price of leisure is equal to unity and
R c(x) is either the rent or the purchase price
per unit of land paid by consumers-workers
settled at distance x. Furthermore, transportation costs to the CBD from distance x are
equal to tx , where t > 0 is the cost per unit of
distance.11 Consumers-workers choose L(x)
and S c(x) to maximize their utility subject to
their budget constraint (which is assumed to
be binding). From equations [2] and [3], we
can solve a constrained maximization problem that yields consumers-workers’ optimal
labor supply and land plot demand:
9 Labor enters as a choice variable because it is an input
for the service operators’ production function.
( )
w ? tx
w
[4]
and
Sc?(x) =
(1 ? ?)(w ? tx)
,
Rc(x)
[5]
where w > tx is assumed to hold. Naturally,
L(x) ? and S c?(x) decrease with w and R c(x),
respectively.12 An increase in transportation
costs creates incentives for consumers-workers to increase their labor supply (i.e., working
time) to afford them and reduces their land
plot demand. Since consumers-workers’ indirect utility function is
[2]
with 0 < ? < 1. We assume that consumersworkers are hired by service operators and receive a fixed and exogenous wage (w) per unit of labor, which they use to cover land rental and transportation expenses. As explained above, consumers-workers are also interested in settling as close as possible to the CBD, whose access entails incurring transportation [3] V= [( ) ]/[ w ? tx (1 ? ?) w w Rc(x)(1 ? ?) ], their bid-rent function, that is, the highest price they are willing to pay for a unit of land at x ? X , becomes 10 Consumers-workers are interested in reducing transportation costs even if this is done at the expense of higher land rents. 11 For the sake of simplicity, we assume identical unit transportation costs t across agent types. 12 Consumers-workers’ leisure demand increases with wages. Higher wages yield a lower labor supply. 92(1) Flores-Fillol, Garcia-Lo?pez, and Nicolini: Land Surrounding Airports: Aerotropolis Rc?(x) = w 1/(1 ? ?) ( ) w ? tx wV , [6] which is increasing with w (since consumersworkers have a higher income) and decreasing and strictly convex with respect to x. Service operators choose S a(x) to maximize profits.14 Using equations [4] and [7], the firstorder condition yields w ? tx pa(1 ? ?) Sa?(x) = 1 ? ? w 1442443 txRa(x) [ ( )][ The activity of the other two groups of agents is strongly connected. Commercial firms need to deliver their production abroad through the airport and service operators are the one in charge of accomplishing this task.13 Namely, service operators provide commercial firms with a full range of services. The action of delivering merchandise from firms’ premises to the airport (i.e., the CBD) implies the existence of transportation costs to be taken into consideration. For the sake of simplicity, we consider that transportation costs are partially assumed by both commercial firms and service operators and are proportional to their distance from the airport. The activity of service operators is provided by using land and all labor supplied by consumers-workers at each location x ? X . Their production function is modeled as the following Cobb-Douglas function with constant returns to scale: [7] with 0 < ? < 1. Service operators (settled at x ? X ) sell their services to commercial firms at a price pa (net of production costs). Revenues earned by service operators are discounted by their transportation costs and equal (p a/tx)Y a(x). Service operators’ production costs comprise labor and land rental expenses and are equal to wL(x) + R a(x)S a(x). Therefore, service operators’ profits are ?a(x) = pa Y (x) ? wL(x) ? Ra(x)Sa(x). tx a 1/? ] , [9] L(x) ? Service Operators Ya(x) = L(x)? Sa(x)1 ? ? , 61 with w > tx . The plot size increases with consumers-workers’ labor supply and with service operators’ marginal revenue (p a/tx),
whereas it decreases with the rental price.15
Competition for land is assumed to extract all
profits (zero-profit condition), yielding
1/(1 ? ?)
() ()
Ra?(x) = (1 ? ?)
pa
tx
?
w
?/(1 ? ?)
,
[10]
where R a?(x) is the bid-rent function for service operators, which decreases with labor’s
unit cost w and increases with marginal revenue. In addition, R a?(x) is decreasing and
strictly convex with respect to x.
Commercial Firms
Finally, commercial firms deliver goods
through the airport and by using the services
supplied by service operators. Therefore, the
activity of commercial firms makes use of
land and the services supplied by service operators as inputs. Their production function is
modeled as the following Cobb-Douglas function with constant returns to scale:
Yi(x) = Ya(x)? Si(x)1 ? ? ,
[11]
with 0 < ? < 1. Therefore, profits for commercial firms settled at x are ?i(x) = pi Y (x) ? Ri(x)Si(x) ? paYa(x), tx i [12] [8] 13 We assume that the final consumers-workers of commercial products are settled abroad, and, therefore, we do not model them. 14 Alternatively, following Glaeser (2008), the optimization problem can be equally solved by defining a measure of labor intensity (Sa(x) = La(x)/Sa(x)) and maximizing ?a(x)/Sa(x) with respect to Sa(x). 15 It is easy to check that the second-order condition always holds. 62 Land Economics where revenues are assumed to decrease with transportation costs (tx ), and their production costs include the payment for the services provided by service operators (i.e., p a Y a(x)) and land rental expenses (i.e., R i(x)S i(x)). Commercial firms choose S i(x) to maximize profits.16 Using equations [7] and [11], the first-order condition yields Si?(x) = 1442443 L(x)? Sa?(x)1 ? ? Y a(x) pi? 1/(1 ? ?) ( ) . txRi(x) [13] The plot size increases with service operators’ supply and with commercial firms’ marginal revenue ( p i/tx ), whereas it decreases with the rental price (the value of S a?(x) can be computed by plugging equation [10] into equation [9]).17 As in the case of service operators, competition for land is assumed to extract all profits (zero-profit condition), yielding 1/(1 ? ?) () () Ri?(x) = (1 ? ?) pi tx ? pa ?/(1 ? ?) , [14] where R i?(x) is the bid-rent function for commercial firms, which decreases with the unit price charged by service operators p a and increases with marginal revenue. Finally, R i?(x) is decreasing and strictly convex with respect to x. Land Equilibrium In the spirit of the von Thu?nian tradition, the three agents compete for land with an auction mechanism. The land equilibrium is driven by the value each type of agent pegs to a land plot at each possible location x ? X , which is given by their bid-rent functions (i.e., equations [6], [10], and [14]). At the equilibrium, we observe that R ? (x) = max{ R c?(x),R a?(x),R i?(x)}, in other words, 16 Alternatively, following Glaeser (2008), the optimization problem can be equally solved by defining a measure of land intensity (Si(x) = Sa(x)1 ? ? /Si(x)) and maximizing ?i(x)/Si(x) with respect to Si(x). 17 It is easy to check that the second-order condition always holds. February 2016 R ? (x) is the upper envelope of the bid-rent curves and land is assigned to the highest bidder at each location x ? X . As a consequence, land is specialized after the bidding process and no land is vacant (as long as bid-rent functions are positive). Looking at equation [1], it is easy to check that n c?(x) = 1/S c?(x) holds in a residential area. Equivalently, we observe n a?(x) = 1/S a?(x) in a services area, and n i?(x) = 1/S i?(x) in a commercial area.18 From inspection of equations [6], [10], and [14], it can be observed that bid-rent functions are continuous, monotonic, and downward sloping with respect to the distance from the CBD, because agents associate a higher value with the land plots located closer to the airport terminals (i.e., their reservation rent decreases with distance).19 The lemma that follows studies the effect of distance on equilibrium land plots and densities. Lemma 1. Equilibrium land plots increase with distance, and equilibrium densities decrease with distance. Equilibrium land plots increase with distance from/to the CBD as the net result of two opposing effects, which can be observed by inspection of equations [5], [9], and [13]. There is a direct negative effect of x on land plot demand functions and an indirect positive effect of x through rental prices R c?(x), R a?(x), and R i?(x), and the indirect effect overcomes the direct effect.20 Therefore, densities after the bidding process decrease with x, a result that clearly matches the empirical evidence. The lemmas that follow perform a comparative-static analysis that studies the impact of the parameters of the model on R c?(x), R a?(x), and R i?(x). First, we focus on the effect of ?, ?, and ?, which determine the land de18 Note that the precise value of n ?(x), n ?(x), and c a ni?(x) can be computed by plugging equation [6] into equation [5], equation [10] into equation [9], and equation [14] into equation [13], respectively. 19 Given that bid-rent functions decrease with x, they become negative at a certain distance. Naturally, land becomes vacant when all of them become negative. 20 The net effect of x on S ?(x), S ?(x), and S ?(x) can be c a i easily computed. More information is available from the authors upon request. 92(1) Flores-Fillol, Garcia-Lo?pez, and Nicolini: Land Surrounding Airports: Aerotropolis mand elasticity of consumers-workers, service operators, and commercial firms, respectively. Lemma 2. As ? increases, R c?(x) moves upward and land demand becomes more inelastic for consumers-workers. In the same way, an increase in ? shifts R a?(x) upward, turning service operators’ land demand more inelastic. Finally, R i?(x) rises with an increase in ? , and land demand becomes more inelastic for commercial firms. As a consequence, the amount of land assigned to each type of agent in equilibrium depends on the relative value of these parameters. The upshot is that there may be cases in which residential areas have a strong influence (i.e., when cities are important) and cases in which either commercial firms or service operators prevail as a consequence of the relative magnitude of their land demand elasticities.21 The remaining comparative-static effects, which are as expected, can be easily derived by inspection of equations [6], [10], and [14] and are summarized in the Lemma 3. Lemma 3. R c?(x), R a?(x), and R i?(x) fall with an increase in the unit transportation cost (t ). An increase in the price (p a) increases the revenues for service operators and the costs for commercial firms, and, therefore, R a?(x) increases and R i?(x) decreases. An increase in the wage ( w ) increases the revenues for consumers-workers and the costs for service operators, and, therefore, R c?(x) increases and R a?(x) decreases. Finally, R c?(x) decreases with V and R i?(x) increases with p i. In line with the idea suggested by Kasarda (2000) and Kasarda and Lindsay (2011), an aerotropolis appears when the spatial sequence services area–commercial area–resi- 21 Of course, a more sophisticated analysis would require considering heterogeneous agents within each category and a polycentric model where agents commute to more than one CBD (e.g., airport, city center). 63 dential area arises as the land equilibrium outcome, in a way made clear in Figure 1.22 This equilibrium outcome occurs for a certain parameter constellation. Given the stylized nature of the model, parameter choices in Figure 1 are necessarily arbitrary, and the analysis is therefore not exhaustive. However, it reveals some interesting insights that are in line with the empirical evidence we will discuss in Section III. Let p a = 2 and p i = 3, so that the price charged by commercial firms is higher than the price they pay to service operators. Additionally, let t = 1 and w = 2, so that consumers-workers’ income can cover land rent and transportation expenses for sufficiently low distances (see equations [3] and [4]). The assumption w > tx implies the upper
bound for distance x < 2.23 Further land plots remain vacant since no agent is willing to make a positive bid to occupy them. Given that R c?(x) decreases with consumers-workers’ indirect utility, we assume V = 0.2, which allows for active consumers-workers in the bidding process. Finally, land demand elasticities are ? = 0.5, ? = 0.6, and ? = 0.5 (the comparative-static effects of the elasticities described in Lemma 2 are also shown in the figure). The following proposition character- 22 By computing the crossing point between the bid-rent functions of commercial firms and service operators (i.e., equations [10] and [14]), we get a parametric measure (xia) of the boundary separating both agent types, that is, xia = ? 1 t 1 p1?? a 1 p1?d i (1 ? ?) ? w (1 ? ?) ? pa () () ? 1?? ? ?1 ? ? ??? (1 ? ?)(1 ? ?) . This point approaches the CBD as transportation costs increase. The reason is that commercial firms evaluate the opportunity costs derived from settling at a certain distance from the CBD, concluding that their valuation of closer locations to the terminals increases as the costs to access the airport increase. 23 It could be argued that the restriction w > tx should
concern only consumers-workers, given that it is required to
ensure Sc?(x) > 0 (see equation [5]). In any case, the aforementioned restriction constitutes a relevant upper bound in
the considered land equilibrium configuration (see Figure 1),
given that consumers-workers occupy the last land plots before land is vacant.
64
Land Economics
February 2016
FIGURE 1
The Aerotropolis Land Equilibrium
izes an aerotropolis land configuration in the
light of the previous analysis.24
We determine from our theoretical framework that the land organization in proximity
of an airport depends on the interplay among
service operators, commercial firms, and con-
sumers-workers. An aerotropolis appears
when the spatial sequence of services area,
commercial area, residential area arises as the
land equilibrium outcome. In this section, we
focus on four U.S. case studies to analyze the
presence and evolution of aeropolitan configurations.
In their book, Kasarda and Lindsay (2011)
assess the importance of airports on firms’
production and distribution chain and their
subsequent impact on the organization of activities around them. They provide a general
overview of interesting case studies across the
world with a clear focus on the United States.
More precisely, they adopt a dynamic perspective to identify four cases in which aerotropolises could be observed:25 (1) the reinforcing aerotropolis, an already existing
aerotropolis that experiences a self-reinforcing process yielding a concentration of regional business activities; (2) the growing
24 Note that degenerated land configurations in which a
certain agent type obtains no land in the bidding process are
not possible in our setting, given that consumers-workers
supply labor to service operators and that service operators
supply services to commercial firms.
25 Although their identification criteria are mostly qualitative, they have the advantage of providing a sensitive and
realistic interpretation of some current (and future) industrial
agglomerations in selected areas.
Proposition 1. An aerotropolis requires a nondegenerated land equilibrium in which both
firm types assign a higher value than consumers-workers to land plots located in the proximity of the airport. In addition, the land demand elasticity of service operators and
commercial firms has to be moderate so that
both of them are active in the bidding process.
This result is illustrated by the empirical evidence, as will be shown in the next section.
III. EMPIRICAL ANALYSIS
92(1)
Flores-Fillol, Garcia-Lo?pez, and Nicolini: Land Surrounding Airports: Aerotropolis
aerotropolis, an area with an unclear land organization that progressively adopts an aerotropolis-type configuration; (3) the declining
aerotropolis, an existing aerotropolis that progressively loses importance due to insufficient
infrastructure and available land to expand the
airport; and (4) the aborted aerotropolis, an
area where an aerotropolis is not formed despite displaying all the relevant features to become an aerotropolis (e.g., available space,
good infrastructure, strategic position for land
deliveries, culture of just-in-time practices),
due to the attraction of a city-downtown preventing any polycentric spatial organization.
Therefore, our empirical strategy consists
of selecting a representative airport for each
of the aforementioned cases, following Kasarda and Lindsay (2011), and then providing
a quantitative assessment of their spatial dynamics. The selected airports are Memphis International Airport (MEM) in Tennessee
(Case 1), Louisville International Airport
(SDF) in Kentucky (Case 2), Los Angeles International Airport (LAX) in California (Case
3), and Newark Liberty Airport (EWR) in