Presented by Mark D. Partridge Swank Professor in Rural Urban
Policy The Ohio State University Presented at LaSapienza University
Rome, Italy July 12, 2012 Central Place Theory: New Wine for Old
Bottles 1
Slide 2
2
Slide 3
I will summarize several papers. Mulligan, G.F., M.D.
Partridge, and J.I. Carruthers. (2012) Central Place Theory and Its
Reemergence in Regional Science. Annals of Reg. Sci. (48): 405-431.
Chen, A. and M.D. Partridge. Forthcoming. When are cities engines
of growth? Spread and Backwash Effects across the Chinese Urban
Hierarchy. Regional Studies. Partridge, M.D., D.S. Rickman, K. Ali
and M.R. Olfert. (2010) The Spatial Dynamics of Factor Price
Differentials: Productivity or Consumer Amenity Driven? Reg. Sci.
and Urban Econ., 40: 440-452. Partridge, M.D., D.S. Rickman, K. Ali
and M.R. Olfert. (2009) Agglomeration Spillovers and Wage and
Housing Cost Gradients Across the Urban Hierarchy. J. of Int. Econ.
78 (1): 126-140. Partridge, M.D. 2010 The Dueling Models: NEG vs
Amenity Migration in Explaining U.S. Engines of Growth. Papers in
Reg. Sci.. 89: 513-536. Partridge, M.D., D.S. Rickman, K. Ali and
M.R. Olfert. (2008). Lost in Space: Population Dynamics in the
American Hinterlands and Small Cities. J. of Econ. Geog. 8:
727-757. 3
Slide 4
Introduction 4 Economic Geography, Regional Science and
Regional/Urban economics have two key models to explain economic
geography and the spatial distribution of cities. First is the New
Economic Geography (Brakman et al., 2009). Monopolistic Competition
with falling long-run average costs and positive transportation
costs create a situation in which endogenous growth/decline takes
place due to proximity to markets and inputs. We can analytically
solve NEG models and develop an urban system. Paul Krugman won the
2008 Nobel Prize in part for the NEG. The JRSs 50 th Anniversary
Issue Points to the NEG as a key reason for the revitalization of
Regional Science after 2000. 2009 World Bank report used NEG for
policy advice.
Slide 5
Introduction 5 Second is Central Place Theory of Christaller
(1933). CPT is a tiering of urban areas from the hinterlands, to
small cities, all the way up to the largest cities based on the
order of service/occupation and the market thresholds needed to
sustain that service. Larger cities have the fullest range of
services and smaller places only have activities with small market
thresholds.
Slide 6
6 K=3 Marketing Area Concept. Source Wikipedia.com,
http://en.wikipedia.org/wiki/Central_place_theory CPTs Hexagons of
Central Places
Slide 7
Introduction 7 CPT is criticized for being static and lacking a
microfoundation. Nonetheless, Mulligan et al. (2012) report a
wealth of empirical evidence suggesting urban systems are organized
in this manner. For instance, CPT is consistent with Zipfs law. One
distinction with NEG is that total market potential matters in NEG
models, not proximity to large or small cities. NEG is a-spatial in
the sense that proximity to different sized cities does not play a
role.
Slide 8
Introduction 8 My aim is to show you that CPT does a nice job
of predicting where economic activity takes place. CPT does better
than NEG in describing location in the US and China. I contend that
CPT was abandoned too soon in the 1980s. CPT research was esoteric.
Also, because existing GIS was primitive, there were only partial
empirical tests of its relevance. Yet, if CPT had held on, GIS
would have extended its life. CPT is helpful in guiding
policymaking for rural and urban areas in illustrating how economic
activity is regional.
Slide 9
What drives spatial patterns? First let me dispel conventional
thinking. Popular commentators instead focus on new technologies
and globalization, which to them makes space much less relevant.
advances in ICT maturing and deconcentration of manufacturing
globalization improved transportation This implies that
agglomeration economies and cities are less important. Economic
activity can occur anywhere. There is not much need for economic
geography, spatial economics or regional science. 9
Slide 10
Death of Distance The Rural Rebound: Recent Nonmetropolitan
Demographic Trends in the United States Recent improvements in the
transportation and ICT infrastructure... thereby diminishing the
effect of distance. 40 Acres and Modem (Kotkin, 1998) Cairncross
Death of Distance (1995, 1997) Thomas Friedman World is Flat
10
Slide 11
11
Slide 12
What drives spatial patterns? Economic Geographers and Urban
and Regional Economists believe distance matters more today. Leamer
(2007) describes how distance costs are now having a bigger effect
on trade. Namely as services rise in importance, distance becomes
more important. Face to face contact vs commodity trade (McCann)
Small policy differences matter more in global economy if resources
are more mobile (Thisse, 2010). Regional Science is where it is at
(Partridge, SRSA Presidential Address, 2005). 12
Slide 13
Review of CPT 13 Review Christaller assumptions from Wikipedia:
an unbounded isotropic (all flat), homogeneous, limitless
surfaceisotropic an evenly distributed population all settlements
are equidistant and exist in a triangular lattice pattern evenly
distributed resources distance decay mechanism perfect competition
and all sellers are economic people maximizing profits consumers
are of the same income level and same shopping behaviour Consumers
have a similar purchasing power and demand for goods & services
Consumers Consumers visit the nearest central places that provide
the function which they demand. They minimize the distance to be
travelled no provider of goods or services is able to earn excess
profit(each supplier has a monopoly over a hinterland)
Slide 14
Christallers Assumptionscont. 14 Therefore the trade areas of
these central places who provide a particular good or service must
all be of equal size there is only one type of transport and this
would be equally easy in all directions transport cost is
proportional to distance traveled in example, the longer the
distance traveled, the higher the transport cost The theory then
relied on two concepts: threshold and range. Threshold is the
minimum market (population or income) needed to bring about the
selling of a particular good or service. Threshold Range is the
maximum distance consumers are prepared to travel to acquire goods
- at some point the cost or inconvenience will outweigh the need
for the good.
Slide 15
What does this mean for China? Many studies of Chinese Growth
Processes. Krugman (2010, subsequently published in Regional
Studies) argues that NEG applies more to China than (say) US. In
these models, market potential (MP) is not affected by its sources.
I will stress Ke an Feser (2010); Chen and Partridge (2011,
Regional Studies); Chen (2010); and Groenewold et al. (2007). They
use CGE models and econometrics. Use CPT, i.e., it matters what
city you are near. 15
Slide 16
Chen and Partridge We first use an aggregate market potential
(MP) variable from NEG. It is positively linked to GDP growth, but
not job growth. We find that Chinas urban growth is positivity
associated with GDP throughout the nation, without statistically
affecting labor migration. This suggests NEG is a good model to
understand Chinas regional development. 16
Slide 17
Chen and Partridge We split MP into that from the three coastal
mega cities, provincial capitals, and prefecture cities. (see
graph) We find evidence of considerable heterogeneity. Having
greater MP from the nearest provincial capital has the strongest
positive link to per-capita GDP growth in smaller
county-urban/rural locales. There are also positive and
statistically significant association for the prefecture MP
variables. 17
Slide 18
18 Figure 1: Illustration of measuring market potential across
the city hierarchy Notes: This map illustrates the market potential
heterogeneity across city hierarchy. Laian Xian is a county in
Anhui province. Chuzhou Shi is Laian Xians nearest prefecture city.
Hefei Shi is Laian Xians own-provincial capital city. Nanjing Shi
is the provincial capital city of Jiangsu province, which is also
the nearest provincial capital city of Laian Xian. Shanghai Shi is
the nearest mega city of Laian Xian. MPB indicates the market
potential in the mega city. MPC indicates the market potential in
the countys own provincial capital city. MPN indicates the market
potential in the countys nearest provincial capital city. MPO
indicates the market potential in the prefecture city.
Slide 19
Chen and Partridge MP from the mega-cities is inversely
associated with per-capita GDP growth. Our results are more
consistent with CPT, not NEG models. Inconsistent with World Bank
(2009) view that urbanization is good for all. Illustrates regional
growth process in most of China. Govt policies that favor the mega
cities may be at the expense of growth elsewhere. 19
Slide 20
Chen and Partridge If balanced growth across the entire country
is an objective, growth in the three coastal mega cities is
detracting from the goal (and may be reducing aggregate growth).
Fallah et al. (2010) find that MP is positively associated with
individual income inequality, creating further social pressures. We
conclude the more nuanced view of growth is correct. This view fits
into CPT augmented by Spread and Backwash. NEG is too blunt for
policy analysis. 20
Slide 21
21 In advanced economies such as the US. I summarize some work
I did with my coauthors including Kamar Ali, Rose Olfert and Dan
Rickman. Central Place Theory (CPT) and NEG both hypothesize that
small urban areas and rural areas support the growth of large
cities: Hinterlands Large Cities Residents in the hinterlands
purchase services in large citiesforming the market for cities This
describes the origination of the urban system through the early 20
th Century
Slide 22
In advanced economies such as the US. But what is better in a
maturing urban system? NEG or CPT? New Information-Communication
Technology Improved transportation Technological improvements in
natural resource industrieslabor saving in rural areas Improved
importance of manufacturing and then services in urban areas.
Decoupling of place of work and place of residence via commuting.
The rise of peri-urban/exurban living. 22
Slide 23
In advanced economies such as the US. These have change the
urban system to a mature system: Now causation seems to run much
more from city to rural areas for economic growth. 23
Slide 24
Do NEG models explain these developments. 24 NEG models
generally predict that falling transport costs imply that there
should be more urban concentration. The US has had falling
transport costs implying US core urban region should have greatly
benefited especially largest cities.
Slide 25
25 US Relative Transportation and Warehousing Costs Compared to
the CPI and GDP Deflator, 1947 - 2009 (2000 = 1) Notes:
Transportation and Warehousing producer price index relative to the
GDP deflator and Consumer Price Index. Source for the
Transportation and Warehousing Producer Price Index and the GDP
deflator is the U.S. Bureau of Economic Analysis [downloaded from
http://www.bea.gov/industry/gpotables/gpo_action.cfm on February
16, 2010] and the source for the Consumer Price Index is the U.S.
Bureau of Labor Statistics [downloaded from
http://data.bls.gov/cgi-bin/surveymost?cu on February 16,
2010].http://www.bea.gov/industry/gpotables/gpo_action.cfm on
February 16 Source: Partridge, 2010.
Slide 26
26 1969-2007 Growth By Metro Area Size in 1969 (%) Notes: Large
MSA is > 3 million population in 1969. There are 8 MSAs in this
category: New York, Los Angeles, Chicago, Philadelphia, Detroit,
Boston, San Francisco and Washington DC. The Large-Medium MSA have
a 1969 population of 1 million - 3 million (27 MSAs). The
Small-Medium Metro Areas are 250,000 - 1 million 1969 population (
85 MSAs). Small MSAs have a 1969 population of 50,000 - 250,000
(230 MSAs). 17 Metros with less than 50,000 in 1969 were omitted
due to a small base. These were generally in UT, NV, and FL and
grew very rapidly. Big metro growth is dominated by Washington DCs
growth. We use 2008 MSA definitions, which makes nonmetro growth
appear especially small. Source: U.S. Bureau of Economic Analysis:
www.bea.gov. Source: Partridge, 2010.
Slide 27
27 1969-2007 Growth For Representative Metro Type (%) Notes:
The Traditional Core includes New York, Boston, Philadelphia and
Chicago. The Rustbelt includes Detroit, Cleveland, Pittsburgh and
St Louis. Sunbelt includes Miami, Atlanta, Phoenix, Tampa, Orlando
and Las Vegas. Mountain/Landscape includes Seattle, Denver,
Portland, and Salt Lake. Source: U.S. Bureau of Economic Analysis:
www.bea.gov. Source: Partridge, 2010.
Slide 28
28 U.S. Population Growth by State: 1960-2008. 016032048064080
Miles Population Growth from 1960 to 2008 (%) 133.9 - 811.5 95.6 -
129.5 52.9 - 88.0 36.8 - 43.4 26.2 - 35.8 -22.5 - 22.3 Map Created
on November 16, 2009 Mean=89.1 Median=43.4 Source, U.S. Census
Bureau. Source: Partridge, 2010.
Slide 29
29 1990-2008 Population Growth by County Source: Partridge,
2010.
Slide 30
The winner? 30 Amenity led growth is a clear winner. NEG fares
poorly, but what about CPT?
Slide 31
Partridge et al. (2008) Empirical Model to capture CPT Regress
county population growth between periods 0 t on initial-period
geographic variables at time 0 DepVar: Cross Sectional: 1950-00,
1950-70; 1970-00, 1980-00, 1990-00 total population growth (one
fixed-effect panel model) This mitigates endogeneity Key distance
variables are exogenous/predetermined %POP ist-0 =+ GEOG ist-l +
AMENITY is + s + ist 31
Slide 32
Defining the Hinterlands 1) Rural, 1,300 rural counties (based
on 2003 U.S. Census definitions) that never achieved urban status
during the sample period; 2) Non-Metro, restrictive (NM-R), adds
the micropolitan area counties to rural counties, yielding
nonmetropolitan areas (NM); (adding another 600+ counties 3)
Non-Metro, inclusive (NM-I), NM-R + 784 counties that were NM in
1950 but were assigned to new and existing MAs between 1950-2003:
2,700+ counties in total 32
Slide 33
Small Urban Areas 1) Small MA-R, includes counties in small MAs
(2003 definition) of less than 250,000 people in 1990, they were
small through the entire period; 2) Small MA-I, adds 218 counties
that were part of a small MA at some time during the sample period
(even though they are currently in a >250k MA). 33
Slide 34
Main Geography Variables Distance: Nearest Metropoltian Area
Rural/NM counties: distance in kms to nearest MA of any size;
(population-weighted centroids) Urban MA counties: distance in kms
to center of urban core if multi-county, 0 for single county urban
area; (pop.-weighted centroids) We also consider nonlinear distance
effects. 34
Slide 35
Geography Variables Incrementally Proximate Higher-Tier Areas
the incremental distances to reach MAs of at least 250k, at least
500k, and at least 1.5m. pop. (for all counties) We have also
considered a highest tier of NY, LA, & Chicago in other
settings. include population of the nearest or actual urban center
(MA) to the county 35
Slide 36
36
Slide 37
Garfield County, a rural Utah county, about 4,000 residents
(1990). 1. The nearest urban area is Cedar City (MICRO) located
88kms away. The nearest MA is St. George (about 90,000 population),
146kms away, an incremental distance of 58kms (146- 88). 2. Nearest
larger MA > 250K, which is Provo-Orem, UT (pop. of 377,000), is
278kms from Garfield County, incremental distance versus St. George
is 132kms (278-146). 3. The nearest MA > 500K, the next higher
tier, Salt Lake City, UT (969,000 people). Salt Lake is 321kms from
Garfield County, incremental distance of 43kms (321-278). 4.
Nearest MA > 1.5 million people, the next higher tier above Salt
Lake, is Phoenix, AZ (3.25 million 1990 pop.). Phoenix is 477kms
away from Garfield County, an incremental distance of 156kms
(477-321). Distance calculations 37
Slide 38
Geography Variables NEG Market Potentialfollows Hanson (2005)
Personal income in surrounding 0-100, 100-200, 200- 300, 300-400,
and 400-500 km rings from the population-weighted center. We use
measures that predate the initial sample period by one year to
mitigate possible endogeneity 38
Slide 39
Other Variables Several amenity variables, population density,
population of the nearest MA. For the cross-sectional models, we
use GMM to account for spatial autocorrelation. 39
Slide 40
40 40 GMM Regressions of U.S. County Population Change
1950-2000 (%) Variables Rural (preferred) NM-RNM-ISmall MA-R
(preferred) Small MA-I Intercept 525.15** (4.50) 870.50** (3.44)
895.42** (3.43) 1723.18** (2.58) 1500.89** (2.31) Distance to
nearest MA -0.685** (-6.39) -1.001** (-5.02) -2.927** (-7.50) n.a.
(Distance to nearest MA) 2 1.5E-3** (4.38) 2.1E-3** (3.85) 7.0E-3**
(6.27) n.a. Distance to center of own MA n.a. 2.022 (0.26) -2.190
(-0.79) (Distance to center of own MA) 2 n.a. -3.7E-2 (-0.19)
8.2E-3 (0.14) Inc dist to metro > 250,000 pop -0.165** (-5.95)
-0.223** (-4.44) -0.648** (-5.91) -0.666* (-1.89) -0.889** (-3.86)
Inc dist to metro > 500,000 pop -0.107** (-2.79) -0.252**
(-3.09) -0.617** (-4.20) -0.601 (-1.41) -0.794** (-2.68) Inc dist
to metro > 1,500,000 pop -0.060** (-2.26) -0.051 (-1.29)
-0.218** (-3.02) -0.517** (-2.56) -0.398* (-1.90) Population
density 50 -0.459** (-4.76) -0.184** (-2.14) -0.356** (-2.22)
-0.503** (-2.89) -0.293** (-3.08) Population of nearest/own MA 50
8.4E-6 (1.41) 3.1E-6 (0.41) 1.3E-5** (2.27) 1.4E-5** (2.02) 5.7E-6
(0.81) Weather/Amenity a YYYYY State fixed effects (FE) YYYYY
Adjusted R 2 0.380.310.350.470.45 No. of counties
129719692752418641 F-statistic: All dist to MA = 0 Inc distance to
MA = 0 15.74** 13.57** 16.24** 12.99** 67.29** 44.48** 3.85**
6.24** 8.49** 14.13**
Slide 41
41 Variables Distance to nearest MA (Distance to nearest MA) 2
Distance to center of own MA (Distance to center of own MA) 2 Inc
dist to metro > 250,000 pop Inc dist to metro > 500,000 pop
Inc dist to metro > 1,500,000 pop Personal income within 100 km
radius, 1969 Personal income within 100-200 km ring, 69 Personal
income within 200-300 km ring, 69 1969 Personal income within
300-400 km ring, 69 1969 Personal income within 400-500 km ring, 69
Weather/Amenity a State fixed effects (FE) Adjusted R 2 No. of
counties F-statistic: All dist to MA = 0 Inc distance to MA = 0
Rural (preferred) NM-RNM-ISmall MA-R (preferred)Small MA-I -0.348**
(-4.47) -0.427** (-4.66) -0.908** (-6.56) n.a. 7.8E-4** (3.43)
9.0E-4** (3.51) 2.1E-3** (5.62) n.a. 0.974 (0.64) 1.464* (1.84)
n.a. -1.6E-2 (-0.39) -2.6E-2 (-1.41) -0.112** (-5.02) -0.137**
(-5.36) -0.233** (-5.76) -0.233** (-4.11) -0.278** (-4.13) -0.074**
(-2.72) -0.123** (-3.18) -0.189** (-3.79) -0.238 (-3.07)** -0.186**
(-2.72) -0.027 (-1.42) -0.016 (-0.78) -0.058** (-2.20) -0.164**
(-2.98) -0.058 (-1.19) 1.6E-03** (2.25) 9.3E-04 (1.25) 5.0E-04**
(1.98) -1.2E-04 (-0.27) 9.2E-04** (3.21) 3.1E-04** (2.45) 1.2E-04
(0.86) -3.6E-04 (-1.54) -2.0E-04 (-0.92) -1.0E-04 (-0.58) 1.6E-05
(0.15) -1.3E-05 (-0.12) -2.3E-04* (-1.75) 3.1E-04** (2.06) 2.6E-04*
(1.65) 1.6E-04* (1.91) 8.5E-05 (0.69) -1.7E-05 (-0.12) 2.5E-04
(1.35) 3.9E-05 (0.22) 2.9E-05 (0.38) -1.2E-04 (-1.33) -2.2E-04**
(-2.09) -4.3E-05 (-0.19) 2.6E-05 (0.14) YYYYY YYYYY
0.470.390.400.520.43 129719692752418641 10.91** 14.32** 14.67**
18.10** 40.57** 30.91** 6.19** 8.75** 8.59** 10.37** GMM
Regressions of U.S. County Population Change 1970-2000 (%) Rural
(preferred) -0.422** (-5.40) 9.1E-4** (3.91) n.a. -0.133** (-5.83)
-0.091** (-3.22) -0.041** (-2.15) N N N N N Y Y 0.47 1297 20.55**
23.74**
Slide 42
Conclusionscontinued Key results Distance penalties are even
bigger for small urban areas than remote areas Distance penalties
are more important than NEG style market potential or proximity to
cities matter more than having an otherwise equal market size.
Summary: At the mean distance from the urban tiers, the typical
Rural county is expected to have 73% less growth than a Rural
county that is adjacent to a MA core (i.e., 2.6% less annual
growth, all else equal). Controlling for MP, population density,
amenities, state fixed effects Distance penalties are growing in
importancedistance is not dead. Mature urban system: cities support
hinterlands 42
Slide 43
43 Variables\SamplesNon- metro Small metro Large metro Mean pop
growth % (std. dev.)32.20 (122.93) 122.47 (271.64) 138.00 (257.38)
Jan temp (diff )-135.58-768.63-731.88 July temp (diff
)94.87323.93255.89 July humidity (diff )57.61215.23162.94 Sunshine
hours (diff DetroitOrlando) 7.69-257.88-248.06 Amenity rank (diff
between (3) and (5) on a 1-7 amenity scale -69.7-153.1-143.1 Mean
distance penalty due to remoteness from urban hierarchy.
-96.6-99.8NA Regression Results for 1950-2000 County Population
Growth: Selected Variables Note: Boldface indicates significant at
10% level. Small metro is counties located in MSAs with 250,000
population, measured in 1990. The difference between Detroit and
Orlando uses their actual values. 1 std dev. represents a
one-standard deviation change in the variable. Other amenity
variables include percent water area, within 50kms of the Great
Lakes, within 50kms of the Pacific Ocean, and within 50kms of the
Atlantic Ocean, and a 1 to 24 scale of topographyi.e., from coastal
plain to extreme mountainous. The models were then re-estimated
with USDA Economic Research Service amenity rank replacing all 9
individual climate/amenity variables to calculate the amenity rank
effects (available online at USDA ERS). The amenity scale is
1=lowest; 7=highest. Most of the regression results reported here
were not reported in Partridge et al. (2008). For more details of
the regression specification, see Partridge et al. (2008b). Source:
Partridge, 2010.
Slide 44
US CaseSummary Large cities in China are rapidly growing,
creating backwash and widening regional differentials. US Large
cities are not necessarily growing rapidly. Nice places are
winningamenity growth. NEG model is not a good predictor and
amenity led growth wins in the USPhil Graves. CPT fares better.
(Partridge et. al 2008, 2009). CPT approaches illustrate the need
for regional approach to governance. 44
Slide 45
45 Figure 3: Distance Penalties (%) for Median Earnings 1999
Source: Partridge et al. (2009) J. of International Economics
Slide 46
46 Figure 4: Distance Penalties (%) for Housing Costs 2000
Source: Partridge et al. (2009) J. of International Economics
Slide 47
Conclusion Space matters! Distance matters and popular folklore
about its death is not true. CPT is very helpful in empirical
models of growth in very different places such as China and the US.
I believe it is equally helpful elsewhere. In both the US and
China, it matters what type of cities/places you are near. US
growth driven by weather/landscape. NEG is rigorous and formal but
it is not a nuanced enough to be good predictor of where economic
activity will occur in both China and the US. at least for policy
purposes. Further mega city growth may be detrimental to Chinese
growth and socioeconomic goals. 47
