Transcript Slide 1

Space Matters: The China and U.S. Case
by
Presented by Mark D. Partridge
Ohio State University/Swank Chair in Rural-Urban Policy
Prepared for presentation at the
International Workshop on Regional, Urban and Spatial Economics in
China
School of Economics, Jinan University
Guangzhou, China
June 15-16, 2012
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Introduction
• Regional Science differs from economics with a
more explicit recognition that space matters.
– Proximity to people, ideas, markets affect
environmental and socioeconomic outcomes.
– Economists usually model things in an a-spatial
manner. Take the famous core H-O model that didn’t
even include transport costs. Likewise, most trade
models do account for a country’s specific neighbors.
• Even in urban economics, the models are often a-spatial and
there is greater interest in methodological contribution
rather than policy innovations.
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Introduction
• Modern geographers are interested in spatial
heterogeneity. They tend to use qualitative
research to identify its source. Their models
can be unclear because of they are difficult to
generalize.
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What drives spatial patterns?
• Regional Science and Regional/Urban economics
have two key models to explain economic
geography and the spatial distribution of cities.
– 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 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.
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What drives spatial patterns?
• 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.
– World Bank’s (2009) report used NEG for policy advice.
• One key distinction between NEG and CPT is CPT is
static. Another is that total market potential
matters in NEG models, not proximity to large or
small cities.
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What drives spatial patterns?
Popular commentators instead focus on new
technologies and globalization, which to them
makes space much less relevant.
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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
spatial economics or regional science.
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Death of Distance
• The Rural Rebound: Recent Nonmetropolitan
Demographic Trends in the United States
(Calvin Beale and Kenneth Johnson)
http://www.luc.edu/depts/sociology/johnson/p99webn.html
– “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
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What drives spatial patterns?
• 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).
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What does this mean for China
• Many studies of Chinese Growth Processes.
• First, 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.
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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 China’s urban growth is positivity
associated with GDP throughout the nation,
without statistically affecting labor migration.
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Chen and Partridge
• We split MP into that from the three coastal mega
cities, provincial capitals, and prefecture cities.
• We find evidence of considerable heterogeneity.
– Having greater MP from the nearest provincial capital
has the most 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.
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Figure 1: Illustration of measuring market potential across the city hierarchy
Lai’an Xian
MPO
Chuzhou Shi
Nanjing Shi
Hefei Shi
Jiangsu Province
Anhui Province
MPO = MP Nearest Prefecture City
MPN = MP Nearest Provincial Capital City
MPC = MP Own-Provincial Capital City
MPB = MP Nearest Mega City
Shanghai Shi
Notes: This map illustrates the market potential heterogeneity across city hierarchy. Lai’an Xian is a county in Anhui
province. Chuzhou Shi is Lai’an Xian’s nearest prefecture city. Hefei Shi is Lai’an Xian’s own-provincial capital city.
Nanjing Shi is the provincial capital city of Jiangsu province, which is also the nearest provincial capital city of Lai’an Xian.
Shanghai Shi is the nearest mega city of Lai’an Xian. MPB indicates the market potential in the mega city. MPC indicates
the market potential in the county’s own provincial capital city. MPN indicates the market potential in the county’s nearest
provincial capital city. MPO indicates the market potential in the prefecture city.
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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.
• Gov’t policies that favored the mega cities may
have been at the expense of growth elsewhere.
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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
correct. NEG is too blunt for policy analysis.
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The US Case
• Summarize some work I did with my coauthors
including Kamar Ali, Rose Olfert and Dan Rickman.
• 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.
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US Relative Transportation and Warehousing Costs Compared
to the CPI and GDP Deflator, 1947 - 2009 (2000 = 1)
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
1947
1949
1951
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
0
Transportation and Warehousing PPI Relative to GDP Deflator
Transportation and Warehousing PPI Relative to CPI
Source: Partridge, 2010.
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]
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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].
1969-2007 Growth By Metro Area Size in 1969 (%)
Source: Partridge, 2010.
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.
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These were generally in UT, NV, and FL and grew very rapidly. Big metro growth is dominated by Washington DC’s growth. We use 2008 MSA definitions, which makes
nonmetro growth appear especially small. Source: U.S. Bureau of Economic Analysis: www.bea.gov.
1969-2007 Growth For Representative Metro Type (%)
Source: Partridge, 2010.
Notes: The Traditional Core includes New York, Boston, Philadelphia and Chicago. The Rustbelt includes Detroit, Cleveland,
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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.
U.S. Population Growth by State: 1960-2008.
Mean=89.1
Median=43.4
Population Growth from 1960 to 2008
(%)
133.9 - 811.5
95.6 - 129.5
52.9 - 88.0
36.8 - 43.4
0
80
160
320
480
640
Miles
26.2 - 35.8
-22.5 - 22.3
Source, U.S. Census Bureau.
Map Created on November 16, 2009
Source: Partridge, 2010.
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1990-2008 Population Growth by County
Source: Partridge, 2010.
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Regression Results for 1950-2000 County Population Growth: Selected Variables
Variables\Samples
Mean pop growth % (std. dev.)
Jan temp (diff − )
NonSmall Large
metro
metro metro
32.20 122.47 138.00
(122.93) (271.64) (257.38)
-135.58 -768.63 -731.88
July temp (diff − )
94.87
323.93
255.89
July humidity (diff − )
57.61
215.23
162.94
Sunshine hours (diff
Detroit−Orlando)
Amenity rank (diff between (3) and
(5) on a 1-7 amenity scale
Mean ‘distance’ penalty due to
remoteness from urban hierarchy.
7.69
-257.88 -248.06
-69.7
-153.1
-143.1
-96.6
-99.8
NA
Source: Partridge, 2010.
Note: Boldface indicates significant at 10% level. “Small metro” is counties located in MSAs with < 250,000 population and “Large 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 topography—i.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
22 et
al. (2008). For more details of the regression specification, see Partridge et al. (2008b).
US Case—Summary
• Large cities in China are rapidly growing, creating
backwash and widening regional differentials. US
Large cities are not necessarily growing rapidly.
• Nice places are winning—amenity growth.
• NEG model is not a good predictor and amenity
led growth wins in the US—Phil Graves.
• What about CPT, does it fare better? Yes, based
on hedonic results and population growth
(Partridge et. al 2008, 2009). NEG MP fares much
worse than distance from different sized cities.
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Figure 3: Distance Penalties (%) for Median Earnings 1999
Source: Partridge et al. (2009) J. of International Economics
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Figure 4: Distance Penalties (%) for Housing Costs 2000
Source: Partridge et al. (2009) J. of International Economics
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Conclusion
• Space matters! Distance matters and popular
folklore about its death is not true.
• 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.
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Thank you for your attention,
[email protected]
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