Space Matters: The China and U.S. Case

by

Presented by Mark D. PartridgeOhio State University/Swank Chair in Rural-Urban Policy

Prepared for presentation at the International Workshop on Regional, Urban and Spatial Economics in

ChinaSchool of Economics, Jinan University

Guangzhou, ChinaJune 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. 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 7

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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

Hefei Shi

Chuzhou Shi

Lai’an Xian

Nanjing Shi

Shanghai Shi

Jiangsu ProvinceAnhui Province

MPO

MPO = MP Nearest Prefecture CityMPN = MP Nearest Provincial Capital CityMPC = MP Own-Provincial Capital CityMPB = MP Nearest Mega City

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.

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)

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

00.20.40.60.8

11.21.41.61.8

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Transportation and Warehousing PPI Relative to GDP DeflatorTransportation and Warehousing PPI Relative to CPI

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].

Source: Partridge, 2010.

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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 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.

Source: Partridge, 2010.

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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.

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U.S. Population Growth by State: 1960-2008.

0 160 320 480 64080

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.

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1990-2008 Population Growth by County

Source: Partridge, 2010.

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Variables\Samples Non-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 − ) 94.87 323.93 255.89July humidity (diff − ) 57.61 215.23 162.94

Sunshine hours (diff Detroit−Orlando)

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 -99.8 NA

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 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 et al. (2008). For more details of the regression specification, see Partridge et al. (2008b).

Source: Partridge, 2010.

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 1999Source: Partridge et al. (2009) J. of International Economics

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Figure 4: Distance Penalties (%) for Housing Costs 2000Source: Partridge et al. (2009) J. of International Economics

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,Partridge.27@osu.edu

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