Journal of Urban Economics - Southern Methodist...

Journal of Urban Economics 100 (2017) 80–103

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Journal of Urban Economics

journal homepage: www.elsevier.com/locate/jue

The birth of edge cities in China: Measuring the effects of industrial

parks policy

�

Siqi Zheng

a , Weizeng Sun

b , Jianfeng Wu

c , Matthew E. Kahn

d , ∗

a Center for Real Estate, and Department of Urban Studies and Planning, Massachusetts Institute of Technology, USA b Institute for Economic and Social Research, Jinan University, China c School of Economics and China Center for Economic Studies (CCES), Fudan University, China d Department of Economics, USC and NBER, USA

a r t i c l e i n f o

Article history:

Received 25 August 2016

Revised 15 April 2017

Available online 19 May 2017

Keywords:

Edge cities

Agglomeration

Place based investments

a b s t r a c t

China’s government has spent hundreds of billions of dollars to invest in new industrial parks with

the intent of boosting the economic growth, by attracting new firms into the parks and also generat-

ing spillovers for the local economy. Do such place-based investments in capital raise urban productivity

or is this another case of the powerful state misallocating capital in China? This paper measures the

localized spillover effects of 110 parks built in eight major cities on firm productivity, wages, and local

manufacturing employment growth. We find that the geographic spillover effect of parks is an increasing

function of the park’s overall human capital level, the FDI share, and its “synergy” with nearby incum-

bent firms (measured by Marshallian factors). Using geo-coded data, we document that the growth in

local employment and wages stimulates nearby local housing construction and retail store openings. The

rise of a new production sub-center causes the emergence of a suburban “consumer city”.

© 2017 Published by Elsevier Inc.

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1. Introduction

Over the last twenty years, place-based industrial policies have

been a crucial component of the development strategy pursued

by the Chinese government. China’s local officials have allocated

millions of acres of land and made huge capital investments to

create industrial parks. As of 2006, there were 1568 national-

level and provincial-level industrial parks distributed in more than

270 Chinese cities, with 9949 square kilometers in total. Although

these parks only occupy around 0.1% of China’s total land area,

they contribute to about 10% of China’s GDP and one-third of

FDI. Several studies have explored China’s industrial parks and

� We thank Nick Mueller and participants at the January 2015 ASSA, USC Eco-

nomics, the USC Price School, LSE and University College, London and UNLV for

useful comments. We are indebted to Zhikuo Liu for helpful discussions. We thank

the UCLA Ziman Center for Real Estate for generous funding. We thank the National

Natural Science Foundation of China (No. 71625004 , No. 71573054 , No. 71273154 ,

No. 71322307 , No. 71533004 ), the National Key Research & Development (R&D)

plan ( 2016YFC0502804 ) for research support. Wu thanks also the MOE Project of

Key Research Institute of Humanities and Social Sciences at the China Center for

Economic Studies (CCES), Fudan University, and the Research Institute of Chinese

Economy (RICE), Fudan University for generous funding and providing access to the

ASIF database. ∗ Corresponding author.

E-mail address: [email protected] (M.E. Kahn).

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http://dx.doi.org/10.1016/j.jue.2017.05.002

0094-1190/© 2017 Published by Elsevier Inc.

heir positive impacts on local economies using a macro approach

Wang, 2013; Schminke and Van Biesebroeck, 2013; Alder et al.,

016 ). These studies use city level panel data and a difference in

ifference estimator to estimate the local effects of new parks. Lu

t al. (2016) use firm data and observe positive effects of industrial

arks on capital, employment and output within the park’s bound-

ry.

The spatial agglomeration literature has emphasized that such

pillovers are often highly localized (see Rosenthal and Strange,

0 03, 20 04 ). This literature suggests that the spillovers of China’s

ew industrial parks are likely to be localized within a city. Based

n this point, we will test for the effects of new parks on local

roduction activity and on consumer behavior in a close proximity

o the new parks. These new parks tend to be built at the edge

f a metropolitan area, with special land, tax, financial and eco-

omic policies to recruit highly productive firms. Physical prox-

mity between firms who seek to co-agglomerate facilitates lo-

al economic growth through stimulating trade by lowering trans-

ortation costs and by facilitating learning and social interaction

Combes et al., 2011 ). Moreover, these new industrial parks stim-

late economic growth by solving a land assembly problem and a

ross firm co-ordination problem and allowing firms to cluster to-

ether in a timely fashion in the context of China. These benefits

re more difficult to be reaped in developed world cities featured

ith pre-existing durable structures and with thousands of firms


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S. Zheng et al. / Journal of Urban Economics 100 (2017) 80–103 81

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1 Industrial parks are authorized by different level governments: state, provin-

cial, or prefecture (or below) government. Those parks authorized by the state

and provincial governments enjoy more favorable policies, such as lower interest

rate loans, larger tax, land price and utility price discounts. We only focus on

those parks because many of the lower-level industrial parks did not obtain for-

mal approval from the central and provincial governments and violated the rele-

vant laws and regulations. In 2003, the central government investigated industrial

parks regarding their potential violation of land use regulations and this resulted

in a large number of those lower-level industrial parks being abolished (see Cartier

(2001) and Adler (2013) ). 2 For the incumbent firms within the park’s boundary which had been estab-

lished before the park was introduced, generally they are unable to enjoy the favor-

able policies unless the park’s administrative committee (AC) re-sign an agreement

with them and change their registration status. Our interviews with some AC offi-

cials show that the latter case seldom happened.

ho do not fully consider the agglomeration externalities when

aking their individual location choice. Successful parks will at-

ract skilled workers who will seek a short commute to work. This

reates an incentive for developers and retail store businesses to

pen up quality housing and shopping close to the new parks.

herefore, a new edge city emerges.

Our study builds on recent work that has documented the het-

rogeneous effects of place-based policies ( Faggio et al., 2017 ).

everal studies have examined the role of place-based policies

n U.S local and regional growth, and their general conclusion is

hat those place-based policies do not lead to net growth ( Rossi-

ansberg et al., 2010; Kline and Moretti 2013; Neumark and Kolko

010 ). As we report below, we find that 70% of the new indus-

rial parks built during 1998–2007 in the eight major Chinese cities

enerate positive TFP spillovers in their vicinity, while 30% of those

arks turn out to have negative or insignificant TFP spillovers.

This paper uses detailed within-city geocoded micro data to

tudy the consequences of Chinese local governments recent in-

estments in industrial parks. We document both the agglomera-

ion benefits within a park’s geographic area and those spillover

ffects beyond its boundary. To test for the extent of the local-

zed agglomeration spillovers, we merge together several geocoded

ata sets in eight Chinese major cities. These cities are the home

f 110 state- and provincial-level industrial parks. We test whether

he creation of industrial parks is associated with production and

onsumption agglomerations based on key outcome indicators in-

luding: TFP for incumbent firms, local job and wage growth, new

ousing construction, home prices, and retail opportunities. Our

pproach allows us to study the economic incidence of this pub-

ic policy and builds on U.S work such as Busso et al. (2013) .

hese outcomes directly benefit the local government officials be-

ause their fiscal revenue is tied to the commercial and resi-

ential land sales, and also the tax collected from productive

rms.

Our study builds on the research estimating localized produc-

ivity spillover effects. Most agglomeration studies focus on the

nited States (see Rosenthal and Strange, 2004; Arzaghi and Hen-

erson, 2008; Greenstone et al., 2010 ). Our new estimates of the

o-agglomeration effects build on the estimates from developed

ities generated by Ellison et al. (2010) . If place-based policies,

uch as industrial parks, help to reallocate labor and capital to its

ighest and best use, local economic boom is likely to emerge.

Our work builds on the consumer city literature which used

ata from the United States to highlight the emergent consumption

pportunities available in cities ( Handbury and Weinstein, 2015;

iamond, 2015; Glaeser et al., 2001 ). The birth of a large industrial

ark creates a spatially concentrated center of employment and

urchasing power far from the city center. Many of the workers in

he park and at the expanding firms located close to the park will

eek out housing and shopping opportunities nearby. This creates

profit motive for retailers and housing developers to co-locate to

upply such goods ( Waldfogel, 2008 ). We demonstrate that such

ositive accumulative forces appear in the suburbs of China’s ma-

or cities when new parks are created.

To investigate the causal effect of an industrial park on local

conomic growth, we follow Greenstone et al. (2010) identifica-

ion strategy of conducting a difference in difference approach for

eographic areas who “won” and “lost” in luring a place based

reatment to their area. Industrial parks are not assigned to geo-

raphic locations at random. A mayor chooses whether and where

o build a park and then recruits firms to locate in the park. After

hese events have taken place, the local benefits (both production

nd consumption opportunities) unfold. To mitigate the concerns

f bias arising from park site selection, we create a control group

y identifying geographic site candidates for industrial parks based

n urban planning documents from the 1950 s to the early 1980 s.

hese candidate areas that were not selected to become industrial

arks form our control group.

Using micro data-based approach, we further test for a rich

et of such heterogeneous effects, and study the key mechanisms

enerating such spillovers. We find evidence of heterogeneous

pillover effects such that older parks, state-level parks, parks that

eature a higher human capital level, a larger share of FDI firms,

smaller share of State Owned Enterprises (SOEs), and a higher

o-agglomeration level of industrial composition, and parks with

better “fit” with the local incumbent industries (in terms of

tronger input-output linkages, labor market pooling, and knowl-

dge spillover) have larger agglomeration effects.

The rest of the paper is organized as follows. In Sections 2 we

ntroduce the institutional background and our conceptual frame-

ork. Section 3 describes data, empirical models and identification

trategies. Sections 4 and 5 present our main results and hetero-

eneity estimates, respectively. We conclude in Section 6 .

. Institutional background and conceptual framework

.1. Institutional background

We focus on the parks authorized by the state or the provincial

overnment in China. 1 Being a host city of such parks has become

favorite strategy of city mayors to compete for FDI and to foster

ocal economic growth ( Wu et al., 2013; Zheng et al., 2014 ). These

arks are approved by the state and provincial governments but

ost of them are implemented at the city level.

China’s industrial parks are opened in a two-step process. First,

city government initiates an industrial park program accompa-

ied with two general packages of policies. One is that city gov-

rnments make large capital investments to improve the trans-

ort infrastructure, utilities, storage, and other service facilities.

he other is associated with a bundle of preferential policies (see

ang, 2013; Alder et al., 2016; Lu et al., 2016 ) including: (1) tax

eductions: corporate income tax rates of 15% −24% as opposed to

he 33% firms normally pay in China are available to foreign enter-

rises, technologically-advanced enterprises, and export-oriented

nterprises; (2) discounted land-use fee and utility prices: to at-

ract productive firms, industrial parks set low land transfer fees

s well as utility prices (electricity, water, etc.), and also favorable

ayment methods; (3) special treatment in securing bank loans:

tate-owned banks put priority on and offer favorable interest rate

o the loan applications from the firms in industrial parks; (4)

aster and easier administrative approval for firm registration for

xporting and importing. 2

Secondly, city mayors are likely to use the “managed hand” to

romote the industrial park policies. China’s unique political sys-

em grants city mayors with powers that far exceed their West-

rn counterparts. On the one hand, the city government represent-

ng the state owns urban land. They can easily convert agricultural

and at the edge of cities into urban use. Moreover, they have the

82 S. Zheng et al. / Journal of Urban Economics 100 (2017) 80–103

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power to allocate a large parcel of land to build an industrial park

and engage in land assembly in ways that are impossible in the

United States. Given that Chinese mayors increase their promotion

chances by raising local economic growth, they have strong incen-

tives to pursue pro-growth strategies. However, this may also bring

in the risk that some city mayors are not smart enough, or they

are too aggressive, so that they make the wrong decision and over-

invest in parks.

Third, the recruitment process in industrial parks is always op-

erated in the lower administrative committee level on behalf of the

city government. The administrative committee in the industrial

park takes the responsibility to direct and to administer the park –

such as project approval, local taxation, land management, finance,

personnel, and public service provision. Private negotiations take

place between the administrative committee staffs and the poten-

tial entrants regarding the exact bundle of subsidies each firm will

receive if it agrees to enter the park. These negotiations lead to

a set of firms choosing to enter the park and then we observe

the subsequent outcomes both within and spilling over outside

of the park. Such recruitment process bears a close resemblance

to the anchor tenant recruitment challenge that arises in creating

a successful shopping mall ( Pashigian and Gould, 1998; Gould et

al., 2005 ) and the development of U.S suburban planned towns

( Henderson and Mitra, 1996 ). The mall owners gain profit from

activities within their mall. If there are positive spillover effects

from a mall to the local community, the mall owner has no claim

to those. But in the Chinese case, the city mayor is in charge of

the entire jurisdiction (including the center city and the new city

around the park) and thus internalizes all of the possible spillover

effects generated by an industrial park . While Chinese mayors

seek to engage in pro-growth strategies, industrial parks are risky

and costly. Below, we will document evidence of significant het-

erogeneous treatment effects induced by these investments.

China’s mayors face capital constraints. In choosing whether

and where to invest in a park, city mayors face a trade-off between

a stream of benefits an industrial park offers and the upfront and

opportunity costs of establishing the park on a large plot of land. 3

The city mayors tend to raise funds through various channels in-

cluding debt financing. 4 In 2011, the total fixed asset investment in

131 state-level industrial parks was 2092 billion RMB yuan ($332

billion US dollars), which was about 35% of the total fixed asset in-

vestment in the whole nation. 5 According to IMF estimates, local-

government debt reached 36% of GDP in 2013, a doubling since

2008, and will increase to 52% of GDP in 2019. Global investors

have worried that such rising debt combined with the slowdown

of the Chinese economy could create a future debt crisis. 6 Such lo-

cal debt crises are less likely to take place if past investments yield

a significant flow of medium term agglomeration benefits.

3 Such farmland is owned by rural villages. If the city government (the upper-

level government of those rural villages) wants to build a new industrial park on

farmland, it needs to covert the use type of that land parcel from agricultural use

to industrial use, and compensate the rural villages who own the land. In most

cases the range of compensation for farmers for land taken is quite low because it

is often based on income generated in agriculture use instead of being tied to its

opportunity cost (the value of the land if allocated to urban use) ( Ding and Song,

2005 ). 4 On average, bank loans, land sale revenue and on-budget fiscal revenue account

for 28%, 25% and 33% of the sources of municipal infrastructure investment in China

in 2010, respectively, and the rest of the money comes from other non-bank finan-

ial channels. See: Ryan Rutkowski. Four Myths about local Infrastructure Invest-

ment in China. China Economic Watch, the Peterson Institute for International Eco-

nomics. http://blogs.piie.com/china/?p=3281 . 5 China Statistics Yearbook, 2012. 6 See http://www.wsj.com/articles/debt- that- once- boosted- its- cities- now-

burdens- china- 1422415981

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.2. Local production and consumption spillovers due to industrial

arks

Here we provide a simple framework to motivate our empirical

pproach. Our starting point is that industrial parks attract produc-

ive firms, and also improve the productivity of incumbent firms

ocated close or within the park. There are two groups of incum-

ent firms – those within and those outside (but close to) a park’s

oundary. We are mostly interested in measuring the spillover

reatment effect on the incumbent firms.

Each incumbent manufacturing establishment in a city is en-

owed with a production function. All else equal, firms located

loser to the city center (where the main agglomeration takes

lace) are more productive. Once an industrial park is built, each

ocation within a city becomes two dimensional as we track the

lant’s distance to the city center and its distance to the closest

ndustrial park. 7 An incumbent firm chooses the amount of labor,

(priced at a competitive wage w ); capital, K (with input price p );

nd land, L (priced in the competitive land market with rent r ), to

aximize its corresponding profit �:

a x K,N,L � = f (A, K, N, L ) − pK − wN − rL

A is the productivity shifter (TFP), and is used to capture the

gglomeration economies this firm enjoys. Output’s price is nor-

alized to one. Here we allow agglomeration externalities, A , to

epend on the firm’s distance to the central business district (CBD)

nd the nearest park’s boundary A = A ( D CBD , D park ) . Where,

A/∂ D CBD < 0 ; ∂ A/∂ D park < 0 .

The positive agglomeration economies increase the productivity

f incumbent plants. This will lead to higher output, ∂ f / ∂ A > 0,

nd thus higher profit, which will trigger the entry or relocation

f firms who are interested in gaining access to such spillovers to

he vicinity of the park. Incumbent firms enjoying increased pro-

uctivity because of their proximity to the new park should also

e less likely to exit. The incumbent firms experiencing productiv-

ty growth will expand by occupying more land and hiring more

orkers. The growth of the park itself and the subsequent entry of

rms and firm expansion in the park’s vicinity leads to competi-

ion for inputs. Wages will be bid up in a vicinity of the productive

ub-center, thus we should observe a negative wage gradient with

espect to distance to the park’s boundary:

w/∂ D park = (∂ w/∂ A ) · (∂ A/∂ D park ) < 0 .

The increases in both employment and wage within and around

he park contribute to the rise in purchasing power, and thus cre-

te the “market potential” ( Hanson, 2005 ), which is the demand

ase for both the housing and retail markets in this new edge city.

Given that commuting is costly, and due to the fact that work-

rs earn higher wages in a vicinity of the park, there will be a

reater density of new retail openings and new home sales in close

roximity to this new employment center, and the house price will

lso be higher. We predict the effects of an increase in distance to

n industrial park as follows:

new home sales /∂ D park < 0

new home prices /∂ D park < 0

new retail opennings/∂ D park < 0

7 If a city has multiple parks, other parks will also have some effect on this plant.

In the empirical analysis we will construct a variable to measure this global impact

of all the parks in the city, but now we abstract from this and only consider the

closest park.

http://blogs.piie.com/china/?p=3281

http://www.wsj.com/articles/debt-that-once-boosted-its-cities-now-burdens-china-1422415981


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8 This information is provided by the Ministry of Land and Resources of China

(MLRC). 9 For those parks that do not have websites or public released information, we

contacted the local officials to obtain the boundary information. 10 The largest park is 64, and the smallest one of 0.2 square kilometers. If we

measure the distance from the centroid of a park to the corresponding city’s CBD,

the average park is located 24.9 kilometers away. The most remote park is 95.1

kilometers from the city center.

.3. Measuring park heterogeneity

There may exist significant heterogeneity in parks’ spillover ef-

ects. As we will document below, 30% of the parks we study gen-

rate insignificant or even negative local spillovers.

We seek to explain the determinants of park heterogeneous

reatment effects. We have collected data that allow us to differen-

iate the parks along four dimensions. The first dimension is based

n the cohort and age of the park. We allow for two cohorts –

lder parks established before 1998 and new ones established in

r after 1998. If the Chinese leaders selected better locations for

he earlier parks to set up growth engines in the country, a di-

inishing returns hypothesis would predict that older parks will

ave larger spillover effects than the newer ones. Within a given

birth cohort” of parks, we expect that the spillover effect grows

ith the park’s age. The second dimension of park differentiation

s the park’s administrative level – whether it is a state-level or a

rovincial-level park. The parks approved by upper level govern-

ent would enjoy more favorable policies packages such as lower

nterest rate loans, larger tax cut, cheaper land price and utility

osts, etc. Thus we expect that all else being equal, a state-level

ark would generate larger spillovers.

The third heterogeneity dimension is based on the likely syn-

rgies between the park’s tenants and local incumbent industries

easured by continuous economic distances between them. Here

ur “thought experiment” is to hold the park’s composition of

rms constant and see how it fits in different neighborhoods. Fol-

owing Glaeser and Kerr (2009), Ellison et al. (2010) and Jofre-

onseny et al. (2011) , we construct four metrics measuring the

ustomers and supplier linkages, sharing a large labor market, and

echnology spillovers between the incumbent firms in a vicinity of

he park and the tenants of the park. We expect that the park’s

pillover effect will be larger if these four forces are larger. In Ap-

endix B, we explain how we construct these metrics.

The fourth dimension is the park’s own composition of plants

nd industries. We hypothesize that a park will generate larger im-

acts if it is larger in size, closer to the CBD (thus stronger linkage

ith the city’s mean economic center), and features a lower share

f state-owned enterprises (SOE) employment, higher share of FDI

rms, higher level of human capital, and higher coagglomeration

etween the industries within the park (see Table 1 for variable

efinitions, and Appendix A for the construction of coagglomera-

ion index).

. Data and estimation strategy

Our study focuses on eight major cities in China: Beijing, Shang-

ai, Shenzhen, Tianjin, Dalian, Wuhan, Xi’ an and Chengdu. We

ave the micro data sets of manufacturing firms, real estate trans-

ctions, and retail shops in those eight cities. These cities include

ll three first-tier cities in China (Beijing, Shanghai, Shenzhen)

nd a couple of the top second-tier cities. We construct four key

eocoded data sets for these eight cities.

.1. Four geocoded data sets

.1.1. The spatial unit of analysis

Within a Chinese city, there are three levels of administrative

nits (from the upper to the lower level): district (or county),

iedao (“zone” thereafter) and juweihui (communities or villages,

small zone” thereafter). For instance, Beijing has 16 districts, 320

ones and 5274 small zones. In the eight cities, the average sizes

f a zone and a small zone are 47.9 and 4.1 square kilometers, re-

pectively. We know the exact geographic boundaries of industrial

arks and zones, but we only have the centroid of a small zone.

or all plants in our data set, we know their zone identifiers; and

or about 60% of them, we know their small zone identifiers.

We know the exact street address of all residential complexes

nd retail establishments. We create two by two kilometer grid cell

aps (the same area as the average size of a small zone) for all

ight cities. This allows us to count the number of new home sales

nd new retail openings by grid cell.

.1.2. The industrial parks data set

According to the 2006 “Bulletin List for the Official Boundaries

f Chinese Industrial Parks” 8 , there are 110 state- and provincial-

evel industrial parks in these eight cities (43 are state-level and 67

re provincial-level), accounting for 8.6% in all such parks in China.

rom the list we know each park’s name, location, and the year

his park was established. From the websites of industrial parks’

Cs we obtain the exact geographic boundary of each park. 9 We

hen geocode the exact boundaries for the 110 parks in the eight

ities ( Fig. 1 ). Each city has several industrial parks – Beijing has

1 parks, and Dalian has 8 parks. The average park’s size is 11.88

quare kilometers. 10

We define the parks established before 1998, the first year of

ur manufacturing plants data set (ASIFs) as “old parks”, and those

stablished during the ASIFs sampling period as “new parks” .

mong the 110 parks, 70 are “old” and 40 are “new” parks.

.1.3. The manufacturing plants data set (ASIFs)

We obtain plant-level data from the Annual Survey of Industrial

irms (ASIFs) dataset conducted by National Bureau of Statistics of

hina (NBSC) from 1998 to 2007. All the state-owned enterprises

SOEs) and non-state owned enterprises with annual sales of more

han 5 million RMB in the manufacturing sector are surveyed, with

etailed information on a plant’s identification, operations and per-

ormance, and all financial variables. Those firms hire roughly 70%

f the industrial employment, generate 90% of the industrial out-

uts and 98% of the exports ( Brandt et al., 2012 ). We link plants

ver time using their information on ID number, name, industry

ode, address (small zone/zone identifier), etc., and construct an

nbalanced panel of 64,759 plants in this ten year period for these

ight cities.

An advantage of this ASIFs data set (compared to the economic

ensus data set) is that it enables us to estimate plant-level total

actor productivity (TFP). We use the data on outputs and interme-

iate inputs, deflated by output and input price indices reported in

randt et al. (2012) , to calculate the real capital stock, real value

dded, and then estimate plant-level TFP (See Appendix A). The

econd key variable is the plant-level wage measure ( Wage ). This

ata set does not contain the wage records for individual work-

rs. Instead, it reports each plant’s annual wage bill and the num-

er of workers employed in each year. Thus we calculate the aver-

ge wage by plant by year. The third variable is the park-specific

o-agglomeration index. We follow Ellison and Glaeser (1997) ’s

ethodology in developing industry pair co-agglomeration index,

hich measures the extent to which the two industries tend to

o-locate in a certain area. Our park-specific co-agglomeration in-

ex is the weighted average of the bilateral co-agglomeration in-

ices for the existing industry pairs in the park (using employ-

ent in each industry pair as the weight). Intuitively, if those in-

ustry pairs that have higher co-agglomeration indices have larger


Table 1

Variable definitions and summary statistics.

Variable Definition Obs. Mean Std. Dev. Min Max

Manufacturing data by plant by year

TFP Total factor productivity (in logarithm) 143,795 8.25 1.25 0.03 15.44

Wage Average wage of employment (yuan RMB) 143,795 16,799.68 11,504.53 2097.38 51,872.73

D_Center Real travel distance (based on the road network) to the city center

(km)

143,795 22.78 19.73 0.11 171.54

Park = 1 if the plant is in an industrial park 143,795 0.04 0.19 0 1

ImpactArea = 1 if the plant is within 2 km from the closest industrial park’s

boundary

143,795 0.10 0.29 0 1

After = 1 if the time is after the establishment of the closest park (or the

park where the plant locates)

143,795 0.62 0.49 0 1

OtherParks The global impact of all parks (except the closest one). See Eq. (2) . 143,795 115.75 82.33 1.03 298.81

SOE = 1 if the plant is an SOE (State Owned Enterprise) 143,795 0.32 0.47 0 1

FDI = 1 if the plant is a FDI (Foreign Direct Investment) enterprise 143,795 0.32 0.47 0 1

Plant_Size Total employment 143,795 279.26 873.00 8 60,834

Plant_Age Age of the plant 143,795 11.76 24.54 1 61

D_Highway Distance to the closest highway of year 2007 (km) 143,795 11.53 14.07 0 138.74

D_Railway Distance to the closest railway station (km) 143,795 7.30 7.46 0.01 73.97

D_Airport Distance to the closest airport (km) 143,795 28.43 17.73 1.34 113.66

D_University Distance to the closest university (km) 143,795 19.84 22.74 0.18 211.47

Industrial park attributes by park by year


(km)

243 28.48 15.75 6.01 61.93

Park_Size The planned area (km

2 ) 243 5.62 6.50 0.50 29.40

SOE_Share Output of SOEs as a share of total output in the park 243 0.43 0.18 0.14 0.82

FDI_Share Employment of FDI firms as a share of total employment in the park 243 0.28 0.15 0.08 0.87

Human_Capital Share of workers with education attainment of college and above 243 0.19 0.06 0.12 0.34

Park-vicinity “synergy indices” by year (see Appendix B for how we construct these variable)

Input_Linkage Input linkages between a park and an industry 5990 0.03 0.07 0 0.77

Output_Linkage Output linkages between a park and an industry 5990 0.03 0.07 0 0.84

Labor_Pooling The size of labor market pooling between a park and an industry 5990 0.02 0.03 0 0.23

Skill_Spillover The knowledge spillover possibility between a park and an industry 5990 0.02 0.03 0 0.27

IVs for park locational choice by zone

Village_Density The number of small zones per square kilometer (/km

2 ) 1689 1.83 3.41 0.01 33.93

Downstream Whether the zone locates in the downstream of its nearest river 1689 0.51 0.50 0 1

Employment density by zone

by year

Employment Manufacturing employment density (/km

2 ) 16,890 390.20 1533.31 0 56,291

OtherParks The global impact of all parks (except the closest one). See Eq. (2) . 16,890 79.95 61.09 0.47 297.80

Housing construction and retail activities by 2 km × 2 km grid by year

New Home Sales Number of new housing sales 215,408 22.19 191.38 0 10,826

ParkVicinity(8 km) = 1 if the grid is within 8 km of an industrial park which was built

between 1998–2006

215,408 0.27 0.44 0 1

New Restaurants Number of new restaurant openings 215,408 1.51 14.89 0 1312

New Entertainments Number of new entertainment establishment openings 215,408 0.40 4.00 0 309

New Shops Number of new retail shop openings 215,408 2.14 23.07 0 2184

ParkVicinity(5 km) = 1 if the grid is within 5 km of an industrial park which was built

between 1998–2006

215,408 0.18 0.39 0 1


(km)

215,408 64.50 40.75 0.46 237.00

OtherParks The global impact of all parks (except the closest one) as of year 2007.

See Eq. (2) .

215,408 84.21 57.99 5.85 299.71

D_Highway Distance to the closest highway of year 2007 (km) 215,408 29.31 26.04 0.00 142.24




Residential complex data by complex by year

New Home Price Average housing sale price (yuan RMB/m

2 ) 182,045 10,303.07 9631.45 10 0 0 170,848


(km)

182,045 15.08 14.54 0.11 104.57

ParkVicinity = 1 if the residential complex within 5 km an industrial park which

was built between 1998–2006

182,045 0.88 0.32 0 1

OtherParks The global impact of all parks (except the closest one) as of year 2007.

See Eq. (2) .

182,045 76.05 51.67 0 203.73

FAR The floor area ratio 182,045 2.95 2.06 0.06 27.45

Green Greening space ratio (%) 182,045 36.76 9.14 0 95

Parking Parking space share 182,045 0.77 0.45 0 6.89

D_Highway Distance to the closest highway of year 2007 (km) 182,045 7.84 9.05 0.00 81.24





Fig. 1. Within-city locations and geographic boundaries of industry parks.

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mployment shares in a park, this park will have a larger park-

pecific co-agglomeration index, which means that the industries

n the park enjoy a higher synergy level (see Technical Appendix A

or details).

We geocode each plant using its zone/small zone identifier. To

easure the spillovers of these industrial parks in their vicinity,

e map plants into industrial parks. We identify the plants inside

he parks and those outside. For those plants outside, we measure
d
heir distances to the city center and to the park’s boundary within

city, respectively.

.1.4. The housing and consumption retail data sets

We obtain the price and quantity data for all newly-built resi-

ential complexes developed by real estate developers from 2006

o 2013 from the local housing authorities in these eight cities. This

ata set contains information on the average transaction price and


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11 The impact area (where the spillovers exist) is likely to vary by park. For the

sake of simplicity, we estimate the average size of the impact area for all parks

and use it in our regressions. For each type of spillovers we have the corresponding

average impact area. We acknowledge this “averaging” may introduce some bias.

For instance, for some very productive parks, we under-estimate their impact area,

and vice versa. 12 “The closest park” is selected based on the distances from the plant to each of

the 40 parks in 2007. 13 We use a travel-distance algorithm written by John Voorheis to construct this

real travel distance measure. This STATA code is available on http://pages.uoregon.

edu/jlv/code.html . 14 In 1992, the Chinese State Council approved the construction of the “7-5” net-

work, which was completed ahead of schedule by the end of 2007 (see Faber

(2014) for details). The construction during that period was much intensive, so to

simply make this control we use the highway network by the end of 2007 to mea-

sure the accessibility.

the number of units sold by residential complex by month. Across

the eight cities, the number of observations varies from 80 0 0 to

40,0 0 0 complex-months. The physical attributes for each complex

include the floor area ratio ( FAR ), green space rate ( Green ), and the

ratio of parking space to the number house units ( Parking ).

Using data on the longitude and latitude of each residential

complex, we geocode all the residential complexes. We calculate

each complex’s locational attributes such as its distance to the city

center, its distance to the closest industrial park’s boundary, and

the 2 km by 2 km grid cell this complex is located in. We also cal-

culate the number of housing units sold for each grid cell in each

year.

We construct a dataset for local private consumption goods

based on dianping.com. a yelp.com in the US. The website covers in-

formation on twelve general categories of urban consumption ser-

vices, and the three biggest are restaurants, entertainment facili-

ties, and retail shops. For these three major categories, as of 2013

there were more than 874,0 0 0 retail establishments in these eight

cities . We know the establishment date for each shop. We geocode

them in the GIS maps and calculate each category’s count density

by grid cell (the data are reliable for years after 2005). Since our

housing and retail data sets only start from 2006 and 2005, while

our manufacturing firm data set is between 1998–2007, what we

observe is the ex-post outcomes in the housing and retail sectors

after the establishments of our sample industrial parks. The cover-

age of the consumption sectors in our study can help study the ef-

fects of an industrial park featuring high-tech companies that pro-

duce Internet services such as Baidu and Alibaba. Such effects are

ignored in previous localized spillover studies that focus on manu-

facturing plants.

Using a major industrial park in Beijing (“Beijing Economic and

Technological Development Zone” ) as an example, Fig. 2 shows the

above geographic units of analysis we use when doing the geocod-

ing work. Table 1 presents the variable definitions and summary

statistics. It also provides information concerning our various geo-

graphic levels of aggregation.

3.2. Econometric models

We seek to study the causal impact of new industrial parks on

the local economy. We hypothesize that the opening of an indus-

trial park will increase the TFP of incumbent firms, local wages and

the density of economic activity in and around the park. The lo-

cation of firms in an industrial park will lead workers to locate

nearby to keep commuting costs down. Workers will bid up price

of land, thus increasing local population density. This triggers in-

creases in local housing prices and new construction as well as

new retail store openings. Our empirical work will study each step

in this “chain” .

3.2.1. Baseline difference in difference specifications

We first focus on measuring the productivity spillovers of new

parks on incumbent firm productivity ( TFP ), incumbent firm wage

rates ( Wage ) and the density of manufacturing jobs in the close

vicinity from new parks ( Employment ). Since we are estimating the

productivity premiums of a geographic area (the park itself or the

park’s impact area) and we have two periods (before and after

the park is established), it is natural to employ the difference-in-

difference (DID) strategy. For the subset of 40 new parks built be-

tween 1998 and 2007 (the sample period of the ASIF data set), we

will employ the DID specification.

As shown in Fig. 3 , we define a dummy variable Park which

equals one for the firms (or zones/small zones, grid cells, housing

units) within the park boundary. Assume that the park’s spillovers

have an “impact area” which ends at x̄ kilometers from the park

oundary, we define a dummy ImpactArea to represent this area. 11

e will identify the average impact area for productivity spillovers,

ousing market spillovers and retail market spillovers, respectively.

dummy Outside is created to represent the area outside the im-

act area. The dummy After equals one for the years after the es-

ablishment of the park.

To be clear, we estimate two separate DID models. First, we

eep the plants within the new parks ( Treatment = Park = 1, treat-

ent group) and the plants in the outside area (the area outside

he impact area, Outside = 1, Treatment = 0, control group), then es-

imate:

it = α0 + α1 · T reatmen t i j + α2 · A f te r jt

+ α3 · T reatmen t i j · A f te r jt + β×X it + city F Es

+ dist rict −year t rend + indust ry −year F Es + μi + ε it (1)

here the subscript i, j, t refers to plant i , industrial park j and

ear t, respectively. For those plants within parks, After jt switches

rom zero to one after park j is opened in year t ; for those plants in

he outside area, After jt switches from zero to one after its closest

ark j is opened. 12 Second, we keep the plants in the impact ar-

as of the new parks ( Treatment = ImpactArea = 1, treatment group)

nd the plants in the outside area ( Outside = 1, Treatment = 0, con-

rol group), then re-estimate Eq. (1) .

In both equations, the control group is the plants in the out-

ide area ( Outside = 1 indicates that the plant is outside the im-

act area). The firms in the impact area will receive spillovers from

he park, and the park may also absorb the firm activities from

his impact area. So the firms in the impact area are not clean

nough to act as the control group. The key coefficient of inter-

st is α3 , which measures the premiums of the outcome Y for the

ark and the park’s impact area in the two equations, respectively.

n TFP and wage regressions, we include city fixed effects, district-

pecific time trends, and industry-specific year dummies. We also

nclude plant fixed effects ( μi ), so we are tracking the within-plant

FP ( Wage ) change of the incumbent plants which is attributed to

he introduction of a new industrial park. Note that the incumbent

lants within the park’s boundary which had been established be-

ore the park’s establishment, are generally not eligible for the fa-

orable policies (see footnote #1), but they do enjoy agglomeration

conomies brought by firms clusters in the park. This is similar to

hose plants in the impact area.

The vector X contains the attributes of firms, including the real

ravel distance to CBD (the driving distance based on the road

etwork in that city) from observation i to the city center, 13 the

lant’s size and age, and whether it is a SOE plant or a FDI one,

ts distances to the closest railway station, airport and university. 14

nother control variable in X is the influence from other indus-

rial parks, besides the closest park. We construct a variable ( Oth-

rParks ) to measure the “global” impact of all the existing parks

except the closest one) in the city. We use the market potential

http://pages.uoregon.edu/jlv/code.html


Fig. 2. Geographic unit of analysis.

Fig. 3. Definitions of park, impact area and outside the impact area.

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ariable ( Hanson, 2005 ) to construct this variable:

ther P ar k s it =

∑

j � = j 0 ρi j · log (Em p jt )

=

∑

j � = j 0

[

1 −(

d i j

d max

)2 ] 2

· log (Em p jt ) (2)

In each year t , we identify all the existing parks in the city, and

he closest park j 0 . For all the parks except park j 0 , we use the in-

erse distance (in quadratic weighting function) from this plant i

o those parks as weights to compute the weighted sum of those

arks’ employment in year t . If a city has more parks and the plant

s relatively closer to those parks, we expect this plant will re-

eive more spillovers and thus have a higher TFP or wage. We will

ontrol for this “global impact” of other parks, and focus on the

pillovers from the closest park.

.2.2. The matched DID specification

In a city, industrial parks are not randomly assigned to loca-

ions. There are many unobservables (unknown to researchers) that

ary across locations. The validity of the above DID specification

Eq. (1) ) depends on the assumption that those unobservables are


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identical between the treatment area (the park or the park’s im-

pact area) and the control area (the outside area nearby the park).

But this may not be true. For instance, infrastructure conditions,

such as transportation accessibility, electricity connection and the

sewerage system, may vary between the treatment and control ar-

eas. We employ the matched DID approach described below to ad-

dress this issue.

In a second set of estimation results, we follow Greenstone et

al. (2010) and implement their “winners and loser pairs” strategy.

We rely on old urban planning documents to identify a subset of

“similar zones” . Since the founding of new China in 1949, urban

planning in China had been greatly influenced by a “top-down”

command-and-control regulation system inherited from the former

Soviet Union, so it had been featured as state-led. In that central

planning era, urban planning was a major tool used by China’s

state and local governments to control and monitor urban infras-

tructure construction and production activities. The main goal of

1980 s urban planning was to facilitate the industrialization pro-

cess in China, based on the concepts of “industrialization without

urbanization” and “work first, live second” . The site selection of

future industrial zones (under different names such as “satellite in-

dustrial towns” and “industrial zones/areas” ) and the placement

of industrial projects were the key elements in the early waves of

“city master plans” .

Once some areas were selected as future “industrial zones” in

a city’s master plan, the city governments would make a large in-

vestment in infrastructure construction and land assembly, to en-

hance that place’s productivity potential. The city government also

had the power to relocate industrial plants to that zone or cre-

ate new plants there. With the input of such resources in several

decades, the industrial base of such zones had been well fostered,

and they became ideal candidates for “industrial parks” when

China initiated the new place-based policy of industrial parks in

late 1980 s and early 1990 s. In this new era, a city has to obtain

approval from the state or provincial governments to establish an

industrial park, so they cannot build as many parks as they want.

Only a portion of the early “industrial zones” set up in old city

master plans became industrial parks. Our interviews with some

senior principals in the municipal urban planning bureaus substan-

tiates our description of the industrial park site selection process. 15

This fact provides us the opportunity to identify the “winners” and

“losers” in this sample of early-defined industrial zones, in terms

of obtaining the industrial park “quota” . 16

Using the old city master plans from the 1950 s through the

1980 s for seven of the eight cities in our sample (Shenzhen is a

new city established in late 1980 s so this strategy does not ap-

ply to it), we identify 245 early-defined industrial zones, of which

about 40% became “winners” . These “winners” were selected to be

included in later developed industrial parks (in many cases a park

covers multiple early-defined industrial zones). Others are called

“losers” – they missed the opportunity to become parks due to the

limited quota of parks approved by the state.

15 In one of the interviews we conducted, Mr. Dong, the former President of Bei-

jing Urban Planning Design and Research Institute (a department within Beijing City

Planning Committee) told us, in late 1980s and early 1990s, the built-up area of

Beijing was small. The infrastructure condition in suburban Beijing was quite poor,

except for those early-defined “satellite industrial towns” (many of them were the

central towns of suburban districts/counties) with favorable infrastructure invest-

ment. Therefore, when choosing the location of a new industrial park, the urban

planning committee would prefer those “satellite industrial towns”. Given the re-

stricted number of new parks that could be built, only some of such “satellite in-

dustrial towns” were selected. 16 Among the 40 new industrial parks which were established during 1998 and

2007 in the eight cities, 30 industrial parks are in the “candidates” (early-defined

industrial zones).

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We use the losers to identify what would have happened to the

roductivity of incumbent firms in the “winner” zones in the ab-

ence of the industrial park policy. Specifically, we assume that in-

umbent firms’ TFP would have treated identically in the absence

f the industrial park in “winner” and “loser” zones. To support the

alidity of this research design, we will test the similarity of the

re-trends in TFP and the balancing of many ex-ante observable

haracteristics of “winner” and “loser” geographic areas. Among

he 40 new parks during 1998–2007, 30 are the “winners” in the

arly-defined industrial zones. We match each of them with the

eographically closest “loser” to assure that they are as similar as

ossible.

We regard the “loser” zone as a runner-up, and define the

rea outside the “loser” zone but two kilometers from the zone’s

oundary as its “hypothetical” impact area. We define the dummy

inner which equals to one for the park and its impact area, and

quals to zero for the “loser” zone and its “hypothetical” impact

rea. In Eq. (3) , instead of using plants in the “Outside ” area as the

ontrol group, we estimate a park’s (or a park’s impact area’s) TFP

r wage premium using its corresponding “loser” zone (or its im-

act area) as the control area. We also include winner-loser pair

xed effects, so that the coefficient α3 measures the within-pair

FP or wage premiums of the park and its impact area.

it = αM

0 + αM

1 · T reatmen t i j + αM

2 · A f te r jt

+ αM

3 · T reatmen t i j · A f te r jt + βM ×X it + P air ID F Es

+ city F Es + district −year trend

+ industry −year F Es + μi + ε it (3)

.2.3. Cross-sectional comparisons for measuring park treatment

ffects

For the parks built before 1998, we do not observe a pre-period

n our data sets. We still seek to compare the economic impact of

hese parks on nearby economic activity. Since we cannot intro-

uce a DID specification here, we instead estimate a type of cross-

ectional model ( Eq. (4) ) where the control group are geographic

reas in the same year, in the same city (and the same district

ithin a city) that are equi-distant to the city center and other

ey locations (highway, railway station, airport and major univer-

ity, which are included in X it ).

it = αC 0 + αC

1 · T reat men t i j + βC ×X it + cit y F Es

+ dist rict −year t rend + indust ry −year F Es + μi + ε it (4)

By estimating Eq. (4) , we rely on the classic urban monocentric

ity model. This model assumes that within a city at a point in

ime that any two locations that are equi-distant to the city cen-

er (and other key locations) are perfect substitutes. The payoff for

s of adopting this framework is that it allows us to construct a

ross-sectional control group for geographic areas treated with an

ndustrial park. The treatment group contains either the plants in

arks, or the plants in parks’ impact areas.

Similarly, when testing the consumption spillovers of industrial

arks (home price, home sales, and the number of new restaurant,

ntertainment facility and retail shop openings in a vicinity of an

ndustrial park), we also estimate the cross-sectional model ( Eq.

4) ) because these consumption data sets exist for the years after

006.

Most of the land within industrial parks is zoned as industrial

and, but a small number of land parcels are zoned for residential

nd commercial uses. So we will see some apartments and retail

hops within parks but high density of them just outside the park

oundary. Since no favorable policy exists for residential and retail

ectors within the park boundary, in later empirical work testing

onsumption spillovers we will combine a park and its impact area

ogether (we call this whole area as ParkVicinity ) and regard it as

he treatment area.


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Table 2

The within city determinants of industrial park locational choice.

Dependent variable: A dummy indicating whether park j is located in zone k

Dependent variable = 1

If zone k is the home to at

least one park in 2006

Dependent variable = 1

If park j is established

in zone k

(1) (2)

log( distance to the

closest existing park )

−0.214

(0.205)

log( D_Center ) 0.0136 ∗ 0.690 ∗

(0.00707) (0.362)

log( D_Highway ) 0.00246 0.205

(0.00344) (0.145)

log( D_Railway ) −0.0101 ∗∗ −0.205 ∗

(0.00428) (0.122)

log( D_Airport ) 0.00724 −0.374

(0.00632) (0.246)

log( D_University ) −0.0434 ∗∗∗ −0.0242 ∗

(0.00689) (0.0126)

Village_Density −0.0126 ∗∗∗ −0.220 ∗

(0.00259) (0.117)

Downstream 0.0195 ∗∗ 0.786 ∗∗

(0.00986) (0.350)

City fixed effects Yes Yes

Joint F-test of IVs 25.22 ∗∗∗ 8.92 ∗∗

(0.0 0 0) (0.012)

Observations 1689 5166

chi2 126.9 31.63

Note: This table reports results from estimating Eq. (6) in the text. Column (1)

reports results from a probit model. The dependent variable equals one if zone k

is the home for at least one industrial park by the end of 2006. The unit of anal-

ysis is a city/zone. Column (2) reports results from a conditional logit model to

examine whether a newly-established park j (we only focus on the 40 industrial

parks established during the years 1998–2006 locates in zone k . Here the unit of

observation is a zone/park where each park locates in one zone. Village_Density

and Downstream are two variables for predicting where a park is located within

a city. See the text for how we construct them. Marginal effects are reported for

probit model (column (1)). Standard errors are reported in parentheses. The stan-

dard errors are clustered at urban district level. ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, ∗∗∗ denotes p < 0.01.

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4

This cross-sectional model is also used to identify the scope

f the impact area in which spillovers exist. For each type of the

pillovers, we will assume the same spatial scope for the impact

reas of all new parks. We define a set of dummies, OutRing (0, x ),

utRing ( x , 2 x ), OutRing (2 x , 3 x ),…, OutRing (( n-1 ) x, nx ), to denote a

et of x -kilometer-width rings ( x = 1 or 2 km) in this impact area,

rom inside to outside. Assume that the impact area ends at n x

ilometer from the park’s boundary. To reveal this width of the

mpact area, we augment Eq. (4) by including the set of OutRing

ummies ( Eq. (5) ). Y can be one of the productivity or consump-

ion spillovers variables. We expect to observe sharp discontinuity

t the boundary of the first several rings. As we move outward, the

oundary discontinuity vanishes at the boundary of a specific ring

namely n x from the park’s boundary, so the coefficient of the Out-

ing dummy changes to be statistically insignificant from n ). Then

e regard n x as the total width of the impact area.

it = αR 0 + αR

1 · P ar k i j +

n̄ ∑

n =0

αR 2 ,n · OutRing (nx, (n + 1) x ) i j + βR ×X it

city F Es + district −year trend + industry −year F Es + μi + ε it

(5)

Table A2 and A3 in Appendix C show how we identify the aver-

ge park’s impact area in terms of TFP and consumption spillover

ndicators. Figure A1 and A2 intuitively show the boundaries of the

mpact areas of production and consumption spillovers. The results

how that the scope of the impact area for TFP spillovers is on av-

rage 2 km from a park’s boundary, while that for housing and

etail market spillovers is on average 8 and 5 km from the park’s

oundary, respectively.

. Main results

.1. The mayor’s park site selection decision

To understand the mayor’s site selection problem, we estimate

discrete choice model where the dependent variable equals one

f city j ’s zone k has at least one park in 2006. The exogenous vari-

bles in this site selection model will be used as instrument vari-

bles in later regressions when we estimate parks’ productivity and

onsumption spillovers. We estimate Eq. (6) below:

prob. (whether zone k is home to park (s ))

= αP 0 +

∑

l

αP 1 l · Z lk +

∑

h

αP 2 h · X hk + city F Es + ε kt (6)

The X vector includes location attributes of zone k , such as its

istance to the city center, highways, the railway station, the air-

ort and the closest university. We include city fixed effects. The Z

ector contains two plausibly exogenous variables. The first is the

ensity of rural villages by zone ( Village_Density ). The city leader

as to negotiate with the heads of the villages occupying the land

bout the compensation fee, so that he can relocate the farm-

rs in those villages to other places, and thus vacant the land for

park. To reduce the compensation paid to those rural villages,

he city leader tends to choose a place with a small number of

illages. The second Z variable is a dummy indicating whether a

one locates in the downstream of its nearest river within the city

oundary ( Downstream ) . 17 City mayors know that some manufac-

uring firms in industrial parks will produce pollution. One strategy

mayor may pursue is to locate the park near the downstream

f the river(s) within the geographic boundary of his jurisdiction.

17 For a river flowing through a city, we identify its entrance point into and its

xit point out of the city. We measure a zone’s distance to the entrance point, and

lso its distance to the exit point. If the latter is shorter than the former, we set

Downstream = 1” for this zone. Otherwise, Downstream = 0.

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n this way, he can keep the job opportunities and tax revenue

his park generates, without suffering from the water pollution this

ark causes ( Cai et al., 2016; Sigman, 2002 ).

Column (1) in Table 2 reports the estimates from a probit model

f Eq. (6) . The dependent variable equals one if zone k is the home

or at least one industrial park by the end of 2006. We can observe

hat industrial parks are more likely to be located further from the

ity center and closer to railway stations. Zones with a lower vil-

age density, or located in river downstream areas are more likely

o have industrial parks by 2006. In later sections we will use the

vector ( Village_Density and Downstream ) from Table 2 as our in-

truments. We will present IV estimates of the productivity and

onsumption spillovers, as a robust check for the OLS estimates.

In Column (2) of this table, we report results from a condi-

ional logit regression model where we study the correlates of the

pening of industrial parks established in our study period (year

998–2006). We match each park with all the zones in that city,

o the observation here is a zone-park pair. We estimate a condi-

ional logit model to examine whether a newly-established park j

atches with zone k . The results are similar.

.2. Productivity spillovers: TFP, wage, employment density

.2.1. Estimating park treatment effect based on cross-sectional model

Conditional on where the parks are built, we now study their

mpacts on the local manufacturing sector by estimating versions

f Eq. (4) . In Table 3 , we report estimates using the sample of all


Table 3

Estimating the impact of parks by comparing geographic areas equal dis-

tance to the city center.

Dependent variables: log( TFP ) log( Wage ) Employment

(1) (2) (3)

OLS OLS ZIP

Park 0.134 ∗∗∗ 0.0819 ∗∗∗ 1.710 ∗∗∗

(0.0321) (0.0153) (0.175)

ImpactArea 0.0796 ∗∗∗ 0.0300 ∗∗ 1.406 ∗∗∗

(0.0278) (0.0133) (0.158)

OtherParks 0.00148 ∗∗∗ 0.00165 ∗∗∗ −0.00136

(0.0 0 0376) (0.0 0 0180) (0.00190)

log( Plant_Size ) 0.0185 ∗∗∗ −0.281 ∗∗∗

(0.00666) (0.00318)

log( Plant _ Age ) −0.0307 ∗∗∗ 0.0337 ∗∗∗

(0.00716) (0.00342)

log( D_Highway ) −0.0141

(0.0420)

log( D_Railway ) −0.0279 ∗ −0.0236 ∗∗∗ −0.167 ∗∗∗

(0.0146) (0.00696) (0.0456)

log( D_Airport ) 0.0516 −0.00627 −0.247 ∗

(0.0401) (0.0191) (0.137)

log( D_University ) −0.179 ∗∗

(0.0848)

Constant −60.62 −408.4 ∗∗∗ 5.696 ∗∗∗

(171.6) (81.84) (0.560)

District-time trend Yes Yes –

Industry-year fixed effects Yes Yes –

Plant fixed effects Yes Yes –

District fixed effects – – Yes

Year fixed effects – – Yes

Observations 125,006 125,006 16,709

R 2 0.719 0.777

Zero obs. 4706

Vuong 30.95

Note: This table reports results from estimating Eq. (4) . All 110 parks are

included when constructing the treatment groups ( Park and ImpactArea ). In

all columns the omitted category is the observations in the “Outside ” area

(located more than two kilometers from the park’s boundary).

In columns (1) and (2), observations are individual plants that have a small

zone identifier. Other controls include the real travel distance to CBD (in

log),the “global” impact of other parks, natural log of the plant’s size and

age, natural log of its distances to the closest railway station and airport,

district-time trend, industry-year fixed effects, and plant fixed effects. Stan-

dard errors are reported in parentheses and they are clustered at the small

zone level. In column (3), the unit of analysis is a zone/year. The num-

ber of manufacturing jobs per square kilometer by zone is regressed on

the real travel distance to CBD (in log), treatment group dummies, park

opening dummies ( After ), four location variables (natural log of zone’s dis-

tance to the closest highway, railway station, airport and university), dis-

trict fixed effects and year fixed effects. The Vuong statistic favors the ZIP

(zero-inflated Poisson) model. In the first stage of the ZIP model (inflate

regression), we regress employment density on location variables includ-

ing distance to the CBD as well as its quadratic term and cubic term, the

natural log of distances to the closest highway, railway station, airport and

university, zone size, the city quadrant this zone locates in (north, south,

east, or, west), district fixed effects and year fixed effects. Incidence-rate

ratios are reported, transformed standard errors are reported in parenthe-

ses, which are clustered at district level.

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18 We have also studied the probability that incumbent firms in the impact ar-

eas remain as producers rather than exiting the industry. The estimation results are

available upon request. All else equal, we find that the probability that an incum-

bent plant dies is lower when this plant is located in the impact area and the park

has opened.

110 parks. In columns (1) and (2), the observation is a plant, and

the control group are manufacturing plants in the same city and

year located more than two kilometers from a park (in the Outside

area). We include city fixed effects, district-time trend, industry-

year fixed effects, and plant fixed effects. In column (3), the unit

of analysis is zone/year, and we include district fixed effects and

year fixed effects. As shown in Table 3 , plants located inside the

parks have on average 13% higher TFP, 8.2% higher wages. In col-

umn (3), the coefficient on “park” indicates that the employment

density in treated zones is 71% higher than in control zones. This

table presents new findings on spillover effects. Recall that the

“ImpactArea ” is the area outside of the park but within two kilo-

meters from the park boundary. We estimate the spillover effect

o be 8% on TFP, 3% on wages and 41% on employment in this im-

act area, compared to the control group (the Outside area).

If industrial parks generate positive spillovers in their impact

reas, we should also observe that, all else equal, firms closer to

arks will have a higher probability to survive. We test this by es-

imating a probit model (see Appendix D). We do find that plants

loser to industrial parks have a lower probability to exit. This is

nother piece of evidence supporting the industrial parks’ produc-

ion spillovers.

.2.2. Difference in difference estimators of the average treatment

ffect

For forty of the parks that were established after 1997, we can

stimate Eq. (1) with the DID specification. Estimates of the aver-

ge treatment effect reveals whether the treated area was more or

ess productive after the park was opened there.

In Table 4 , we report six regression estimates of Eq. (1) in

hich the dependent variable is a plant’s TFP in a given year. In

ll columns the control group are plants located more than two

ilometers away from the park boundary (in the Outside area). In

olumns (1)–(3), we focus on estimating TFP premiums within the

ark itself by dropping all plants in the “ImpactArea ” . As shown

n column (1), these new parks were built in the originally less

roductive areas of the city. The average TFP for plants located

n areas where parks would be built in the future but have not

een built yet is 26% lower than those in the control plants. The

lants (including incumbent plants and new entrants) in the parks

re 25.7% more productive after the introduction of the park. In

olumn (2), we use two Z variables ( Village_Density and Down-

tream ) to instrument for Park . The TFP premium in this IV re-

ression is significantly positive and is of a similar size (a little

arger) as that in column (1). The first-stage estimates indicate

he validity of our IV specification (see the joint-F test in Table

. In addition, the Sargan test of over identification is rejected,

nd the weak instrument test is passed). In column (3), we in-

lude plant fixed effects, and find that the incumbent plants in

he park experience a 22% increase in productivity when the parks

pen. These results indicate that these less productive firms at

he baseline enjoyed a productivity boost from the opening of the

ark.

We are more interested in the estimates of TFP spillovers. In

olumns (4)–(6) of Table 4 , the control group includes the plants

ocated more than two kilometers away from the park. In this case,

e drop all plants in the park as we focus on estimating spillover

ffects on the “impact area” . We find that the impact areas ex-

erience a boost in TFP when the park opens. The ImpactArea ∗After

oefficient equals roughly 12.7%, so the TFP spillover effect is about

alf of the TFP premium within the park (25.7% in column (1)). 18

he IV regression in column (5) yields a similar coefficient esti-

ate. When we include plant fixed effects this coefficient becomes

little smaller (10.9%).

Other explanatory variables in Table 4 are found to have intu-

tive signs. State owned enterprises and firms that attract less FDI

re less productive. Firms closer to the city center are more pro-

uctive. Two surprising findings are that larger plants and younger

lants are more productive.

In Table 5 , we switch the dependent variable from TFP to wages

nd the local employment density. In Panel A, the wages in parks

re 12.7% higher after the introduction of the park. When plant

xed effects are considered and we track those incumbent plants


Table 4

The impact of parks on firm TFP for firms located inside and close to a park’s boundary.

Dependent variable: log( TFP )

Variables The sample includes plants within the new

parks and plants located more than two

kilometers from the park’s boundary

Variables The sample includes all plants not located in

the park

(1) (2) (3) (4) (5) (6)

OLS IV OLS OLS IV OLS

log( D_Center ) −0.0255 −0.00707 log( D_Center ) −0.0242 −0.0175

(0.0187) (0.00877) (0.0182) (0.0150)

Park −0.258 ∗∗ −0.290 ∗∗∗ ImpactArea −0.0202 −0.0406

(0.112) (0.0952) (0.0373) (0.0396)

After −0.0439 ∗∗∗ −0.0499 ∗∗∗ −0.0349 ∗∗∗ After −0.167 ∗∗∗ −0.152 ∗∗∗ −0.0931 ∗∗∗

(0.0168) (0.0141) (0.0123) (0.0172) (0.0186) (0.0164)

Park ∗After 0.257 ∗∗ 0.302 ∗∗∗ 0.220 ∗∗ ImpactArea ∗After 0.124 ∗∗∗ 0.150 ∗∗∗ 0.109 ∗∗∗

(0.116) (0.0982) (0.0976) (0.0349) (0.0422) (0.0391)

OtherParks 0.00111 ∗∗ 0.00149 ∗∗∗ 0.00155 ∗∗∗ OtherParks 0.00152 ∗∗∗ 0.00207 ∗∗∗ 0.00239 ∗∗∗

(0.0 0 0527) (0.0 0 0441) (0.0 0 0467) (0.0 0 0532) (0.0 0 0486) (0.0 0 0611)

SOE −0.158 ∗∗∗ −0.181 ∗∗∗ SOE −0.165 ∗∗∗ −0.151 ∗∗∗

(0.0166) (0.0135) (0.0165) (0.0173)

FDI 0.119 ∗∗∗ 0.123 ∗∗∗ FDI 0.116 ∗∗∗ 0.110 ∗∗∗

(0.0157) (0.0130) (0.0161) (0.0168)

log( Plant_Size ) 0.113 ∗∗∗ 0.118 ∗∗∗ 0.0133 log( Plant_Size ) 0.114 ∗∗∗ 0.105 ∗∗∗ 0.0169

(0.0 070 0) (0.00548) (0.00809) (0.00712) (0.00721) (0.0154)

log( Plant _ Age ) −0.185 ∗∗∗ −0.193 ∗∗∗ −0.0308 ∗∗∗ log( Plant _ Age ) −0.184 ∗∗∗ −0.185 ∗∗∗ −0.0346 ∗∗∗

(0.00748) (0.00608) (0.00854) (0.00740) (0.00756) (0.0128)

log( D_Highway ) −0.00687 0.00344 log( D_Highway ) −0.00488 −0.00585

(0.00675) (0.00578) (0.00679) (0.00573)

log( D_Railway ) −0.0163 ∗∗ −0.0151 ∗∗ 0.0216 log( D_Railway ) −0.0223 ∗∗∗ −0.0407 ∗∗∗ −0.0292

(0.00796) (0.00668) (0.0199) (0.00815) (0.00854) (0.0217)

log( D_Airport ) −0.00617 0.0192 0.0529 log( D_Airport ) −0.00327 0.0378 ∗∗∗ 0.0827

(0.0213) (0.0172) (0.0512) (0.0209) (0.0128) (0.0583)

log( D_University ) 0.00438 −0.0237 ∗ log( D_University ) 0.00532 −0.0515 ∗∗∗

(0.0179) (0.0134) (0.0176) (0.0156)

Constant 85.54 ∗∗∗ 5.504 18.51 Constant 80.64 ∗∗∗ 7.991 ∗∗∗ −554.4 ∗∗∗

(27.37) (24.51) (185.0) (27.12) (0.0818) (190.6)

City fixed effects Yes Yes – City fixed effects Yes Yes –

District-time trend Yes Yes Yes District-Time trend Yes Yes Yes

Industry-year fixed effects Yes Yes Yes Industry-year fixed effects Yes Yes Yes

Plant fixed effects – – Yes Plant fixed effects – – Yes

Sargan test of overid. restrictions:

chi2(2) 3.948 1.530

(p-value) (0.139) (0.465)

Shea’s adjusted partial R 2 0.028 0.045

Minimum eigenvalue statistic 38.238 72.794

Observations 87,423 87,423 87,423 Observations 89,333 89,180 89,333

R 2 0.230 0.231 0.719 R 2 0.237 0.218 0.719

Note: This table reports results from estimating Eq. (1) . Plants with small zone identifier are included. In all columns the omitted category is the plants in the “Outside ” area

(located more than two kilometers from the park’s boundary).

Other controls include natural log of the real travel distance to CBD, the “global” impact of other parks, natural log of the plant’s size and age, dummies of whether the plant

is a SOE plant or a FDI one, natural log of its distances to the closest highway, railway station, airport and university, city fixed effects, district-time trend, and industry-year

fixed effects. The time-invariant variables (natural log of the real travel distance to CBD, whether the plant is a SOE plant or a FDI one, and natural log of its distances to

the closest highway and university) are omitted when including plant fixed effects in columns (3) and (6).

Columns (2) and (5) show 2SLS regression results of Eq. (3) . In column ( 2 ), the two exogenous variables in Table 2 ( Village_Density and Downstream ) are used as IVs for PARK

(and their interactions with AFTER as IVs for PARK • AFTER ). In column (5), we construct the IVs for ImpactArea in the following way: for each plant, we calculate the average

of this IV’s values in zones which are located within 2 kms from the plant, and use these average variables as the IVs for ImpactArea . The Sargan test of over identification

is rejected, and the weak instrument test is passed (minimum eigenvalue statistic far exceed the 5% Wald test critical values of 11.04). These tests, together with the joint F

test in Table 2 , all indicate the validity of our IV specification.

Plant fixed effects are included in columns (3) and (6), therefore the time-invariant variables (natural log of the real travel distance to CBD, whether the plant is a SOE plant

or a FDI one, and natural log of its distances to the closest highway and university) are omitted.

Standard errors are reported in parentheses which are clustered at small zone level. ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, ∗∗∗ denotes p < 0.01.

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hose wages are observed before and after the park, this wage

remium shrinks a little to 9.27%. The estimates in terms of wage

pillovers in the impact area are about 6%–6.6%. The IV results

re quite robust. Similar to TFP estimates, the wage spillovers in

he impact area are also about half of the wage premiums in the

ark. Panel B reports employment density regression results. Zones

ithin the park and the impact area have experienced 80% and

0% increases in manufacturing employment density after the park

s established, respectively.

It is interesting to compare the sizes of TFP and wage spillovers

n the park’s impact area. Firms face a trade-off between the in-

reasing agglomeration economies and the increasing input costs.

he positive agglomeration economies increase the productivity of

earby incumbent plants, as indicated by the 12% increase in TFP

n the impact area. This leads to entry or relocation of firms who

re interested in gaining access to the spillovers to the vicinity

f the park. The expansion of existing firms and the subsequent

ew entry lead to competition for inputs, so incumbent firms face


Table 5

The impact of parks on wages and employment for firms located in and close to the park.

PANEL A: Wage Impacts

Variable The sample includes plants within the new parks

and plants located more than two kilometers from

the park’s boundary

Variable The sample includes all plants not located in the

park

(1) (2) (3) (4) (5) (6)

OLS IV OLS OLS IV OLS

Park ∗After 0.127 ∗∗ 0.134 ∗∗ 0.0927 ∗∗ ImpactArea ∗After 0.0661 ∗∗∗ 0.0693 ∗∗ 0.0599 ∗∗∗

(0.0535) (0.0545) (0.0466) (0.0254) (0.0281) (0.0196)

Plant fixed effects – – Yes Plant fixed effects – – Yes

Other controls Yes Yes Yes Other controls Yes Yes Yes

Observations 87423 87423 87423 Observations 89333 89180 89333

R 2 0.375 0.375 0.771 R 2 0.379 0.310 0.766

PANEL B: Employment Effects

Variable The sample includes zones within

the new parks and zones located

more than two kilometers from

the park’s boundary

Variable The sample includes all zones not located in the

park

(7) (8) (9) (10)

ZIP IV ZIP IV

Park ∗After 1.828 ∗ 1.981 ∗∗

ImpactArea ∗After

1.505 ∗∗∗ 1.604 ∗∗∗

(0.592) (0.617) (0.175) (0.272)

Controls Yes Yes Controls Yes Yes

Observations 13881 13881

Observations

14293 14293

Zero obs. 4325 4325 Zero obs. 4389 4389

Vuong 21.22 21.30 R 2 21.15 21.14

Note: This table reports results from fitting versions of Eq. (1) .

In panel A, observations are individual plants that with small zone identifier. Specifications of columns (1) to (6) are same as those reported in Table 2 .

Other controls include the natural log of the real travel distance to CBD, the “global” impact of other parks, natural log of the plant’s size and age, dummies of whether

the plant is a SOE plant or a FDI one, natural log of its distances to the closest highway, railway station, airport and university, city fixed effects, district-time trend, and

industry-year fixed effects. The time-invariant variables (natural log of the real travel distance to CBD, whether the plant is a SOE plant or a FDI one, and natural log of its

distances to the closest highway and university) are omitted when including plant fixed effects in columns (3) and (6). Standard errors are reported in parentheses. They

are clustered at small zone level. In panel B, the unit of analysis is a zone/year. The number of manufacturing jobs per square kilometer by zone is regressed on natural log

of the real travel distance to CBD, treatment group dummies ( Park in columns (7) and (8), ImpactArea in columns (9) and (10)), park opening dummies ( After ), four location

variables (natural log of zone’s distance to the closest highway, railway station, airport and university), district fixed effects and year fixed effects.

In columns (2) and (8), the two exogenous variables in Table 2 ( Village_Density and Downstream ) are used as IVs for PARK (and their interactions with AFTER as IVs for

PARK • AFTER ). In columns (5) and (10), we construct the IVs for ImpactArea in the following way: for each zone, we calculate the average of this IV’s values in zones which

are located within 2 km of the zone, and use these average variables as the IVs for ImpactArea .

Our IV specifications have passed the over identification restriction and weak instrument tests. The Vuong statistics all favor the ZIP (zero-inflated Poisson) model. In the

first stage of the ZIP models in column (7) to (10), inflate regression, we regress employments density on some location variables (distance to CBD as well as its quadratic

term and cubic term, natural log of distances to the closest highway, railway station, airport and university), zone size, the city quadrant this zone locates in (north, south,

east, or, west), district fixed effects and year fixed effects.

Incidence-rate ratios are reported, transformed standard errors are reported in parentheses, which are clustered at district level.

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higher prices for labor and land. The magnitudes of the changes in

the labor or land prices depend on their supply elasticities. When

the agglomeration economies generated by the industrial park are

strong enough, the magnitude of the spillovers exceeds the in-

creases in production cost. In the short run, profits will be positive

for incumbent firms that reside close to the parks. These positive

profits will disappear over time as the prices of local factors are

bid up.

Tables 4 and 5 show that in the impact area TFP and wages

increase by 12% and 6%, respectively. In our sample, labor ac-

counts for roughly 24% of total costs, so the estimated 6% increase

in wages implies that manufacturers’ costs increase by approxi-

mately 1.44%. The increased production costs due to higher wages

are therefore 12% of the gain in TFP. This suggests that incumbent

plants in the impact area gain a short run increase in their profits.

To mitigate the concern that the park’s productivity premium

comes from the selection effect—the industrial parks are selected

to be put in those locations with higher production potential, we

conduct a pre-trend test . We decompose After jt in Eq. (1) into var-

ious year dummies both before and after the industrial park’s in-

troduction, and set the previous year before the establishment of

the park as the benchmark year. Table 6 reports the estimated re-

ults. The coefficients of the event time indicators in columns (1)

nd (2) respectively reflect yearly mean TFP of firms within indus-

rial parks and in the outside areas relative to the previous year of

he park’s introduction. Column (3) reports the difference between

olumns (1) and (2). The control variables in Table 6 are the same

s those in column (3) in Table 4 . The estimated coefficients of

re-event time indicators in column (3) are all insignificant, which

ndicates that the trends in firm’s TFP patterns in industrial parks

nd control areas were similar prior to the establishment of in-

ustrial parks. The gap between the treatment and control areas

tarted to emerge after the park opens.

Fig. 4 A graphs the estimated coefficients of this pre-trend test.

he top panel separately plots the mean TFP of firms within parks

nd in the outside areas (columns (1) and (2) of Table 6 ). The bot-

om panel plots the differences in the estimated treatment and

ontrol groups’ coefficients (column (3) of Table 6 ). The same pre-

rend test is also applied to wage and employment density, and the

esults are shown in Fig. 4 B and Fig. 4 C, which also show that the

reas selected as industrial parks did not perform better ex ante

ompared to the areas far away.

One possible concern is that the new economic activity do not

epresent “net growth” but are shifted from other places (“spatial


Table 6

Pre-trend test of plant TFP before and after the park opening baseline DID: within parks vs. outside areas (outside

the impact area).


(1) (2) (3)

Within parks Outside areas (outside the impact area) Difference ( 1 )-( 2 )

7 years before park opening 0.0376 0.0342 0.00343

(0.148) (0.0418) (0.0963)

6 years before park opening −0.0691 −0.0500 −0.0191

(0.107) (0.0374) (0.0568)

5 years before park opening −0.0208 −0.00792 −0.0129

(0.140) (0.0295) (0.0904)

4 years before park opening −0.0336 −0.0582 ∗∗ 0.0246

(0.0821) (0.0267) (0.0328)

3 years before park opening −0.0210 −0.0374 ∗ 0.0164

(0.139) (0.0203) (0.0895)

2 years before park opening −0.0475 0.0133 −0.0608

(0.199) (0.0165) (0.0995)

1 year before park opening 0 0 0

Park opening 0.117 −0.0580 ∗∗∗ 0.175 ∗∗∗

(0.0900) (0.0182) (0.0506)

1 year before park opening 0.136 −0.0663 ∗∗∗ 0.200 ∗∗∗

(0.101) (0.0194) (0.0616)

2 years before park opening 0.165 ∗ −0.0531 ∗∗ 0.218 ∗∗∗

(0.0970) (0.0234) (0.0578)

3 years before park opening 0.130 −0.0372 0.167 ∗∗∗

(0.101) (0.0272) (0.0619)

4 years before park opening 0.214 ∗∗ −0.0277 0.242 ∗∗∗

(0.108) (0.0298) (0.0681)

5 years before park opening 0.206 ∗∗ 0.0334 0.171 ∗∗∗

(0.103) (0.0358) (0.0628)

6 years before park opening 0.176 −0.0409 0.217 ∗∗∗

(0.109) (0.0414) (0.0683)

7 years before park opening 0.144 −0.0588 0.202 ∗∗

(0.179) (0.0471) (0.0867)

Control variables Yes

District-time trend Yes

Industry-year fixed effects Yes

Plant fixed effects Yes

Observations 87,423

R 2 0.587

Note: This table reports results from fitting versions of Eq. (1) . Plants with small zone identifier and that are

within the new parks or in the outside area (the distance to the closest park is larger than 2 km) are included.

Columns ( 1 ) and ( 2 ) report coefficients from the same regression. Control variables include the “global” impact of

other parks, natural log of the plant’s size and age, and natural log of its distances to the closest railway station

and airport.


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(

ransfer” ), and thus represent a city level “zero sum game” . Figs.

A–C can partially mitigate this concern. As illustrated in Fig. 4 A

nd B, the plant-level TFP and wage in the outside area (the rest

f the city which is not affected by parks) do not show a declining

rend after the parks are established, while both of them within

arks do experience a significant increase. In Fig. 4 C, the employ-

ent density in the outside area has a slight decline after the in-

roduction of parks, indicating the spatial shift of some firms from

ther places towards the designated parks. Nevertheless, the TFP

nd wage results in Fig. 4 A and B do confirm our parks generate

ositive premiums without causing the productivity losses in the

est of the city.

If we think about the park itself and its impact area, the impact

rea receives spillovers from the park, but at the same time there

ay be a “gravitational pull” such that activity in the impact area

oves into the park or the park’s firms produce the output more

roductively and drive out some of the impact area’s incumbent

rms. What we observe is a “net impact”, and it will be hard to

eparate these two effects. Our em pirical results show that this net

mpact is positive.

.2.3. Estimating the park’s impact based on matched winner and

oser areas

In this section, we mimic the Greenstone et al. (2010) strategy

o compare outcome dynamics for geographic areas treated with

park relative to geographic areas that just missed based on our

eading of the Chinese CCP old urban planning documents. To be-

in to study this issue, we first present the productivity dynamics

f the average plant in the winner (industrial park) versus loser

early-defined industrial zones which did not receive a park) areas

or each year before and after the park was opened. In Table 7 ,

olumns (1) and (2) report estimated parameters and their stan-

ard errors from a version of Eq. (3). Specifically, the natural log

f TFP is regressed on the “global” impact of other parks, natural

og of the plant’s size and age, and natural log of its distances to

he closest railway station and airport. We include city fixed ef-

ects, district-time trend, industry-year fixed effects, plant fixed ef-

ects, winner-loser pair fixed effects, and the event time indicators.

he reported coefficients on the event time indicators reflect yearly

ean TFP in winner areas (column (1)) and loser areas (column

2)), relative to the year before the park was established. Column


(a)

(b)

Fig. 4. (A) Pre-Trend test of plant TFP before and after the park opening baseline DID: within park vs. the outside area (outside the impact area). (B) Pre-trend test of plant

wage before and after the park opening baseline DID: within park vs. the outside area (outside the impact area). (C) Pre-trend test of employment density before and after

the park opening baseline DID: within park vs. the outside area (outside the impact area). Note: See the corresponding regressions in Table 6 . Note: The unit of analysis in

Figure 4B is a firm, and that in Figure 4C is a zone. The underlying regressions have a similar identification as those in Table 6 .


(c)

Fig. 4. Continued

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3) reports the yearly difference between estimated mean TFP in

inner and loser areas.

Fig. 5 A graphs the estimated coefficients of this pre-trend test

rom Table 7 . The top panel separately plots mean TFP in winner

nd loser areas (columns (1) and (2) of Table 7 ). The bottom panel

lots the differences in the estimated winner and loser coefficients

column (3) of Table 7 ). 19 The same process is also applied to wage

nd employment density, and the results of this pre-trend test are

hown in Fig. 5 B and C.

As shown in Table 7 and Fig. 5 A–C, we cannot reject the hy-

othesis that there is no pre-trend differential for the treatment

roup and the control group before the park is opened for all three

roductivity measures. After the park opens, the gap between the

inners and losers appears and a linear relationship is observed. It

s notable that the loser areas also experience TFP growth but at a

esser rate than the winner areas.

In Table 8 , we report estimates of Eq. (3) with the DID specifi-

ation. The control group is the loser area (runner-up) and its “hy-

othetical” impact area. The sample size is smaller now relative to

he results reported in Table 4 because now we focus on a subset

f the treated plants and on a subset of the control plants.

In Table 8 ’s Panel A’s left column, we estimate a roughly 20%

reatment effect of being in a new park on plant TFP. These re-

ults are robust to including plant fixed effects (see column 2).

ased on the results reported in Panel A’s right column we find

hat plants located in the impact area (but outside) of new parks

xperience a 10% increase in TFP. Again, this is evidence of the spa-

ial spillover effect, and the size is about half of the park’s own

19 We also test the balancing of many ex-ante observable characteristics of “win-

er” and “loser” geographic areas. The results are available upon request.

T

f

a

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FP premium. These estimates are relatively smaller than those in

able 4 (with baseline DID specification), so our matched DID spec-

fication helps to mitigate the problem of omitted variables (they

ffect park’s location choice and the TFP premiums/spillovers with

he same direction). We find similar positive effects for wage gains

t 7% for new park plants and at 4.4% for impact area plants (see

anel B) and again large impacts on treated zone overall employ-

ent growth (see Panel C).

.3. Consumption spillovers: housing sales, prices and new retail

penings

We now turn to estimating how new parks affect local real es-

ate construction and pricing and the openings of new retail stores.

ur consumption data sets start from 2006, after the establish-

ent of all the 110 parks in our sample. To study this we estimate

q. (4) .

Table 9 reports four count models (new home sales, new

estaurant/ entertainment/ retail openings) and one hedonic pric-

ng model (new home prices). The unit of analysis is by grid cell by

ear for the count models, and it is by residential complex (with

xact address) by year for the hedonic pricing model. The spa-

ial boundary of the impact area is 8 km from the park’s boundary

or new home sales, and 5 km from the park’s boundary for new

estaurant openings, entertainment facilities and retail shops (see

able A3). Figure A2 graphs the distribution of these spillover ef-

ects at various distances (with confidence intervals). According to

survey conducted by Baidu.com , the average one-way commut-

ng distance in these eight cities varies between 13 km to 19 km


(a)

(b)

Fig. 5. (A) Pre-trend test of plant TFP before and after the park opening matched DID: winners vs. losers. (B) Pre-trend test of plant wage before and after the park opening

matched DID: winners vs. losers. (C) Pre-trend test of employment density before and after the park opening matched DID: winners vs. losers. Note: See the corresponding

regressions in Table 7 . Note: The unit of analysis in Figure 5B is a firm, and that in Figure 5C is a zone. The underlying regressions have a similar identification as those in

Table 7 .


(c)

Fig. 5. Continued

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5

n 2015. 20 An industrial park can be regarded as an edge city or a

ubcenter, which attracts nearby workers, so the commuting dis-

ance should be shorter than the city average. The 8-km radius for

he housing market spillovers seems to be a reasonable size.

All else equal, home prices are roughly 12% higher in the vicin-

ty of an industrial park (the park and its impact area). The counts

f new home sales, new restaurants, entertainment facilities and

etail shops also rise sharply in the vicinity of the park- 43%, 51%,

9% and 50% more than a comparable place. In results available on

equest, we have re-estimated these results using an IV specifica-

ion using the variables listed at the bottom of Table 2 . We find

hat our IV results are quite similar. These results are available on

equest.

Based on the results reported above, we calculate the rough

enefits the local government could gain by building an industrial

ark. The land value premium in this average park’s impact area

s about 23 billion RMB. Given Chinese land market rules, this is a

netime payment but it is quite large as that amount equals 33%

f the average city’s annual fiscal revenue flow in the year 2007.

he land where the park is placed could have been auctioned off

s residential and commercial real estate. Using standard hedonic

ricing methods, we estimate the opportunity cost of the average

ark’s land to be 4.1 billion RMB. 21 This opportunity cost under-

20 http://hr.yjbys.com/xinchouguanli/560940.html 21 The land sale revenue from residential and commercial land auctions in the

ark’s vicinity is much larger. Here we estimate the residential land sale revenue

o give an example. Our estimates (column ( 2 ) in Table 9 ) show that the av-

rage industrial park increases home prices in its vicinity by 11.7%. We assume

hat land prices increase by the same rate. We obtain the land sale auction price

ata for the year 2007, and use the spatial gradient estimated from the housing

rice hedonic regression to predict the price of land across the city. We acknowl-

dge that we do not take general equilibrium effect into account here and we

se post-improvement prices (after the public project investment) instead of pre-

mprovement prices when conducting the estimation. On the cost side, to attract

t

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stimates the total cost of the park because there are other often

idden costs. It is difficult to obtain the cost parameters of infras-

ructure investment, land, tax and utility subsidies for building an

ndustrial park because such cost information is always regarded as

secret” and is hidden by local governments. Our interview with an

fficer at NDRC (National Development and Reform Commission of

hina) gives us a rough estimate of 2.5 billion of such costs for an

verage park in those cities. Combining the above costs together,

hey only accounts for 28.7% of the land value premium in its im-

act area. Therefore, even without counting the long term annual

ows of tax revenue from firms, the huge net benefit from land

ales (land value premium in the impact area minus the opportu-

ity land cost within the park) already creates incentives for may-

rs to build industrial parks. But of course such huge land sale

evenue depends on that the park itself is successful in terms of

enerating productivity gains. Since China’s mayors face term lim-

ts with the average term length running to around four years, the

p-front land revenue incentives may even be stronger than col-

ecting a long term annual flow of tax revenue from manufacturing

rms.

. Measuring park treatment effect heterogeneity

In this section, we first present estimates documenting the dis-

ribution of park heterogeneous treatment effects and then we use

deas from the economic agglomeration literature to test various

ypotheses. In Fig. 6 , we graph the treatment effects for 40 parks

roductive firms, local governments always sell the industrial land within the park

o them at very low subsidized prices, and in some cases even at a price of zero.

e estimate the opportunity cost of this favorable land subsidy by calculating the

arket value of the land within the park if it could have been developed into resi-

ential uses (assuming zero prices for industrial uses). This subsidy is valued at 4.1

illion RMB for the average park.

http://hr.yjbys.com/xinchouguanli/560940.html


Table 7

Testing for pre-trends based on plant TFP before and after the park opening: matched DID.


(1) (2) (3)

In winner zones In loser zones Difference ( 1 )-( 2 )

7 years before park opening −0.0197 −0.0629 0.0432

(0.126) (0.0722) (0.128)

6 years before park opening 0.00538 −0.0203 0.0257

(0.140) (0.0709) (0.143)

5 years before park opening 0.0 0 0349 −0.0127 0.0131

(0.0806) (0.0590) (0.0816)

4 years before park opening 0.0560 0.00423 0.0518

(0.0706) (0.0541) (0.0784)

3 years before park opening −0.0221 0.0288 −0.0509

(0.0608) (0.0440) (0.0663)

2 years before park opening −0.00566 −0.0311 0.0254

(0.0479) (0.0369) (0.0535)

1 year before park opening 0 0 0

Park opening 0.146 ∗∗∗ −0.0544 0.201 ∗∗∗

(0.0529) (0.0351) (0.0572)

1 year before park opening 0.191 ∗∗∗ −0.0437 0.234 ∗∗∗

(0.0529) (0.0387) (0.0582)

2 years before park opening 0.269 ∗∗∗ 0.00345 0.265 ∗∗∗

(0.0614) (0.0475) (0.0642)

3 years before park opening 0.184 ∗∗∗ 0.0188 0.165 ∗∗

(0.0609) (0.0544) (0.0664)


(0.0604) (0.0576) (0.0627)


(0.0726) (0.0648) (0.0727)

6 years before park opening 0.250 ∗∗∗ −0.0537 0.303 ∗∗∗

(0.0838) (0.0790) (0.0798)

7 years before park opening 0.134 −0.0936 0.228 ∗∗

(0.103) (0.0870) (0.0871)

Control variables Yes

District-time trend Yes

Industry-year fixed effects Yes

Plant fixed effects Yes

Winner-loser pair fixed effects Yes

Observations 24,608

R 2 0.588

Note: This table reports results from fitting versions of Eq. (3) . Plants with small zone identi-

fier and that are within the new parks or in the outside area (the distance to the closest park

is larger than 2 km) are included.

Columns (1) and (2) report coefficients from the same regression. Control variables include

the “global” impact of other parks, natural log of the plant’s size and age, and natural log of

its distances to the closest railway station and airport.

Standard errors are reported in parentheses which are clustered at small zone level. ∗denotes p < 0.10,

∗∗ denotes p < 0.05, ∗∗∗ denotes p < 0.01.

Fig. 6. Estimating the TFP premium for each of the forty new parks. Notes: The figure reports results from a fitting version of Eq. (1) which reports the parameter estimates

of α3 for each of the 40 parks built between 1999 and 2006. They are calculated by interacting park_ID dummies (or Impact_Area_ID dummies) with After in column (3)

in Table 4 . The control group contains the plants in the outside area. The Y axis denotes the sizes of the TFP premium in parks and the TFP spillovers in impact areas (in

percentage) compared to the outside area.


Table 8

Estimating the impact of parks using the “winners and losers” subsample.

PANEL A: TFP Premiums

Variable The sample includes plants within

the new parks and plants located

more than two kilometers from the

park’s boundary

Variable The sample includes all plants not

located in the park

(1) (2) (3) (4)

OLS OLS OLS OLS

Park ∗After 0.212 ∗∗∗ 0.195 ∗∗∗ ImpactArea ∗After 0.123 ∗∗ 0.0985 ∗

(0.0528) (0.0598) (0.0480) (0.0546)

Plant fixed effects – Yes Plant fixed effects – Yes

Winner-loser pair fixed effects Yes Yes Winner-loser pair fixed effects Yes Yes

Other controls Yes Yes Controls Yes Yes

Observations 24,608 24,608 Observations 15,329 15,329

R 2 0.256 0.718 R 2 0.276 0.746

PANEL B: Wage Premiums

Variable The sample includes plants within

the new parks and plants located

more than two kilometers from the

park’s boundary

Variable The sample includes all plants not

located in the park

(5) (6) (7) (8)

OLS OLS OLS OLS

Park ∗After 0.0858 ∗∗∗ 0.0713 ∗∗ ImpactArea ∗After 0.0562 ∗∗∗ 0.0443 ∗

(0.0253) (0.0294) (0.0194) (0.0226)

Plant fixed effects – Yes Plant fixed effects – Yes

Winner-loser pair fixed effects Yes Yes Winner-loser pair fixed effects Yes Yes

Other controls Yes Yes Controls Yes Yes

Observations 24,608 24,608 Observations 15,329 15,329

R 2 0.386 0.762 R 2 0.418 0.796

PANEL C: Impact Areas Employment Premiums

Variable The sample includes zones within the

new parks and zones located more

than two kilometers from the park’s

boundary

Variable The sample includes all

zones not located in the

park

(9) (10)

ZIP ZIP

Park ∗After 1.507 ∗∗∗ ImpactArea ∗After 1.318 ∗∗

(0.215) (0.150)

Winner-loser pair fixed effects Yes Winner-loser pair fixed effects Yes

Controls Yes Controls Yes

Observations 2966 Observations 2752

Zero obs. 535 Zero obs. 504

Vuong 10.66 R 2 12.73

Note: This table reports results from estimating Eq. (3) with the matched DID (“winner-loser”) specification. The samples are restricted to those in “candidate zones” (winner

zones and loser zones).Winner-loser pair fixed effects are included in all the regressions. Other specifications reported in panel A, B and C are the same as those in Table 4 ,

panel A of Table 5 , and panel B of Table 5 , respectively.

In panel A, the standard errors are reported in parentheses and are clustered at the small zone level. In panel B, standard errors are clustered at the district level. In panel

C (zero-inflated Poisson models), incidence-rate ratios are reported, and transformed standard errors are clustered at district level. ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, ∗∗∗ denotes p < 0.01.

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orted from lowest to highest for which we observe their produc-

ivity before and after the park was built based on the DID model

we include park-specific interaction terms with After ) reported in

q. (1) (with plant fixed effects).

Fig. 6 highlights the fact that some of the parks have a neg-

tive but statistically insignificant effect on plant productivity. If

e count numbers, 75% of the new parks built during 1998–2007

n the eight cities generate positive TFP premiums within park

oundary; while 25% of those parks turns out to have negative TFP

remiums. Regarding TFP spillovers to nearby incumbent firms,

0% of the parks have positive spillovers; and 30% have negative

pillovers (most of the negative coefficients are insignificant).

In Section 2.3 we already define four dimensions measuring

ark heterogeneity—the cohort and age of a park; its administra-

ive level; the extent to which this park “fits” with the local incum-
w
ent industries; and its own composition of plants and industries.

ow we examine the heterogeneous productivity and consumption

pillovers along those dimensions.

.1. Heterogeneous productivity spillovers as a function of park

ttributes and Marshallian factors

In panel A of Table 10 , we allow the impacts of an industrial

ark to vary by a park’s age. We find that older parks do have

arger spillovers. As time passes, the spillovers for both old and

ew parks become larger. We interpret this as evidence of a “mul-

iplier” effect. In Panel B, we find that the state-level parks gener-

te larger TFP spillovers compared with provincial-level parks, but

hey have little difference in wage spillovers. In Panel C of Table 10 ,

e follow Ellison et al. (2010) and explore how the various Mar-


Table 9

The impact of parks on housing markets and retail store openings.

New home sales New home prices New restaurants New entertainments New shops

(1) (2) (3) (4) (5)

ZIP OLS ZIP ZIP ZIP

ParkVicinity 1.432 ∗∗∗ 0.117 ∗∗∗ 1.506 ∗∗∗ 1.391 ∗∗∗ 1.507 ∗∗∗

(0.119) (0.0180) (0.0898) (0.0778) (0.114)

Controls Yes Yes Yes Yes Yes

Observations 215,408 182,045 215,408 215,408 215,408

R 2 0.771

Zero obs. 203,295 194,460 200,860 194,770

Vuong 91.30 41.56 28.42 41.82

Note: This table reports estimates of Eq. (4) . The area of ParkVicinity includes both the park itself, and its impact

area. The spatial boundary of the impact area is 8 km from the park’s boundary for new home sales, and 5 km

from the park’s boundary for new restaurant openings, entertainment facilities and retail shops.

In columns (1), (3), (4), (5), we use zero-inflated Poisson model and the unit of analysis is 2 × 2 km grid by year.

The dependent variable of columns (1), (3), (4), (5) are the count of new home sales (columns (1)), new opening

restaurants (columns (3)), entertainment facilities (columns (4)) and retail shops (columns (5))by 2 × 2 km grid by

year, respectively. The control variables include the “global” impact of other parks, natural log of distance to CBD,

the closest highway, railway station, airport and university, district fixed effects and year fixed effects. Incidence-

rate ratios are reported. Standard errors are reported in parentheses which are clustered at district level.

In column (2), the hedonic model is a regression of each home sale’s price on its location attributes including

the “global” impact of other parks, natural log of distance to CBD, the closest highway, railway station, airport

and university, physical attributes including floor area ratio, greening space ratio and parking space share, district

fixed effects, year fixed effects. Standard errors are clustered at grid (2 × 2 km) level. ∗denotes p < 0.10, ∗∗denotes p < 0.05,

∗∗∗ denotes p < 0.01.

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shallian factors correlate with spillover effects. We find that knowl-

edge spillovers have the largest effect on TFP and wages. We also

find some evidence supporting the roles of input and output link-

ages, and labor pooling in local economic growth. Finally in Panel

D, we describe the parks based on a variety of dimensions and

find that parks featuring a higher human capital share, a higher

foreign direct investment, and a higher co-agglomeration effects

within the park have a higher spillover effect. Perhaps surprisingly,

we do not find that parks with higher share of state owned enter-

prises have a smaller TFP effect. However, we find that the wage

impacts on the impact area are smaller for parks with a larger SOE

share. The magnitudes of these results are large. A ten percentage

point increase in the share of park workers who are college grad-

uates is associated with a 26% increase in the TFP spillover in the

impact area.

5.2. Heterogeneous consumption spillovers as a function of the park’s

composition of new firms

Our final piece of the empirical work examines how the con-

sumer city spillovers are affected by the park’s administrative level

and its attributes.

These results are qualitatively very similar to the results re-

ported for the producer spillovers in Table 10 ’s Panel C and Panel

D. All else equal, for state-level parks and parks featuring more hu-

man capital, greater co-agglomeration, more FDI and fewer SOEs,

we find a larger spillover impact on nearby housing construction,

home prices and new retail openings. These results support our

claim of the heterogeneous impacts of parks and the key role

that the composition of these parks plays. The finding that the

co-agglomeration index has a positive spillover effect in both the

TFP regressions and in the consumer city regressions supports the

claim that this is new synergistic activity rather than simply be-

ing reshuffling of economic activity that would have taken place

somewhere in the same city in the absence of the park’s creation.

As shown in Table 11 , parks featuring a larger SOE share have a

smaller impact on the local housing market and on the creation of

new retail outlet and restaurants.

.

. Conclusion

Our paper provides strong support for Marshallian theories of

ocalized production and consumption agglomeration in a lead-

ng developing country. Using the opening of 110 industrial parks

cross eight major Chinese cities, we quantify the spillover effects

n productivity, manufacturing employment, incumbent firm sur-

ival, real estate construction, real estate pricing, and retail store

penings for economic activity close to these new suburban cen-

ers of productivity. Consistent with Marshall’s core hypotheses, we

nd that proximity to the parks facilitates trade and growth be-

ause such co-agglomeration reduces the costs of moving goods,

eople, and ideas.

We have also documented that the parks differ with respect

o their productive spillover effects. A recent literature in macroe-

onomics measures the productivity wedges across Chinese firms

Hsieh and Klenow, 2009 ). This research argues that government

nduced distortions act like a subsidy for unproductive firms. For

xample, state owned enterprises receive special treatment in re-

eiving cheaper land and capital access. Our results indicate that

overnment policies that encourage the creation of high human

apital industrial parks are more likely to facilitate productivity

rowth.

Unlike previous evaluations of government subsidizes for place-

ased programs, we have explicitly measured impacts for both the

roduction and consumption sides of the economy. The new park

reates a spatially concentrated increase in local market potential

s well paid workers seek nearby housing and retail opportunities.

iven that Chinese urbanites spent a great deal of time commuting

o work, household quality of life is improved by having suburban

mployment options where they can live and shop with access to

uch shorter commutes. Unlike in the classic monocentric Chinese

ity, this means that the new parks lead to sharp improvements in

orker quality of life and this should attract talented workers to

eek these jobs ( Duranton and Turner, 2011; Monte et al., 2015 ).

Our results clearly show that not all industrial parks are “suc-

essful”, even in our sample of 110 parks in the first-tier cities and


Table 10

Testing for heterogeneous productivity spillovers as a function of park age and Marshallian factors.

PANEL A: Heterogeneity w.r.t. Cohort and Park Age

Dependent variables: log( TFP ) log( Wage )

Old parks New parks Old parks New parks

(1) (2) (3) (4)

Average effect 0.169 ∗∗ 0.0880 ∗∗∗ 0.0626 ∗ 0.0261 ∗(0.0809) (0.0296) (0.0380) (0.0142)

0–5 years 0.127 0.0913 ∗∗∗ 0.0423 0.0278 ∗(0.0842) (0.0298) (0.0405) (0.0143)

6–10years 0.185 ∗∗ 0.105 ∗ 0.0723 ∗ 0.0119

(0.0821) (0.0577) (0.0395) (0.0277)

10–15 years 0.188 ∗∗ 0.0734 ∗(0.0823) (0.0396)

> 15 years 0.209 ∗∗ 0.0751 ∗(0.0850) (0.0409)

Controls Yes Yes

Observations 89,333 89,333

R 2 0.716 0.768

PANEL B: Heterogeneity w.r.t. park administrative level


(7) (8)

State-level Park 0.0766 ∗∗∗ 0.00624

(0.017) (0.0314)

Controls Yes Yes


R 2 0.719 0.766

PANEL C: Heterogeneity w.r.t. spillover synergies


(5) (6)

Input_Linkage 0.670 ∗∗ 0.284 ∗(0.311) (0.159)

Output_Linkage 0.902 ∗ 0.306

(0.475) (0.223)

Labor_Pooling 0.652 1.496 ∗(1.823) (0.793)

Skill_Spillover 4.246 ∗∗ 2.255 ∗∗∗(2.023) (0.843)

Joint F-test 11.21 ∗∗∗ 4.96 ∗∗∗(0.0 0 0) (0.0 0 0)

Controls Yes Yes


R 2 0.698 0.769

PANEL D: Heterogeneity w.r.t. park attributes


(9) (10)

log( D_Center ) 0.138 ∗∗∗ 0.0700 ∗∗(0.0433) (0.0307)

Park_Size 0.00618 ∗∗ 0.00315 ∗∗(0.00260) (0.00136)

SOE_Share −0.301 −0.258 ∗(0.319) (0.154)

FDI_Share 0.469 ∗ 0.104

(0.263) (0.124)

Human_Capital 2.652 ∗∗ 1.981 ∗∗∗(1.108) (0.541)

Coagglomeration 0.884 ∗∗∗ 0.404 ∗∗∗(0.259) (0.124)

Joint F-test 5.32 ∗∗∗ 7.41 ∗∗∗(0.0 0 0) (0.0 0 0)

Controls Yes Yes


R 2 0.720 0.771

Note: This table reports results from fitting versions of Eq. (1) . We use the baseline DID specification to examine the heterogeneous treatment effect in a park’s impact

area.

In panel A, we add the interaction terms between Park ∗After and the indicated park cohort dummies and age group dummies. In panel B we add interaction terms between

Park ∗After and the indicated variables of how a park “fits” with the local incumbent industries measured by continuous economic distances between firms within the park

and the incumbent firms outside but nearby the park. These industry linkage measures are defined and described in Table 1 , and Appendix B provides how we construct

those variables. In panel B we add interaction terms between Park ∗After and a dummy indicating whether a park is a state-level one. In panel D we add interaction terms

between Park ∗After and the indicated measures of a park’s own “economic power”. These park feature measurements are defined and described in Table 1 .

Other controls include DID dummies, the “global” impact of other parks, natural log of the plant’s size and age, natural log of its distances to the closest railway station

and airport, city fixed effects, district-time trend, industry-year fixed effects, and plant fixed effects.



Table 11

Testing for heterogeneous consumption spillovers as a function of the park’s attributes.

Employment (by zone) Housing market Retail market

New home sales (by grid) New home prices New restaurants (by grid) New entertainments (by grid) New shops (by grid)

(1) (2) (3) (4) (5) (6)

ZIP ZIP OLS ZIP ZIP ZIP

PANEL A: Heterogeneity w.r.t. park type

State-level Park 0.777 ∗ 0.0651 0.0742 ∗∗∗ 0.0264 ∗∗∗ 0.0474 ∗∗∗ 0.0214 ∗∗∗

(0.467) (0.0849) (0.0157) (0.00501) (0.0101) (0.00423)

Observations 14,293 215,408 182,045 215,408 215,408 215,408

R 2 0.772

Zero obs. 4389 203,295 194,460 200,860 194,770

Vuong 21.12 91.42 41.56 28.35 41.75

PANEL B: Heterogeneity w.r.t. park attributes

log( D_Center ) 0.705 ∗∗∗ 0.343 ∗∗∗ 0.0114 0.0120 −0.0570 −0.0985

(0.175) (0.0885) (0.0184) (0.0584) (0.0482) (0.0871)

Park_Size 0.0174 ∗∗∗ 0.00784 ∗∗ 0.0623 ∗∗∗ 0.00729 ∗∗ 0.00426 ∗∗ 0.00606 ∗∗∗

(0.00663) (0.00344) (0.0166) (0.00289) (0.00184) (0.00225)

SOE_Share −1.628 −0.540 ∗∗∗ −0.172 ∗∗ −1.126 ∗∗∗ −0.569 ∗∗∗ −0.845 ∗∗∗

(1.327) (0.199) (0.0683) (0.279) (0.214) (0.279)

FDI_Share 0.150 ∗∗∗ 0.517 ∗∗ 0.125 ∗∗∗ 0.790 ∗∗∗ 0.457 ∗∗∗ 0.852 ∗∗∗

(0.0530) (0.223) (0.0430) (0.223) (0.169) (0.225)

Human_Capital 6.194 ∗ 1.431 ∗ 0.866 ∗∗∗ 3.139 ∗∗∗ 1.773 ∗∗∗ 2.196 ∗∗∗

(3.722) (0.755) (0.185) (0.772) (0.592) (0.737)

Coagglomeration 9.159 ∗∗∗ 0.168 0.272 ∗∗∗ 1.729 ∗∗∗ 1.490 ∗∗∗ 1.751 ∗∗∗

(3.408) (0.608) (0.0933) (0.254) (0.217) (0.252)

Joint F-test 47.65 ∗∗∗ 29.68 ∗∗∗ 8.99 ∗∗∗ 57.63 ∗∗∗ 61.45 ∗∗∗ 78.18 ∗∗∗

(0.0 0 0) (0.0 0 0) (0.0 0 0) (0.0 0 0) (0.0 0 0) (0.0 0 0)

Control variables Yes Yes Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes Yes Yes

District fixed effects Yes Yes Yes Yes Yes Yes

Observations 14,293 215,408 182,045 215,408 215,408 215,408

R 2 0.769

Zero obs. 4389 203,295 194,460 200,860 194,770

Vuong 21.01 91.18 40.22 27.87 40.31

Note: Column (1) reports results from fitting version of Eq. (1) , column (2)-(6) report results from fitting versions of Eq. (4) . We employ zero-inflated Poisson model for

all columns except of column (3) (where we use OLS). In panel A we add interaction terms between Park ∗After/ImpactArea and a dummy indicating whether a park is a

state-level one. In Panel B we add interaction terms between Park ∗After/ImpactArea and the indicated measures of a park’s own “economic power”. These park feature

measurements are defined and described in Table 1 . Other specifications of column (1) are the same as those reported in column (9) of Table 5 . The other specifications

reported in columns (2)-(6) are same as those reported in Table 8 . ∗ denotes p < 0.10, ∗∗ denotes p < 0.05, ∗∗∗ denotes p < 0.01.

m

n

“

b

t

c

p

M

S

f

R

A

A

relatively large cities. 22 The geographic spillover effect of parks is

an increasing function of the park’s overall human capital level,

the FDI share, the SOE share and its “synergy” with nearby in-

cumbent firms. Nevertheless, on average we do find positive pro-

duction and consumption spillovers. Other studies on China’s in-

dustrial parks also confirm such positive impacts on local eco-

nomic growth. Therefore it is interesting to understand why this

place-based policy did not work well in many developed coun-

tries ( Busso et al., 2013; Glaeser and Gottlieb, 2008; Papke, 1994 ),

but has an optimistic outcome in China. We propose two possible

(and speculative) explanations: First, in developed countries, place-

based policies often target those lagged-behind regions. China’s in-

dustrial park policy incorporates both efficiency and equity moti-

vations together with the additional target of experimenting with

market reforms. On the efficiency side, industrial parks pursue the

reduction of pre-existing distortions in the old central planning

regime and the exploitation of agglomeration effects ( Alder et al.,

2016 ). Second, the active role of the government has been crucial

for China’s development because it supported a fast move towards

22 If data permits, this study should be expanded to less developed cities to see if

industry parks’ positive impacts diminish when this policy extends down the hier-

archy of cities. We tried our best but still cannot obtain this micro-level firm data

beyond these eight cities. Therefore, we view this to be a valuable future research

topic. In the conclusion, we mention the possibility that if we look at relatively

small cities and those cities in China’s middle and western regions, it is possible

that the probability of “unsuccessful” parks will rise.

B

C

ore modern and productive sectors which have positive exter-

alities on the whole economy ( Rodrik, 2006 ). In our case, the

managed hand” of Chinese city governments solve a land assem-

ly problem and a cross firm co-ordination problem allowing firms

o cluster together in a timely fashion, which is unimaginable in

ities featuring pre-existing durable structures. It may also not be

ossible in a US city where such projects would face more “Not In

y Back Yard” (NIMBY) opposition.

upplementary materials

Supplementary material associated with this article can be

ound, in the online version, at doi:10.1016/j.jue.2017.05.002 .

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