Transport Infrastructure and the - Ricerca sull'intelligenza artificiale specializzata al MIT

Transport Infrastructure and the
Decentralization of Cities in the People’s
Republic of China
Nathaniel Baum-Snow and Matthew A. Turner∗

It is widely believed that transport infrastructure has important impacts on
the development of cities. Until recently, Tuttavia,
there has been little
systematic evidence with which to evaluate claims about the effects of transport
infrastructure on the development of cities and regions. in questo documento, we
describe the evolution of transport infrastructure in the People’s Republic of
China and how it relates to the evolution of location patterns of population
and production in Chinese cities and their surrounding regions. We summarize
empirical evidence from Baum-Snow et al. (2017) on the causal effects of
various types of transport infrastructure on the decentralization of cities in
the People’s Republic of China. Finalmente, we put our results in the context of
the existing literature on the effects of infrastructure on productivity and the
allocation of resources across locations.

Keywords: infrastructure, People’s Republic of China, railroads, roads
JEL codes: O20, R40

IO. introduzione

Over the past 2 decades, the People’s Republic of China (PRC) has made
huge investments in highways and railroads. Between 1990 E 2010, an average
prefecture in the PRC saw its railroad network length increase from 151 kilometers
(km) A 218 km. More dramatically, there were no limited access highways in

∗Nathaniel Baum-Snow: Associate Professor, Rotmann School of Management, University of Toronto. E-mail:
Nate.Baum-Snow@rotman.utoronto.ca; Matthew A. Turner (corresponding author): Professor, Department of
Economics, Brown University. E-mail: matthew_turner@brown.edu. We are grateful to the International Growth
Centre (grant RA-2009-11-013) for generously funding this research. Matthew Turner is grateful to the Canadian
Social Science and Humanities Research Council for funding and the Population Studies and Training Center at
Brown University, which receives funding from the National Institutes of Health (P2C HD041020), for general
support. We also thank the many research assistants who helped on this project: Magda Besiada; Rong Zhang Wang;
Jie Ciao; Huaihong Su; Yujin Cao; Hyunjoo Yang; Xiaolu Li; E, particularly, Zhi Li and Zhi Wang. We are also
grateful to Byron Moldofsky, the University of Toronto Cartography lab, and the Neptis Foundation for their support
and for their assistance with GIS data. We would also like to thank the participants at the Asian Development Review
conference on Urban and Regional Development in Asia held in Seoul in July 2016, the managing editor, and an
anonymous referee for helpful comments and suggestions. in questo documento, the Asian Development Bank recognizes
“China” as the People’s Republic of China. The usual disclaimer applies.

Asian Development Review, vol. 34, NO. 2, pag. 25–50

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

26 Asian Development Review

the PRC in 1990, but by the end of 2010 there were 215 km of limited access
highways in the average prefecture. The PRC is not alone. All over the world,
developing countries are making enormous investments in transport infrastructure.
The importance of infrastructure has also been recognized by development
assistance agencies. Per esempio, between 2012 E 2016, approximately 12%
of the World Bank’s lending was for transport-related projects (World Bank
2016).

It is widely believed that transport infrastructure has important impacts on the
development of cities. Speciﬁcally, we expect that improvements to urban transport
infrastructure change location incentives for people and ﬁrms and inﬂuence the
amount of driving and mode of transport choices, conditional upon residential and
ﬁrm location. Such infrastructure improvements may ultimately generate welfare
beneﬁts by reducing commuting and shipping costs. Tuttavia, they may also affect
the physical layout of cities and the organization of activities within cities, yet what
evidence we have is primarily for the effects of roads on cities in the developed
mondo (Baum-Snow 2007, Baum-Snow et al. 2017, Duranton and Turner 2011,
Garcia-López and Holl 2015, Hsu and Zhang 2014). There has been little empirical
investigation of these phenomena in a developing country context. Inoltre, there
is almost no evidence about the effects of modern railroads or the conﬁguration of
urban road and rail networks for promoting urban development.

This paper has two main goals. The ﬁrst is to describe the evolution of
location patterns of transport infrastructure, population, and production in the PRC,
and how transport infrastructure is allocated between cities and their surrounding
regions. The second is to describe our work in Baum-Snow et al. (2017) on the
effects of transport infrastructure on the spatial distribution of population and
production in urban areas in the PRC. Finalmente, we put our estimates in the context
of the existing literature on the effects of infrastructure on urban and regional
development. In sum, we describe the state of infrastructure and cities in the
PRC, and summarize what is known about the ways that infrastructure affects the
development of its cities.

The decentralization of cities is of interest for several reasons. Primo, IL
expansion of cities has immediate implications for land use, travel behavior, E
the availability of agricultural land. Secondo, the decentralization of cities appears
to be an important part of the process of economic development. Early in the
industrial revolution, Western cities tended to be dense and highly centralized:
workers typically traveled under their own power to centrally located factories.
With the advent of the internal combustion engine, manufacturing moved to urban
peripheries where land was cheaper, allowing business services that require less
land to occupy central business districts (CBDs). In developed countries, incomes
have increased many times over since the beginning of the industrial revolution
E, while our understanding of this growth process is incomplete, it is now widely
accepted that much of the innovation responsible for this growth occurred in

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Transport Infrastructure and the Decentralization of Cities in the PRC 27

the densest parts of cities.1 More speciﬁcally, there is evidence that large cities
with centers occupied by diverse and predominantly white collar industries are
engines of economic growth. Così, our interest in the role of infrastructure in
the decentralization of cities is justiﬁed not only by the importance of the effects
that decentralization has on land use and travel demand, but also by the likely
importance of decentralization in the evolution of cities from places organized
around manufacturing into places organized around innovation.

Beyond these general motivations, understanding the effects of transport
infrastructure on the decentralization of cities in the PRC is of interest for three
reasons. Primo, over the past 20 years more than 100 million people in the PRC have
migrated from the countryside to cities, one of the largest human migrations in
history. This has naturally led to very high population densities in Chinese cities
and is probably partly to blame for the current restrictions on internal migration.
These restrictions, known as the hukou (household registration) system, limit access
to schools and health care for rural migrants and limit rural dwellers’ ability to
access urban housing and labor markets. To the extent that transport infrastructure
facilitates the decentralization of cities, it may also reduce crowding and make
cities in the PRC more open to rural migrants. Secondo, cities in the PRC have a
history of centrally planned land allocation. A legacy of the planning economy is
that some cities have centers dominated by large manufacturing establishments.
An important feature of the process of urban growth and development is the
decentralization of manufacturing to urban peripheries and its replacement by
younger and more dynamic industries that are less land intensive and beneﬁt
more from local productivity spillovers and the availability of a broader range
of inputs and ideas. If infrastructure can help cities in the PRC overcome this
legacy of planning and become centers of innovation, then it is important to learn
what elements of the transport network are most effective at facilitating industrial
decentralization. Third, policy makers in the PRC are particularly interested in
food security. To the extent that transport infrastructure causes the conversion of
agricultural land to urban use, such transport expansions may also reduce food
production.

Baum-Snow (2007) ﬁnds the extent of population decentralization due to
radial urban highways in the United States (US) to have been slightly larger in
magnitude than our estimates for cities in the PRC. Given the higher incomes and
much higher rates of auto commuting in the US, the larger estimated impacts in
the US are not surprising. Using data from the US, Duranton and Turner (2011)
show that the amount of driving responds proportionately to the amount of road
capacity available. Infatti, we have also seen this in the PRC’s cities in which newly
constructed highways quickly become congested. Duranton and Turner (2012) ﬁnd

1See Rosenthal and Strange (2004) for a review of the empirical evidence on local spillovers.

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

28 Asian Development Review

that highways draw migrants to cities, consistent with our arguments for Chinese
cities showing how highways can allow a city to accommodate more residents.

There exists a relevant descriptive literature about

the process of
decentralization of industrial production as countries grow and establish additional
infrastructure. Henderson and Kuncoro (1996) show how manufacturing facilities
near Jakarta decentralized with the establishment of a highway linking the
Indonesian capital to nearby hinterlands. Deng et al. (2008) descriptively show how
a similar process has occurred in several cities in the PRC. Così, the narrative that
industry decentralizes ﬁrst to allow cities to specialize in more productive activities
that require less land has been documented empirically, though descriptively, In
several contexts.

While there is much ongoing research and debate about the most relevant
mechanisms, a number of recent papers argue convincingly that reductions in
transport costs promote economic growth. Using lights-at-night data, Storeygard
(2016) shows that as the costs of shipping between interior African cities and
nearby markets fall, these interior cities grow. Donaldson (forthcoming) ﬁnds very
large effects of roads and railways on growth in Indian cities in the late 19th and
early 20th centuries. Banerjee, Duﬂo, and Qian (2012) ﬁnd moderate effects of
railroads on rural gross domestic product (GDP) levels, but not on growth, In
the PRC. Michaels (2008) ﬁnds consistent evidence that roads in the US affect
factor shares and output levels in rural counties, which is also consistent with
Chandra and Thompson’s (2000) evidence. Duranton and Turner (2012) ﬁnd small
effects of roads on urban population growth between 1980 E 2000, suggesting
small effects on productivity in the US during this period. They also ﬁnd that
marginal roads in the US are probably not welfare improving. Garcia-López and
Holl (2015) ﬁnd similar results for Spain between 1960 E 2010. This is broadly
consistent with Duranton, Morrow, and Turner’s (2014) evidence indicating that
roads did not affect the value of intercity trade in the US in 2007. The dominant
mechanism considered in these papers is that transport infrastructure lowers trade
costs, thereby allowing cities to specialize in the production activities for which
they have comparative advantages. Generalmente, evidence from the literature suggests
that returns to infrastructure are large in poor countries, but decline with increases
in income levels and the extent of the network.

II. Background and Basic Facts

Our study area consists of the 26 provinces that make up the eastern PRC.
Provinces in the PRC comprise prefectures, which in turn are made up of three
types of counties: xian (rural counties), xianji shi (county towns), and qu (urban
districts). There are 257 prefectures in our study area and about 2,500 counties.
Each prefecture contains at most one core city. Core cities are administrative units
and consist of all urban districts within the prefecture. The extent of our study

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Transport Infrastructure and the Decentralization of Cities in the PRC 29

area is indicated by the green area in Figure 2, prefectures are indicated by the red
boundaries in Figure 5, and the extent of core cities is indicated by the tan regions
in Figure 2.

We are primarily interested in two geographic units: the prefecture and
the core city drawn to constant 1990 boundaries. One goal of this paper is to
review evidence on the extent to which transport infrastructure contributes to
decentralization in the PRC’s cities. More precisely, we evaluate the extent to which
transport infrastructure inﬂuences changes in the share of prefectural economic
activity and population within 1990 prefectural city boundaries between 1990 E
2010.

UN.

Transport Infrastructure in the PRC: Data and Basic Facts

To construct data describing the PRC’s road and railroad infrastructure, we
digitize large-scale national road maps. Questo è, we measure highways and railroads
as lines on maps. We rely on national maps rather than more detailed provincial
maps to ensure consistency of measurement across locations. Per esempio, a red line
describes the same class of road in two provinces if both provinces are on the same
map. Figura 1 illustrates the way our data is constructed for Beijing in 2010. In all,
we construct digital maps for the following networks: (io) limited access highways in
1995, 2000, 2005, E 2010; (ii) railroads in 1924, 1962, 1980, 1990, 1999, 2005,
E 2010; E (iii) smaller highways in 1962, 1980, 1990, 1999, 2005, E 2010.
We also construct data on the PRC’s river networks.2

Figura 2 shows the development of the PRC’s network of limited access
highways. The construction of this network began in the early 1990s. In the top
left panel of Figure 2, we see that a few segments had been constructed by 1995,
most of them near Beijing and Shanghai. The top right panel of Figure 2 shows
the highway network in 1999. By this time, routes connecting Hong Kong, China
to both Beijing and Shanghai were complete, with fragments scattered broadly
throughout the country. The bottom left panel of Figure 2 shows the road network
just 5 years later in 2005. We see that the network is well developed along the coast,
but that the coastal network is not well connected to the central part of the country.
The bottom right panel of Figure 2 shows the limited access highway network in
2010. We see that the highway network in the central part of the country is now
connected to the coastal network. We also see that by 2010 a large majority of core
prefecture cities, indicated by the tan regions, have been connected to the network.
Figura 3 presents corresponding maps of the PRC’s rail network. The top left
panel shows the rail network in 1990. This network is concentrated in the northeast
and while almost all core cities in this region are connected to the network, cities
in the south and west of the PRC are much less likely to be connected. The top

2Detailed bibliographical information is available in Baum-Snow et al. (2017).

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

30 Asian Development Review

Figura 1. Digital Maps of Beijing

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Notes: Construction of digital maps from paper source maps for the region around Beijing in 2010. The top panel
shows a region around Beijing in a 2010 national road map. The bottom panel shows the resulting digital road map.
The green region in the right panel indicates the extent of the 1990 prefectural city. The yellow region indicates
expansion of this administrative region by 2005.
Fonte: Authors’ illustration.

Transport Infrastructure and the Decentralization of Cities in the PRC 31

Figura 2. Limited Access Highways in the People’s Republic of China

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Note: The top left panel is for 1995, the top right is for 1999, the bottom left is for 2005, and the bottom right is for
2010. Green indicates the extent of our study area. Tan indicates 1990 prefecture city boundaries. Blue dots signify
central business districts.
Fonte: Authors’ illustration.

right panel of Figure 3 shows the rail network in 1999. While the network is clearly
more extensive than in 1990, the rate of growth is nowhere near as fast as for the
highway network. IL 1999 network is denser everywhere, but many core cities
in the south and west are still not on the network, while many spur lines serve the
regions around cities in the northeast. The lower left panel of Figure 3 shows the rail
network in 2005, which is more extensive than the 1999 rete. Just as changes
to the 1999 network allowed major northeastern cities to interact with smaller cities
nearby, so did new rail lines built between 1999 E 2005. The bottom right panel of

32 Asian Development Review

Figura 3. Railroads in the People’s Republic of China

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Figura 3 shows the rail network in 2010. The main change from 2005 is the addition
of an east–west line to connect the rail network in the central part of the country to
the coastal network.

A few comments about these maps are in order. Primo, we can observe that
the rail and highway networks are obviously different from one another. The rail
rete, while more extensive, grew more slowly. In addition to connecting major

Transport Infrastructure and the Decentralization of Cities in the PRC 33

cities to each other, the rail network was designed to connect major cities to the
smaller cities that surround them. The highway network, on the other hand, is more
specialized in connecting major cities to each other. Secondo, the PRC relies heavily
on railroads for long-haul and even short-haul freight (World Bank 1982). In 1978,
less than 5% of freight in the PRC (in terms of ton–distance units) was carried on
highways. By 2004, this number had risen to almost 15%, but was still much lower
than in the US. We will ultimately ﬁnd that highways and railways have different
effects on the decentralization of cities in the PRC. Highways affect the location
of people within urban areas, while railroads affect the location of manufacturing.
These different effects may reﬂect the intrinsic comparative advantages of highways
and railroads for moving goods and people. Alternatively, they may also reﬂect the
fact that the road and rail networks were laid out to serve different purposes. While
the data in Baum-Snow et al. (2017) do not permit distinguishing between these two
possibilities, the second alternative seems consistent with an inspection of the way
the networks are laid out in the PRC.

To proceed with our investigation of how transport infrastructure affects the
decentralization of cities in the PRC, we need measures that quantify the road
and rail networks in each core city and its surrounding region. We construct three
variables for each network. The ﬁrst is simply the length of each network in each
prefecture and in each 1990 core city. The second and third are radial and ring
road indexes, rispettivamente. The radial road index describes the ability of a network
to carry trafﬁc radially in and out of the central business district, while the ring
index measures the ability of a network to carry trafﬁc in a circle around the central
business district.

The top panel of Figure 4 illustrates how we construct our radial road index.
To begin, we draw two circles around the CBD of each core city, one with a radius of
5 km and one with a radius of 10 km. We then count the number of times a transport
network intersects each ring. The smaller of these two numbers is our measure of
radial road capacity in the 5–10 km donut surrounding the CBD. In the top panel
of Figure 4, we illustrate this process for the 2010 highway network surrounding
Beijing. This network intersects the smaller ring six times and the larger ring eight
times. In questo caso, our radial road index takes a value of six, which is exactly what
one would choose if doing the calculation by eye.

Calculating our ring road index is more involved. We proceed quadrant by
quadrant. For the northwest quadrant, as illustrated in the bottom panel of Figure
4, we begin by drawing two rays out from the CBD, one to the west and one to
the northwest. We then restrict attention to the portion of each ray that lies between
9 km and 15 km from the CBD and count the number of times the transport network
intersects each ray. The ring road capacity of the network in the northwest quadrant
between 9 km and 15 km is the smaller of these two counts of intersections. For the
example illustrated in the lower panel of Figure 4, we count one ring road in the
northwest quadrant. We repeat this procedure for each of the four quadrants and for

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

34 Asian Development Review

Figura 4. Construction of the Radial Road and Ring Road Indexes

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Notes: The top panel illustrates the construction of our radial road index. The bottom panel illustrates the construction
of our ring road index.
Fonte: Authors’ illustration.

Transport Infrastructure and the Decentralization of Cities in the PRC 35

Tavolo 1. Railroad and Highway Network Growth in the People’s Republic of China

Year

1962

1980

1990

1999

2005

2010

Panel A: Railroads

32
99
1.16
0.52
0.11
0.10
0.02
0.02

40
139
1.43
0.60
0.16
0.16
0.02
0.02

44
151
1.54
0.67
0.20
0.18
0.03
0.03

Panel B: All Roads Visible on Maps

60
349
2.04
0.79
0.19
0.17
0.05
0.05

74
463
2.47
0.84
0.35
0.29
0.14
0.11

84
517
2.89
0.94
0.28
0.24
0.09
0.08

Panel C: Express Highways Only

47
177
1.64
0.68
0.18
0.16
0.04
0.04

89
469
2.89
0.88
0.46
0.35
0.18
0.13

55
214
1.81
0.76
0.24
0.23
0.05
0.05

118
649
3.37
0.93
0.9
0.53
0.27
0.21

55
218
1.85
0.76
0.24
0.22
0.04
0.04

137
746
3.81
0.94
1.27
0.63
0.44
0.29

0
0
0
0
0
0
0
0

Mean total km, central city
Mean total km, entire prefecture
Mean radial index
Share radial index > 0
Mean ring index
Share with ring index > 0
Mean peripheral ring index
Share with peripheral ring index > 0
km = kilometers.
Notes: Infrastructure measures are reported for the 257 prefectures with distinct prefecture cities in 2010.
Central cities are deﬁned using prefecture city geographies in 1990 or at the time of upgrading to prefecture
city status. The types of roads that contribute to numbers in Panel B differ markedly over time, with single-lane
dirt roads predominating in 1962 E 1980, and large highways predominating in 2010.
Fonte: Authors’ calculations.

43
160
0.86
0.46
0.50
0.36
0.12
0.12

52
215
0.95
0.46
0.66
0.44
0.16
0.14

13
49
0.33
0.19
0.14
0.13
0.05
0.05

0
0
0
0
0
0
0
0

roads that lie between 15 km and 25 km from the CBD. To construct our ring road
capacity index, we sum over all quadrants and the small and large donuts. We are
also able to restrict attention to prefectural ring roads that lie outside 1990 core city
boundaries. The construction of this measure of peripheral ring road capacity is the
same as the one described above, but considers only roads outside the boundaries of
IL 1990 core city. As we discuss below, these peripheral ring roads appear to have
been most important in shaping cities in the PRC since 1990.

Tavolo 1 uses our three city-level statistics—total kilometers, radial roads, E
ring roads—to describe the evolution of transport infrastructure in prefectures and
their core cities. Panel A describes the rail network. In 1962, an average prefecture
had about 99 km of rail, of which 32 km was in the core city. The rail network grew

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

36 Asian Development Review

steadily between 1962 E 2005, at which point an average prefecture contained
Di 214 km of rail, 55 km of which was in the core city. There was little change
in the network between 2005 E 2010. The top panel of Table 1 bears out what we
see in Figure 3. The rail network increased steadily over the 1990–2005 period, con
much of the expansion devoted to rail lines that connect core cities to satellite cities
just outside their administrative boundaries.

Panel A in Table 1 also describes the conﬁguration of the rail network in an
average prefecture. In 1962, an average core city had one radial rail line and about
half of all core cities had no rail lines. By 1990, the ﬁrst year illustrated in Figure 2,
the average core city had about 1.5 radial rail lines and only one-third of core cities
had zero. By 2005, radial railroads in core cities had increased only marginally. An
average core city in 2005 had about 1.8 radial rail lines and the share of core cities
without radial rail lines had declined to about one-quarter.

The ﬁnal four rows of Panel A in Table 1 show that prefectures typically
have little ring rail capacity. In 1990, an average prefecture had 0.2 units of ring
rail capacity. This means that an average prefecture city in 1990 had a rail line
that traveled about 18 degrees around its perimeter. With this said, only 18% Di
core cities had any ring rail capacity in 1990. Così, Tavolo 1 shows that almost
all cities with ring rail capacity had exactly one unit, questo è, a rail line traveling
Di 90 degrees around the CBD. Suburban ring rail capacity is even scarcer.
In 1990, an average core city had 0.03 units of peripheral ring rail capacity and
only about ﬁve core cities had any peripheral ring rails at all. Ring rail capacity
increased gradually until 2005. Between 2005 E 2010, ring rail capacity actually
dropped. This appears to reﬂect measurement error. Our rail maps are hand drawn
so the location of any given rail line will move slightly from year to year. Since our
ring road algorithm is sensitive to such small changes, we can observe year-to-year
variation in the ring index even when, as was the case between 2005 E 2010, IL
underlying network is little changed.

Data reported in Panel B in Table 1 is analogous to that in Panel A except
it describes the evolution of the PRC’s road network. Roads in our data were of
different quality in different years depending on what appeared on the national maps
that were digitized. In 1962 E 1980, almost all roads were single-lane dirt tracks
that were unsuitable for trucks. By 1990, many of these had been paved. During
the 1990s and 2000s, the express highway network was developed so that by 2010
Di 35% of urban roads were express highways, with the rest being mostly wide
boulevards with some limits on access. The result is that the average city had almost
four radial highways in 2010, with almost all cities having at least one.

Panel C in Table 1 shows the growth in express highways. A comparison
of Panels B and C suggests that much of the road improvements experienced in
cities involved the construction of new or upgrading of existing roads to express
highways. By 1999, an average prefecture had about 49 km of express highway, con
Di 13 km of this located in the prefecture’s core city. Unlike the rail network,

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Transport Infrastructure and the Decentralization of Cities in the PRC 37

which appears to be mostly complete by 2005, the highway network continued
to grow through 2010, when an average prefecture contained 215 km of limited
access highways, of which 52 km were in the boundaries of the 1990 core city.
As for railroads, the share of highways in prefectures’ core cities falls slightly
over time. The amount of radial express highway increased from 0.33 per city in
1999 to about 0.95 In 2010, and the share of cities with at least one radial express
highway increased from 0.19 to about one-half. Unlike for the rail network, there
was substantial ring road capacity by 2010. An average core city had more than
twice as much ring road capacity as ring rail capacity in 2010, while almost two-
thirds of all core cities had some ring road capacity. If we restrict attention to
peripheral roads, the contrast with rail is even more dramatic.

Tavolo 1 conﬁrms the impressions formed by inspection of Figures 2 E 3.
The rail network grew rapidly over the review period and was largely completed
by 2005, while the limited access highway network grew much faster than the rail
network and this growth continued through 2010. More subtly, the rail network is
relatively more specialized in radial capacity and the highway network is relatively
more specialized in ring capacity. In the PRC, people and goods moving in and out
of CBDs are more likely to travel by rail. If they are moving around city centers,
they are more likely to travel by highway.

Population and Production in the PRC: Data and Basic Facts

We have two primary measures of production: lights-at-night satellite images
and explicit measures of prefecture and core city GDP from various PRC censuses
and yearbooks. We have one measure of population taken from various PRC
censuses and yearbooks. We begin by discussing the lights-at-night data before
turning to GDP and population data. We rely on six separate lights-at-night images
of the PRC (National Geophysical Data Center 1992–2009). These images are for
1992, 2000, 2005, E 2009, with two sets of data for 2000 E 2005. For each
cell in a regular 1-km grid covering our study area, these data report an intensity
of nighttime lights ranging from 0 A 63. The codes for 0 A 62 indicate intensity,
while 63 is a topcode. Although it is common in developed countries, topcoding is
rare in the PRC during our study period. Henderson, Storeygard, and Weil (2012)
show that lights at night are a good proxy for GDP at the country level. As we
discuss below, lights and GDP are also strongly correlated at the prefecture level in
the PRC. While lights at night are related to production, they are surely also related
to other human activities, including those occurring in residences, which may not
be directly related to production. Così, while lights at night give us a more detailed
picture of where activity occurs than is available from administrative data, some
caution in interpreting these images is required.

Figura 5 presents lights-at-night images for our study area for 1992, 2000,
2005, E 2009. Lighter shades indicate higher nighttime intensity of light and red

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

38 Asian Development Review

Figura 5. Lights at Night in the Eastern People’s Republic of China

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Notes: The top left panel is for 1992, the top right is for 2000, the bottom left is for 2005, and the bottom right is for
2010.
Fonte: Authors’ illustration.

indicates prefecture boundaries. These images show that lights are concentrated in
the northeastern part of the country in much the same area where the early railroad
network is concentrated. Over time, lights expand to the whole country, but light in
the region between Beijing and Shanghai expands most rapidly while in the western
PRC it grows less dramatically. While the major cities such as Beijing; Shanghai;

Transport Infrastructure and the Decentralization of Cities in the PRC 39

Tavolo 2. Prefecture and Central City Growth in the People’s Republic of China

1990–1992 1995 2000 2005 2009–2010

Panel A: Lights and Geographic Shares

Entire prefecture (1992 = 1)
Central city share of prefecture

1
0.38

1.43
0.33

1.59
0.33

1.88
0.33

Panel B: GDP and Geographic Shares (189 Prefectures)

All GDP: Entire prefecture (CNY100 million)
All GDP: Central city share of prefecture
All GDP: Prefecture outside central city share
Industrial GDP: Entire prefecture (CNY100 million)
Industrial GDP: Central city share of prefecture
Industrial GDP: Prefecture outside central city share

34
0.45
0.55
16
0.59
0.41

n.a.
n.a.
n.a.
n.a.
n.a.
n.a.

112
0.41
0.59
53
0.45
0.55

Panel C: Population and Geographic Shares

Entire prefecture (millions)
Central city share of prefecture
Prefecture outside central city share

3.91
0.24
0.76

n.a.
n.a.
n.a.

4.27
0.28
0.72

213
n.a.
n.a.
107
n.a.
n.a.

n.a.
n.a.
n.a.

2.41
0.32

404
0.45
0.55
207
0.44
0.56

4.57
0.32
0.68

CNY = Chinese yuan, GDP = gross domestic product, n.a. = not available.
Notes: Reported numbers in Panels A and C are averages across prefectures in the primary estimation sample of 257.
Reported numbers in Panel B use the sample of 189 prefectures for which we have full GDP information in 1990.
Missing GDP fractions in 2005 are because we have fewer than 189 observations for prefecture cities in this year
only. GDP is reported using provincial deﬂators.

and Hong Kong, China clearly grow over our study period, growth is not conﬁned
to these cities. Small cities all over the country grow rapidly as well.

Panel A in Table 2 quantiﬁes this growth in lights. The ﬁrst row of this table
reports the total amount of light in our study area, con 1992 normalized to 1.
Consistent with inspection of the images in Figure 5, we see a steady, rapid increase
in the total amount of light in the PRC, with almost 2.5 times as much light in 2010
as in 1992. The second row of Panel A in Table 2 reports the share of all lights
In 1990 core cities. We see a gradual decrease in the share of lights in core cities,
from 38% In 1990 A 32% In 2010. Così, as with the road and rail networks, lights
increase faster outside the 1990 boundaries of core cities than inside them.

We now turn our attention to direct GDP measures. For 1990, noi usiamo
GDP and industrial sector GDP information from various national and provincial
data year books (China Statistics Press 1992b, 1992C). For 2000–2010, noi usiamo
output information from the University of Michigan’s Online China Data Archive
at the rural county, county city, and core prefecture city levels according to
these data with prefecture-level
contemporaneous deﬁnitions. We supplement
printed yearbooks. We note that prefecture-level GDP data is not available for our
full sample in all years. Therefore, reports on GDP in Panel B in Table 2 use a
sample of 189 prefectures.3

The top panel of Figure 6 shows the percentage change in GDP between
1990 E 2010 for constant boundary core cities and for the residual portion of each

3More detail about data construction is available in Baum-Snow et al. (2017).

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

40 Asian Development Review

Figura 6. Changes in Gross Domestic Product and Population at the Prefecture Level

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Notes: The top panel shows the percentage change in gross domestic product between 1990 E 2010 In 1990
prefecture cities and in the residual prefectures. The bottom panel shows the corresponding changes in population
between 1990 E 2010.
Fonte: Authors’ illustration.

Transport Infrastructure and the Decentralization of Cities in the PRC 41

prefecture. Gray signiﬁes missing data. For cities and prefectures, GDP growth rates
are assigned to one of six color-coded categories: (io) less than 600%; (ii) less than
800%; (iii) less than 1,000%; (iv) less than 1,200%; (v) less than 1,400%; E (vi)
almeno 1,400%. Lighter colors indicate lower growth rates. While our GDP map is
somewhat incomplete, as expected it shows that development was faster along the
coast, both in prefecture cities and in the surrounding prefectures. Close inspection
does not suggest a pattern of either decentralization or centralization. In many cases,
central cities appear to grow more quickly than the surrounding prefecture and vice
versa.

We separately observe the industrial component of GDP in the tabular
dati. Infatti, we believe that industrial GDP is better measured than overall GDP,
especially in earlier years. For this reason and because GDP results can be inferred
from industrial GDP results as described below, the regression analysis will only
look at industrial GDP. Industrial GDP accounts for 46% of measured total GDP
In 1990, rising to 51% In 2010. Panel B in Table 2 describes these data. Between
1990 E 2010, prefecture mean GDP increased by about a factor of 12. We also
see that between 1990 E 2010 there was a marked decentralization of industrial
production. IL 59% share of industrial GDP in 1990 core cities decreased to 46%
In 2000 and to 44% In 2010. This decentralization is more rapid than for overall
GDP, which has a stable central city share of about 45% throughout our study
period.

Overall, Tavolo 2 bears out our inspection of Figure 6 and our results based on
lights-at-night data. There is a rapid overall increase in GDP and a decentralization
of economic activity focused on industry. Together with the lights-at-night data, IL
GDP data suggest that the PRC’s cities are adopting a modern form of organization
often seen in developed countries. Much production activity occurs in the CBDs
of cities in developed countries, but as countries become wealthier, manufacturing
moves to the periphery of big cities.

Finalmente, we consider population growth and migration. We assemble
population data from the 1990, 2000, E 2010 population censuses. For 1990, we
rely primarily on the 100% count Chinese census data aggregated to the prefecture
core city, rural county, and county city levels (China Statistics Press 1992a). For
2000 E 2010, our census data are the 100% count aggregated to the urban district
and rural unit levels (China Statistics Press 2002, Lianxinwang 2012). We note that
In 1990, census data reports place of legal residence rather than place of actual
residence; Perciò, using census data to ﬁgure out the resident population is a
subtle exercise. More detail on data construction is available in Baum-Snow et al.
(2017).

The bottom panel of Figure 6 uses our data to illustrate population changes
in the PRC between 1990 E 2010. This ﬁgure is similar to the top panel of Figure
6. Lighter colors indicate slower rates of population growth.

Unsurprisingly, this ﬁgure shows high rates of population growth near
Beijing; Shanghai; and Hong Kong, China. Generally, it shows high rates of

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

42 Asian Development Review

population growth along the PRC’s east coast. Perhaps more surprising, it shows
a number of regions with high population growth in the interior and the western
parts of the PRC as well. Così, the widely reported coastal migration of the Chinese
population appears to be only a part of the story of migration in the PRC. A second
pattern is also clear: 1990 core cities experienced higher rates of population growth
than the surrounding areas of prefectures during the study period. Così, while the
large-scale patterns of migration appear to be complicated, at a small scale they are
clear. People are moving from the countryside to the city.

Panel C in Table 2 further describes our population data. The ﬁrst row
reports mean population for entire prefectures in 1990, 2000, E 2010. In 1990,
an average prefecture was home to about 4 million people. This number had grown
A 4.6 million by 2010. The second row reports the share of an average prefecture’s
population within the boundaries of the 1990 core city. The share of population in
1990 core cities increases between 1990 E 2000, and again between 2000 E
2010. In 1990, one person in four lived in a core city. By 2010, one person in three
did. Così, consistent with what we saw in Figure 6, the population in core cities is
growing much more rapidly than in the surrounding areas. Some simple calculations
illuminate the scale of the rural-to-urban migration underlying these data. In 1990,
an average core city had a population very close to 1 million. By 2010, this ﬁgure
had increased to about 1.5 million. Con 257 prefectures in our primary sample,
this suggests that the population of 1990 core cities increased by about 127 million
between 1990 E 2010.

III. Transport Infrastructure and the Decentralization of Cities in the PRC

Our data describe three signiﬁcant changes in the PRC’s economy between
1990 E 2010. Primo, we see a large increase in GDP. Secondo, we see a
huge migration of people from the countryside to the major cities. Third, we
see a dramatic decentralization of manufacturing. That the decentralization of
manufacturing GDP is so much larger than of total GDP suggests a countervailing
centralization of services. During the same period, our data indicate a dramatic
increase in the extent of the railroad network and the wholesale creation of a
network of limited access highways. We now describe the results of Baum-Snow
et al. (2017) on the role that highways and railroads played in the centralization of
population and the decentralization of manufacturing in the PRC’s cities between
1990 E 2010.

UN.

Econometric Method

Baum-Snow et al. (2017) investigate the extent to which road and rail
networks contributed to the decentralization of cities in the PRC using instrumental
variable (IV) regressions analysis. We begin by describing our approach and
providing some intuition about how it works.

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Transport Infrastructure and the Decentralization of Cities in the PRC 43

Baum-Snow et al. (2017) conduct regressions of the following form using

information for prefectures indexed by i:

(1)

(cid:2)tln(Outcomei) = A + B × (Infrastructureit ) + C × Controlsi + errori
Here, (cid:2)t indicates the 1990–2010 difference, and the outcomes of interest are
core city population and industrial GDP. The infrastructure measure is one (O
more) of our measures of road or rail infrastructure described in Panels A and B
of Table 1 as of 2010. B is the parameter of interest. Control variables include
the 1990–2010 change in log prefecture population, which we include so that B
captures the extent to which infrastructure reallocates economic activity between
cities and prefecture remainders holding the total scale constant. Excluding this
control would make B capture a combination of the effects of infrastructure on
prefecture size and the allocation of economic activity between central cities and
prefecture remainders. Other control variables measure city and prefecture size plus
1982 prefecture economic conditions. The reasons for including these variables are
explained below in our discussion of the identiﬁcation strategy.

The coefﬁcient of interest, B, is the percentage of central city population
or industrial GDP displaced to prefecture remainders for each unit change in the
infrastructure variable. For highways, we treat the number of 2010 rays as identical
to the 1990–2010 change because the types of roads that appear on our map in 2010
are of much higher quality than those in 1990.4 Tuttavia, considerable railroad
infrastructure existed in 1990. As is discussed in Baum-Snow et al. (2017), we also
use 2010 as the measurement year for railroads because in 1990 the central planning
regime in the PRC rendered any market responses to the location of transport
infrastructure impossible at that time. Only after 1990 did market forces begin to
inﬂuence the allocation of land to different uses in urban areas.

We must be fundamentally concerned that infrastructure was assigned to
prefectures in ways that are driven by or correlated with unobserved factors in the
error term that drive shifts in the allocation of economic activity between central
cities and prefecture remainders. Per esempio, suppose that highways are assigned
to prefectures by a planning authority in response to anticipated growth in core city
population. In questo caso, estimates of equation (1) will yield a positive coefﬁcient:
roads are built in prefectures where the core city population grows. Alternatively,
suppose that roads are assigned to prefectures at random. In questo caso, we expect the
estimation of equation (1) to return negative estimates of B. Questo è, we expect that
by reducing transport costs, additional roads allow the population to decentralize
and the share of the core city population in the prefecture to decline.

Therefore, the process by which roads are assigned to cities is fundamental
to this investigation. To understand the extent to which infrastructure causes

4The empirical analysis in Baum-Snow et al. (2017) uses 2010 radial and ring road capacities for all types of

roads we observe on the map, not just express highways.

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

44 Asian Development Review

decentralization, it is important to examine cases in which infrastructure was
either assigned randomly or by a process that is unrelated to variables for which
we cannot control but may affect outcomes of interest. To resolve this problem,
Baum-Snow et al. (2017) rely on an IV estimation. Our implementation of this
technique essentially randomizes across cities the amount of infrastructure that is
received by relying on variations in historical infrastructure networks to predict
modern networks. This process can be thought of as occurring in two stages. IL
ﬁrst stage of this process picks out infrastructure that was assigned to cities in a
way that is plausibly unrelated to unobservables that predict central city growth
or decentralization. We use as instruments infrastructure variables measured as of
1962.

As is discussed at length in Baum-Snow et al. (2017), roads in the PRC
In 1962 were of low quality and primarily existed to move local agricultural
goods to market and not to facilitate travel within urban areas. Tuttavia, their
existence established rights of way over which modern highways could be built
at lower cost. A credible IV estimation in this case thus requires inclusion of
control variables that may be correlated with prefecture agricultural productivity,
as this may predict subsequent city or prefecture growth. Allo stesso modo, because
railroads in 1962 were disproportionately allocated to serve provincial capitals
and manufacturing-oriented cities, we control for these two factors as well. Tutto
such control variables use measures from 1982. The second estimation stage uses
quasi-randomized infrastructure measures that come out of the ﬁrst estimation stage
to recover estimates of B that are “as if” infrastructure had been assigned at random.
Our IV estimates capture the extent of decentralization in prefectures that
received more 2010 infrastructure only because of 1962 infrastructure differences,
holding constant the prefecture industry mix, historical population, and central
city area. To the extent that roads or railroads cause cities of different proﬁles to
decentralize at different rates, we can only recover one local average treatment
effect per type of infrastructure with this procedure (Imbens and Angrist 1994).
Wrapped into these local average treatment effects are likely to be cocktails of
treatment effects that depend on underlying observed and unobserved prefecture
heterogeneity. Questo è, our estimated treatment effects apply only to the set of
prefectures for which 1962 infrastructure affected 2010 infrastructure. Attempts
to unpack which types of prefecture are most affected by infrastructure reveals that
more developed areas in the eastern PRC experienced larger decentralization effects
of infrastructure than other areas. Inoltre, infrastructure responses primarily
occurred within 10 years of construction. Tuttavia, our results are not driven
by the largest cities. Limited statistical power precludes us from disaggregating
heterogeneity in treatment effects much further. We note that as with any IV
estimator in which the treatment is not truly randomized with full compliance, our
estimates apply only to the types of cities that experienced infrastructure upgrades
because of the level of infrastructure in place in 1962. It may well be that some

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Transport Infrastructure and the Decentralization of Cities in the PRC 45

cities with fewer gains to be made from upgrades chose not to do so, even though
1962 roads and rails gave them lower-cost infrastructure upgrade options. Questo è,
we can only recover “treatment-on-the-treated” type estimates.

We note that while IV estimation is subtle, it is pervasive in applied
microeconomics. Inoltre, similar IV estimation strategies have been successfully
employed in other papers looking at the effects of transport infrastructure, including
Baum-Snow (2007); Duranton, Morrow, and Turner (2014); Duranton and Turner
(2011); Duranton and Turner (2012); Hsu and Zhang (2014); and Michaels (2008).
This collection of papers gives us enough experience with the general estimation
strategy to be conﬁdent that Baum-Snow et al. (2017) provide credible estimates of
the causal effects of infrastructure on the spatial organization of cities in the PRC.

The Effects of Infrastructure on Population Decentralization
in Cities in the PRC

In regressions like equation (1) where the outcome variable is the change in
log core city population between 1990 E 2010, and the infrastructure measure is
the index of radial road capacity for major highways, Baum-Snow et al. (2017) ﬁnd
that each highway ray causes a 4%–6% decrease in core city population depending
on the details of the regression. Additionally, the existence of some ring road
capacity decentralizes about 25% of core city population to prefecture remainders.
We ﬁnd no discernable effects on population decentralization of any other transport
measures studied, including radial rail capacity, the extent of the prefecture road
rete, the extent of the rail prefecture network, or ring rail capacity.

Consistent with evidence for the US in Baum-Snow (2007) and Duranton
and Turner (2012), differences between ordinary least squares and IV highway ray
coefﬁcients suggest that the 1999 E 2010 radial road indexes are not assigned to
cities at random. Speciﬁcally, more roads are assigned to cities whose populations
grow faster relative to the surrounding prefecture. Così, more roads were built in
prefectures containing rapidly growing core cities, even as these roads were causing
populations to decentralize from these cities. Results in Baum-Snow et al. (2017)
show that while more rapidly growing cities in the PRC received more transport
infrastructure of various types, the decentralization that occurred because of this
infrastructure was overwhelmed by the growth that precipitated the construction of
this infrastructure.

The Effects of Infrastructure on Production Decentralization
in Cities in the PRC

Baum-Snow et al.

transport
infrastructure on the decentralization of production from central cities. Speciﬁcally,
they estimated versions of equation (1) in which the outcome variable is the change

(2017) also investigate the effects of

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

46 Asian Development Review

in log industrial GDP between 1990 E 2010. With industrial GDP measured much
more precisely than entire GDP in 1990, we focus here on estimating effects on the
industrial GDP measure. To maintain consistency in regression speciﬁcation, noi usiamo
the same set of control variables as in the population analysis described in section
III.B. We lose 16 prefectures from the sample for which we do not observe 1990
GDP information.

Results indicate that neither highway rays nor network length have
measurable effects on the decentralization of core city economic activity. Tuttavia,
railroads cause economic activity to decentralize. Each railroad ray is estimated to
displace 24%–34% of core city industrial GDP. Because industrial GDP is about
half of total GDP, and we ﬁnd that these effects primarily apply to the industrial
sector, railways’ effects on total GDP are about half as large. Similar strong results
hold for prefecture railroad network length. Tuttavia, Baum-Snow et al. (2017) do
not have the statistical power to jointly estimate the effects of these two railroad
network measures in one regression. Baum-Snow et al. (2017) ﬁnd large additional
statistically signiﬁcant negative effects of the existence of a ring road on prefecture
city economic activity in addition to the effects of rail rays. The estimated effect of
peripheral ring road capacity on industrial GDP is −0.71 log points in addition to
−0.236 log points for each radial railroad. These estimates are robust to including
other transport measures in the regression and to minor changes in the details
of the regression equation. As with highways and population, results reported in
Baum-Snow et al. (2017) suggest that more railroads have been assigned to central
cities with more rapid GDP growth.

We believe that railroads are important for industrial decentralization because
they dominate trucking as the primary intercity shipping mode. More radial
railroads provide more options for manufacturers to move out of central cities
and maintain access to the national railroad network through sidings and ring
road connections. Industrial decentralization is likely a desired reorganization of
urban production activities since cheaper land and rural labor is well suited for the
land-intensive, low-skilled manufacturing sector. Allo stesso tempo, CBD land can
be repurposed toward services that are less land intensive and typically beneﬁt more
from local agglomeration spillovers.

Employment versus Population

Evidence in Baum-Snow et al. (2017) indicates that radial roads cause
population decentralization, radial railroads cause industrial decentralization, E
ring roads cause both. While this might seem contradictory since industrial
employment that has decentralized because of railroads requires more suburban
workers, these results can be squared by looking at decentralization effects on
employment by industry. Estimated effects of roads and railroads on the number of
working residents are very similar to those reported in section III.B for population.

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Transport Infrastructure and the Decentralization of Cities in the PRC 47

Tuttavia, estimated effects on employment in manufacturing only are similar
to those reported for industrial GDP in section III.C, whether employment is
aggregated to residential or work locations. This suggests that railroads did indeed
cause some people to decentralize (or to not move to cities when they otherwise
would have), while radial roads promoted either more intensive commuting from
suburbs to central cities or the decentralization of nonmanufacturing jobs. Noi
thus have evidence that highways promote decentralization of service jobs and
workers, while rails promote decentralization of manufacturing jobs and workers.
That railroads do not affect the allocation of the total working population between
cities and suburbs means that railroads likely promote central city shifts toward the
service sector. Ring roads decentralize all types of activities.

Other Effects of Infrastructure

Baum-Snow et al. (2017) make two further ﬁndings. The ﬁrst is that
neither road nor railroad infrastructure inﬂuences prefecture population levels. In a
regression like equation (1) where the dependent variable is a change in prefectural
population rather than central city population, the coefﬁcient on radial highways is
small and statistically indistinguishable from zero. They obtain similar results for
other infrastructure measures.

This clariﬁes the interpretation of their results in an important way.
Regressions showing changes in the central city share of population or GDP could
reﬂect increases in suburban population or GDP, decreases in central city population
or GDP, or the migration of activity from CBDs to the suburbs. The fact that
the overall level of prefectural population and GDP does not depend on within-
prefecture measures of infrastructure tells us the effects of this infrastructure are
purely redistributive. Within-prefecture infrastructure appears to operate primarily
by reorganizing activity within the prefecture by encouraging the radial migration
of population and GDP that we have discussed at length.

Finalmente, Baum-Snow et al. (2017) investigate the extent to which the effects
of infrastructure on industry location differ by industry. To accomplish this, Essi
perform regressions like those for industrial GDP, like equation (1), dove il
dependent variable is the change in sectoral employment decentralization. They
partition manufacturing sectors into three groups based on the weight of a given
value of output using data from Duranton, Morrow, and Turner (2014). Per esempio,
primary metals and wood and paper processing are in the “heavy” category,
fabricated metals and furniture are in the “medium” category, and textiles and
high-tech are in the “low” category.

Given our results for overall

that certain
industries decentralize in response to radial railroads and ring highway capacity,
but do not respond to other infrastructure measures. This is broadly true, Anche se
the more disaggregated analysis suggests a slightly subtler story. All three weight

industrial output, we expect

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

48 Asian Development Review

classes respond to radial railroads, although light goods respond more than medium
goods, which in turn respond more than heavy goods. The effect of ring roads on
decentralization is most pronounced for high-tech goods. Medium-weight goods
also decentralize in response to radial highways. This suggests that heavy goods
manufacturing is often stuck in place by big, immobile capital investment, while
other classes of manufacturing are more footloose and able to decentralize to ﬁnd
cheaper land.

IV. Policy Implications and Broader Lessons

We have so far described how the spatial organizations of population,
production, and infrastructure evolved in cities in the PRC between 1990 E 2010.
We have also reported our ﬁndings from Baum-Snow et al. (2017) on the extent
to which transport infrastructure has affected the organization of population and
production in Chinese cities. A grandi linee, we ﬁnd that radial railroads and ring roads
have caused the decentralization of economic activity, while radial roads and ring
roads have caused the decentralization of populations. There is some heterogeneity
across industries in how they respond to infrastructure. There is no evidence
that prefecture-level infrastructure affects the population level or GDP within a
prefecture. Così, the decentralization effect described in Baum-Snow et al. (2017)
probably reﬂects the radial migration of central city populations and manufacturing.
The data conspire against a compelling welfare assessment of infrastructure
construction in the PRC between 1990 E 2010. Consider the following two facts.
Primo, we have established that there are high levels of mobility in the PRC. Noi
estimated that approximately 127 million people migrated into central cities in the
PRC between 1990 E 2010. Secondo, there is no evidence that prefecture-level
infrastructure affects the overall level of prefecture population. If we take the high
rates of population mobility as evidence that mobility costs are low, then the fact
that people are not attracted to prefectures with better infrastructure should indicate
that these policy innovations are not making the prefectures better places to live.
Questo è, infrastructure investments are not improving welfare, at least at the margin.
D'altra parte, we can calculate from Table 1 that GDP per person in
urban areas in 2010 was nearly double that in rural areas. This ratio is much higher
than the rural–urban wage gap in developed countries (World Bank 2009). If, COME
the high level of mobility in the PRC suggests, mobility is not costly, such a wide
gap can be sustained only if cities in the PRC are sufﬁciently more unpleasant than
the countryside to offset wage differences, or if there is some institutional barrier
to migration. Given the existence of the hukou system, it is natural to suspect that
institutional barriers to migration help preserve the large rural–urban wage gap.
Tuttavia, this calls into question the logic of the preceding paragraph. If people are
not able to move to exploit the large rural–urban wage gap, then prefecture-level
infrastructure could, in principle, have large effects on welfare without affecting

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Transport Infrastructure and the Decentralization of Cities in the PRC 49

prefectural-level population. Until we are able to resolve these conﬂicting lines of
argument, any welfare analysis of infrastructure expansion in the PRC is necessarily
quite speculative.

On a purely theoretical basis,

there is good reason to think that

IL
PRC’s infrastructure expansion improved welfare. Transport infrastructure reduces
transport costs and allows ﬁrms and people to consume more land while holding
the cost of travel constant. This reduction in land costs reduces the costs of both
housing and production, thereby increasing real incomes and proﬁts. Using US data
and a simulation model, Baum-Snow (2007) indicates welfare gains of 2%–3% per
additional highway ray for US cities as a consequence of these effects. It is not clear
whether we should expect these effects to be larger or smaller in the PRC. Allo stesso modo,
we expect an analogous effect on production but have no basis for quantifying its
magnitude.

More generally, while it is clear on theoretical grounds that improvements to
infrastructure increase welfare, it is less clear if this increase is sufﬁcient to justify
the associated costs.

Riferimenti

Banerjee, Abhijit, Esther Duﬂo, and Nancy Qian. 2012. “On the Road: Transport Infrastructure

and Economic Development.” NBER Working Paper No. 17897.

Baum-Snow, Nathaniel. 2007. “Did Highways Cause Suburbanization?” Quarterly Journal of

Economics 122 (2): 775–805.

Baum-Snow, Nathaniel, Loren Brandt, J. Vernon Henderson, Matthew Turner, and Qinghua
Zhang. 2017. “Roads, Railroads and Decentralization of Chinese Cities.” Review
of Economics and Statistics. http://www.mitpressjournals.org/doi/abs/10.1162/REST_a
_00660#.WMK1-7YrJE4

Chandra, Amitabh, and Eric Thompson. 2000. “Does Public Infrastructure Affect Economic
Activity? Evidence from the Rural Interstate Highway System.” Regional Science and
Urban Economics 30 (4): 457–90.

China Statistics Press. 1992UN. 1990 Provincial Census Books. Beijing.
_____. 1992B. 1990 China Urban Statistical Yearbooks. Beijing.
_____. 1992C. 1990 Provincial Statistical Yearbooks. Beijing.
_____. 2002. 2000 Census County Statistics. Beijing.
Deng, Xiangzheng, Jikun Huang, Scott Rozelle, and Emi Uchida. 2008. “Growth, Population,
Industrialization, and Urban Land Expansion in China.” Journal of Urban Economics 63
(1): 96–115.

Donaldson, Dave. 2017. “Railroads of the Raj: Estimating the Impact of Transport Infrastructure.”

American Economic Review. Forthcoming.

Duranton, Gilles, Peter M. Morrow, and Matthew A. Turner. 2014. “Roads and Trade: Evidence

from the US.” Review of Economic Studies 81 (2): 681–724.

Duranton, Gilles, and Matthew A. Turner. 2011. “The Fundamental Law of Road Congestion:

Evidence from US Cities. American Economic Review 101 (6): 2616–52.

_____. 2012. “Urban Growth and Transport.” Review of Economic Studies 79 (4): 1407–40.

D
o
w
N
o
UN
D
e
D

F
R
o
M
H

T
T

:
/
/

D
io
R
e
C
T
.

io
T
.

e
D
tu
UN
D
e
v
/
UN
R
T
io
C
e
–
P
D

F
/

3
4
2
2
5
1
6
4
4
3
1
8
UN
D
e
v
_
UN
_
0
0
0
9
4
P
D

B
sì
G
tu
e
S
T

o
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

50 Asian Development Review

Garcia-López, Miquel-Ángel, and Adelheid Holl. 2015. “Suburbanization and Highways in Spain
when the Romans and the Bourbons Still Shaped Its Cities.” Journal of Urban Economics
85 (C): 52–67.

Henderson, J. Vernon, and Ari Kuncoro. 1996. “The Dynamics of Jabotabek Development.”

Bulletin of Indonesian Economic Studies 32 (1): 71–95.

Henderson, J. Vernon, Adam Storeygard, and David Weil. 2012. “Measuring Economic Growth

from Outer Space.” American Economic Review 102 (2): 994–1028.

Hsu, Wen-Tai, and Hongliang Zhang. 2014. “The Fundamental Law of Highway Congestion
Revisited: Evidence from National Expressways in Japan.” Journal of Urban Economics
81 (C): 65–76.

Imbens, Guido, and Joshua Angrist. 1994. “Identiﬁcation and Estimation of Local Average

Treatment Effects.” Econometrica 62 (2): 467–75.

Lianxinwang. 2012. Compendium of Reports from Local Statistics Bureaus. http://www.luqyu.cn

(accessed June 21–August 25, 2013).

Michaels, Guy. 2008. “The Effect of Trade on the Demand for Skill: Evidence from the Interstate

Highway System.” Review of Economics and Statistics 90 (4): 683–701.

National Geophysical Data Center. 1992–2009. 4 DMSP-OLS Nighttime Lights Time Series.

https://www.ngdc.noaa.gov/eog/download.html (accessed July 15, 2011).

Rosenthal, Stuart, and William Strange. 2004. “Evidence on the Nature and Sources of
Agglomeration Economies.” In Handbook of Urban and Regional Economics, Volume
4, edited by J. Vernon Henderson and Jacques-François Thisse, 2119–71. Amsterdam:
Elsevier.