Infrastructre and Dev in China

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Journal of Comparative Economics29, 95117 (2001)

doi:10.1006/jcec.2000.1693, available online at http://www.idealibrary.com on

Infrastructure Development and Economic Growth:An Explanation for Regional Disparities in China?1

Sylvie Demurger

CERDI-IDREC, CNRSUniversit e d Auvergne, 65, boulevard Francois Mitterrand,

63 000 Clermont-Ferrand, France

E-mail: [email protected]

Received March 10, 2000; revised September 14, 2000

Demurger, SylvieInfrastructure Development and Economic Growth: An Explanation

for Regional Disparities in China?

This paper provides empirical evidence on the links between infrastructure investment

and economic growth in China. Using panel data from a sample of 24 Chinese provinces

(excluding municipalities) throughout the 1985 to 1998 period, the estimation of a growth

model shows that, besides differences in terms of reforms and openness, geographicallocation and infrastructure endowment did account significantly for observed differences in

growth performance across provinces. The results indicate that transport facilities are a key

differentiating factor in explaining the growth gap and point to the role of telecommunication

in reducing the burden of isolation. J. Comp. Econ., March 2001, 29(1), pp. 95117.

CERDI-IDREC, CNRSUniversite dAuvergne, 65, boulevard Francois Mitterand, 63 000

Clermont-Ferrand, France. C 2001 Academic Press

Key Words: infrastructure; economic growth; regional inequalities; panel data; China.

Journal of Economic LiteratureClassification. Numbers: C33, H54, O11, R11.

1. INTRODUCTION

In two decades of market-oriented reforms, China has been one of the worlds

fastest-growing economies with per capita real incomes more than quadrupling

since 1978. Some of the key features of this evolution have been the dramatic

1This paper is drawn from a research program on Economic Policy and Growth funded by the

OECD Development Centre. I am indebted to the Development Research Center of the State Council

(China), the Academy of Macroeconomic Research of the State Planing Commission (China), and

the National Bureau of Statistics (China) for useful discussions on a earlier version, as well as to

participants at the International Conference on the Chinese Economy (Clermont-Ferrand, Oct. 2223,

1998) and the ASSA meeting (Boston, Jan. 79, 2000). I am also grateful to two anonymous referees

for valuable comments and suggestions. I remain solely responsible for errors and omissions.

95 0147-5967/01 $35.00Copyright C 2001 by Academic Press

All rights of reproduction in any form reserved.


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96 SYLVIE DEMURGER

growth in international trade and the inflow of huge amounts of foreign direct

investment, which have accompanied the open-door policy and are usually high-

lighted as main engines of Chinas growth performances. However, Chinas tran-

sition to a market-based economy has created new problems, among which is the

growing inequality in per capita income between coastal and interior provinces.

Achieving balanced growth so as to reduce those disparities appears to be one of

the major policy challenges that China now faces in order to maintain both its

current GDP growth rate and social stability.

From this perspective, enhancing the growth potential of inland provinces is

necessary either directly, through appropriate economic policies, or indirectly, by

facilitating growth spillovers from rapidly developing coastal regions to backward

interior provinces. The first issue has been documented extensively in the recenteconomic literature, which shows that a large part of economic growth in coastal

provinces comes from their deeper implementation of industrial and foreign trade

reforms.2 However, the question of how to take the best advantage of all these

reform and opening-door policy measures has, up to now, received little attention.

Considering Chinas huge size, important regional differences arise naturally in ge-

ography and in natural resource endowments. These may have a substantial impact

on the economic returns of any kind of reform. To compensate for these naturalconstraints, the availability of an appropriate infrastructure might prove helpful in

facilitating communications between provinces and with the outside world. Thus,

this issue may be important when we are evaluating provincial growth performance

and regional growth spillovers.3 Using a database covering 24 Chinese provinces

and autonomous regions over the period from 1985 to 1998,4 this paper provides

empirical evidence on why economic performance has been so different from one

province to another throughout the reform period and on the specific relation be-

tween the distribution of infrastructure endowments and the distribution of growthperformances.

The paper is organized as follows. Section 2 gives an overview of economic

growth and income level disparities among Chinese provinces. Section 3 sum-

marizes the evolution of infrastructure availability at both national and provin-

cial levels. Section 4 describes the conceptual and methodological background for

quantitative assessment made in Section 5 on the extent to which economic growth

may be accounted for through geography and infrastructure-related characteristics.Section 6 provides and discusses a growth decomposition exercise, which leads

to a grouping of provinces according to their economic structure as well as their

2 For recent contributions, see Chen and Fleisher (1996), Mody and Wang (1997), Raiser (1998),

Wu (1999), and Demurger (2000).3 As will be documented further below, attempts to evaluate, at least partially, the role of infrastructure

in China have been made by Fleisher and Chen (1997) and Mody and Wang (1997).4 As will be seen, the Beijing, Tianjin, and Shanghai municipalities that are directly under the central

government are not considered in the quantitative analysis since their characteristics make them hardlycomparable with other provinces.


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INFRASTRUCTURE AND GROWTH IN CHINA 97

education level and infrastructure endowment as an explanation for their growth

performances. Section 7 concludes with several economic policy related remarks.

2. REGIONAL PATTERNS OF ECONOMIC GROWTH IN CHINA

The issue of rising or declining inequality among Chinese provinces has been

debated extensively in recent years. If provincial unequal development is far from

being a new phenomenon in China, the renewed interest in this question partly

comes from the fact that the redistribution system has been abandoned with the

implementation of reforms (Naughton, 1999; Wang and Hu, 1999). Hence, this pol-

icy is no longer available to prevent a widening dualism between provinces. China

has experienced growing interprovincial inequality during its transition process

to a market-based economy. Indeed, large disparities occurred in growth perfor-mances among provinces from 1978 to 1998. The gap between the most dynamic

province in terms of GDP per capita, Zhejiang, with an annual growth rate of

12.5%, and the least dynamic, Qinghai, is 7.1 percentage points.

A broader regional classification of provinces reveals that, on average, coastal

provinces grew faster than inland provinces. This growth concentration along

the coastline brought about changes in regional income disparities. First, it led

to a slight downward trend in the cross-section dispersion of per capita GDP.However, from the 1990s onward, it has been accompanied by an increase in the

relative disparity between regions. Indeed, the rapid growth of the coastal provinces

did not contribute to divergence in incomes until 1990 since the fastest-growing

coastal provinces started from a below-average level of per capita income. The

convergence process came to an end after 1990, and regional incomes began to

exhibit widening trends of divergence as these provinces caught up and growth also

accelerated in the richest coastal provinces. Lorenz curves can be used to visualize

these trends in Fig. 1. They suggest that, when we exclude municipalities that seem

highly atypical,5 interprovincial inequalities remained rather stable up to the early

1990s but have tended to increase since then. As a result, inland provinces such

as Guizhou and Yunnan remained far behind in terms of GDP per capita in 1998

while most coastal provinces had caught up with the richest municipalities. Thus,

due to growth concentration along the coast, the most pronounced disparities have

arisen mainly between coastal and noncoastal provinces.6

These opposite movements, with a catch-up process at the top of the scale anda divergence phenomenon between regional zones from the end of the 1980s

onward, summarize the complex regional development of China since the imple-

mentation of reforms. This complexity explains why it is so difficult to discern

5 As municipalities do have very particular characteristics in terms of size and economic structure,

they may appear as outliers in the overall sample (Wu, 1998). Fleisher and Chen (1997, Fig. 1) also

show that their estimated total factor productivity level is much higher than the average.

6 To give an example, Guangdong province had a level of GDP per capita slightly inferior to that ofQinghai in 1978, and nearly three times higher in 1998.


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FIG. 1. Evolution of GDP per capita disparities among Chinese provinces, difference between the

Lorenz curve and the 45 line, 19781998. Beijing, Tianjin, and Shanghai municipalities, as well as the

Tibet autonomous region, are excluded from the sample. The curves here represent differences between

the Lorenz curve and the 45 line, so that a lower curve corresponds to lower inequality. Sources: State

Statistical Bureau (various issues) and authors calculation.

a convergence relationship between Chinese provinces.7 Indeed, as suggested by

Fig. 2, there is no clear relationship between initial GDP per capita and subsequent

growth when considering all provinces but excluding the municipalities.8

3. INFRASTRUCTURE PROVISION IN CHINA

3.1. National Trends in Infrastructure Investments

During the pre-reform period, the centralized decision-making structure applied

to all kinds of investments, including those in infrastructure equipment. Central-

ization implied that infrastructure investments were made according to priorities

established in a general development strategy. Among different aspects of this

strategy were the emphasis on heavy industries development and, mainly from the

end of the 1960s onward, the emphasis on provincial self-sufficiency. Both have

had a particular influence on infrastructure equipment, especially transportation.

They favored the development of the transport network in Northern China, where

heavy industries were located. More specifically, they favored railway develop-

ment versus other types of transportation modes, to carry huge quantities of raw

material and resources at a lower cost per kilometer from resources-rich provinces,

e.g., Shanxi for coal, to industrializing Northern provinces. As a consequence, the

7 See Raiser (1998) and Wu (1998) for a discussion on the existing literature on this issue.8 The only significant relationship seems to be between the coastal provinces and the municipalities

alone (Demurger, 2000).


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FIG. 2. Per capita income and growth performance of Chinese provinces, 19781998. Beijing,

Tianjin, and Shanghai municipalities, as well as the Tibet autonomous region, are excluded from the

sample. Sources: State Statistical Bureau (various issues) and authors calculation.

central government made substantial efforts to expand the railway network rather

than upgrade existing routes (Huenemann, 1992) so that it more than doubled in

length between 1952 and 1978, from 22,900 to 48,600 km. Despite these efforts,

transport equipment investments, which were implemented mainly at the state

level, remained rather small compared to basic needs in terms of both moderniza-

tion of existing equipment and construction of new facilities, until the beginningof the reforms. On the telecommunication side, investments were nearly nonex-

istent during the pre-reform period. Altogether, at the beginning of the 1980s,

China was a relatively poorly endowed country in terms of both transport and

telecommunication network.

During the development process, infrastructure investments have become nec-

essary in order to alleviate infrastructure endowment related constraints and to

facilitate the development of economic activities. However, while the implemen-

tation of a priority investment program at the beginning of the 1980s set energy

and infrastructure sectors as key sectors to be developed and financially supported

(Naughton, 1996), investment in transport as well as in the telecommunication and

energy sectors lagged behind that in industry for the ensuing decade. The share of

transportation and telecommunication services remained stable at around 10% of

state fixed-assets investment, while that of the energy sector was steady at around

20%.

In the context of growing interregional trade, the inherited primitive infrastruc-ture associated with relatively low investments during the reform period resulted in


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100 SYLVIE DEMURGER

chronic shortages of transport services and increased urban congestion. From the

beginning of the 1990s, investment in infrastructure has been reasserted progres-

sively as a major national policy priority,9 leading to a large increase in the share

of transportation and telecommunication services in state fixed-assets investment,

up to 30% in 1998. As road development lagged far behind that of the railway

throughout the pre-reform period, great efforts have been made from the 1980s

onward to fill the gap by building new roads and opening up remote areas. Besides

this quantitative aspect, concerns about quality issues have appeared during the

reform period, with a shift in priorities toward upgrading existing routes.

3.2. Spatial Distribution of Infrastructure Equipment

Since the beginning of the reform period, the sources of infrastructure fundinghave been diversified and include local governments expenditures,10 international

organizations loans, and foreign capital. However, the decentralization process

that has been accompanying Chinas transition toward a market-based economy

has had contrasting effects on infrastructure provision at a regional level. On the

one hand, as local governments have better information about local conditions and

preferences, efforts have generally been reoriented to meet the demand for public

goods. In particular, this has been the case for the road network, which has beenexpanded according to local needs in the eastern provinces. On the other hand, the

larger autonomy given to local governments at every level of the administrative

hierarchy has produced unexpected effects on the level of infrastructure provision.

As discussed by Jin (1994), the reform process led local governments to pay

less attention to investment in transportation and telecommunication because they

were focusing on their productive activities and investing in industrial capacities.

This has been true at least until the beginning of the 1990s, when infrastructure

shortages were so severe that they required real investment efforts.While local governments had more power in terms of fixed-assets investments

compared to the center, they tended to neglect their role as a public good provider

and to concentrate on their role as an entrepreneur. This unique feature of the

Chinese economic system, assigning a dual identity to local governments, gave

way to irrational behavior as these two objectives may produce conflicting in-

terests. Moreover, autonomy in decision making given to local governments fa-

vored individualistic behaviors and noncoordinated decisions, which sometimeslead to a wasteful duplication of infrastructure facilities11 or, in contrast, exacer-

bated shortages in infrastructure capacity. Finally, besides the potentially inefficient

use of resources, the decentralization process induced a de factorise in inequal-

ity between provinces since the capacity to raise funds to finance infrastructure

9 In particular, an ambitious investment policy was announced in February 1998 to build roads,

railways, and power stations, with the total expenditure over three years planned to be US$ 750 billion.10Every level of local government is now concerned with public goods and services provision.11 A striking example of this kind of inefficiency can be found in the number of airports built in

Guangdong province since the beginning of the reforms (Sung et al., 1995).


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investments mainly depends on local government revenues and/or their ability to

negotiate with the central government.

Table 1 gives a broad overview of provincial infrastructure endowment dispari-

ties, including both transportation and telecommunication. The most pronounced

regional difference in the availability of transport infrastructure can be found

TABLE 1

Infrastructure Availability and Structural Characteristics by Province, 19851998 Average

Transport network density Telecom- Coal

(km/1,000 km2) munication Population production Electricity

(telephones/ density (tons/1,000 productionProvincesa Railway Highway Waterway 1,000 persons) (km2) persons) (kWh/person)

Zhejiang 9 310 104 50 418 31 672

Fujian 9 350 32 38 250 289 594

Guangdong 4 372 61 59 360 137 796

Jiangsu 7 245 231 45 660 357 727

Shandong 14 301 12 23 547 803 646

Hainan 6 401 10 26 199 2 291

Henan 13 273 7 14 521 1,048 461Anhui 12 226 43 16 409 636 406

Hubei 9 259 46 23 294 197 667

Hebei 17 252 0 25 327 1,085 748

Jiangxi 10 199 29 15 229 552 357

Xinjiang 1 17 0 17 9 1,443 558

Jilin 19 151 6 36 132 1,008 864

Inner 4 38 1 19 19 2,410 957

Mongolia

Sichuan 5 173 15 12 192 687 383Yunnan 4 154 3 14 96 628 419

Guangxi 7 163 19 13 183 237 356

Shaanxi 9 188 4 20 161 1,018 560

Shanghai 42 547 370 139 2193 0 2,620

Beijing 58 617 0 149 665 859 1,114

Tianjin 42 370 15 90 798 0 1,275

Hunan 11 276 48 11 291 653 393

Liaoning 24 276 3 44 271 1,315 1,192

Shanxi 15 214 1 14 189 9,777 1,268Guizhou 8 178 10 9 187 1,298 456

Gansu 5 76 0 11 50 750 845

Ningxia 8 125 6 27 72 2,931 1,416

Heilongjiang 11 102 10 31 76 2,131 896

Qinghai 2 23 0 21 6 620 1,194

National 13 237 37 35 338 1,134 798

average

a Provinces are classified according to their GDP per capita average annual growth performance

from 1978 to 1998. Sources: State Statistical Bureau (various issues) and authors calculations.


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102 SYLVIE DEMURGER

FIG. 3. Average transport network density at a provincial level, 19851998, including railways,

highways, and inland waterways (km/1000 km2): dark areas, over 500; medium-dark areas, 350500;

medium-light areas, 200350; light areas, less than 200.

between coastal and interior provinces. Looking more closely at transportation

modes, the coastal/inland divide appears particularly in road network density,

which is lower and lower the closer provinces are to the west. As roads have been

developed very rapidly during the last 20 years, this inequality illustrates the un-

even development that occurred throughout the reform process between coastal

and noncoastal provinces.Figure 3 shows that, among noncoastal provinces, those that are relatively well

endowed in terms of transportation facilities are located next to coastal provinces.

Their endowments can be attributed either to a strategic role as a coal producer,

e.g., Shanxi, or to their position relative to the Yangtze River, e.g., Hubei and

Anhui. On the opposite end, transport network density remains very low in remote

provinces, which nonetheless are energy resources provinces, such as Ningxia,

Inner Mongolia, or Xinjiang.

On the telecommunication side, the regional gap is even deeper and reinforces

transport disparities. Provinces in which the number of telephone sets per capita is


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higher than average are also those where overall transport network density is over

350 km per 1,000 km2. In addition, the distribution of telecommunication facilities

indicates that the three northern provinces of Liaoning, Jilin, and Heilongjiang are

above-average provinces, which can be at least partly explained by their inherited

strategic situation as state-owned industry bases during the pre-reform period.12

4. INFRASTRUCTURE ENDOWMENT AND ECONOMIC GROWTH:

CONCEPTUAL AND METHODOLOGICAL FRAMEWORK

In the following two sections, we try to identify whether differences in infras-

tructure endowment have played a role in per capita income growth in Chinese

provinces. From a theoretical point of view, the perception of the role of produc-

tive public expenditures as an engine of economic growth has changed markedlyover the last few years. It was recently re-examined in the framework of endoge-

nous growth theory when, following the empirical work of Aschauer (1989), new

growth theory models began to take account of public spending as a factor for

self-sustaining productivity gains and long-term growth (Barro, 1990).

It is now usually recognized that investment in physical infrastructure, including

transport services, telecommunication, power, and irrigation, can improve the pro-

ductivity of all inputs in the production process and thus strengthen long-run growthperformance by facilitating market transactions and the emergence of externalities

among firms or industries (Jimenez, 1995). In this respect, total factor productivity

growth is a function of infrastructure endowment under the assumption that where

infrastructure facilities are developed, it is easier for entrepreneurs to adopt new

technologies and consequently generate technical progress and economic growth.

In the case of China, where distances are huge and where technological progress

is mainly imported rather than created by local R&D activities, this argument may

apply particularly well. Moreover, both transportation and telecommunication in-

frastructures may be of particular importance since industrial activities in China

tend to be located far from energy and raw materials resources. This is the case

for energy resources, e.g., coal and natural gas, which are located mainly in the

west while industrial centers are based on the east coast. As the latter need more

and more energy resources throughout their development process, a weak infras-

tructure network across provinces might imply inefficiency in the transportation

of raw materials as well as potential increases in their price.13 Hence, withoutthe basic infrastructure that links producers to raw materials providers and final

goods consumers, both inefficiencies and competitiveness problems may impede

economic development.

Chinas economic growth has been the focus of a growing recent literature that

only rarely raises the question of the specific relationship between infrastructure

12Those three provinces were already among the best endowed in terms of telecommunication as

early as 1978, when telecommunication facilities were nearly nonexistent.13 A World Bank discussion paper (Harral et al., 1992) pointed out this problem in the case of

Guangdong.


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104 SYLVIE DEMURGER

availability and growth. Among others, Fleisher and Chen (1997) use the trans-

portation route length in 1986 as an explanatory variable for total factor produc-

tivity (TFP) level and growth in Chinese provinces from 1978 to 1993. However,

they do not find any significant contribution of transport infrastructure. Despite

these weak results, they retain the estimated value of the corresponding coefficients

when evaluating policy implications. Mody and Wang (1997) use panel data on

the output of 23 industrial sectors for seven coastal provinces from 1985 to 1989

and emphasize several determinants of coastal Chinas growth during the second

half of the 1980s. They find that both transport, measured only by road network

length, and telecommunication facilities have been engines of growth during this

short time period from 1985 to 1989.

Our analysis aims at assessing the relationship between infrastructure develop-ment and economic growth in China by using both a larger database and as many

different infrastructure indicators as possible. The database used in the economet-

ric analysis covers 24 Chinese provinces and autonomous regions over the period

from 1985 to 1998.14 One of our main concerns is the role of transport equip-

ment in growth differentials. Hence, it seemed relevant not to include the three

municipalities of Beijing, Tianjin, and Shanghai in the analysis in order to keep

a comparable basis in terms of size and of the role of transport as a transactionfacilitator. Moreover, due to time length limitations of our sample and in order to

eliminate partly short-term fluctuations, we use three-year weighted and centered

moving averages15 for all the variables. This approach increases the number of

time-series observations in our panel data set. Data sources are described in the

Appendix.

We estimate a growth equation by using the now-standard Barro-type frame-

work, which allows testing for conditional convergence by adding to a Solow-type

equation a set of variables reflecting differences in the steady-state equilibrium. Inour case, we try to account for differences in investment in both physical and human

capital and in economic environment, including the relative degree of openness

and reform in different provinces as well as their geography and their infrastructure

endowment.

Hence, the growth equation estimated on panel data has the form

gi t= i + t+ Ln(yi t1) + Xi t+ Zi t+ Wi t+ ui t, (1)

where g represents the average annual growth rate of real GDP per capita, y

14 Due to missing values, Hainan, Qinghai, and Tibet have been excluded from the statistical analysis.

Moreover, as the Chongqing area was given a municipality status only from 1997 onward, data before

1997 do not allow us to distinguish between Sichuan and Chongqing municipality, which have therefore

been combined.15 A three-year average is certainly not long enough to capture long-term phenomena and serves

only to smooth short-term cycle variations. Moving average computations give a higher weight to thecurrent year; for the computation formula, see Table 2 and its notes.


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represents the level of real GDP per capita,Xcontains a set of variables intended to

account for production factors accumulation, basically physical and human capital,

Wis a matrix of variables intended to account for differences in reform implemen-

tation and economic structure, Zcontains measures of geographical constraints

and infrastructure endowment, and i and tare province- and time-specific pa-

rameters, respectively. The former stands for productivity level differences among

provinces and takes into account unmeasured characteristics of provinces, includ-

ing economic reforms, natural resources, or geographical location differences. The

latter is introduced to control for temporary shocks or policy changes that might

have affected all provinces at the same time. This may be particularly relevant

during the 1990s, when both economic reforms and growth accelerated. Hence,

the potential shift in Chinas growth process during the 1990s is accounted forusing a dummy variable covering the 1991 to 1998 period. Moreover, as will be

seen in the regression results, the period dummy is introduced both as an additional

term and in interaction with a coastal dummy variable in order to take into account

any potential differences between coastal and inland provinces in terms of policy

implementation.16

Finally, some statistical features must be stressed. First, due to the panel form

of the data set, several tests have to be performed in order to choose the correctspecification. In particular, the Hausman test indicates that a fixed-effects model

is preferred to a random-effects model (see Table 2). However, presenting the two

different estimation techniques is a way to check the robustness of our results, and,

as pointed out by Mody and Srinivasan (1998), the random-effects model gives

a composite picture, since it combines the within and between perspectives.17

Moreover, the use of a random-effects model allows us to introduce infrastructure-

related variables that are available only at one point in time and can be considered

as environmental variables. Second, several variables used as explanatory variablesare likely to be affected by economic development and consequently to be inversely

correlated to growth. Both domestic and foreign investments in fixed assets may be

to some extent demand-induced, in that they may follow as well as lead economic

development. To get consistent results, we also present estimations computed

through a two-stage least-squares (2SLS) procedure.

5. ECONOMETRIC EVIDENCE

Regressions reported in Table 2 provide estimation results on the role played

by geographical constraints, and human efforts made to alleviate these natural

constraints, along with structural factors in the economic performances of Chinese

provinces. Different indicators are used, a measure of the urbanization rate, two

16 The distinction between the two periods (19851991 and 19921998) has been suggested by an

anonymous referee to capture any policy shift toward developing the coastal region during the 1990s.17 As suggested by an anonymous referee, the use of different panel data techniques is important

both to extract the most information from the data and to test the robustness of our estimates.


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106 SYLVIE DEMURGER

TABLE2

Determ

inantsofPerCapitaEconom

icGrowth,

19851998a

(1)

(2)

(3)

(4)

(5)

(6)

(7)

DP(2)

0.189(10.9

9)

0.1

93(10.90)

0.12

8(10.2

1)

0.1

94(1

1.5

3)

0.1

20(11.80)

0.1

27(10.3

1)

0.1

92(12.65)

vestment

0.2

13(5.2

1)

0.3

39(3.3

5)

0.14

0(4.5

5)

0.2

12(5.2

5)

0.3

57(3.6

1)

0.135(4.4

6)

0.1

96(2.8

0)

condary

0.5

11(3.1

3)

0.7

09(2.9

4)

0.12

7(1.9

4)

0.6

36(3.9

0)

0.8

91(3.8

0)

0.139(2.1

4)

0.6

05(3.5

3)

educationlevel

areofagriculture

0.3

18(3.4

5)

0.2

40(2.6

0)

0.13

5(2.2

8)

0.3

63(3

.65)

0.2

75(2.8

8)

0.146(2.49)

0.4

24(4.6

9)

areofcollective

0.0

75(2.3

1)

0.0

67(2.1

5)

0.13

4(4.9

1)

0.0

79(2.4

1)

0.0

67(2.1

7)

0.1

32(4.9

0)

0.1

03(3.5

0)

sector

reigndirect

0.6

10(7.8

4)

0.5

18(5.0

7)

0.56

6(6.2

4)

0.4

35(5.0

9)

0.3

94(3.5

7)

0.4

43(4.3

4)

0.3

67(3.7

6)

investment

921997period

0.0

30(5.9

2)

0.0

22(2.5

7)

0.043(10.9

3)

0.0

25(4.8

5)

0.0

15(1.7

5)

0.0

42(10.5

1)

0.0

25(4.1

4)

dummy

oastaldumm

y

0.58102(0.6

9)

oastperiod

0.0

20(2.9

7)

0.0

21(3.1

4)

0.013(2.3

7)

0.0

24(4.0

3)

banization

0.0

37(2.0

4)

0.0

36(2.0

0)

0.06

4(3.8

7)

0.0

34(1.8

9)

0.0

30(1.7

3)

0.063(3.8

4)

0.0

41(2.4

3)

ansport

0.6

98(3.4

03)

0.7

54(3.6

9)

0.19

8(1.7

4)

0.5

78(2.7

9)

0.6

44(3.1

4)

0.166(1.4

7)

0.5

53(3.0

2)

ansport2

1.198(5.13)

1.1

21(4.7

7)

0.73

0(3.0

2)

0.9

20(3

.75)

0.8

57(3.4

1)

0.6

46(2.6

7)

0.7

31(3.4

2)

pulation

0.14103(0.5

4)0

.42103(1.3

1)0.3

5103(3.8

1)0.23103(

0.90)0.5

4103(1.6

8)0.3

1

103(3.3

3)

density


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ansport

0.59

103(1.9

2)

0.3

9

103(1.2

0)

0.93

103(4.0

9)

0.24

10

3(0.7

2)

0.2

6

103(0.77

)

0.8

1

103(3.4

9)

population

density

lephone

0.443(3.9

0)

0.3

26(2.5

0)

0.304(2.7

4)

0.395(3

.44)

0.2

43(1.8

4)

0.2

73(2.4

8)

0.4

19(3.5

6)

stanceto

0.102(1.7

5)

0.0

88(1.5

3)

townmore

than10km

llageaccessible

0.040(3.0

8)

0.0

36(2.5

4)

bytelephon

e

umberof

288(=24

12)

288(=24

12)

288

(=24

12)

288(=24

12)

288(=24

12)

288(=24

12)

28

8(=24

12)

observation

s

timationmethod

fixed-effects

fixed-effects

random-effects

fixed-effects

fixed-effects

random-effects

fixed-effects

(2SLS)

(2SLS)

(2SLS)

shertest

7.8

6[F(23,

249)]

7.9

6[F(23

,247)]

ausmantest

37.712[2(6)]

56.7

95[2(7)]

djustedR2

0.7

86

0.779

0.5

81

0.79

1

0.785

0.5

84

0.7

94

aAllvariablesareexpressedasweightedmovingaverages.

IfXitistheobservedvalueofXf

orprovinceiatdatet,

itsw

eightedmovingaverageX

it,usedinthe

gression,ismeasuredasXit

=

(Xi,t1

+

2

Xi,t+

Xi,t+1)/4.Th

evaluesshownbetweenbracketsarethet-values.

The

estimatedstandarddeviationshavebeen

rrectedusingaWhitematrix.

Theabbreviation2SLSindicatestwo

-stageleast-squares.

Instrum

entsareexogenousvariables(includingfixedeffects),twice-lagged

vestmentand

foreigndirectinvestmentv

ariables,theannualrealwage(weightedbytheshareofthelaborforceintotalpop

ulation),agriculturaltoindustrialrelative

ices,andthe

ratioofexportsplusimportsoverGDP.TheadjustedR

2oftheinstrumentationequationsis0.8

6(and0.8

6whe

naddingcoastperiod)forinvestment

d0.9

3(and0.9

5whenaddingcoastperiod)forforeigndirectin

vestment.


14/23

108 SYLVIE DEMURGER

measures of the relative isolation of rural areas in the province, i.e., the share

of villages whose distance to town is more than 10 km and the share of villages

accessible by telephone, a measure of the development of telecommunications, i.e.,

the number of telephones per capita, and several measures of the overall transport

network density and related congestion issues.

Together with the implementation of market-economy reforms, a reallocation of

production factors, in particular labor, from agricultural to industrial and services

activities has contributed to growing urbanization all over China. One of the most

striking evolutions in the past 20 years has been the increase in disparities between

rural and urban areas, and, from a spatial point of view, very important variations

arose among rural areas between inland and coastal provinces. Indeed, while rural

areas in coastal provinces largely benefited from the overall economic improve-ment, many rural areas in inland provinces remained very poor. To account for the

relative isolation of some provinces as well as for the urban/rural divide, we use the

urbanization rate taken as an imperfect proxy for all kinds of geographical char-

acteristics related to the provincial economic structure. Estimation results confirm

that the urban-biased development strategy implemented since the beginning of

reforms leads urbanized provinces to grow at a higher rate than rural provinces.

Data from the First National Agricultural Census in China, issued in 1997,provide additional evidence on the negative role of geographical constraints in

economic growth. They give information on the percentage of villages located

more than 10 km from the home township or town as well as on those accessible

by telephone. They suggest that provinces where the percentage of isolated villages

is above 20% are also the poorest and the most remote provinces in China, e.g.,

Yunnan, Guizhou, and Inner Mongolia. Moreover, while nearly all villages had

electricity and were accessible by roads and postal services in 1997,18 less than half

were accessible by telephone and there were huge differences among provinces.Using both indicators in a random-effects specification confirms the negative re-

lationship between rural isolation and economic growth. Estimations also indicate

that the development of telecommunication in rural areas helps reduce the burden

of isolation and has a positive impact on economic growth. The introduction of the

number of telephones per capita, in both urban and rural areas, as another proxy

for telecommunication development confirms this significant impact on growth

performances and corroborates Mody and Wangs (1997) results on the positiveeffect of telecommunication growth.

Turning to the relation between transportation infrastructure and economic

growth, the indicator chosen to account for regional differences in transport en-

dowment is the density of railway, road, and inland navigable waterway networks.

These factors have been taken into account together, since it is difficult to dis-

tinguish benefits from one transportation mode to another. Of course, a density

18According to the First National Agricultural Census, 96% of the administrative villages in China

had electricity in 1997; 87% were accessible by road vehicles and 90% by postal services.


15/23


measure gives only quantitative information on transportation infrastructure and

does not say anything about its quality, e.g., accessibility and road conditions.

In the absence of information on transport infrastructure quality at the provincial

level, a quantitative assessment for a country where distances are huge can nev-

ertheless give useful primary information on the relative importance of means of

communication in economic performance. Moreover, the relation between infras-

tructure and growth may not be linear since the impact of quantity, rather than

quality, enhancing investments in infrastructure may be lower with economic de-

velopment. Our results show a nonlinear and concave relationship for the impact

of transport endowment on economic growth.19 The positive effect of transport

equipment is decreased with its development. This finding suggests that, although

investing in network expansion of transport-poor provinces can prove to be veryuseful for economic growth, the best strategy for transport-rich provinces is to

invest in upgrading or quality-improvement of existing facilities.

Finally, in measuring the role of infrastructure in economic growth, it is impor-

tant to distinguish between opening-up and congestion related issues. The former

can be measured through a density measure, as previously done; the latter needs

to account for population density when infrastructure endowment is measured.

Following Mody and Wang (1997), we introduced an interaction term betweentransport density and population density in the regressions, together with popu-

lation density itself. As indicated by the, respectively, positive and negative co-

efficients, our results tend to suggest that a higher transport density also reduces

congestion-related constraints and thus facilitates growth. However, these are much

less robust than other results to specification changes and should be taken with

care.

Econometric estimations of Eq. (1) reported in Table 2 provide additional ev-

idence on the determinants of provincial economic growth from 1985 to 1998.These determinants have already been documented, at least partially, in the exist-

ing literature on Chinas economic growth. However, as some of our specifications

differ from the usual ones, our results require further comment. First, the signifi-

cant and negative coefficient associated to the logarithm of lagged GDP per capita

(GDP(2))20 indicates a catch-up phenomenon among Chinese provinces. If not

convincing evidence of a conditional convergence relationship between Chinese

provinces,21

this result suggests that those provinces that were farther from theirsteady-state equilibrium level tended to grow at a higher rate, at least in the short

run. Moreover, this result holds even when municipalities are excluded from the

sample. This may be an important contribution to the debate on convergence issues

19 They corroborate Mody and Wangs (1997) findings about the positive but subject to diminishing

returns effect of a road network on economic growth for a limited time and regional data set.20As regressions use three-year centered moving averages, the level of GDP per capita is twice

lagged in order to prevent any overlapping with the dependent variable.21 Testing for conditional convergence would require a longer time period, or at least averages on

more than three years.


16/23

110 SYLVIE DEMURGER

in the sense that, while there is no evidence of an absolute convergence relationship

between provinces only, conditional convergence is likely.

Second, the setXof explanatory variables included on the right-hand side of the

growth equation accounts for production factors-related differences. As physical

capital is a major production factor, different rates of accumulation in fixed-assets

should contribute to growth differences, and this hypothesis is confirmed by our

estimation results. Moreover, referring both to Lucas (1988) work on the contribu-

tion of human capital to economic growth and to the empirical debate on the issue,

we use a secondary education level variable to account for the impact of human

capital availability on technical progress. The proportion of total population with

at least secondary education measures the level of education.22 As indicated in

Table 2, the availability of educated labor improves economic performance. Fromthe perspective of the decentralization process, this result is very important because

education funding falls mainly within the jurisdiction of local governments. This

means that further inequalities are likely to arise if poor provinces cannot raise

enough funds to invest in education capacities.

A final set of explanatory variables includes differences in the implementation

of reforms that can be accounted for by a wide range of indicators. The volumi-

nous literature dealing with this issue shows that, among important reforms, thosethat seem to have been engines of growth differences reflect productive structure

and openness. Thus, the matrix Wincludes indicators on the share of agricultural

activities, on the share of the collective sector in total industrial production, and

on the rate of foreign direct investment. The basic idea for introducing the share

of agriculture in total value-added is that agricultural provinces may have fewer

opportunities for productivity growth than industrial provinces and may thus grow

substantially more slowly. This difference may be important since our period of

interest does not cover the beginning of the 1980s, when agriculture productiongrew at a sustained rate. Indeed, Table 2 indicates that the share of agriculture

in total GDP has the expected negative impact on growth. Moreover, to control

for differences in the internal reform process, we introduce the share of collective

enterprises in total industrial production as a proxy. These enterprises, including in

particular township and villages enterprises (TVEs), are usually said to have been

the most dynamic. Contrary to the state sector, they may have benefited relatively

more from both technical progress and the introduction of market economy mech-anisms. As suggested by the positive and significant coefficient, it is indeed the

case that provinces in which the share of collective enterprises, as opposed to state

enterprises, is higher have experienced higher growth performance.23 Turning to

22 The computation of this variable is based on the 1982 population census, which gives information

on population by educational level and by province. The values for the number of educated people

in subsequent years are inferred with the perpetual inventory method using available information on

graduation and mortality.23 Note that when we use the nonstate share of industrial production rather than the collective share,

results are similar. Unfortunately, because of missing data, we could not test for the contribution of

private industrial production alone.


17/23


the impact of openness on regional economic growth,24 the positive and highly

significant coefficient confirms the standard conclusions as to the role played by

foreign direct investment in Chinas provincial growth process since the imple-

mentation of reforms. Finally, the use of the 1991 to 1998 period dummy and its

interaction with a coastal dummy indicates, other things being equal, an acceler-

ation of economic growth from 1991 onward, which has been significantly more

pronounced in coastal provinces.

6. GROWTH DECOMPOSITION

While the estimation results reported in Table 2 provide some evidence of the

marginal contribution of each identified determinant to economic growth, they

do not give any direct indication of their relative average contribution. To dealwith this issue, this section proposes a retrospective analysis of growth based on

a simple growth accounting exercise. Its aim is to give a picture of the economic

development of Chinese provinces in light of their structural and economic char-

acteristics and to highlight the growth engines that should be targeted in order to

achieve a balanced and sustainable growth process. The decomposition involves,

first, computing the gap between the predicted per capita income growth rate of

each province and the national average, approximated by the average of our samplesimulated growth rates, and then decomposing this gap into various components

that reflect the differences in each of the variables included in the growth regres-

sions presented in Table 2.

Average gaps over the 1985 to 1998 period with respect to the national mean

and their decomposition into each component for each province are reported in

Table 3. As indicated in this table, a broad regional classification can be drawn from

the results taking into account both differentiated development strategies and geo-

economic characteristics. The first group includes eastern coastal provinces, which

have had on average higher per capita GDP growth rates. Their above-average per-

formances come from a relatively developed transport and telecommunication

network as well as economic activities oriented toward nonagricultural industries.

In addition, for the southeast provinces of Guangdong and Fujian, foreign direct

investment has played a particularly important role, while for Shanghais neigh-

boring provinces of Jiangsu and Zhejiang, nonstate activities, and particularly

collective enterprise activities, have been important engines of growth.The second group gathers northern provinces in which growth performances

have been much less impressive. While urbanized, relatively well-endowed in

24 In terms of openness, two categories of indicators can be considered. One deals with export

promotion, the other with foreign direct investment (FDI) attraction. For the former, neither openness

indicators, e.g., the ratio of exports over GDP or the ratio of exports plus imports over GDP, nor export

growth indicators appeared to be significant and robust to specification changes. Hence, openness is

measured here only by its FDI component. The weak results on the role of export promotion, indicating

that it would have been a less important determinant of provincial growth in China, are discussed in

Demurger (2000).


18/23

112 SYLVIE DEMURGER

TABLE3

C

ontributionstoProvincialG

rowthGapsRelativetothe

NationalMean(19851998

),inPercentagesa

Contribution

ofeachvariabletothedifferenceinpercapitagrowthrelativetothenationalmean

Growthdifference

Fixed

Initial

Coll.

predicted

effects

GD

P

Invest.

Education

Agriculture

sector

FD

I

Urban.

TransportTelecom.

East:Nonagricultural,transport-rich,successfulprovinces

Guangdong

2.97

2.6

4

8.5

0

0.2

2

0.5

4

2.4

9

0.2

9

2

.00

1.2

7

1.7

9

1.31

Fujian

2.87

2.6

5

2.2

9

0.56

4.1

6

1.1

9

0.7

3

2

.11

0.37

2.38

0.4

5

Zhejiang

2.40

2.24

8.7

3

0.14

0.3

5

2.9

8

2.8

2

0.1

2

0.55

2.28

0.8

6

Jiangsu

2.25

1.60

10.6

3

0.5

2

1.80

3.1

6

3.0

5

0

.37

0.06

1.67

0.6

5

Shandong

1.40

0.2

9

2.7

6

0.2

0

0.37

0.1

9

1.3

8

0

.29

0.47

2.00

0.0

6

Hebei

0.98

1.9

8

0.9

7

0.0

7

0.58

1.3

5

1.1

2

0.2

1

0.31

1.4

5

0.0

0

N

orth:Nonagricultural,Education-rich,unsuccessfulprovinces

Jilin

0.15

2.9

2

3.3

7

0.1

6

6.23

0.3

1

0.81

0.1

7

0.9

0

0.64

0.4

8

Liaoning

0.87

4.84

12

.21

0.1

8

8.05

4.9

8

0.35

0

.19

0.7

7

1.9

9

0.7

2

Shanxi

1.45

10.08

0.0

2

0.5

6

3.65

3.9

5

0.4

3

0.4

0

0.1

7

0.6

4

0.3

9

Heilongjiang

1.71

0.38

5

.51

0.4

8

5.9

6

1.60

1.1

3

0.2

4

0.5

4

2.3

4

0.2

6

Center:Agricultural,transport-rich,unsuccessfulprovinc

es

Jiangxi

0.26

4.5

9

3.4

7

0.9

2

3.0

6

2.87

0.8

1

0.2

7

0.5

3

0.9

9

0.3

4

Henan

0.23

4.6

8

5.0

0

0.1

9

0.1

0

0.9

2

0.7

5

0.2

9

0.7

4

1.7

9

0.38

Hubei

0.31

0.6

6

1.5

1

0.90

0.7

4

0.9

9

0.4

4

0.2

1

0.6

6

2.1

4

0.04

Anhui

0.36

0.2

9

4.7

3

0.66

3.6

8

2.3

4

0.5

2

0.2

8

0.2

9

1.65

0.3

0

Hunan

0.73

2.8

9

5.1

8

1.7

4

0.46

2.2

4

0.1

1

0.2

5

0.30

2.3

1

0.4

7


19/23


S

outhwest:Agricultural,edu

cation-poor,unsuccessfulbackwardprovinces

Guangxi

0.3

4

1.25

10.7

1

1.0

7

2.96

3.7

8

0.7

4

0.2

7

0.1

7

0.2

8

0.4

0

Yunnan

0.4

8

5.30

7.39

0.0

1

7.63

2.1

5

0.8

1

0.3

6

0.68

1.1

3

0.4

0

Sichuan

0.7

2

0.68

4.79

0.67

3.12

1.2

3

0.0

4

0.3

0

0.31

0.1

0

0.43

Guizhou

2.0

7

1.9

8

12.5

8

0.16

6.71

2.9

6

1.5

5

0.40

0.3

3

0.0

0

0.55

N

orthwest:Transport-poor,unsuccessfulprovinces

Xinjiang

0.4

9

8.2

0

1.77

2.7

0

3.8

7

4.0

0

1.36

0.4

0

0.4

0

7.0

9

0.2

5

Gansu

0.51

5.7

8

2.1

3

0.3

2

2.89

1.32

1.04

0.4

2

0.6

0

3.9

8

0.4

8

Shaanxi

0.84

5.2

2

2.0

4

0.7

9

2.2

3

0.35

0.5

6

0.1

2

0.3

6

0.1

4

0.1

4

I.Mongolia

0.86

6.3

1

0.4

6

0.0

1

2.4

1

1.33

1.0

6

0.4

3

0.2

5

5.84

0.2

0

Ningxia

2.1

4

3.12

0.6

5

3.24

0.23

1.3

0

1.3

9

0.3

8

0.4

9

1.8

2

0.1

0

aThefixedeffectscolumniscalculatedfrombothfixedeffectsestimatedinEq.(7),Table2,andtheinteractiontermbetweenthecoastdummy

andtheperioddummy.Thus,itcomprisesanindividual

effectaswellasameasure

ofpolicyshifttowardcoastalprovinces.Thetransportcolumn

givestheoverallcontributionoftransportinfrastructuretothedifferenceinpercapitagrowthrelativetothenatio

nalmean,includingboththelinear

termandthesquaredterm,butexcludingthecongestion-relatedcoefficients,w

hicharenotestimatedinEq.(

7),Table2.Source:authorscalculation,

fromEq.

(7),Table2.


20/23

114 SYLVIE DEMURGER

terms of education and, for some of them, also in terms of transport or telecommu-

nication facilities, these provinces grew at relatively moderate rates. An important

part of their weak performances is captured through fixed effects and, thus, re-

mains to be analyzed further. However, the industrial structure of these provinces,

based either on natural resources extraction, i.e., Shanxi, or on heavy industry, i.e.,

Liaoning, Jilin, and Heilongjiang, might be a potentially important explanatory

factor. In the case of Shanxi, coal abundance has indeed induced a de factospe-

cialization of the province in coal extraction and may have created some type of

Dutch disease by diverting resources from more productive activities. In the case of

the northeast provinces of Liaoning, Jilin, and Heilongjiang, the state-dominated

and inefficient production structure based on heavy industry might have acted

as one of the most important brakes on economic growth. This effect might becaptured partly by the collective sector variable, whose contribution is below the

national average, and partly through fixed effects. Indeed, following Lin et al.

(1998), we argue that the centrally determined specialization of these provinces in

heavy industry might not reflect their actual comparative advantages, which could

have affected negatively their economic performance.

The third group is composed of a set of central provinces, i.e., Henan, Jiangxi,

Anhui, Hubei, and Hunan, which have been relatively unsuccessful on average.While transport-rich thanks to their geographical position along the Yangtze River,

these provinces did not overcome their main handicap based on a specialization

in agricultural activities. The neighboring provinces along the Yangtze River are

hardly industrialized despite their natural resources endowment, in terms of both

coal and hydraulic resources, and a relatively favorable geographic position. Being

located next to fast developing eastern provinces, they could have benefited from

growth spillovers. However, their low investment in both physical and human capi-

tal seems to have impeded their growth performance, as can be seen by the negativesign of the gap in investment, foreign direct investment, and education level.

The southwest provinces of Guangxi, Yunnan, Sichuan, and Guizhou com-

pose the fourth group, characterized by a backward, agricultural, and unsuccessful

economy; they are very poorly endowed in education and to some extent in trans-

portation facilities. These provinces remain very poor on average compared to the

rest of the country and have not taken advantage of the reform process. They look

like forgotten provinces in the overall economic landscape since many types ofinvestment, in domestic or foreign capital, in education, in transportation, or in ur-

banization, as well as industrial reforms, remained far below the national average.

While southwest China does have important natural resources, its isolation and the

absence of investment make these resources inefficiently utilized or underutilized

so that its economic performance remains very low.

Finally, the northwest group consists of unsuccessful provinces for which the

lack of infrastructure investment and the low implementation of economic re-

forms explain much of the lower growth rates during the 1985 to 1998 period.

While the availability of transport infrastructure explained an important part of the


21/23


above-average performance of the eastern provinces, the lack of adequate infras-

tructure endowment in Inner Mongolia, Gansu, and Xinjiang strongly constrained

their growth potential. In this respect, the case of Xinjiang deserves particular

attention since, despite an above-average rate of investment in fixed assets and

a high average education level,25 its economic performance has been among the

poorest. An explanation can be found in its underdevelopment in terms of transport

facilities which, in a strongly agricultural province, makes goods transportation to

and from markets extremely difficult and expensive.

7. CONCLUSION

This paper investigates the relationship between interprovincial disparities in the

availability of infrastructure and economic growth in China from 1985 to 1998.Since infrastructure improvement may be considered a byproduct as well as a

necessary condition for market development and economic growth, it is important

to determine whether underdeveloped infrastructure networks are likely to lead to

growing regional disequilibrium in China. Using panel data for a sample of 24

Chinese provinces, the estimation of a growth equation indicates that differences

in geographical location, transport infrastructure, and telecommunication facilities

do account for a significant part of the observed variation in the growth perfor-mances of provinces. Moreover, this conclusion is reinforced in the growth gap

decomposition exercise, in which the transport variable appears as one of the most

regularly differentiating factor.

The regional classification drawn from this exercise also gives a broad picture

of the extent to which economic policy induced and naturally inherited elements

combine to explain provincial economic growth differences. These findings have

different policy implications in terms of both redistribution by the central gov-

ernment, and the related decentralization process, and public investment targeting.

They suggest that economic policy measures that can improve infrastructure equip-

ment may have a nonnegligible impact in promoting per capita income convergence

among Chinese provinces. Moreover, setting policy priorities and targeting public

investments toward those that have the highest growth payoff would help to im-

prove regional as well as nationwide growth prospects. In this respect, expanding

and upgrading the network of transportation, storage, and distribution services, as

well as developing the telecommunication network, would be particularly usefulin rural areas, to allow for the development of efficient, competitive markets and

for the diffusion of economic growth.

STATISTICAL APPENDIX: SOURCES AND DEFINITIONS

The database has been constructed from a number of different official Chinese

sources, including Hsueh et al. (1993), State Statistical Bureau (various issues

25 This high level of education is attributable to two main sources, the pre-reform redistributive

system that put emphasis on western regions and the presence of Koranic schools.


22/23

116 SYLVIE DEMURGER

and 1996), National Bureau of Statistics (1999), and National Agricultural Census

Office of China (1999). All current values are expressed in local currency (RMB

yuan). Real GDP has been computed with the implicit deflator given by the State

Statistical Bureau. Other variables expressed in real terms were deflated using

the provincial overall retail price index based on 1978 price structure. The list of

indicators used is the following:

Investment: gross fixed capital formation over GDP.

Secondary education level: number of people who have completed at least

secondary education divided by the total population.

Share of agriculture: primary sector real GDP divided by total real GDP, in

percentage terms (deflators are not similar).

Share of collective sector: share of collective enterprises in total industrial

production.

Foreign direct investment: foreign direct investment in constant prices (1978=

100) as a share of real GDP.

Urbanization: urban population divided by the total population.

Transport: railway, road, and inland navigable water network length per square

kilometer.

Population density: total population per square kilometer.Telephone: number of telephone sets per capita.

Distance to town more than 10 km: percentage of villages located more than

10 km from the home township or town.

Village accessible by telephone: percentage of villages accessible by tele-

phone.

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