Market Integration and Inter-Province Freight Flows in China

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タイトル Market integration and inter-province freight flows in China Title 著者 Lin, Huang Author(s) 掲載誌・巻号・ページ The annals of the Graduate School of Business Administration, Kobe Citation University,44:69-90 刊行日 2000 Issue date 資源タイプ Departmental Bulletin Paper / 紀要論文 Resource Type 版区分 publisher Resource Version 権利 Rights DOI JaLCDOI 10.24546/81003695 URL http://www.lib.kobe-u.ac.jp/handle_kernel/81003695

PDF issue: 2021-10-06 MARKET INTEGRATION AND INTER-PROVINCE FREIGHT FLOWS IN CHINA

Huang Lin*

Abstract

This paper investigates the changes in the inter-province freight flows in China for the period 1978-97 and examines the effect of economic growth and infrastructure development on the interdependencies between provinces. The author shows that inter-province dependencies have increased after 1985, and the physical infrastructure investment is the most important factor. In addition, The expansion of consumption increases the economic autonomy of provinces and a long period of time is required to achieve market integration. Key words: market integration, inter-province dependencies, external dependence rate

Introduction

This paper investigates the changes in the inter-province freight flows in China and the changes in inter-province dependencies for the period 1978-97. The primary goal is to examine the effect of economic growth and infrastructure development on the interdependencies between provinces. The political divisions of China consist of 23 provinces, 5 autonomous regions and 4 municipalities administered centrally. In the following pages, the concept of 'province' is limited to the above 32 regions. Market integration within province or intra-province integration will not be discussed in this paper. Many studies related to the role of distribution system in economic development have been conducted using the theory of distribution systems. The economic development of China has brought structural changes to its distribution system. Therefore, it is important to explain

* Associate Professor, Graduate School of Business Administration, Kobe Univer- sity. 70 Huang Lin

such structural changes in two processes, namely 'transition to market' and 'market system development'. Empirical research on the transition of the Chinese economy, using the above two process has been conducted by a number of authors testing different hypotheses. The research on China highlights the problems of market fragmentation, which was one of the characteristics of the Chinese economy in the 1980's, as well as the problems of regional inequality. However, there has been little research on the market integration of China or the inter-province dependencies. In the 1990's, there was a series of papers published when the inter-dependencies between provinces became a crucial indicator for market integration (Kato 1997). However, most of these research did not examine the inter-province freight flows in China. Therefore, first, it is necessary to review and put all the previous studies related to inter-province freight flows in order. Second, It is important to clarify the mechanism which market integration appears when the dependency between provinces increases. Based on previous studies of distribution systems, it has been ar- gued that the development of infrastructure supporting physical distribution activities is as important as improvement of trading and information transmission activities, especially in the case of developing countries. In addition, this research suggests that improvement in physical distribution networks and technology will cause market system development, and such changes lead to a positive feedback in market integration. Consequently, it is important to show the impacts of Chi- nese infrastructure on the inter dependencies between provinces. This paper consists of four parts. The first section reviews the previous studies on the relationship between market integration and economic development. The next section analyses long-term changes in the inter dependencies between provinces using the data from inter-province railway cargo matrix (the volume of inland freight Originating and Destination = OD Table). It also investigates the conditions of market integration from the standpoint of inter-province freight flows. The third section analyses the determinants of the inter-province freight flows and their impacts. The final section discusses future research topics for analyzing inter-province relationships in China. MARKET INTEGRATION AND INTER-PROVINCE FREIGHT FLOWS IN CHINA 71

I. The Relationship Between Market Integration and Economic Development

I .1. The Inter-Province Dependencies and Market Integration

Market integration is a multidimensional and complex concept. When a province is used as the unit of analysis, market integration leads to increase inter dependencies between provinces and causes specialization and differentiation of provinces. On the other hand, market fragmentation leads to lower inter dependencies between provinces. The provinces of China are formed by independent industrial system based on the existing planning economic system and 'self-reliance' policy, and these provinces show notably low interdependencies. By analyzing the inter-province dependencies, we should be able to verify whether increased inter-province dependencies will cause market integration across provinces. The inter-province dependencies can be explained by the distribution of flows between provinces. Previous studies on China also empha- size the importance of analyzing the development of the Chinese market system from the standpoint of market fragmentation or integration. For example, the World Bank (1994) claimed that the fragmentation of China's domestic market has been strengthened through economic development. On the other hand, the study by Kato (1997) has a different conclusion on market integration, regional inequality, and inter- province flows in China. He concludes that the period from the late 1980's to the early 1990's can be considered as a turning point. In particular he argues that "market integration is in the process regardless of the disturbance from many system and non-system obstructive factors". Nevertheless, the former arguments are widely accepted. The common opinion states that the transition and development of market system in China causes domestic market fragmentation, thus, there will be a lower inter-province dependencies. Based on this point, we present the following proposition concerning the changes in inter-province freight flows in China.

Hypotheses 1: The inter-province dependencies have decreased since 1985. 72 Huang Lin

Investment in physical infrastructure is considered as one of the sources of economic development. In the case of developing countries, the formation of physical flow networks and improvement in transport technology and logistic services often lead to the development of their market systems. The importance of physical infrastructure, institutional infrastructure and human infrastructure are emphasized in the development of market systems. Physical infrastructure has been well developed in all countries that have achieved a certain stage of economic development. In addition, physical infrastructure consists of fa- cilities and a nationwide network that support the supply of services which are required by household for their existence. Mode of physical infrastructure includes railway services, roads, airports, ports, and communications. Besides acting as a nationwide network for economic activities and the basis for people's livelihood, physical infrastructure is particularly important for market integration. Research concerning market integration in China emphasizes institutional factors and policy factors. For example, one of the causes of market fragmentation is structural factor, that is the similarity of regional industrial structures. However most of the existing research emphasizes the institutional or policy factors, such as district decentralization, financial contractual system, or protectionism by the local governments. However, at present, China's physical infrastructure and its nationwide physical flow network are still insufficient. In addition, the effect of institutional reform such as changes in the taxation system on the inter-province dependencies is not clear. Therefore, although it is clear that institutional and policy factors have also had significant effects, investments in physical infrastructure are theoretically considered as an important source of market integration.

Hypotheses 2: Investment in physical infrastructure will increase inter- province dependencies

I .2. The Determinants of Inter-province Freight Flows

Many studies on inter-province freight flows use the spatial interaction (gravitation) model of economic geography. Economic geography is primarily concerned with the effect of distance on inter-regional MARKET INTEGRATION AND INTER-PROVINCE FREIGHT FLOWS IN CHINA 73

freight flows whereas interaction models use variables such as the size of destination market for the incoming and outgoing shipments. The regional population is normally used as the indicator for market size in empirical research. For example, Chisholm and O'Sullivan (1978) analyze the inter- regional freight flows of road cargo and railway cargo in the United Kingdom using population size, the number of workers and the total retail sales of the regions. Unfortunately, all the variables do not show high explanatory power with regards to railway cargo. In addition, Holsman (1975) analyzes inter-regional freight flows of air, rail, road and water transportation in Australia. He found that the regional population size has a significant effect on the regional road and railway cargo. Moreover, Knudsen (1985) examines the changes in inter- regional railway cargo flows of the United States from 1972 to 1982, and found that changes in outgoing and incoming shipments for each state are more important factors than changes in transportation cost. Existing empirical analysis on railway cargo in China have focused on analyzing the relationship between GDP growth rate of provinces and the inter-province freight flows. For example, Meng (1995) analyzes the changes in outgoing regional freight flows and the expansion of outgoing freight flows from the coastal provinces, using the inter- province freight flow matrix (OD Tables) of National Railway Cargo in 1985 and 1992. He argues that the notable increasing of inter-province freight flows from and to Guangdong was caused by its high growth. This research also showed that an increased volume of railway cargo has strong positive relationship with the increasing of agriculture and industry production. On the contrary, using data from 1985 to 1993, Chin (1998) examines the elasticity coefficient, i.e. the degree to which increased incoming shipment is influenced by the regional GDP growth. The elasticity coefficient of province j can be measured in the following formula (1). He found that the effect of GDP growth of the coastal provinces such as Guangdong, Shanghai and Jiangsu is relatively smaller than Hebei, Tianjin, Liaoning and Hubei.

Formula (1) EINj = rate of increase in incoming freight flows to province j / GDP growth rate of province j 74 Huang Lin

However, the effect of economic development on inter-regional freight flows must be explained from two dimensions, i.e. production and consumption. Hence, an explanation based on one factor, such as the population size, agriculture and industry production or GDP growth rate is not sufficient. For example, Huang (1992) analyzes the effect of regional production capacity (emissiveness or discharge ability), regional market size (attractiveness or absorptive capacity) and regional freight flow networks (separation or distance effect) on the inter-regional distribution flows in Japan. The results showed that the effect of economic development on inter-regional dependencies is a complex phenomenon (pp.195-209). Theoretically, an expansion of regional production capacity will increase its discharge ability. Majority of railway cargo in China is heavy industrial product. The production of heavy industrial products such as glass plate, chemical fertilizer and steel are scattered among provinces. In addition, coal, petroleum and mineral ores account for more than half of the railway cargo in China. Chin (1994) presents a case study of market fragmentation. Based on the industrial structure of coastal provinces (mainly processing industry) and inland provinces (concentrating on energy and raw materials), local governments limited outgoing shipments from these province from 1979 to 1982, and the incoming shipment to these province from 1985 to 1988. Consequently, movements of crops and raw materials between the provinces were significantly obstructed. After 1990, policies that restrict incoming shipments are still imposed on common production goods and daily industrial goods. Therefore, if the above argument is correct, the relationship between a regional heavy industrial production and its inter- province freight flows in the 1990's can be explained by the following hypotheses.

Hypotheses 3: The expansion of a province's heavy industrial production capacity will increase inter-province freight flows.

On the other hand, looking at the current situation of the Chinese market, the heavy consumption is still limited to cities with high in- comes. The expansion of consumption within provinces has not reached the stage sufficient to attract the primary commodities and materials from other provinces. Therefore, we can say that an expansion of MARKET INTEGRATIONAND INTER-PROVINCEFREIGHT FLOWSIN CHINA 75 consumption within a province does not have a large effect on the inter-province dependencies.

Hypotheses 4: At present, an expansion in consumption within a region will not increase inter-regional freight flows in China

II. External Dependency and Market Integration

This section analyses the long-term changes of external dependency of provinces in China and reveals its changing pattern by using the data from National Railway Cargo Matrix (OD Table).

ILL Inter-province Freight Flows Matrix of Railway Cargo The inter-province freight flow matrix of national railway cargo (OD Table) is the only time series data that can be used to measure the Chinese inter-province dependencies based on freight flows. The weight volume of freight flows (in ten thousand ton) is shown in Na- tional Railway Cargo OD Table. In this paper, data from 1985 through 1995 is analyzed.

Table 1: Transition of Transportation Volume Share of Modes in China

Modes 1978 1980 1985 1990 1995 1997 Railways 54.4% 47.5% 44-.2% 40.5% 36.0% 34.3% Waterways 38.4% 42.0% 42.1% 44.2% 49.1% 50.3% Highways 2.8% 6.4% 10.4% 12.8% 13.1% 13.8% •4 Pipelines 4.4% .1% 3.3% 2.4% 1.7% 1.5% Civil Aviation 0.010% 0.012% 0.023% 0.031% 0.062% 0.076% Total (100 million ton-km) 9831 546537 745763 970602 1234811 1275511 Source: 'Year Book of China Transportation & Communications'.

However, there are some limitations of this data. First, as shown in Table 1, the share of rail cargo in China has been gradually decreasing and reached 34% in 1997. By contrast, the share of river (inland water) cargo has gradually increased and reached 50% of total transport volume in 1997. Furthermore, the share of roads cargo also increased 76 Huang Lin

from 3% in 1978 to 14% in 1997.

Table 2: Transition of Average Transportation Distance of Modes in China

Modes 1978 1980 1985 1990 1995 1997 Railways 485 514 622 705 776 772 Waterways 873 1184 1221 1447 1551 1694

Highways 32 20 35 46 50 54 Pipelines 418 467 442 398 386 362 C ivil Aviation 1516 1584 2128 2216 2206 2334

Source: 'Year Book of China Transportation & Communications'.

Base on the modes of transport, highway and road transportation is suitable for short distance freight flows within a province while inland water transportation is especially suitable for long distance inter- province freight flows. Such relationships can be understood by examining the average transportation distance for different modes which are shown in Table 2. In 1997, the average transportation distance for rails was 772 kilometers whereas the average distance for inland waterways was double this figure. However, because inland water transportation is largely confined to area such as the `Yangtse River', it is not an appropriate measure for analyzing inter-province freight flows in China. On the other hand, the average transportation distance for roads is only 54 kilometers. We can conclude that road transportation is used mainly for freight flows within provinces. In addition, data of inter-province freight flows by roads is not available, thus, the analysis of inter-province freight flows in China can only be done by using railways data. Since the transport volume of the National Railway Cargo accounts for more than 95% of the railway shipments in China, the National Railway Cargo OD Table provides a fairly representative picture for the railway transportation. However, the second limitation of this data is that the measure is in weight, causing the primary commodities and materials to account for more than half of railway cargo in China. Table 3 shows that the shipments mainly consist of primary commodities such as coal and petroleum (about 50%), metal ores (about 13%), food and cotton (about 4.5%), and other materials such as steel, MARKET INTEGRATION AND INTER-PROVINCE FREIGHT FLOWS IN CHINA 77

cement, and chemical fertilizer (about 13%). Therefore, since the Na- tional Railway Cargo data consists of primary commodities and materials, inter-province movement of general manufactured goods and daily industrial products cannot be analyzed.

Table 3: Weight of Freight Shipments of China National Railway Cargo

1978 1985 1988 1995 1997 Total 107500 127516 140600 159346 161880 Coal 38.7% 41.9% 41.7% 44.6% 43.5% Petroleum 5.7% 4.6% 4.6% 4.6% 4.8% Steel & Iron 5.5% 5.8% 5.6% 5.9% 6.1% Metal Ores 11.8% 10.8% 10.6% 12.6% 13.0% Mineral Building Materials 15.8% 12.4% 10.6% 6.6% 6.2% Cement 1.9% 2.4% 2.3% 3.0% 2.4% Timber 3.5% 3.7% 3.1% 2.5% 2.4% Chemical Fertilizers 1.6% 1.8% 2.1% 2.7% 2.6% Grain 2.4% 3.6% 4.1% 4.3% 4.5% Salt 0.9% 0.7% 0.8% 0.6% 0.7% Others 12.2% 12.2% 14.6% 12.6% 13.9%

Source: 'Year Book of China Transportation & Communications'.

The pattern of freight flows is also determined by the railway network. The center of the Chinese railway network is at Dongbei (north- east region) and Hwabei (northern region). This biases the data shown in the OD Table. Chin (1998) analyzed the incoming freight flows for Hebei, Tianjin and Hubei, and he found a large elasticity coefficient. This is partly because the cities in these provinces are hubs in the Chi- nese railway network. Despite of the above limitations, data from Rail- way Cargo OD Table is sufficient to analyze inter-province freight flows. First, the long-term changes in the inter-province dependencies can be examined. The advantage is that time series data exists for more than ten years. Second, although the spatial structure of road transportation will change significantly when new highways are built, the structure of inter-province freight flows by railway is fairly stable. Third, as compared to inter-province transaction data which only exists for a few regions, the Railway Cargo Matrix (OD Table) covers 28 78 Huang Lin

territories, only excludes Taiwan, Hainan, Chongqing and Tibet.

II.2. Definition of External Dependence Rate

In general, inter-regional interaction can be explained by using four types of indicators based on data shown in an OD Table (Huang 1992). 1) Indicator for intra-regional dependency, or inter-regional dependency. 2) Indicator for concentration inter-regional flows. 3) Indicator for inclination of inter-regional flows. 4) Indicator for widening of inter-regional flows.

Destination j

X11 X12 X],

X21 X22

OD Table = Origin i X11

Xi, i,j=1,2, • • • ,n

The diagonal line elements (X11,X22, ,X„„) are intra-regional flows.

This paper will only use external dependence rate as an indicator for inter-regional dependencies. External dependence rate is measured by the percentage of outgoing (incoming) volume from (to) a region in the total volume of this region. Since the intra-regional dependency is measured base on the diagonal line element to the total volume of a region, the external dependence rate of incoming freight flows (INit) or outgoing freight flows (OUTit) of region i in year t will be measured by applying the following formula (2).

Formula (2) INit = 1 — Xiit/E Xji, j=1,2....n, OUTit = 1 — Xiit/ E Xij, j=1,2....n,

External dependence rate of a province calculated using data of outgoing shipments and incoming shipments from Railway Cargo OD Table. The former is considered as an indicator of external dependency for outgoing freight flows and the latter is the indicator of external MARKET INTEGRATION AND INTER-PROVINCE FREIGHT FLOWS IN CHINA 79

dependency for incoming freight flows. Since the direction of freight flows of each province is different, these two indicators are not ex- pected to be equal. From the view point of a time series, although these two indicators for a particular province fluctuate, as long as the inter- provinces dependencies remain unchanged, the average nationwide indicators should maintain a certain level within a fixed period. The changes in inter-province dependencies can be examined by analyzing changes in the average level of external dependence rate over time. Specifically, if the mean value of an indicator become smaller, the inter-province dependencies are decreasing and market fragmentation is occurring. On the contrary, if the mean value of an indicator become larger, the inter-province dependencies will increase and market integration will occur. Proposition 1 is examined by using the indicators described in the following pages.

Table 4-1: Changes of External Dependence Rate of Railway Cargo

Railway Cargo External Dependence Rate Annual Changes Total Volume Incoming (10000 ton) Mean Min Max Change Min Max SD 127514 IN85 51.8% 29.1% 94.2% 132221 IN86 53.5% 28.3% 95.0% 1.7% -3.9% 4.9% 1.9% 136948 IN87 54.5% 30.5% 95.2% 1.0% -2.6% 3.2% 1.5% 140133 IN88 54.8% 29.3% 95.8% 0.3% -8.9% 7.6% 2.7% 146382 IN89 55.5% 33.8% 95.8% 0.7% -2.7% 10.5% 2.4% 146212 IN90 56.6% 30.4% 95.1% 1.1% -3.6% 4.6% 1.8% 147543 IN91 57.2% 31.0% 95.0% 0.6% -4.2% 6.1% 2.0% 152317 IN92 58.6% 33.5% 95.4% 1.4% -2.9% 4.5% 1.6% 156270 IN93 59.3% 34.4% 96.0% 0.7% -2.5% 4.0% 1.2% 156809 IN94 60.4% 35.7% 95.5% 1.0% -2.6% 4.8% 1.9% 159065 IN95 61.2% 35.5% 95.8% 0.8% -1.4% 4.6% 1.5% Outgoing 127514 OUT85 50.6% 26.1% 90.5% 132221 OUT86 53.0% 26.7% 91.4% 2.4% -0.6% 11.8% 2.5% 136948 OUT87 54.3% 29.2% 91.1% 1.4% -2.8% 3.7% 1.6% 140133 OUT88 54.9% 28.8% 91.8% 0.5% -3.4% 8.3% 2.2% 146382 OUT89 54.9% 28.1% 91.8% 0.0% -4.2% 5.2% 2.1% 146212 OUT90 55.9% 29.2% 91.0% 1.0% -4.6% 7.5% 2.5% 147543 OUT91 57.0% 31.3% 91.9% 1.1% -6.0% 6.7% 2.3% 152317 OUT92 58.5% 31.8% 92.6% 1.5% -1.1% 4.9% 1.5% 156270 OUT93 59.4% 32.6% 92.5% 0.9% -2.3% 3.9% 1.7% 156809 OUT94 60.5% 33.2% 91.3% 1.1% -4.2% 5.8% 2.3% 159065 OUT95 61.4% 34.7% 91.5% 0.9% -4.1% 6.9% 2.4%

Mean is the average external dependence rate of 28 provinces. 80 Huang Lin

11.3. Changes and Transition of External Dependence Rate

Table 4-1 shows the mean value of the external dependence rate for incoming and outgoing freight flows for 11 years. A few important conclusions can be drawn from Table 4-1. The most important finding is that the Chinese inter-province dependencies have increased over the 11 years. First, the volume of railway cargo has been increasing almost every year. Although it dropped 0.1% between 1989 and 1990 due to re- cession, the annual increase in railway cargo is 2.2% for the 10 years. Second, the mean value of the external dependence rate of 28 provinces in 1985 was 51.8% for incoming freight flows and 50.6% for outgoing freight flows. The nationwide average external dependence rate increased about 1% each year and reached 61.2% and 61.4% respectively in 1995. Third, the external dependence rate of Shanghai exceeded 90% in 1995 whereas the external dependence rate of Liaoning, Heilongjiang, Inner Mongolia and Sichuan were extremely low. Fourth, the annual changes in external dependence rate and the fluctuation coefficient were notably larger for the period before and after 1989. In addition, the external dependence rate of railway cargo is still rising even when coal shipments are excluded. According to the OD Ta- ble, coal transportation represents 40% of railway cargo. After sub- tracting coal shipments from the total shipments between 1993 to 1995, the mean value of external dependence rate of railway cargo for 1993,1994 and 1995 are 63.4%, 64.8% and 65.6% respectively, which are higher than the results shown in Table 4-1. In Table 4-2, provinces that have a lower external dependence rate than the previous year are arranged in descending order. For example, the incoming volume of railway cargo to Ningxia decreased 8.9% in 1988 and its outgoing volume also decreased to 6.0% in 1991. Other provinces that faced decreasing external dependency are Guizhou and Fujian. The external dependency of Fujian province has notably decreased. When we look at Table 4-2 in detail, it is clear that the external dependence rate for almost all the provinces became minus in 1989 and then became positive in 1992. In addition, the number of province that faced decreasing external dependence rate has been increasing since 1994. This includes provinces like Shanghai and Beijing. MARKET INTEGRATION AND INTER-PROVINCE FREIGHT FLOWS IN CHINA 81

Table 4-2: Provinces Have Lower External Dependence Rate of Freight Flow Than The Previous Year

Incoming

IN86 Ningxia Xinjiang Heilongjiang Fujian

N87 Qinghai Inner-Mongolia Shanxi Fujian Tianjin Xinjiang IN88 Ningxia Guizhou Inner-Mongolia Guangxi Shanxi Yunnan Fujian Jilin Xinjiang IN89 Shaanxi Zhejiang Tianjin Henan Fujian Inner-Mongolia Sichuan Shandon Guangxi Jiansu Liaoning Xinjiang IN90 Heilongjiang Yunnan Qinghai Shanghai Xinjiang Guizhou Beijing IN91 Guizhou Anhui Ningxia Qinghai Shanxi Inner-Mongolia

IN92 Qinghai Anhui Xinjiang Sichuan IN93 Anhui Qinghai Inner-Mongolia Guangdong Gansu Jiangsu IN94 Guizhou Fujian Inner-Mongolia Ningxia Hubei Shanghai Heibei Tianjin Beijing IN95 Fujian HXinjiang. Zhejiang Hunan Sichuan Guangxi Guizhou eilongjiang Liaoning Shandong

Outgoing

OUT86 Shandong Fujian Liaoning

OUT87 Inner-Mongolia Jiangsu Fujian Shanxi Shanghai OUT88 Fujian Ningxia Guizhou Guangxi Xinjiang Hubei Sichuan Gansu Tianjin Yunnan OUT89 Shaanxi Tianjin HXinjiang . Zhejiang Henan Guizhou Ningxia Sichuan Fujian eilongjiang Anhui Yunnan OUT90 Zhejiang Jilin Fujian Jiangsu Liaoning Shanghai Qinghai Hebei OUT91 Ningxia Guizhou Zhejiang Anhui Shandong Hebei Guangxi

OUT92 Xingjiang Guizhou Anhui Qinghai

OUT93 Tianjin Guangdong Shaanxi Hubei Fujian Liaoning Shanghai

OUT94 Fujian Zhejiang Beijing Shanghai Liaoning Hubei Guizhou Qinghai OUT95 Zhejiang Guizhou Hunan Jiangxi Yunnan Hebei Henan Henan Heilongjiang

Such changes can be explained to a certain extent by political factor and economic development. In order to counter the sudden rise in prices, the central government imposed tight monetary and fiscal policies in September of 1988 and in June of 1993. As a result of these policies, investment and imports declined, and the outgoing and incoming freight flows decreased for many provinces. In summary, economic policy and changes in economic growth have strong influences on inter- province dependencies. 82 Huang Lin

Base on the above analysis, the overall results do not support Hy- pothesis 1. This paper concludes that the inter-province dependencies has continued to increase after 1985. Although we have seen some evi- dences of market fragmentation, inter-province freight flows for primary commodities and materials have been increasing due to reform policies such as expanding the market distribution.

IQ. Determinants of External Dependencies

What is the reason for the increased inter-province dependencies since 1985? Apparently, previous studies have failed to explain such phenomenon effectively with industrial structure factors or political factors. Of course, market fragmentation partly occurred due to the decentralization of authority to the provinces. However, during the reform process to a market system, some reforms such as a transfer of authority to the private sector and liberalization of distribution activities have strengthened the inter-province dependencies and bring market integration. The influence of factors such as economic growth, expansion of production and consumption, and physical infrastructure investment on the degree of external dependency of provinces will be analyzed in the next section.

Measures and Method

The dependence variables are the external dependence rate of incoming and outgoing freight flows of railway cargo. Since there are only 28 provinces, in order to increase the number of provinces used in the analysis, data for 1993, 1994 and 1995 are considered as a panel data. Thus, the total number of sample becomes 84. The explanatory variables are defined using data from the China Statistics Yearbook. In order to examine whether differences between groups of eastern, central and western provinces have influenced the inter-province dependencies, two province dummy variables are introduced. In addition, in order to check the impact from different time point, two dummy variables are also introduced and regression analysis is conducted. The explanatory variables are defined as the following. MARKET INTEGRATION AND INTER-PROVINCE FREIGHT FLOWS IN CHINA 83

1. Economic growth: GDP growth rate of the province 2. Production: Total production amount of heavy industry of the province. 3. Physical Infrastructure Investment: Fixed investment on transportation and communication industries. However, the number of sample became 79 due to the data of Fujian province became deficit value. 4. Consumption: Final consumption expenditure of the province. 5. Eastern dummy: 1 = Liaoning, Hebei, Beijing, Shanghai, Tianjin, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Guangxi, 0 = other provinces. Central dummy: 1 = Heilongjiang, Jilin, Inner Mongolia, Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan, 0 = other provinces. 6. Dummy94: 1 =1994, 0=1993, 0=1995, Dummy95: 1=1995, 0=1993, 0=1994.

The process of analysis begins with the full (basic) model that in- cluded all the explanatory variables. Then final model is determined by removing variables that do not have a statistically significant impact on the external dependence rate of railway cargo. In the next step, an analysis on external dependence rate excluding coal shipments is conducted. 84 Huang Lin

Table 5 Result of Regression Analysis for External Dependence Rate

Explanatory Outgoing Freight Flows Incoming Freight Flows Removing Coal Shipments

Variables Basic Model Final Model Basic Model Final Model Outgoing Incoming

GDP Growth 0.06 0.27 b 0.18 c 0.22 0.32 b Rate 0.45 2.39 1.92 1.46 2.50 Production of 0.37 c 0.54 a 0.28 0.28 c 0.43 c 0.41 b Heavy Industries 1.78 3.18 1.63 1.67 1.92 2.06 Consumption in -1.09 a -1.14 a -0.61 a -0.58 a -0.10 a -1.01 a The Province -5.01 -5 .58 -3 .46 -3.30 -4.21 -4.92

Infrastructure 0.51 a 0.60 a 0.18 0.23 c 0.44 a 0.46 a

Investment 3.34 4.10 1.43 1.93 2.66 3.20

Eastern Dummy 0.28 0.56 a 0.57 a -0.08 0.01 1.51 3.72 4.91 -0.40 0.06

Central Dummy 0.19 -0.02 -0.17 -0.33 a 1.49 -0.20 -1.25 -2 .77 Dummy94 0.09 0.11 0.10 0.12 0.79 1.15 0.82 1.09 Dummy95 0.15 0.20 c 0.17 0.21 c 1.15 1.84 1.20 1.67

Adjusted R.' 0.28 0.27 0.52 0.52 0.16 0.35 n= 79 The level of significance: a = 1%, b = 5%, c= 10%. The numbers are $ — coefficient + and t — statistic.

111.2. Findings

The result of regression analysis of the determinants for external dependence rate of railway cargo is shown in Table 5. In this analysis, we assume that external dependence rate of outgoing and incoming shipments can be explained by the same set of explanatory variables. However, the result shows that the adjusted R2 of external dependence rate for outgoing and incoming shipments has significant difference.

MI.2.1. Determinants of External Dependence Rate of Outgoing Freight Flows

The determinants of external dependence rate for outgoing freight flows are production, consumption and physical infrastructure invest- MARKET INTEGRATION AND INTER-PROVINCE FREIGHT FLOWS IN CHINA 85

ment. Since the adjusted R2 of the basic model is 0.28, there is a room for improvement by introducing new explanatory variables. The stan- dard regression coefficients ( )3-coefficient) in Table 5 show the direction of influence on the external dependence rate for each factor. First, the main items of railway cargo are the products of heavy industry, such as coal, steel and chemical raw materials. Therefore, an increasing production capacity will create inter-province freight flows and subsequently cause the external dependence rate for outgoing freight flows to increase. In contrast, increasing consumption of provinces causes a fall in the external dependence for outgoing freight flows. Moreover, in the basic model, (3-coefficient of the former is 0.37 whereas the latter is -1.09, which is about 3 times larger than the former. It means that the negative effect of consumption on outgoing freight flows is 3 times larger than the positive effect brought by the expansion of production of heavy industry. In most situations, primary commodities such as coal, petroleum, minerals and wood, and other materials are determined by the resource conditions of the province. Therefore, if the production of heavy industry is concentrated within a province, the inter-province dependencies will increase. However, at present, expansion of province's consumption will rise the autonomy of a province and will not increase the attractiveness of a province. Local authorities have even imposed some policies to protect primary commodity and material industries in the provinces. With regards to the expansion of consumption of the provinces and its external dependence rate for outgoing freight flows, it suggests that decreases in the outgoing freight flows of the province cannot be directly linked to market fragmentation. The results of Table 5 show that the positive effect of infrastructure investment is larger than the effect of production of heavy industries. This conclusion remain unchanged even after coal shipments are taken out. Bases on the findings from the basic model shown in Table 5, the GDP growth of a province does not influence the external dependency of outgoing freight flows. In addition, both dummy variables also do not have any impact on the external dependency of outgoing freight flows. Although the basic structure of the model is not changed after coal shipments are taken out, it is clear that the effect of consumption in the province becomes smaller. 86 Huang Lin

111.2.2. Determinants of External Dependence Rate of Incoming Freight Flows

The determinants of the external dependence rate for incoming freight flows are GDP growth rate consumption and the dummy eastern province variable. In the final model, the infrastructure investment and the total production amount for heavy industry are also statistically significant. When the coal shipments are removed, the dummy central provinces become significant and have negative effect. Since the dummy year of 1995 has no meaning in the final model, we shall ignore its influence. Based on the result of analysis in Table 5, the following conclusions are made. First, the consumption in a province has a negative impact on the external dependency for incoming freight flows. Therefore, the results suggest that in the 1990's, the expansion of consumption in the provinces has increased its autonomy and integration of regional market has not been progressing in China. When coal shipments are removed, the negative effect of consumption on incoming freight flows for a province becomes greater. Second, we found that the positive effect of the eastern dummy variable is almost same with the absolute value of /3-coefficient of consumption in the province. However, when the coal shipments are removed, the central dummy variable has a negative effect. Therefore, incoming and outgoing coal shipments contribute to such effects. When coal shipments are removed, the provinces that have a high external dependence rate on incoming cargo are Shanghai, Tianjin, Qinghai, Ningxia, Jiangsu, Guangdong, Beijing, Shanxi and Henan. On the other hand, the provinces that have a notably low external dependence rate are Sichuan, Heilongjiang, Inner Mongolia and Liaoning. Excluding coal, majority of railway cargo consists of primary commodities and materials, thus, it is the effect of the spatial distribution of industrial production in China. The findings suggest that the spatial distribution of industrial production have a strong influence on the incoming freight flows. In order to test the above statement, interactive effect such as dummy eastern area with GDP growth rate, consumption with production of heavy industry and physical infrastructure investment are introduced to the model. The results show that GDP growth rate and physical infrastructure investment become unsignificant even at 10% level. By contrast, MARKETINTEGRATION AND INTER-PROVINCEFREIGHT FLOWS IN CHINA 87 interactive effects of the heavy industry production with consumption, eastern dummy variable with GDP growth rate, and consumption with physical infrastructure investment become significant at the 1% level. This result shows that the economic development of eastern provinces and the open-door policy for the coast have caused big changes to external dependencies of incoming freight flows. In addition, owing to the fact that physical infrastructure investment was concentrated in eastern provinces until 1992, the formation of positive feedback of between economic growth, physical infrastructure investment, inter-province dependencies (i.e. regional integration) is in the process within the coast in China. Third, the results from Table 5, also as shown in previous research, show a significant relationship between the GDP growth of a province and its incoming freight flows. Although the effect from the GDP growth is smaller than the heavy industry production, the economic growth of provinces increases its external dependence rate of incoming freight flows. Fourth, the effect of fixed investment on transportation and communication is not statistically significant in the basic model. However, a significant effect appears when the coal shipments are removed, and it has a greater positive effect on external dependence rate of incoming freight flows.

IR .3. Discussion

This paper analyzes the external dependence rate of incoming and outgoing railway cargo between Chinese provinces, there are two key findings. First, majority of inter-province freight flows in China use railway and inland waterway. When the study is limited to railways, it shows that the external dependency of inter-province freight flows in China continues to grow. Therefore, physical infrastructure investment is the most important factor supporting the expansion of inter-province dependencies. In addition, the expansion of production of heavy industry is also considered to be a crucial factor. In summary, the above findings support Hypotheses 2 and Hypotheses 3. Physical infrastructure investment and expansion of production are considered to be the positive factors of market integration in China. 88 Huang Lin

However, depending on conditions such as the spatial structure of heavy industries and the spatial distribution of railway networks, increases in the outgoing shipments of natural resources and materials from inland provinces to coastal provinces support the rapid economic growth of the coast. The above mentioned relationship and regional inequality of inland and coast already been discussed in many studies. In this paper, the results suggest such relationship still maintain until 1995. Second, even after 1995, expansion of consumption in provinces is still causing a fall in the inter-province dependencies. It means that the expansion of consumption in provinces is increasing the economic autonomy of provinces. In the case of Japan in the 1980's, the expansion of regional population size and regional consumption expenditure decreased its economic autonomy, and promoted spatial integration and regional specialization in the economy (Huang 1992). Since the consumption in provinces has a notably negative effect on the inter- province dependencies, long period of time is required to achieve the stage whereby market integration and inter-province dependencies in China can be strengthened by the growth of consumption in provinces. The results support for Hypotheses 4 and show that the expansion of consumption in provinces still works to increase the autonomy of the provinces.

CONCLUSION

This paper suggests several areas for future research in the Chi- nese market integration and inter-province dependencies. In the previous studies on regional inequality and homogeneity of regional industrial structure in China, the provinces were divided into eastern and central districts. However, such division of provinces is fairly arbitrary with respect to an analysis of inter-province dependencies. In order to understand the expansion or dissolution mechanism of regional inequality, it is necessary to analyze the inter-regional dependencies at province level. The continuous expansion of the external dependence rate of railway cargo after 1985 is an important fact. In addition, it is also an important fact that the fixed investment on transportation and communication is a crucial factor to expand the external dependencies MARKET INTEGRATION AND INTER-PROVINCE FREIGHT FLOWS IN CHINA 89

between provinces. Although the examination of highway cargo is diffi- cult due to the limitation of data, improving road and highway network may also have a strong impact on the inter-dependencies between Chi- nese provinces. The technological advancement of road networks and highway transportation is considered to be an important factor for regional economic integration. In the future, as highway networks are gradually formed, an analysis of inter-province freight flows from the standpoint of highway and road transport becomes an important sub- ject. The analysis of this paper is restricted to external dependency, thus, regarding market integration, research on the regional specialization needs to be deepened. For example, the ratio of heavy industry to total industry production in Liaoning increased from 67.8% in 1980 to 75.6% in 1995. This ratio for Jiangsu also increased form 45.4% to 50.4% during the same period. Since the national heavy industry ratio is 56% in 1995, the external dependence rate is relatively high for a province which has a high light industry ratio. When total production amount of light industry in provinces is introduced as a new variable into the model, both the effects on external dependence rate of railway cargo and the explanatory power of the model also increase. After en- tering the 90's, a high degree of concentration of consumer durable goods and daily industrial products started to appear in certain provinces. Therefore, the classification of light and heavy industry may be overly simplistic. In fact, it is probably important to analyze the regional specification and inter-province dependencies should use a de- tailed industry classification. Although the market fragmentation behaviors of local governments obstruct the inter-province freight flows, there are probably other hid- den factors that are causing changes in inter-province dependencies. These factors include system factors such as the financial contractual system, macro economic policy factor and other factors like spatial distribution of resources. These factors should been discussed in terms of their effect on changes in inter-province dependencies. Therefore, future research should look at these factors.

Received October. 6. 1999 90 Huang Lin

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