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Spatial-temporal contrasts in integrated urban-rural development in China, 1990-2010.

infrastructure.

INTRODUCTION

For a long period following the establishment of People's Republic of China in 1949, urban and rural areas were treated separately and differently. Since 1978, China has experienced over three decades of rapid economic growth, with annual growth rates of over 9 per cent. However, despite this "economic miracle", the country has also witnessed ever-enlarging urban-rural inequalities across a range of indicators such as income, education, medical care, provision of infrastructure and social insurance. For example, while the per capita urban household income increased from 343 yuan in 1978 to 13,041 yuan in 2007, over the same period the per capita rural household income increased from 134 yuan to only 3,998 yuan. (1) A cluster of studies that investigated urban-rural inequalities in China have attributed the inequalities to factors such as a dualistic urban-rural structure, (2) urban-biased development strategy, (3,4) market forces (5) and taxation. (6) Urban-biased policies and related measures like the household registration system (hukou) were initially formulated on the basis of the typical socio-economic conditions in China in the period after 1949. These policies and measures have, however, intentionally diverted resources (capital, labour and materials) from rural to urban areas, and induced greater urban-rural inequality in China. (7)

In 2002, the 16th National Congress of the Communist Party of China (CPC) for the first time stressed the importance of the countryside's achieving moderate prosperity (xiaokang), and declared that China's socio-economic development must incorporate both urban and rural areas alike. This declaration caused a paradigm shift to the long-standing separation between the urban and the rural, by placing urban and rural development under the same framework. Thereafter, a series of policies and measures to achieve integrated urban-rural development in China was formulated and implemented. However, China is a vast country of huge socio-economic and geographic differences--as such, levels of integrated urban-rural development can be expected to be very different among provinces within different time periods. (8) This is particularly evident in the different reactions of the provinces with respect to the transitions of decentralisation, marketization, urbanisation and globalisation in the post-reform era. The way in which integrated urban-rural development has evolved in China has not, to the authors' knowledge, been clearly analysed at the provincial level. Thus, the aim of this paper was to investigate the spatial-temporal distribution of levels of integrated urban-rural development in China in the post-reform period.

The structure of the article is as follows. The first section provides an understanding of urban-rural linkages and integrated urban-rural development in China.

The second introduces the research methodology and data, and the third assesses urban-rural linkages in each province and analyses spatial-temporal contrasts in integrated urban-rural development. The conclusion offers a discussion on the research findings.

RESEARCH BASIS AND ANALYTICAL FRAMEWORK

The Evolution of Integrated Urban-Rural Development in China

Generally, the strategy of integrated urban-rural development in China was first proposed in the 1980s to combat inequalities between urban and rural areas. However, the local governments charged with implementing this strategy share two major misunderstandings: first, that integrated urban-rural development means an integrated distribution of industries (a mix of agricultural and non-agricultural industries coexisting in the same area); and second, that integrated urban-rural development refers to building urban and rural areas alike (i.e., transforming villages into cities, and peasants into citizens). (9,10) The major shortcoming in the interpretation of integrated urban-rural development lies in the oversight of the unique economic, social and geographic features of urban and rural areas--that is, the productivity and other socio-economic factors in cities and rural areas are different, and cities and rural areas in each province practise different approaches in the distribution of land, capital, labour and technology. (11) In fact, the core concept of integrated urban-rural development regards industry and agriculture, cities and countryside, and citizens of China and peasants as an integrated whole instead of isolated parts. (12) According to Chen and Li and Luo and Li, the fundamental purpose of integrated urban-rural development in China has been to break down institutional barriers, and achieve coordinated urban and rural development by strengthening urban-rural linkages. (13,14) Zhang et al. also point out that integrated urban-rural development should promote reasonable resource flows (capital, people, materials and information), agglomeration and profit allocation between urban and rural areas. (15) Ye lists three main rubrics of integrated urban-rural development in China: deployment of key factors between urban and rural areas, supply of primary public goods and services (infrastructure, compulsory education, health care and social insurance) in urban and rural areas, and allocation of public resources between urban and rural areas (this includes the previous two rubrics). (16) In this sense, integrated urban-rural development can be observed and interpreted as the consequence of factor flows (labour, capital, goods, information and technology) and of factor agglomeration between urban and rural areas. Naturally, an imbalanced distribution of resources may benefit one while disadvantaging the other: as such, the manner in which urban and rural areas are linked impacts upon the integration of development between the two areas.

Integrated urban-rural development in China has been deeply influenced by government policies. In the early period after the founding of the PRC, the government emphasised the development of capital-intensive heavy industries. Against this historical background, rural areas became the sources of capital, labourers and raw materials for industrialisation and urban development in China. This economic strategy has seen the emergence and implementation of many policies and measures, such as the state-set procurement price for trading of agricultural products, the hukou system, and agricultural and rural taxation that disfavoured rural areas. Thus, rural areas in China gradually lagged behind the cities. Lin and Liu and Lin and Chen point out that the heavy industry-oriented development strategy had a strong impact and resulted in higher urban-rural income inequality in China. (17,18) Luo and Li also argue that urban-biased policies have in fact limited the direct linkages between cities and the countryside, and induced urban-rural isolation.

China's reform and opening up since 1978 marked a new transitional era of integrated urban-rural development. Linkages between cities and the countryside were strengthened. Cities that were granted substantial autonomy in decision-making shifted from being passive agents of the central government to active actors responsible for local prosperity. The flourishing urban economy provided large market and job opportunities to the villages and peasants. The household responsibility system was implemented in rural China in the early 1980s, replacing the collective farming of the people's commune. The introduction of this system, which enabled peasants to deal with their surplus, greatly increased their enthusiasm for agricultural production. The post-reform era has also seen large-scale rural migration to the cities. Foreign or Sino-foreign joint enterprises flourished in the manufacturing industries in eastern China and employed large numbers of rural labourers, who came mainly from inland provinces such as Guizhou and Sichuan. According to the China Statistical Yearbook 1999, the contribution of rural-urban migration to the urbanisation growth remained at over 60 per cent annually from 1978 to 1989. (19) The large number of rural migrants, who represented the legacy of cheap labour, contributed greatly to the urban development of the eastern provinces.

As a strategic way of achieving "urbanisation from below", township and village enterprises (TVEs) achieved rapid development in rural China. The strategy--which is to "strictly control the growth of large cities, rationally develop medium-sized cities, and vigorously promote the development of small cities and towns"--encourages peasants to work in TVEs in small towns and medium-sized cities instead of migrating to large cities. Employment in the rural non-farm sectors expanded from 9.2 million in 1980 to 191 million in 2004, and the share of rural non-farm sectors in total rural employment increased from 3 per cent to 38.4 per cent in the same period. (20)

The early 21st century has seen a historic shift in China from urban-biased policies to a period of "industry nurturing agriculture and cities supporting the countryside". The central government has adopted policies that promote and coordinate harmonious economic development in both urban and rural areas to achieve overall well-being in the Chinese society. (21) A slew of measures were taken to realise this shift, for example rural tax and fees reduction, agricultural subsidies, support for rural infrastructure construction and social development. These rural-favoured policies and measures (which all focused on issues concerning agriculture, villages and farmers) resulted in the drafting of nine consecutive "No. 1 Central Documents" issued by the Central Committee of the CPC and the State Council from 2004 to 2012.

Generally, integrated urban-rural development at the provincial level in China has entered a stage that favours rural areas by strengthening urban-rural linkages. However, eastern, central and western regions differ greatly in terms of their level of integrated urban and rural development. These differences are predominantly due to socio-economic inequalities between the regions. Following the notion of "allowing some regions to get rich first and in turn helping other regions to gradually become rich", the eastern provinces, due to their socio-economic and geographic advantages, were the first ones to become rich. In particular, facing competition and opportunities of globalisation, the eastern provinces received special economic status from the central government and were subject to preferential policies (e.g., tax breaks; favourable terms of loan, credit and subsidies; and higher foreign exchange retention rates). According to the China Foreign Economic Statistical Yearbook (1980 and 1990), (22,23) China's actual usage of foreign capital increased from USD109 million in 1979 to USD3,392 million in 1989. However, over 85 per cent of the foreign direct investment in the 1980s agglomerated in eastern China. Gradually, the inequalities between the eastern and inland provinces in China increased.

Urban-Rural Linkages: The Theoretical Background

Generally, urban and rural areas maintain strong links in the form of resource flows of people, capital, goods, information and technology. (24) Potter et al. point out that urban-rural linkages are initiated in an attempt to take advantage of differentials or complementarities between urban and rural areas. (25) As both areas differ in productivity and other factors, such as labour quality and infrastructure, economic activities in rural areas are primarily agricultural, whereas those in urban areas are primarily non-agricultural.

To understand how resources flow between urban and rural areas, central place theory (26) provides the key concept of placing rank order on the economic activities flowing between villages, towns and cities: cities are the main suppliers of high-order services like medical services and education to the surrounding areas, which in turn supply low-order services like food and other resources to the central place. The core-periphery model, (27) which was mainly based on the unequal distribution of power in economy, society and polity, indicates that the core area is the central realm upon which the surrounding rural periphery is dependent for the supply of high-order services. The core area could, as such, evolve into an urban or metropolitan centre with high potential for innovation and growth while peripheral areas would experience slow growth or even stagnation, adding to their dependency on the core area.

The "new economic geography" explains how increasing returns to scale, agglomeration economies, transport costs and product differentiation lead to a highly differentiated spatial organisation of economic activities. Krugman argues that interrelated industry concentrations, reliable infrastructure, accessibility to the market and high production returns drive a cumulative process that may result in a core-periphery economy. The research essentially reveals the relationship between economic growth and geographic conditions. (28) Redding and Venables provide evidence that the geography of access to markets and sources of supply is statistically significant and quantitatively important in explaining cross-country inequalities in per capita income. (29) In this sense, resources tend to agglomerate in core (urban) areas of high profit returns and accessibility to the market, while the fortunes of rural peripheries are highly reliant upon those urban areas.

In the field of demographics, rural-urban migration is mainly considered to be induced by socio-economic factors. In dual economy theory, Lewis states that surplus rural labourers move from agricultural sectors to modern industrial sectors due to differences in production efficiency. (30) Schultz considers migration from lower-productivity sectors to higher-productivity sectors as a choice made after balancing the migration cost and potential profit. (31) In economics, rural-urban interactions between sectors are known as sectoral linkages, which include rural activities that take place in cities (e.g., urban agriculture) and activities that are usually classified as urban (e.g., manufacturing and services) taking place in the rural areas. (32) These linkages have developed beyond the traditional division whereby rural areas are primarily seen as agricultural producers and urban areas are perceived non-agricultural producers. In most cases, in rural areas, peasants have been found to undertake non-agricultural jobs that increase and diversify their income, while in and around cities, growing urban poverty and the lack of formal employment have catalysed the development of urban agriculture.

A preliminary assumption of the above analysis is that the level of integrated urban-rural development of an area (i.e., a province) is closely related to the flows and agglomeration of resources between cities and the countryside, and is sensitive to spatial-temporal dynamics in post-reform China. Thus, we attempted to examine spatial-temporal contrasts in China's integrated urban-rural development at the provincial level.

METHODOLOGY AND DATA

Construction of an Urban-Rural Linkage Index

There are 27 provinces and four provincial-level cities (Beijing, Tianjin, Shanghai and Chongqing) in mainland China (see Figure 1). These administrative units are grouped into eastern, central and western regions.

In the above analysis, the integrated urban-rural development at the provincial level is assumed to be a consequence of flows and agglomeration of resources between urban and rural areas. In this connection, the hypothesis is that the level of integrated urban-rural development in each province varies in response to the presence and strength of urban-rural linkages. Thus, urban-rural linkages can serve as a proxy for integrated urban-rural development. The urban-rural linkage index (URLI) is then constructed to show the level of integrated urban-rural development of the Chinese provinces. High URLI scores indicate tight urban-rural linkages and a high level of integrated urban-rural development.

Generally, it is not easy to monitor resource flows between urban and rural areas, since such flows involve complex processes. Lin identifies the spatial form of urban-rural linkages in the Pearl River Delta in China by quantifying selected variables of urban-rural linkages. (33) However, these variables, such as population, employment and land use intensity, are considered inadequate. Furthermore, the study relied on data from the year 1991 alone and did not show the variation of urban-rural linkages over time. Normally, the flows and agglomeration of resources between cities and the countryside will induce demographic and economic changes like urbanisation and shifts in economic structure and income levels, etc. Due to data availability, five variables are selected to assess the URLI in each province (see Table 1):

(i) Urbanisation level (the percentage of urban population to the total population). This variable is selected to show population mobility between cities and the countryside.

(ii) Ratio of TVE workers to the total rural population. This variable is chosen to show changes in the employment structure of secondary and tertiary industries in the countryside. Sectoral linkages have contributed to the development of nonagricultural industries in rural areas and employed many rural labourers in off farm industries.

(iii) Ratio of the number of TVEs per thousand people. This variable describes the development of non-agricultural industries in the rural areas of each province.

(iv) Ratio of wage income to rural household income. In China, rural household income includes wage income, household operating income, asset income and transfer income. Wage income indicates the income generated by peasants' employment in off-farm industries. This variable describes the change of rural household income due to sectoral linkages that have diversified a rural economy and increased rural household income.

(v) Urban household food consumption. Generally, food consumption in cities relies heavily on the rural supply. This variable is chosen to show the material linkage between the countryside and cities.

Considering the possible correlations among these variables, principal component analysis (PCA) was applied to transform the variables into a smaller number of uncorrelated variables (named principal components) that account for most of the variance in the selected variables. PCA can reduce data dimensionality by introducing a covariance analysis between factors. The extraction of a few orthogonal components gives a concise summary of the different variables.

Exploratory Spatial Data Analysis (ESDA)

Upon construction of the URLI, we investigated spatial-temporal contrasts in levels of integrated urban-rural development at the provincial level. The central aspect of ESDA is the notion of spatial autocorrelation or spatial association, the phenomenon in which similarity of location (observations in spatial proximity) is matched by value similarity (attribute correlation). (34) Moran's I was mainly used to test spatial autocorrelation. Global Moran's I measures the overall clustering and is assessed by examining a null hypothesis. Rejection of the null hypothesis suggests a spatial pattern or spatial structure. Local Moran's I examines the spatial autocorrelation and shows where the clusters or outliers are located and what kinds of spatial correlation are most important. (35) Global Moran's I is defined as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

where [x.sub.i] is the ith observation's value (i = 1, 2 ... n), n is the number of observations, [w.sub.ij] is a binary system of an n*n spatial weight matrix. Moran's I ranges from -1 to +1. Positive values of Moran's I indicate spatial clustering of similar values, while negative values of Moran's I suggest that high values are found in the vicinity of low values.

We used a Z score to test the statistical significance that will determine the acceptance or rejection of the null hypothesis. The critical Z score values are from -2.58 to +2.58 standard deviations if a 99 per cent confidence level is used. If the Z score is within the standard deviations (-2.58, +2.58), the research cannot reject the null hypothesis, implying that overall clustering is very likely to be a random pattern. If the Z score falls outside the standard deviations (-2.58, +2.58), then the null hypothesis should be rejected and the overall clustering displays a significant clustered pattern. The formula of the Z score is written as:

Z = I - E(I)/[square root of (VAR(I))]

E(I) = -1/n - 1, VAR(I) = [n.sup.2][w.sub.1] + n[w.sub.2] + 3[w.sub.0.sup.2]/[w.sub.0.sup.2](n - 1)(n - 2)(n - 3) - [E.sup.2](I) (2)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where [w.sub.i.] and [w.sub..i] are the sum of the figures in ith column and ith row respectively.

Thus, local Moran's I can be written as:

Locall = [z.sub.i] [n.summation over (j=1)] [w.sub.ij][z.sub.j] (3)

where [z.sub.i] = [x.sub.i] - [bar.x], [z.sub.j] = [x.sub.j] - [bar.x] are the deviation of the observed value and the mean value.

The data were taken from the China Statistical Yearbook and China Village and Township Enterprise Yearbook for the three calendar years 1991, 2001 and 2011. Taiwan, Hong Kong and Macau were not included.

EMPIRICAL RESULTS AND INTERPRETATION

Provincial URLI Scores in the 1990-2010 Period

According to the conventional rule of extracting components that have eigenvalues greater than one, two components were extracted by PCA in 1990, accounting for 84.4 per cent of the variance (see Table 2). The first component, which accounted for 62.9 per cent of the variance, was the most important component. According to the variable loadings, this component included variables of urbanisation, the ratio of TVE workers to the total rural population, the ratio of wage income to rural household income and the ratio of food consumption to urban household consumption of nonagricultural products. The second component, which accounted for 21.4 per cent of the variance, included the ratio of the number of TVEs per thousand people.

The model for the two principal components in 1990 was formulated according to the factor loadings of the components and the initial eigenvalues. The coefficients indicated that the five variables all contributed positively to the URLI.

F = 0.203[X.sub.1] + 0.452[X.sub.2] + 0.146[X.sub.3] + 0.391[X.sub.4] + 0.342[X.sub.5] (4)

where [X.sub.i] (i = 1,2 ... 5) are the variables; F denotes the URLI.

The URLI score of each province in 1990 was computed according to equation (4). The scores were uncorrelated and represented the selected five variables (Table 3). Similar computations were made for 2000 and 2010. The URLI scores of provinces in these years are listed in Table 4 and Table 5 respectively.

Spatial-temporal contrasts were distinct in the provincial URLI scores as shown in Figure 2. Generally, provinces of high URLI scores were located in the eastern parts of China, while the far western provinces were characterised by low URLI scores. The URLI scores in central China remained at the medium level. In 1990, provinces of high URLI scores included provinces in all three regions: those in the eastern region included Beijing, Tianjin, Liaoning, Jiangsu, Shanghai, Zhejiang and Guangdong; whereas those in the central and western regions included Jilin and Guangxi, respectively. There were also provinces in the three regions that generated lower URLI scores. Sharp contrasts in URLI scores between provinces were also evident in 2000. Apart from Hainan, the ten other provinces in eastern China generated high URLI scores. The URLI scores of the western provinces were low, while those of the central region stood at a medium level. In 2010, high URLI scores were found to be concentrated in the southern and southeastern provinces, and surrounded by inland provinces with medium URLI scores. Most of the western provinces, as well as Heilongjiang and Henan, generated low URLI scores. The URLI scores generated imply that integrated urban-rural development in the eastern provinces remained high, followed by the inland provinces.

Spatial-Temporal Contrasts in Integrated Urban-Rural Development

This section examines the degree of clustering in the distribution pattern of URLI scores and the factors that attributed to spatial-temporal contrasts in provincial URLI scores. The global Moran's I in 1990, 2000 and 2010 were 0.303, 0.315 and 0.437 respectively, at the 1 per cent level of significance. This means that there is less than 1 per cent likelihood that this clustered pattern could be the result of random chance. Thus, the spatial-temporal distribution of integrated urban-rural development at the provincial level was highly clustered in the research period. Moreover, the clustered pattern is prominent in the research period, as evidenced by the increases in Moran's I from 0.303 to 0.437.

The LISA (local indicators of spatial association) cluster map was generated by the GeoDA 095 software programme (see Figure 3). A Moran scatter plot categorised the nature of spatial autocorrelation into four types, corresponding to the four different quadrants. Observations with high values surrounded by observations with high values are in the upper right quadrant (high-high), while observations with low values surrounded by those of low values are in the lower-left quadrant (low-low). Observations with low values surrounded by those of high values are in the upper-left quadrant (low-high), while the bottom-right quadrant contains observations with high values surrounded by those of low values (high-low).

In 1990, over 77 per cent of the provinces fell into the high-high (26 per cent) and low-low (52 per cent) quadrants, indicating a polarised clustering pattern of provinces of high or low URLI scores. Provinces in the high-high quadrant are situated in eastern China, while provinces in the low-low quadrants are mainly located in central and western China. In 2010, over 84 per cent of provinces fell into the high-high (35 per cent) or low-low (49 per cent) quadrants. This change indicated an increased clustering pattern among provinces of high or low URLI scores during the research period. This research finding is in line with the results shown in Figure 3.

Spatial Econometric Model of the Driving Factors

A spatial econometric model was used to analyse the driving factors behind the spatial-temporal contrasts in China's integrated urban-rural development at the provincial level. Six variables were selected to examine the driving factors behind the provincial URLI scores: per capita GDP (Pgdp), the ratio of non-agricultural production to total production (Nap); the ratio of exports and imports to total GDP (Openness), the ratio of people holding a junior college degree or higher educational qualification to total population (Edu), the length of highways per square kilometre (Highway) and the location of the province (L) (where L is the dummy variable indicating an eastern province if L equals one, and an inland province if L equals zero). These variables described the economic level, economic structure, international trade, education level of the population, infrastructure and location of provinces.

Generally, the spatial error model (SEM) and spatial lag model (SLM) are two important methods in which spatial interaction is modelled in spatial regression analysis. The SEM is expressed by equation (5):

Y = X[beta] + [epsilon], [epsilon] = [lambda][W.sub.[epsilon]] + [mu] (5)

where [beta] is the coefficient of the explanatory variable X; [epsilon] is the random error term; and [lambda] is the auto-regression parameter, which measures the spatial dependence. The spatial dependence means the influence direction and degree from the observed value Y of the adjacent provinces. W is the spatial weights matrix, [mu] is the random error term of normal distribution.

The SLM is expressed in equation (6) as:

y = [rho][W.sub.y] + X[beta] + [epsilon] (6)

where [rho] is the spatial auto-regression coefficient and [w.sub.y] estimates the spatial correlation degree of the model and adjusts the influence of other explanatory variables.

Table 6 presents the regression results of the driving factors to the URLI scores. Generally, economic structure, international trade and infrastructure were the significant factors with respect to a province's URLI score. The education level of the population and location of the province, however, were irrelevant to the provincial URLI scores. In 1990, only the Nap was found to be positive and significant at a 5 per cent level. Apart from location and education level, all the other factors presented positive significance with regard to the URLI scores in 2000. In 2010, the estimated coefficients of Nap, Openness and Highway were positive and significant at the 5 per cent level.

The findings showed that provincial URLI scores are highly related to the development of non-agricultural industries, international trade and highway density. With manufacturing as the main pillar of the economy, China's international trade can help to advance the development of non-agricultural industries, which will strengthen cooperation in industrial production between enterprises in the cities and TVEs in the countryside. Labour mobility, capital and material linkages between urban and rural areas can also be expected to strengthen in response to increased production cooperation. Highway density can contribute to increased accessibility to urban and rural markets, to rural materials and to the labour force. As shown in Table 7, the average Nap, Openness and Highway were higher in eastern China than in central and western China (both in 1990 and 2010). Correspondingly, the URLI scores of provinces in eastern China in 1990 and 2010 were also higher than those of the inland provinces.

CONCLUDING REMARKS

This article provides evidence of spatial-temporal contrasts in the levels of integrated urban-rural development at the provincial level in post-reform China. Generally, integrated urban-rural development in China became spatially clustered over the period of 1990 to 2010, a pattern that became more evident in the last decade. The eastern provinces possess a high level of integrated urban-rural development, followed by the central and western provinces. Such spatial-temporal contrasts in the level of integrated urban-rural development are attributed to the development of non-agricultural industries, international trade and highway density in each province.

The level of integrated urban-rural development was revealed through an assessment of urban-rural linkages in each province. The eastern provinces were found to have tighter urban-rural linkages than the inland provinces, indicating that the development of the urban and rural areas in the eastern provinces became highly interwoven and codependent in the post-reform era. Corresponding to the research assumption, the results also imply that balanced flows and agglomeration of resources between urban and rural areas in eastern China constituted resource linkages that shaped these two areas alike. Turning to the central and western provinces, large urban-rural disparities exist due to resource flows that predominantly benefit cities and disadvantage the countryside. Moreover, non-agricultural industries, international trade and infrastructure in the inland provinces are still lagging behind the eastern provinces. In particular, the out-migration of educated and skilled labourers to the cities, especially those in the eastern provinces, had an impact on the level of integrated urban-rural development in the inland provinces. Thus, the development of non-agricultural industries, international trade, and infrastructure construction in the inland provinces, as well as the coordination of resource flows between eastern and inland provinces, could contribute to reducing spatial-temporal contrasts in the levels of integrated urban-rural development in China.

While the research findings gave proof of the connection between the level of integrated urban-rural development at the provincial level and the flows and agglomeration of resources between cities and the countryside, there is still a lack of theoretical support for integrated urban-rural development clustering across the provinces.

ACKNOWLEDGEMENTS

This research was supported by two programmes of the National Natural Science Foundation of China (41130748, 41301190).

Li Yuheng (liyuheng@igsnrr.ac.cn) is Research Associate at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. He obtained his PhD in Urban and Regional Studies from the Royal Institute of Technology (KTH), Sweden. His research interests are urban-rural transformation and integrated urban and rural development.

Zhang Zhenghe (zhangzhenghe@cau.edu.cn) is Professor of Agricultural Economics at the College of Economics and Management in China Agricultural University, Beijing, China. He obtained his PhD in Agricultural Economics from China Agricultural University. His research interests include agricultural economics, regional agriculture in China.

Liu Yansui (liuys@igsnrr.ac.cn) is Professor of Land-Use Planning and Rural Development at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. He obtained his PhD in Physical Geography from Nanjing Normal University. His research interests include land-use management and urbanisation in China.

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(23) Ibid., pp. 25-125.

(24) Li Yuheng, "Resource Flows and the Decomposition of Regional Inequality in Beijing-Tianjin-Hebei Metropolitan Region, 1990-2004", Growth and Change 2 (2012): 335-57.

(25) Robert Potter, Tony Binns, David W. Smith and Jennifer Elliott, Geographies of Development (London: Pearson Prentice Hall, 2004), pp. 110-358.

(26) W. Christaller, Die zentralen Orte in Suddeutschland (The Central Places in Southern Germany) (Jena: Verlag von Gustav Fischer, 1933), pp. 30-123.

(27) John Friedmann, Regional Development Policy--A Case Study of Venezuela (Cambridge: MIT Press, 1966), pp. 34-210.

(28) Paul Krugman, "Increasing Returns and Economic Geography", Journal of Political Economy 3 (1991): 483-99.

(29) S. Redding and A.J. Venables, "Economic Geography and International Inequality", Journal of International Economics 1 (2004): 53-82.

(30) Lewis W. Arthur, "Economic Development with Unlimited Supplies of Labour", The Manchester School 2 (1954): 139-91.

(31) Theodore W. Schultz, "Reflections on Investment in Man", Journal of Political Economy 5 (1962): 1-8.

(32) Tacoli Cecilia, "Rural-Urban Interactions: A Guide to the Literature", Environment and Urbanization 1 (1998): 147-65.

(33) Lin George C.S., "Evolving Spatial Form of Urban-Rural Interaction in the Pearl River Delta, China", Professional Geographer 1 (2001): 56-70.

(34) L. Anselin, S. Sridharan and S. Gholston, "Using Exploratory Spatial Data Analysis to Leverage Social Indicator Databases: The Discovery of Interesting Patterns", Social Indicators Research 2 (2007): 287-309.

(35) L. Anselin, "Local Indicators of Spatial Association--LISA", Geographical Analysis 2 (1995): 93-115.

TABLE 1

Description of the Selected Variables

Selected variables                Mean             Std. Dev

                            1990     2010      1990     2010

Urbanisation level           0.31      0.49     0.17      0.15
Ratio of TVEs workers to     0.11      0.23     0.07      0.19
  total rural population
Ratio of number of TVEs     11.32     15.07     6.96     10.82
  per thousand people
Ratio of wage income to      0.09      0.39     0.13      0.13
  rural household income
Urban household food       792.48   4637.55   186.03   1074.18
  consumption

Selected variables                Min                  Max

                            1990      2010      1990      2010

Urbanisation level           0.140      0.24      0.77      0.89
Ratio of TVEs workers to     0.002      0.02      0.31      0.91
  total rural population
Ratio of number of TVEs      0.250      0.67     24.21     38.97
  per thousand people
Ratio of wage income to      0.003      0.12      0.53      0.69
  rural household income
Urban household food       540.000   3052.57   1267.00   7776.98
  consumption

TABLE 2

Rotated Component Matrix of Analysed Period (1978)

                                                   Factor loadings

Selected variables                               Factor 1   Factor 2

Urbanisation level                                  0.885     -0.068
Ratio of TVE workers to total rural population      0.908      0.284
Ratio of number of TVEs per thousand people        -0.217      0.966
Ratio of wage income to rural household income      0.826      0.178
Urban household food consumption                    0.900     -0.149
Initial eigenvalues                                 3.147      1.072
% of variance                                      62.935     21.439

TABLE 3

URLI Scores of Provinces in China, 1990

Province   Beijing     Tianjin        Hebei
Score      2.175       1.608          -0.094

Province   Jilin       Heilongjiang   Shanghai
Score      -0.804      -1.362         3.833

Province   Fujian      Jiangxi        Shandong
Score      0.839       0.193          0.116

Province   Guangdong   Guangxi        Hainan
Score      1.491       -0.484         -0.826

Province   Xizang      Shaanxi        Gansu
Score      -1.358      0.46           -1.061

Province   Shanxi    Neimenggu   Liaoning
Score      -0.655    -1.002      0.73

Province   Jiangsu   Zhejiang    Anhui
Score      1.103     1.79        -0.408

Province   Henan     Hubei       Hunan
Score      -1.09     -0.466      0.326

Province   Sichuan   Guizhou     Yunnan
Score      -0.421    -0.81       -0.831

Province   Qinghai   Ningxia     Xinjiang
Score      -1.052    -0.341      -1.533

TABLE 4

URLI Scores of Provinces in China, 2000

Province   Beijing        Tianjin    Hebei      Shanxi
Score      2.012          1.816      -0.173     -0.069

Province   Heilongjiang   Shanghai   Jiangsu    Zhejiang
Score      -0.435         4.402      0.456      0.97

Province   Shandong       Henan      Hubei      Hunan
Score      0.161          -0.778     -0.192     0.087

Province   Chongqing      Sichuan    Guizhou    Yunnan
Score      -0.459         -0.49      -0.948     -0.991

Province   Qinghai        Ningxia    Xinjiang
Score      -0.788         -0.312     -1.066

Province   Neimenggu   Liaoning   Jilin
Score      0.077       0.535      -0.006

Province   Anhui       Fujian     Jiangxi
Score      -0.585      0.387      -0.371

Province   Guangdong   Guangxi    Hainan
Score      0.607       -0.586     -1.015

Province   Xizang      Shaanxi    Gansu
Score      -1.42       -0.162     -0.662

Province
Score

TABLE 5

URLI Scores for China's Provinces, 2010

Province   Beijing        Tianjin    Hebei      Shanxi
Score      3.381          1.997      -0.68      -0.09

Province   Heilongjiang   Shanghai   Jiangsu    Zhejiang
Score      -0.226         1.423      0.624      0.461

Province   Shandong       Henan      Hubei      Hunan
Score      -0.049         -0.498     -0.213     -0.464

Province   Chongqing      Sichuan    Guizhou    Yunnan
Score      -1.294         -0.517     -0.524     -0.273

Province   Qinghai        Ningxia    Xinjiang
Score      -0.837         -0.598     -0.38

Province   Neimenggu   Liaoning   Jilin
Score      -0.474      0.582      2.081

Province   Anhui       Fujian     Jiangxi
Score      -0.567      -0.026     -0.775

Province   Guangdong   Guangxi    Hainan
Score      0.795       0.07       -0.933

Province   Xizang      Shaanxi    Gansu
Score      -1.237      -0.64      -0.119

Province
Score

TABLE 6
Parameter Estimation of LM Lag and LM Error Models, 1990-2010

Year                Cons.       Pgdp                     Nap

1990   Coef.        -6.11 ***   9.88 x [10.sup.-6]       5.50 **
       Std. Error    2.12       2.12 x [10.sup.-5]       2.56

                                Log L = -19.42, AIC = 52.84, SC = 62.88

2000   Coef.        -3.80 ***   2.41 x [10.sup.-3] ***   3.27 **
       Std. Error    1.12       6.21 x [10.sup.-5]       1.46

                                Log L = -13.51, AIC = 41.02, SC = 51.06

2010   Coef.        -6.11 ***   9.88 x [10.sup.-6]       5.50 **
       Std. Error    2.12       2.12 x [10.sup.-5]       2.56

                                Log L = -19.42, AIC = 52.84, SC = 62.88

Year                Openness   Edu     Highway   L      [R.sup.2]

1990   Coef.        0.05       45.66   0.36      0.14   0.86
       Std. Error   0.42       37.84   0.71      0.26

                    Log L = -19.42, AIC = 52.84, SC = 62.88

2000   Coef.        1.13 **    6.86    0.55 **   0.45   0.89
       Std. Error   0.46       14.01   0.27      0.29

                    Log L = -13.51, AIC = 41.02, SC = 51.06

2010   Coef.        1.33 **    7.60    0.56 **   0.45   0.86
       Std. Error   0.36       15.01   0.27      0.29

                    Log L = -19.42, AIC = 52.84, SC = 62.88

Notes: LM denotes Lagrange Multiplier; LogL denotes Log likelihood;
AIC denotes Akaike info criterion; SC denotes Schwarz criterion, ***,
** and * indicate the significance levels of 1%, 5% and 10%.

TABLE 7

Variations of Driving Factors in the Three Regions

Region           Year   URLI    Non-agricultural
                        score      industries

Eastern          1990    0.69         0.78
China            2010    1.16         0.92
  Annual increase (%)    5.36         1.66
Central          1990   -0.53         0.68
China            2010   -0.09         0.88
  Annual increase (%)   18.94         2.57
Western          1990   -0.71         0.66
China            2010   -0.57         0.87
  Annual increase (%)    2.14         2.86

Region           Year   International   Highway
                            trade       density

Eastern          1990       0.36          0.34
China            2010       0.69          1.12
  Annual increase (%)       6.56         12.62
Central          1990       0.07          0.21
China            2010       0.11          0.90
  Annual increase (%)       4.36         15.98
Western          1990       0.07          0.11
China            2010       0.10          0.46
  Annual increase (%)       4.17         15.29
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Title Annotation:COMMENTS & NOTES
Author:Li, Yuheng; Zhang, Zhenghe; Liu, Yansui
Publication:China: An International Journal
Article Type:Report
Geographic Code:9CHIN
Date:Dec 1, 2013
Words:6857
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