DEVELOPMENT OF URBAN INFRASTRUCTURES AND POPULATION CHANGE IN CHINA

Transcription

1 DEVELOPMENT OF URBAN INFRASTRUCTURES AND POPULATION CHANGE IN CHINA A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in Public Policy By Yuhan Nan, LL.B. Washington, D.C. April 14, 2018

2 Copyright 2018 by Yuhan Nan All Rights Reserved ii

3 DEVELOPMENT OF URBAN INFRASTRUCTURES AND POPULATION CHANGE IN CHINA Yuhan Nan, LL.B. Thesis Advisor: Andrews Kern, Ph.D. ABSTRACT The provision of urban infrastructure is crucial for the sustainable development of cities, and it has been proven that the growth of urban infrastructure should be in line with the population change in cities. However, evidences have shown that it is not the case in Chinese cities. Furthermore, there are disparities in the development of urban infrastructures across sector, and these disparities are also related the administrative level of a city. Using the data from China City Statistical Yearbook, this paper mainly finds two conclusions: (1) compared to other sectors, population condition (the size of household registered population) is more in line with the development of urban infrastructures that can improve the attractiveness of the cities to businesses and investments, and (2) population size and population density are more likely to be negatively associated with the development of urban infrastructures in second level cities. The findings of this paper suggest that local governments should emphasize the development of infrastructures of a more diverse range, and that second level cities need more attention in further development. iii

4 ACKNOWLEDGEMENTS The research and writing of this thesis is dedicated to everyone who helped along the way. Many thanks, Yuhan Nan iv

5 TABLE OF CONTENTS INTRODUCTION BACKGROUD: URBANIZAITON OF CHINA LITERATURE REVIEW: URBAN INFRASTRUCTURE IN CHINA AND ITS INTERACTION WITH POPULATION DYNAMICS CHALLENGES PUT FORWARD BY POPULATION GROWTH MISMATCH OF URBAN INFRASTRUCTURE AND DEMOGRAPHIC CONDITION CURRENT STUDIES ON URBAN INFRASTRUCTURE IN CHINA OUTCOME AND PERFORMANCE OF URBAN INFRASTRUCTURE INTERACTION BETWEEN URBAN INFRASTRUCTURE AND URBANIZATION HYPOTHESIS AND CONCEPTUAL FRAMEWORK EMPIRICAL ANALYSIS EMPIRICAL MODEL DESCRIPTIVE STATISTICS REGRESSION RESULTS ROBUSTNESS OF EMPIRICAL RESULTS CONCLUSION AND DISCUSSION POLICY IMPLICATIONS POTENTIAL LIMITATIONS APPENDIX BIBLIOGRAPHY v

6 INTRODUCTION China has witnessed a rapid urbanization process since its economic reforms in the 1990s (Yusuf, Nabeshima, 2008). This process has benefited from the national development policy that took urbanization as a primary driver of economic growth and absorbing surplus rural labor (Su and Zhao, 2017), among other factors such as industrialization, marketization, political reform, and globalization. China s urbanization is exceptional in terms of the expansion of urban lands, the increase of urban population, the upgrade of industry, and its extraordinary speed. Given the rapid urbanization rate, it is the time to consider the quality of urbanization, especially the coordination between the three aspects of urbanization: landscape urbanization, demographic urbanization, and industrial urbanization. Particularly, this paper focus on the coordination between landscape and demographic urbanization, and takes the development of urban infrastructure as a representative of the landscape urbanization because it is the foundation of living and economic activities. China s urbanization features a threefold mismatch between urban construction and population conditions: first, it is not uncommon that the rate of landscape urbanization out-speeds urban population growth in many cities (Wang, Hui, Chonguill, Jia, 2015), rendering them not dense enough to exploit the economies of scale and agglomeration (Henderson, 2009); second, the distribution of urban infrastructures may not be in line with the distribution of population; and third, China s urbanization process features a property-led pro-growth model (Vytautas Snieskaa and Ineta Zykieneb, 2015; Keith Head and John Ries, 1996), which incentivizes local government to emphasize more on the development of infrastructures that can improve the attractiveness of cities to businesses and investment, leading to a disparity across sectors of urban infrastructures. 1

7 These mismatches raise the necessity of further investigation into the relationship between the physical development and the population change in the background of China s urbanization. Therefore, the objective of this paper is to figure out to the extent to which the population condition is in line with the development of urban infrastructures across different sectors of infrastructure, and the difference across administrative level of cities. To achieve the objective stated, this paper will test three hypotheses. First, the development of urban infrastructures is positively associated with the population conditions but not significantly associated with the population density. The growth of urban population increases the demand for urban infrastructures and raises the financial capacity of local government to provide more infrastructures; therefore, it should have a positive influence on the development of urban infrastructures. On the other hand, evidences suggest that the development of urban infrastructures is not taking population density into sufficient consideration. Second, considering that local government have more likely to build transportation infrastructures to stimulate economic growth and increase government revenue from land leasing (Li, 2017), I expect that there are disparities across different sectors of infrastructures. Particularly, since literature shows that among other factors, the accessibility of the city, road infrastructure, labor force would influence the choice of location of investment and businesses (Snieskaa and Zykieneb, 2015), it is expected that infrastructures regarding transportation and education would be more well development compared to other sectors. Third, I expect that the effect of population condition on the development of urban infrastructures tends to be different across city level. This reflects the different urbanization stages of cities of different administrative level. First level cities are the most developed ones and at the 2

8 most advanced stage of urbanization, while cities of lower levels are still in the process of developing and at an earlier stage of urbanization. Therefore, cities of different levels may have disparate demands for infrastructures and capacity to provide them. Using data from China City Statistical Yearbook, this paper will adopt a fixed effect model to test the three hypotheses. This paper embraces a broader definition of urban infrastructure that is the physical structure for societal operation (Cheng and Lu, 2017) and the provision of basic services to industries and households (Li, 2017). Urban infrastructures are divided into four sectors: education, transportation, sustaining system (that provides necessities for living and economic activities), and cultural or social infrastructures. Population condition is captured by population size, density, and growth rate, with a distinguish between permanent population and household registered population. Control variables include economic condition, public finance, and city specific factors. The regression results indicate that all urban infrastructures except for sustaining system is significantly associated with the size of household registered population, but not significantly associated with population density and the size of permanent population. When the difference in city levels is considered, population size is positively associated with all urban infrastructures except for education infrastructures in first level cities, but is negatively associated with all urban infrastructures except for education infrastructures in second and lower levels of cities. Particularly, the strength of negative association is the largest in second level cities. Based on the findings, this paper suggests local governments emphasize a more diverse range of infrastructures, especially the social and cultural infrastructures, to make the cities more 3

9 livable. Also, the second level cities deserve more attention to develop a more balanced and sustainable portfolio of urban infrastructures. 1. BACKGROUND: URBANIZATION OF CHINA China has witnessed a rapid urbanization process since its economic reforms in the 1990s (Yusuf, Nabeshima, 2008). China s urbanization has benefited from the national development policy that took urbanization as a primary driver of economic growth and absorbing surplus rural labor (Su and Zhao, 2017), among other factors such as industrialization, marketization, political reform, and globalization. The definition of urbanization can be divided into three aspects: (1) landscape urbanization that features the expansion of urban land and built-up areas a, (2) industrial urbanization meaning the up-grade of industries from primary industries to secondary and tertiary industries, and (3) demographic urbanization, namely the increase of urban population and the change in life style. China s urbanization is outstanding in all three aspects. As for landscape urbanization, up to 2010, China expanded its urban lands at an average rate of 3.1% per year, and the absolute value of urban land was 23,600 square kilometers, which took up two-thirds of the total urban land in East Asia (World Bank, 2015). And the Pearl River Delta was honored as the largest single urban area in the world, in terms of both population and area (World Bank, 2015). China s urbanization is incomparable also in terms of demographic urbanization. From 1978 to 2013, urban population grew from 170 million to 730 million (Wang, Hui, Chonguill, Jia, 2015), and its proportion in total population grew from 19% to 54% from 1980 to 2014 (Li, 2017). The rapid increase in urban land a Bai, Chen, Shi (2012): Land conversion is one of the key process that characterizes urbanization. 4

10 and urban population makes cities in China a growth pole for economic development. The process of urbanization is accompanied with an increase of productivity in secondary and service industry. Employment in secondary industry grows from 70 million people to 225 million people from 1978 to 2011; and the employment in service industry (tertiary industry) increases from 49 million people to 273 million people during the same period (World Bank, 2011). China s urbanization is also exceptional for its extraordinary speed. It is predicted that the urbanization rate in China would exceed 60% in 2020, with the urban population increase by 200 million. This figure is high even compared to other countries under rapid urbanization in East Asia (Yusuf, Nabeshima, 2008). Given the rapid increase of urbanization rate, it is the time to start considering the quality of urbanization. To achieve a sustainable urban development, the three aspects of urbanization should be in line with each other, while it may not be the case in some Chinese cities. There are debates over whether China s urbanization is of the right speed compared to its industrialization, reflecting the controversies on the relation between industrial and landscape urbanziation (Mingxing Chen, Weidong Liu, Xiaoli Tao, 2012). The large number of immigrant workers and the urban-rural gap also indicate the disparity between demographic change and the other two aspects of urbanization. Particularly, this paper focuses on the delivery of urban infrastructure as representative of landscape urbanization because it is the foundation of living and economic activities. This paper embraces a broader definition of urban infrastructure that is the physical structure for societal operation (Cheng and Lu, 2017) and the provision of basic services to industries and households (Li, 2017). China s urbanization features a threefold mismatch between their urban construction and their population conditions. First, it is not uncommon that the rate of landscape urbanization 5

11 out-speeds urban population growth in many cities (Wang, Hui, Chonguill, Jia, 2015), rendering them not dense enough to exploit the economies of scale and agglomeration (Henderson, 2009). Many prefecture-level cities are only half of their efficient size (Henderson, 2009), but even in mega-cities of higher density, they are criticized as high-density sprawl, because of failing to capture the benefits (World Bank, 2015). This low-density expansion model increases the cost of infrastructure and public services, induces energy inefficiency and environmental problem (Bhatta B. 2010). Second, the distribution of urban infrastructures may not be in line with the distribution of population. In this situation, though the number of infrastructures may be in line with the population size, the infrastructures may underserve residents in some area in the city. Third, China s urbanization process features a property-led pro-growth model (Vytautas Snieskaa and Ineta Zykieneb, 2015; Keith Head and John Ries, 1996), which incentivizes local government to emphasize more on the development of infrastructures that can improve the attractiveness of cities to businesses and investment, leading to a disparity across sectors of urban infrastructures. This mismatch raises the necessity to further investigate the relationship between the physical development and the population change in the background of China s urbanization. Urbanization puts forward requirements for urban infrastructure development to accommodate the growth in population and economic activities (Su and Zhao, 2017). Sufficient and welldesigned infrastructure, in turn, benefits the urbanization process and ensures sustainable economic growth in the long-run. The facilitating function of urban infrastructures for economic growth comes from three mechanisms: first, urban infrastructure system, especially the transportation system, reduces the transaction cost and optimizes the distribution of economic activity (Li, 2017); second, the investment in and construction of infrastructure creates 6

12 employment (Tao Liu, Qi, Cao, Hui Liu, 2015); third, better urban infrastructures attract further investment in industries (Bai, Chen, and Shi, 2012), and improve productivity in the private sector (Li, 2017). Not only the economy, but also the well-being of urban residents is subject to the provision of urban infrastructures. For example, as shown by Yusuf (2008), the livability of large cities is subject to the development and design of the transport system. As for urbanization, infrastructures and the related services attract more people into urban areas, thus promoting demographic and landscape urbanization (Li, 2017). Acknowledging its importance in urbanization, this paper focuses on urban infrastructure and its coordination with urban population. 2. LITERATURE REVIEW: URBAN INFRASTRUCTURE IN CHINA AND ITS INTERACTION WITH POPULATION DYNAMICS Theoretically speaking, the development of urban infrastructure should be in harmony with both economic and population growth for the urbanization to be sustainable and efficient. However, as shown in numerous literature, it s not the case in Chinese cities, which causes challenges to further urban development (Liu, 2017; Gao, 2015; World Bank, 2015). Particularly, this paper focuses on the mismatch between urban infrastructure development and demographic urbanization, focusing on the coordination between urban infrastructure development and the growth of urban population. 7

13 2.1 CHALLENGES PUT FORWARD BY POPULATION GROWTH Although the expansion of urban land and the reclassification of some rural areas as cities played an important role in the early stages, urbanization in China is mainly driven by internal migration, namely the migration from rural to urban areas. Internal migration provides labor of lower cost for economic development (Hu, 2013) and facilitates the poverty reduction in sending areas (Heikkila, 2007). However, one thing special about the population dynamic of China is its hukou system (also known as household registration system), which regulates the movement of people by assigning every person a specific locality where they are supposed to live (Yusuf, Nabeshima, 2008). This system not only distinguishes urban from rural residents, but also limits the number of people moving into certain cities (Yusuf, Nabeshima, 2008). People who move from their registered locations to other places for better economic or education opportunities become a floating population. They are employed by urban industries but enjoy limited access to some of the infrastructures and public services. Up to 2014, there are about 253 million floating populations in China, which also raises challenges for the urban infrastructure development. b Despite of the growth population size, the overall population density in Chinese cities is decreasing. And in 2010, the population density in Chinese urban areas was 5,300 people per square kilo meter, which is lower than the average of East Asia (World Bank, 2015). This is partly because the urbanization in China takes a relatively expansive model, in which urbanization is b National Health and Family Planning Commission. 8

14 featured by expanding the size of urban area rather than increase the density within already constructed areas (Chen, Jia, Lau, 2008). 2.2 MISMATCH OF URBAN INFRASTRUCTURE AND DEMOGRAPHIC CONDITION The sustainable development of urban areas requires urban infrastructure to increase in line with its economic growth, landscape conversion, and population growth. However, Chinese urbanization is facing a mismatch in urban infrastructure and demographic condition in terms of population growth rate, population density, and distribution. This renders the supply side and the demand side of urban infrastructure not well-coordinated with each other. The provision of urban infrastructure in China is more supply-driven than demand-driven after 2008, during which period the central government used urban infrastructure investment as a tool to stabilize the economy (Li, 2017). In addition to that, since local governments take the responsibility of both urban infrastructure delivery and economic development, the former one is usually of lower priority (Liu, 2005). Even within the urban infrastructure development, there are disparities across sectors. Local governments have more incentive to invest in real estate development and constructions of physical infrastructure, because they are more profitable and can stimulate short-term GDP growth (Li, 2017; Koppenjian and Enserink, 2009). While insufficient provision of public services and infrastructures in large, affluent cities leads to congestion and the deterioration of urban environment (Yusuf, Nabeshima, 2008), some Chinese cities are over-supplying urban infrastructures compared to their population growth, which further exacerbates the urban expansion. It is reported that 62% of urban areas in China with 9

15 populations over 100,000 are experiencing decreasing population density due to the expansion of urban infrastructures; and about 50 smaller urban areas are constructing more roads and buildings even though their population has declined (Gao, 2015; World Bank, 2015). The loss in population density undermines the cost-efficiency of urban infrastructure development, and even creates ghost cities where the infrastructures remain idle with no residents nor economic activities there (Gao, 2015; World Bank, 2015). Furthermore, the urbanization in China is project-oriented and scattered without an integrated infrastructure system. This urbanization pattern prevents cities from achieving the optimal density to take advantage of agglomeration and increases the construction cost of new infrastructures (Chen, Jia, Lau, 2008). The mismatching also occurs in the urban infrastructure investment. Although the investment in urban infrastructure has been growing rapidly overtime, it remains relatively modest compared to the average investment of developing countries, in terms of the share of total national investment. c 2.3 CURRENT STUDIES ON URBAN INFRASTRUCTURE IN CHINA Urban infrastructure in China has long been studied in literature, most of which focuses on topics including finance system for urban infrastructure and the decentralization related to it, evaluation of outcomes and performance of urban infrastructure investment, and the interaction c According to a World Bank s report in 1994, developing countries averagely spend 50% of local government investment, 20% of total national investment, and 4% of GDP in urban infrastructure investment. Comparing to China, Shanghai spent 12.5% of total investment or 5.9% of regional GDP to urban infrastructure over the period of Ninth Five-Year Plan; the number of Beijing was 13.7% of total investment, and 6.3% of regional GDP. 10

16 between urban infrastructure with economic growth and urbanization. Although the unresponsiveness of urban infrastructure development to population indicators has long been noticed by researchers, the majority of them focus on the investment and finance, and consider both population and urban infrastructure as a whole. This paper enriches the existing literatures by trying to distinguish different sectors of urban infrastructure, especially the disparities between conventional infrastructures and social infrastructure. China has taken action to accelerate the development of urban infrastructure by increasing investment. During the 12 th Five-Year Plan (2011 to 2015), averagely 7 trillion yuan (1.03 trillion US dollars) was invested in urban infrastructure (Song, 2013); and 2015 alone witnessed an investment in urban infrastructure of more than 9 trillion CNY (Bai, Chen, Shi, 2012). Although efforts have been taken, the development of urban infrastructure in China still faces the problem of insufficient provision, disparities across regions and city levels, cost-inefficiency, financial shortage, and lack of design (Song, 2013) OUTCOME AND PERFORMANCE OF URBAN INFRASTRUCTURE Assessment of the outcome and performance of urban infrastructure is another hot topic related to urban infrastructure development. Researchers analyze the impact of urban infrastructure development, and evaluate the efficiency of urban infrastructure investment. There is mounting evidence that associates urban infrastructure development to economic growth, with casualty flowing in both directions. The World Bank analyzes the function of urban infrastructure by pointing out that improved road networks and public transit can lead to mixed 11

17 land use, which may contribute to the sustainability of urbanization (World Bank, 2015). Song (2013) reinforces that urban infrastructure development is linked with higher level of human settlement and can raise land value, which in turn increases the probability of landscape urbanization (Song, 2013). Researchers are also interested in the evaluation of investment efficiency of urban infrastructure development. Chen and Lu (2017) assesses the investment efficiency at the national level, drawing the conclusion that the technical efficiency of urban infrastructure investment declines nationally, but the overall efficiency increased due to the improved management skills of governments. Besides, due to the influence of changing economic conditions and national policies, the performance of urban infrastructure investment is unstable overtime (Cheng and Lu, 2017). Li (2017) compares the urban infrastructure growth with the urbanization rate in China and assesses the efficiency of urban infrastructure investment. It concludes that the investment in roads is less efficient compared to the US, and the inland regions are still facing under investment problems (Li, 2017) INTERACTION BETWEEN URBAN INFRASTRUCTURE AND URBANIZATION There is abundant literature that focuses on the interaction between urban infrastructure development and other aspects of urbanization, especially its interaction with demographic conditions. Although researchers have proven the positive influence of population density on the availability and efficiency of urban infrastructure, the responsiveness of urban infrastructure 12

18 investment to population growth is not significant. Per capita investment in urban infrastructure is actually decreasing with the increase of population density, indicating that urban infrastructure development is determined by other factors like financial capacity or local development strategy instead of the demand of population (Chen, Jia, Lau, 2008). Cheng and Lu (2017) conducts a factor analysis (FA) and a STIRPAT (Stochastic Impacts by Regression on Population, Affluent and Technology) analysis to figure out the critical influencing factors of urban infrastructure development. They find that at the local level, urban infrastructure development is mainly influenced by income, industrialization, and investment of local government; while at the national level, urban infrastructure is mainly influenced by national public investment (Cheng and Lu, 2017). In either case, population indicators are not emphasized by local government when determining the investment in and location of urban infrastructure. 3. HYPOTHESIS AND CONCEPTUAL FRAMEWORK This paper will examine the coordination between urban infrastructure and demographic conditions, especially the disparities across different sectors of urban infrastructures. It is also expected that region and city s level will exert an influence on the result. The interaction between urban population and urban infrastructure development is presented in Figure 1, where the population is measured in three aspects: the total size of urban population, population density, and population growth rate. On the other side, urban infrastructure is measured by indicators provided in China City Statistical Yearbook. Population growth drives the development of urban infrastructure by (1) increasing living and economic activities thus increasing the demand for urban infrastructures, (2) enhancing cities capacity of developing urban 13

19 infrastructures through increased labor force and enlarged tax base, and (3) reducing the cost of urban infrastructure per person due to higher density and economy of scale. Therefore, a positive association between urban infrastructure and population growth is expected. However, since the urban development in China is more supply-driven after 2008 (Li, 2017), I expect that the growth of urban infrastructure is not in line with the growth of urban population, especially in terms of population density. Evidences suggest that the development of urban infrastructures is not taking population density into sufficient consideration. Some cities are constructing infrastructures ignoring their declining population density, giving raise to the occurrence of ghost cities as mentioned before (Gao, 2015). Even some highly developed cities share the problem. Taking Shanghai as an example, its urban area increased by 117% from 2000 to 2010, while the population grew only 73% (Gao, 2015). Although the expansion of urban land cannot be equalized to the growth of urban infrastructure, this trend suggests the possibility of higher urban infrastructures per capita compared to population growth rate. On the other hand, some cities fail to provide sufficient infrastructures to accommodate their increasing population, causing congestion problem. As pointed out by Liu (2005), the growth of urban population usually exceeds the planned population target in some cities, making the demand for urban infrastructures and public services increases faster than predicted and planned. These phenomena lead to Hypothesis 1 that urban infrastructure indicators are not significantly associated with the population density. Related to the mismatch between urban infrastructures and population density comes Hypothesis 2 that there are disparities across different sectors of infrastructures. Li s (2017) report shows that local government have more likely to build transportation infrastructures to stimulate 14

20 economic growth and increase government revenue from land leasing. This trend may result from local governments emphasis on economic development, generating a property-led, pro-growth urbanization model in Chinese cities. Literature shows that among other factors, the accessibility of the city, road infrastructure, labor force would influence the choice of location of investment and businesses (Snieskaa and Zykieneb, 2015). Therefore, it is expected that infrastructures regarding transportation and education would be more responsive to population condition. Hypothesis 3 is that the effect of population condition on the development of urban infrastructures tends to be different across city level. As explained before, the economic return of different types of urban infrastructures varies across region and city level. For example, research has found out that infrastructures like telecommunications can promote local GDP in affluent eastern cities, but not in cities in western region; while the investment in conventional infrastructures like roads tends to gain better economic return in western regions (Li, 2017). Acknowledging that the development of urban infrastructures would be determined by a wide range of factors, this paper will take into account city specific factors that may influence the development of urban infrastructure as control variables, including government finance indicators, economic conditions, and the characteristics of cities. 15

21 Figure 1: Conceptual Frameworks and Operationalization 4. EMPIRICAL ANALYSIS This paper uses the data from China City Statistical Yearbook, supplemented by data from China City Construction Statistical Yearbook. China City Statistical Yearbook is issued annually by the National Bureau of Statistics of the People s Republic of China, recording the data related to the social and economic development of all cities in China. Aspects of information include population, economic conditions, industry, transportation, post and telecommunications, investment in fixed assets, education, culture, living conditions, social security, public utilities, and environment. d Considered that prefectural level cities and county level cities are using different indicator systems since 1997, and the fact that prefectural cities are overall more developed in urban infrastructures, this paper only uses that data from prefectural and upper levels of cities. d Source: 16

22 This part is developed as following. First, this part introduces the fixed-effect model that is adopted to analyze the data. Second, it presents the dependent and central independent variables and their measurement. Third, the results of regression will be presented and interpreted. Finally, the fourth part will be the robustness checks and the summary of findings. 4.1 EMPIRICAL MODEL This paper will estimate a fixed effect model for the development of urban infrastructures in each city. The equation is given as following: UI it = β 0 + β 1 population it + β 2 GRP it + β 3 GRP per capita it + β 4 GRP growth rate it + β 5 public expenditure it + β 6 year t + β 7 city level it + α i + μ i Where α i is the unobserved characteristics for each city that does not change over time (city fixed effect), and μ i is the error term. Variable population it is the population condition of a city in a given year, which is captured by either household registered population or permanent population. GRP it is the Gross Regional Product of a city in a given year, representing the economic condition of the city. GRP per capita it and GRP growth rate it also capture the economic condition of a city in a given year from the perspective of living condition and the stage of development. Variable public expenditure it is the public finance indicator that captures the public expenditures of a city in a given year. Variable city level it indicated the level of cities, containing five levels and reflecting the stage of development of cities. 17

23 4.2 DESCRIPTIVE STATISTICS This paper uses data from China City Statistic Yearbook, from the year 2004 through China s city system at prefectural level experienced radical changes from 1997 to 2003, featured by an increasing number of prefectural level cities at a rate of about 10 cities per year and modifications of city level. e Taking account of this radical change, and the fact that the measurement and scope of statistic indicators was not sufficiently stable until 2004, only data after 2004 is included in this paper to keep consistence of city numbers and variables. Four categories of variables will be discussed in this part: population indicators including population size, population growth rate, and population density; city economic indicators including Gross Regional Product (GRP), GRP growth rate, and per capita GRP; public finance indicators including public income and public expenditure; and indicators for infrastructures across section. Detailed descriptive statistics are reported in Appendix 1. This part highlights the distributions and characteristics of primary variables. Only data from districts under cities are included in the discussion because of two reasons. First, considering that the data regarding urban infrastructures and utilities only covers the district under cities, it would be more consistent to discuss other variables in the same scope. Second, total city also includes some counties under the jurisdiction of cities, which should be excluded from the discussion of urban infrastructure because it is an administrative concept rather than the actual urban areas. Also, cities of province Tibet are not included because there are large amounts of e Policy Research Center, Ministry of Civil Affairs of the PRC: 18

24 missing data. The city of Sansha and Tibet province are also excluded because of the incomplete data collection. The average household registered population of districts under cities is about 1.55 million, but the distribution of population is not even across cities, which is reflected by the large standard deviation. Chongqing has the largest registered population, with a size of about million; while Lijiang has the smallest population size of 150,000 thousand inhabitants. The average population natural growth rate of district under cities is 6.79%, and cities polarizes in this respect. There are 38 cities with negative population growth rate, while the population growth rate of Anshun has reached 27.75%. Variation across cities also occurs in terms of population density. The average of population density in total area of cities is about 840 persons per square kilometer. Heihe has the lowest population density of persons per square kilometer, while Xuchang owns the highest population density of persons per square kilometer. The gross regional production (GRP) indicates the economic condition of cities, while GRP per capita reflects the standard of living. Chinese cities have an average GRP of 1482 billion yuan, with a high standard deviation of 3032 billion yuan. The average per capita GRP is 62, yuan, and the disparity across cities is also large. The variation across cities is also reflected in the growth rate of GRP. On average cities have a GRP growth rate of 7.54%, however, economic growth does not happen in every city. Cities like Chaoyang experienced a negative growth rate of -9.98, while the city of Anshun has a growth rate as high as 15.3%. Public finance is the foundation of the development of urban infrastructures, which reflects both the capacity and the willingness of cities to invest in urban infrastructures. In general, public 19

25 expenditures excesses public income in Chinese cities. The average public income of cities is billion yuan, and the average public expenditure is billion yuan. As part of the soft power, cities average expenditure on technology and education is 7.53 billion yuan and billion yuan respectively. Cities have approximately 155 units of primary schools, and the standard deviation is about units. The variation in number of schools can be attribute to either the difference in economic condition and financial capacity, or the difference in demand. The total collection in public libraries and the collections per 100 persons have huge standard deviations. On average, cities have a total collection of approximately 2 million copies/pieces, but the standard deviation reaches about 6.5 million copies. The average collections per 100 persons is copies/pieces, with a high standard deviation of copies/pieces. In summary, the large standard deviations show that there are huge disparities across cities in the development of urban infrastructures. part of the variations can be explained by the difference in demand for infrastructures, and the difference in economic and public financial conditions of cities. While population living in cities is a main factor that generates the demand for urban infrastructures, it is not always the most important determinant for the provision of infrastructures in cities. Therefore, it would be beneficial to analyze that on what extent is the development of urban infrastructures in line with the population conditions in cities, as well as the disparity across city level. 20

26 4.3 REGRESSION RESULTS This paper uses a fixed-effect model to analyze the relationship between urban infrastructures of different sectors and population indicator. Economic conditions (GRP, GRP growth rate, per capita GRP), public finance conditions (public expenditure) and time variable (year) are controlled. Considering that the indicators of infrastructures like transportation infrastructures and supply of water and energy only includes district under cities, this paper focuses of the data from districted under cities for all infrastructures to make the result consistent. Moreover, because of the large standard deviation in most of the variables, population size, GRP, per capita GRP, public expenditure, number of primary schools, collections of public libraries per 100 persons, number of beds in hospitals and health centers, gas supply, per capita area of paved road, and the number of public transport vehicles per 10,000 population are logged. Table 1 Model 1: Urban Infrastructures with Population Size (Household Registered Population), Baseline Variables Population (log) GRP(log) Size Per Capita GRP (log) Number of Primary Schools Collections of Public Libraries Per 100 Persons Number of Beds in Hospitals and Health Centers Gas Supply Per Capita Area of Paved Road Number of public transport vehicles per 10,000 population 1.067*** *** 0.216*** *** *** (0.108) (0.140) (0.0473) (0.241) (0.111) (0.0952) *** *** 0.207*** (0.0833) (0.119) (0.0446) (0.233) (0.0874) (0.0664) ** (0.0467) (0.0266) (0.0143) (0.0456) (0.0450) (0.0154) ** ( ) ( ) ( ) ( ) ( ) ( ) *** 0.111*** 0.318*** 0.116*** (0.0307) (0.0888) (0.0301) (0.104) (0.0371) (0.0350) GRP Growth Rate *** Public Expenditure (log) Year 21

27 Constant 2.157** 5.202*** 3.369*** *** 2.670*** (0.975) (1.641) (0.479) (2.931) (1.053) (0.793) Observations 3,388 3,364 3,390 2,885 3,372 3,106 Number of city R-squared Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Model 1 is the baseline model of regression, in which only time and city fixed effect are included, and population size is the major population indicator. Population size is measured by household registered population because of the data completeness. As presented in Table 1, the population indicator is significant in all urban infrastructures except for gas supply, but the direction varies. For the number of primary schools, the coefficient on population size is positive, indicating that one percent increase in population size is associated with 1.07% increase in the number of primary schools in the urban area. On the other hand, population size is negatively associated with the collections of public libraries per 100 persons, indicating that the growth in total collections of public libraries is not as high as the growth in population size. The result in the third column shows that one percent increase in population size is associated with 0.22% increase in the number of beds in health-related institutions. In terms of transportation system, one percent increase in population size is associated with 0.76% decrease in the per capita area of paved road, and 0.88% decrease in the number of public transport vehicles per 10,000 population. This is reasonable because larger population within a city would lead to more competition in the access with resources, but it also indicates that the growth in transportation infrastructures is not in line with the increase in population size. As for the control variables, GRP is positive and significant associated with the number of beds in health-related institutions, per capita area of paved road, and the number of public transport 22

28 vehicles. The coefficient on per capita GRP, however, is significant at 95% confidence level only with the gas supply. GRP growth rate has significant coefficients on the number of beds in healthrelated institutions and the per capita area of paved road, but the absolute value is relatively small. This result indicates that the economic conditions of cities is the foundation of urban infrastructures because cities with higher GRP are more capable to invest in infrastructures. But the development of urban infrastructures does not necessarily reflect the growth rate or the standard of living. It is not surprising that public expenditure has positive significant coefficients on most of the dependent variables, except for number of primary schools and the number of public transport vehicles per 10,000 population. Due to the decentralized financial system and the concentration of resources in cities of a higher level, I expect that the relationship between urban infrastructures and urban population to be different across city level. Therefore, Model 2 is developed to examine the effects of population size on the development of urban infrastructures in cities of different city level. In consideration that the classification of city levels in China has changed a lot over time, this paper uses the classification announced in 2015 to avoid inconsistency. Current city system of China divided cities into five categories. Beijing, Tianjin, Shanghai, and Shenzhen are categorized as first-class cities, and Shenzhen was added into this category in There are 31 cities that are classified as second-class cities. The number of third-class cities fluctuated between 54 and 55 for years, and settled at a number of 57 after Fourth-class cities contain more than 90 cities, and the number of fifth-class cities changes around 100. Since the regression results of third- fourth- and fifth-class cities are quite similar, these three categories are combined together. 23

29 When the difference across city levels is considered, as shown in Table 2, population size is positive and significantly related to all urban infrastructures except for the number of primary schools in first-class cities. It may indicate that the provision of infrastructures is more in line with the population conditions in first-class cities. However, the coefficients on population size are negative for second-, third-or-below class cities. Exceptions include the coefficient of population size on number of primary schools for second-level cities is not significant, while its coefficient for third- and below class cities is positive and significant. This result further confirms the disparity across city levels in the provision of urban infrastructures. 24

30 25 Table 2 Model 4: Urban Infrastructures with Population Size (Household Registered Population), Across City Levels Collections of Public libraries per 100 Number of Beds in Hospitals and Health Number of Public Vehicles per 10,000 Number of Primary Schools Per Cpita Area of Paved Road persons Centers Population City Level L1 L2 L3 L1 L2 L3 L1 L2 L3 L1 L2 L3 L1 L2 L3 Population Size GRP (log) Per Capita GRP (log) * 1.124*** 1.663*** *** *** 0.661*** *** 1.954** *** *** *** *** (0.452) (0.325) (0.104) (0.294) (0.184) (0.155) (0.0550) (0.121) (0.0469) (0.617) (0.185) (0.110) (0.161) (0.188) (0.0982) * *** 0.419*** 0.161*** *** * 0.200*** (0.833) (0.191) (0.0829) (0.226) (0.300) (0.126) (0.0401) (0.0922) (0.0464) (1.116) (0.209) (0.0823) (0.225) (0.182) (0.0689) *** (0.388) (0.0871) (0.0383) (0.164) (0.190) (0.0221) (0.0747) (0.0528) (0.0128) (0.306) (0.112) (0.0304) (0.0787) (0.0814) (0.0123) GRP Growth Rate * e *** *** (0.0186) ( ) ( ) ( ) (0.0145) ( ) ( ) ( ) ( ) (0.0226) ( ) ( ) (0.0238) ( ) ( ) *** *** * (0.255) (0.0840) (0.0316) (0.208) (0.317) (0.0935) (0.0665) (0.0742) (0.0318) (0.323) (0.141) (0.0336) (0.173) (0.0986) (0.0366) Year Public Expenditure Constant ** ** 11.88** 4.481*** 4.177*** 5.006** 3.230*** *** 4.502*** ** 5.661** 2.443*** (15.99) (2.924) (0.994) (2.151) (4.614) (1.711) (0.103) (2.029) (0.486) (14.62) (2.731) (1.008) (1.901) (2.558) (0.814) Observations , , , , ,729 Number of city R-squared

31 4.4 ROBUSTNESS OF EMPIRICAL RESULTS The provision of urban infrastructure is subject to several additional factors; therefore, a robustness check is necessary. Various models have been tested, and they generate similar results, indicating the robustness of the empirical results is confirmed. In this robustness check model, the independent variables are lagged in the consideration that the current level of urban infrastructures is largely depend on their previous development. For example, cities that have already got well developed paved roads are more likely to have more paved roads in the following year. Table 3 shows the regression results where all right-hand side variables are lagged. This model generates similar results compared to Model 1, indicating that the baseline model is sufficiently robust. 26

32 Table 3 Model 3: Urban Infrastructures with Population Size (Household Registered Population), Right-Hand Variabled Lagged Variables Population Size GRP (log) Per Capita GRP (log) GRP Growth Rate Public Expenditure Acknowledging that the development of urban infrastructures is subject to complicated factors, different models are estimated in this paper, and the results are presented in appendixes. Model 3 uses population density as the major indicator of population condition, whose result is reported in Appendix 2. The result shows that the coefficients of population density are not significant on all urban infrastructures except for the number of primary schools, further confirming that the development of urban infrastructures are not in line with the distribution of urban population or the population density into consideration. Model 4 uses the population growth rate as the major population indicator, and the result is reported in Appendix 3. The coefficients of population growth rate are small in absolute value, indicating that the effect of population growth is relatively small on the development of urban infrastructures. Number of Primary Schools Collections of Public Libraries Per 100 Persons Number of Beds in Hospitals and Health Centers 27 Gas Supply Per Capita Area of Paved Road Number of public transport vehicles per 10,000 population 0.803*** *** 0.133*** *** *** (0.143) (0.0500) (0.204) (0.106) (0.0953) *** *** 0.189*** (0.0857) (0.135) (0.0412) (0.251) (0.0722) (0.0604) *** (0.0497) (0.0301) ( ) (0.0466) (0.0106) (0.0152) *** ** ( ) ( ) ( ) ( ) ( ) ( ) *** *** (0.0369) (0.0826) (0.0262) (0.114) (0.0294) (0.0379) Year Constant 2.893*** 4.309** 4.881*** ** (1.080) (1.847) (0.515) (2.934) (1.010) (0.799) Observations 3,089 3,067 3,091 2,691 3,076 3,089 Number of city R-squared Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

33 In consideration that the household registered population may not necessarily reflect the actual population condition in China due to the large number of internal migrants, this paper also uses permanent population in each city as the major independent variable in Model 5. The result is reported in Appendix 4. The coefficients of permanent population are of the same direction compared to the baseline model, but the absolute value is smaller. The result is not stable because of the incomplete data on permanent population and the large number of missing values, therefore it is not included in the baseline data. 5. CONCLUSION AND DISCUSSION By analyzing the data on urban infrastructures and population conditions of Chinese cities at prefectural level, this paper finds the disparity across different sectors of urban infrastructures. Compared to other sectors, education and transportation related infrastructures are more in line with the population size for they can improve the attractiveness of cities to businesses and investments. The development of urban infrastructures does not take sufficient consideration of population density, population growth rate, and permanent population. Furthermore, the second level cities require more attention to develop a more balanced portfolio of urban infrastructures. 5.1 POLICY IMPLICATIONS This study confirms the literature that local governments take effort in developing infrastructures regarding transportation and education to increase the attractiveness of cities to businesses or investment. Thus, my findings are in line with a property-led pro-growth urbanization process of Chinese cities. While this model provides incentives to local governments to improve the conditions of these infrastructures, it also incentivizes local government to 28