Housing-choice hindrances and urban spatial structure: Evidence from matched location and location-preference data in Chinese cities

Journal of Urban Economics 60 (2006) 535–557 www.elsevier.com/locate/jue

Housing-choice hindrances and urban spatial structure: Evidence from matched location and location-preference data in Chinese cities Siqi Zheng a , Yuming Fu b,∗ , Hongyu Liu a a Institute of Real Estate Studies, Tsinghua University, Beijing 100084, China b Department of Real Estate, National University of Singapore, 4 Architecture Drive, Singapore, 117566

Received 13 March 2005; revised 8 May 2006 Available online 3 July 2006

Abstract In a monocentric city with a well-functioning residential market, Pareto-efficient spatial equilibrium entails the sorting of residents according to their bid–rent gradient in descending order away from city center. Violation of this sorting condition creates opportunities for Pareto-improving trading of locations and can be sustained only if the market is hindered. We propose a simple ordered-location-choice model using matched location and location-preference data of individual households to examine violations of the Pareto-efficient spatial sorting condition. In so doing we are able to identify population groups facing housing-choice hindrances. We find in a sample of Chinese cities undergoing housing market liberalization that poor marketability of the previously state-provided homes, inadequate provision of housing finance, and spatial mismatch between job-market and housing-market opportunities contribute to a Pareto-inefficient spatial structure. © 2006 Elsevier Inc. All rights reserved. Keywords: Bid–rent gradient; Urban spatial structure; Location choice; Housing markets; Transition economies

* Corresponding author. Fax: (65) 6774 8684.

E-mail addresses: [email protected] (S.Q. Zheng), [email protected] (Y.M. Fu), [email protected] (H.Y. Liu). 0094-1190/$ – see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.jue.2006.05.003

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1. Introduction Understanding the location choice of households and the consequent spatial structure in cities has always been an important focus of urban economics (excellent recent surveys on this topic include Mieszkowski and Mills [24], Mills and Lubuele [26], Anas, Arnott and Small [1], and Glaeser and Kahn [18]).1 The monocentric city model of Alonso, Mills and Muth (AMM) laid the foundation for urban spatial analysis four decades ago. The AMM model captures the fundamental tradeoff in residential location choice between central-city oriented commuting cost and housing consumption (see Fujita [13]). To this, the recent contribution by Brueckner, Thisse and Zenou [7] adds the demand for urban amenities, whose location pattern is in part determined by the urban history. A related body of literature on local public finance examines the location choice of households across communities within metropolitan areas, focusing on the tradeoff between housing cost and local public good provision (e.g. Epple, Filimon and Romer [12]). Empirical studies of the residential location choice along both lines generally assume an unhindered housing market so that the spatial equilibrium reveals the location preferences of the households (e.g. Quigley [33], Gabriel and Rosenthal [17], Nechyba and Strauss [28], and Bayer, McMillan and Rueben [3]). However, the housing market can be hindered even in developed market economies. Fiscal zoning, for example, restricts the types of home available in suburban communities (e.g. Gyourko [21]), credit rationing prevents liquidity-constrained households from attaining their long-term optimal housing choices (e.g. Rosenthal, Duca and Gabriel [34]), and rent control discourages residential mobility (e.g. Ault, Jackson and Saba [2]); these housing-market hindrances can prevent the spatial equilibrium from fully reflecting the location preferences of the urban residents. In countries undergoing transition from a planned economy to a market economy, housing market can be hindered further by deficient private property rights in privatized homes. In this paper, we depart from the extant empirical studies of residential location by incorporating stated location preferences into a location choice model. In so doing we seek to separate the determinants of location preferences from the effects on location choices due to possible hindrances to individual housing choices. Our analysis is built on the basic spatial equilibrium principle of the AMM model. Consider a monocentric city with an unhindered housing market. Residents compete for space at different locations according to their willingness-to-pay for these locations, i.e. their bid–rent curves. In equilibrium, the location of different population groups in the city must be sorted according to the gradient of their bid–rent curves; those with a higher gradient (a steeper bid–rent curve) locate nearer to the city center (see Fujita [13, Chapter 2]).2 This spatial sorting condition must hold in the housing market in equilibrium because at the boundary of the different residential zones, where the bid rents of the competing population groups equalize, the group with a higher bid– rent gradient will out bid the other group for the more central location. Once the spatial order of the population groups is determined by the sorting condition, the types of homes (and hence the residential density) within each zone will be determined by the housing demand of the population group in the zone. 1 Mills [25] defines urban spatial structure as the location pattern of various sectors within urban areas. 2 This result applies also when capital can be used to substitute for land in producing housing space. When the capital-

to-land ratio is chosen optimally to maximize the land value, the gradient of the land bid–rent curve is increasing in the capital-to-land ratio, which in turn is increasing in the bid rent for space (see, for example, Fu and Somerville [15]). Note also that the comparison of the bid–rent gradients is independent of the size of housing consumption; it is the bid rent per unit of space that matters.

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This spatial sorting condition has been the corner stone of urban spatial structure analysis. Wheaton [37], for example, examines the income elasticity of demand for land relative to that of the value of commuting time—a higher value for the former implies a declining bid–rent gradient with income but a higher value for the latter, a rising bid–rent gradient with income. He finds that the two elasticity coefficients are too close for income to have any significant effect on the bid–rent gradient and hence to account for the postwar suburbanization of the rich in the US. More recently, Glaeser, Kahn and Rappaport [19] show that the different commuting technologies available to different income groups can account for the differences in their bid–rent gradients, which support the central-city versus suburban segregation between the poor and the rich. Whereas the poor rely on the cheaper but more time-consuming public transit services available mostly in central cities, the rich can afford the more expensive but time-efficient automobile-based commuting and hence have a relatively lower bid–rent gradient. We examine the spatial structure in transition-economy cities, focusing on the spatial impact of housing-market hindrances. For this purpose, we propose a simple ordered-location-choice model derived from the spatial sorting principle of the AMM model. Our method is based on a simple idea. Absent hindrances to housing choices, the individuals’ bid–rent gradient alone would determine their relative locations in the city according to the AMM spatial sorting condition. The resulting AMM spatial equilibrium is Pareto efficient. Violation of this sorting condition creates opportunities for Pareto-improving trading of locations and the violation can be sustained only if the housing market is hindered. Thus, the hindrance to housing choice by different population groups in a city can be identified by comparing their actual residential locations against the locations predicted by their bid–rent gradient according to the AMM spatial sorting condition. We demonstrate our method by employing a dataset of individual locations and location preferences in a sample of Chinese cities. We present a threefold analysis. First, we describe the spatial structure of the Chinese cities as reflected by our household location data. Second, we examine how the location preference in terms of bid–rent gradient varies across individuals. Third, combining the location and location-preference data, we investigate the extent to which the location of various population groups in these cities violates the AMM spatial sorting condition. We find systematic violations indicative of hindrances to housing choices due to deficient property rights in newly privatized homes, inadequate access to housing finance, and the supply of affordable homes spatially mismatched with job-market opportunities. Our empirical findings add to the understanding of residential market transition in the formerly centrally planned economies. Extant studies on transition-economy urban spatial structure largely focus on the impact of housing market liberalization on residential density gradients and land price gradients (e.g. Bertaud and Renaud [4] and Dale-Johnson and Brzeski [10]; Bertaud and Malpezzi [5] surveys this literature). In contrast, we attempt to separate the influence of the market force (i.e. the spatial sorting incentives) versus that of the housing-market hindrances on urban spatial structure. Compared with the studies on “wasteful” commuting (e.g. Hamilton [22], and Small and Song [36]), which also examine the spatial sorting of residents among metropolitan areas, our analysis provides clearer policy implications. The “wasteful” commuting studies focus on the validity of the assumptions underlying the spatial equilibrium model that predicts the necessary amount of aggregate commuting. Violation of the necessary-commuting condition (the presence of “wasteful” commuting) refutes the validity of the spatial equilibrium model. Our analysis, in contrast, aims to identify violations against the Pareto-efficient spatial sorting. The evidence of such violation would suggest possible improvements in the land-use efficiency and the welfare of the residents when housing-market hindrances are removed.

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In the next five sections we first provide the institutional background for our empirical analysis in Section 2, where we briefly review the development of the urban form and housing reform in China and highlight the presence of various hindrances to housing choices that may impact on urban spatial structure. In Section 3, we describe our ordered-location-choice model. The basic ordered-choice model derived from the AMM spatial sorting condition is extended to allow for violations specific to different population groups due to various housing-market hindrances. We discuss the data and the variables in Section 4 and, in Section 5, we document the urban spatial structure and individual location preferences in our sample of Chinese cities and examine the impact of housing-market hindrances on urban spatial structure. We conclude in Section 6 and discuss the policy implications. 2. The emerging urban land market and housing reform in China After forty years of a planned economy, China reinstated the urban land market in the early 1990s as it opened its economy more fully to foreign direct investment. The real estate market took off, and massive land redevelopment took place in many Chinese cities, especially in the coastal regions where the economic growth was remarkably strong. Before the reinstatement of the urban land market, Chinese cities were typically very compact with a predominantly mixed pattern of residential and non-residential land uses (see Sit [35]). The majority of the urban workforce would commute by foot or bicycle to work near their home. Such a land-use pattern, although largely an outcome of central planning, would be consistent with the prediction of the endogenous urban spatial model of Ogawa and Fujita [31] and Lucas and Rossi-Hansberg [23] under the conditions of a high commuting cost and low external economies in production. Such conditions indeed apply to Chinese cities during the planned economy. However, the old central areas of Chinese cities were always the commercial, cultural and administrative hub, even during the period of the planned economy.3 With massive investment in urban transport infrastructure and the rapid growth of the service sector in Chinese cities since the beginning of the 1990s, a more specialized land-use pattern has emerged: the central business district (CBD) has greatly expanded and the residential land use has been extended into suburbs, whereas industrial land use has been pushed towards the outlying urban locations. At the same time, land-rent profiles sloping down from the traditional city centers also emerged in the land market, reflecting the importance of the old central area for business and consumption.4 This new urban form is consistent with the prediction of the endogenous urban spatial model under the conditions of a relatively low commuting cost and large external economies in production. These conditions now generally characterize the new urban economy of Chinese cities, where commuting trips increasingly rely on rapid rail transit and private automobiles and where personal networking and face-to-face communications become crucial for private enterprises. This remarkable transformation of urban form at the macro-level demonstrates the powerful market force at work in the emerging land market in Chinese cities. Before 1980s, urban housing in China was allocated to urban residents as a welfare good by their employer (the work unit) through the central planning system. Workers enjoyed different levels of housing welfare according to their office ranking, occupational status, working experience and other merits. Governments and work units were responsible for housing construction 3 This is reflected by the monocentric population density profiles in Chinese cities that decay quickly from the city center, as documented in Bertaud and Malpezzi [5]. 4 Fu et al. [14] examine the urban form and redevelopment pattern in Shanghai during the early 1990s.

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and residential land was allocated through central planning. Workers would also obtain services, such as child care, child education and family health care, from their work unit. Larger and more prominent work units would provide better housing and other welfare. Residents had little opportunity to choose their residential location according to their income and other household characteristics in the absence of a housing market. Since 1980s, most of the work-unit housing units have been privatized. By the end of the 1990s, housing procurement by work units for their employees had officially ended and new homes would be built and sold in the market.5 Developable land was supplied and regulated by the government through long-term leases.6 Large amounts of old homes in central urban areas were demolished to make way for new transport infrastructure, commercial developments and up-market housing projects. Urban built-up areas were quickly expanded and new mass housing projects (including economy-housing projects built on subsidized land) were largely built around the fast expanding urban fringes. Despite the remarkable change in urban forms and the increasing liberalization of the housing and urban land markets in the 1990s, there remained hindrances to individual housing choices in the housing market preventing the Pareto-efficient spatial sorting in Chinese cities. In particular, resale home market was slow in development, housing finance was limited, and the supply of affordable housing was skewed towards urban fringes. We give some stylized facts regarding these housing-market hindrances below as the background to our empirical analysis in Sections 4 and 5. The poor marketability of the old housing stock was reflected by the low turnover of existing homes relative to new home sales in Chinese cities.7 One contributor to the thin resale market was the deficient private property rights in privatized work-unit-provided dwelling units—the owneroccupants’ legal title to their homes was ambiguous and not fully marketable. These home owners might be required to sell their home to their work unit below market prices in case they wanted to liquidate their home equity, or to share the resale profit with their work unit. Individuals who purchased their home from the market with subsidies from their work unit might also be subject to resale restrictions and resale profit sharing. In addition, the resale market institutions, including real estate listing services, title transfer and brokerage were still under development. These resale hindrances would prevent the market from re-allocating existing housing resources according to the Pareto-efficient spatial sorting condition. Although residential mortgage market expanded rapidly since 1997, the total supply of housing credit was still relatively small in recent years. The total mortgage loans outstanding as a percentage of total domestic bank loans to non-financial sectors increased from 0.49% in 1998 to 8.89% by the end of 2005.8 Buyers of privatized homes in the resale market were often subject to low loan-to-value-ratio limits and extra transaction costs in obtaining bank mortgage loans. Such lending restrictions, together with the restrictions for buyers to assume the existing mortgage on resale homes, contribute to the slow development of the resale home market. Furthermore, mortgage lending has been unfavorable to elder home buyers and the government support for low-income households to obtain housing finance is largely undeveloped. At the same time, 5 See Fu, Tse and Zhou [16] for a review of the housing reform and housing market development in Chinese cities. 6 See Fu and Somerville [15] for a review of the emerging land market in Chinese cities. 7 According to the sales record at the Central Real Estate Exchanges in Shanghai and Guangzhou, resale of existing

homes accounts for only about half the total home sale volume in 2005. In Beijing, resale accounts for less than 15% of the total home sale volume. 8 People’s Bank of China, China Monetary Policy Report, Quarter Four, 2005.

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home prices are high relative to the average income in Chinese cities.9 The inadequate supply of housing credit together with high housing prices creates liquidity constraints for many home buyers, compromising their ability to buy homes according to their bid–rent gradients. Finally, the spatial structure in Chinese cities may be distorted by the predominant land supply policies with little consideration to the spatial aspects of housing demand. In the past decade, public land leases for new mass residential projects, many of which were built for households displaced by urban redevelopment in old central areas, were mostly located in urban fringes where land was more abundant but employment opportunities more distant.10 The urban redevelopment projects in the central areas, however, provided little new supply of modest homes for medium and low-income working families. 3. An ordered-location-choice model Within the AMM framework, Pareto-efficient spatial equilibrium is characterized by the spatial sorting of residents in a city in descending bid–rent gradient from the city center. For the purpose of our analysis, we divide a city into three zones: zone 3 in the center, followed in turn by zone 2 in the middle and zone 1 in the fringe. Let BRGj denote the bid–rent gradient of individual j and g2 and g3 the minimum bid–rent gradient in zones 2 and 3 respectively, with g2 < g3 . The AMM spatial equilibrium is characterized by the following spatial sorting conditions: Resident j locate in zone 1

iff BRGj < g2 ;

Resident j locate in zone 2

iff g2  BRGj < g3 ;

Resident j locate in zone 3

iff BRGj  g3 .

(1)

These conditions constitute an ordered-location-choice model, which we refer to as Model (1). Note that BRGj alone is sufficient to determine individuals’ spatial position in the city. When the housing market is interfered with by various restrictions, BRG alone would no longer be sufficient to determine the spatial order of individuals within the city. Instead, the observed spatial structure would depend also on the history of housing allocation and the constraints faced by different population groups in their housing market participation. For instance, individuals who have obtained their home in the previous housing welfare system might be discouraged from relocating, even though they may find themselves better off moving to a more distant but cheaper location, due to the deficiency in property rights that impairs the marketability of their home. These individuals would be living nearer to city center than their BRG would justify. Conversely, individuals who have inadequate access to housing finance or have few housing opportunities in central localities may compromise their location choices and live farther away from city center than their BRG would predict. Let Xj be a vector of household characteristics for individual j , capturing the differences in housing history, housing opportunities, and access to housing finance across population groups. We introduce a spatial displacement index ψ(Xj ), representing the discrepancy between individuals’ current spatial position in the city and 9 The ratio of median home price to median household annual income ranged between 7 and 13 among the five cities in our sample in 2003, according to statistics at www.sofang.com.cn. 10 According to a survey by the Geography Center at the Chinese Academic of Science, inhabitants in affordable housing projects (provided to medium and low income households) in Beijing have an average commuting time of 45.5 minutes, much higher than the average commuting time of 38.1 minutes in the city.

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their “optimal” position according to the AMM spatial order as determined by Model (1), such that the spatial position of individual j (or the population group who share the characteristics Xj ) is determined by the following augmented ordered-location-choice model, Model (2): BRGj + ψ(Xj ) < g2 ,

for individuals who live in zone 1;

g2  BRGj + ψ(Xj ) < g3 , for individuals who live in zone 2; BRGj + ψ(Xj )  g3 ,

(2)

for individuals who live in zone 3.

A positive spatial displacement, ψ > 0, indicates that the population group Xj is in a position of “surplus centrality,” meaning that their residential location appears more central than their bid– rent gradient BRGj would warrant, whereas a negative spatial displacement, ψ < 0, indicates a position of “deficient centrality.” In either case the AMM spatial sorting principle is violated. Those with surplus centrality could trade locations with those with deficient centrality at a differential land rent that would make both parties better off—or housing developers could profitably step in to offer new housing opportunities appealing to these population groups in the respective locations according to the AMM spatial order. The fact that the spatial displacements from the AMM spatial order are not eliminated by trading or by spatial arbitrage in housing development/redevelopment shows the presence of housing-market hindrances. The selection of the population group characteristics Xj in Model (2) will be guided by our institutional knowledge of the potential sources of the housing-market spatial distortion that affect different population groups in Chinese cities. The identification of the coefficients of ψ(Xj ) depends crucially on a reliable measure of individuals’ bid–rent gradient BRGj . The measure we have is obtained from a contingent-valuation (CV) survey. Specifically, the survey asks individuals to consider the situation where they were to buy a new home in the city. The respondents are requested to indicate how much more or less they would be willing to pay, in terms of the percentage of housing price, for the same home 15 minutes nearer to city center. We use the expressed willingness to pay for this hypothetical choice as our measure of individuals’ BRG. This measure of BRG in percentage term makes it comparable across individuals regardless of their housing demand; notice that it is the bid rent per unit of housing space that matters in spatial equilibrium.11 The CV method is criticized for its potential biases and validity problems when it is applied to assess the value of public goods, such as environmental damages and the value of public recreational facilities (see, e.g., Diamond and Hausman [11] for critiques). Individuals’ statement of their willingness to pay regarding such public goods may be unreliable because they do not usually make active use of these goods to be aware of their willingness to pay. Individuals may also express their willingness to pay as a political or moral statement rather than what they are prepared to pay. They may also understate their willingness to pay so as to enjoy a free ride. These problems, however, are unlikely to undermine the validity of our BRG measure. Residential location provides access to urban amenities and job markets and these accessibilities are private goods. Residents in a booming city would often use and contemplate these accessibilities to be aware of their willingness to pay for them. Moreover, their willingness to pay for living in a more accessible location is unlikely to be influenced by their political or moral persuasion or free-ride incentives. Perez, Martinez and Ortuzar [32], for example, find individuals’ value of time implied 11 An individual who is willing to pay 20% more for the same home 15 minutes nearer to the city center would be willing to pay 20% more in price per square meter of housing space. If the differential willingness to pay were measured in dollar amount, the implied BRG would depend on the respondents’ housing demand.

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by their sated preferences over residential locations consistent with the estimates based on the observed transport-mode-choice data.12 Moreover, since the equilibrium spatial order predicted by Model (1) is invariant to a monotonic transformation of the measured BRGj , the estimates of ψ(Xj ) would be qualitatively robust to general measurement biases in BRG due to, for example, the framing of the CV question (Boyle [6]), as long as the biases are consistent across the participants in the survey. Furthermore, our estimates of ψ(Xj ) will be unbiased if the measurement error in BRGj is uncorrelated with the observed characteristics Xj . We also construct an alternative measure of BRG using other indicators and covariates of individuals’ location preferences. This alternative measure is based on the premises that the CVbased BRG, though a noisy measure of individuals’ bid–rent gradient, has an average value across population groups consistent with the variation in the underlying bid–rent gradient across these groups, so that the underlying bid–rent gradient can be predicted by a regression equation of the CV-based BRG on those other indicators and covariates of location preferences. 4. Data and variables The data are collected from a survey of urban residents undertaken by the Institute of Real Estate Studies at Tsinghua University in August 2003. The survey was conducted in five cities, including Beijing (the national capital), Shanghai and Guangzhou (major coastal cities of high income), and Wuhan and Chongqing (major interior cities on the Yangtze River of relatively low income). Table 1 provides selected urban economic characteristics of these cities. A total of 1124 completed questionnaires were collected; they are about equally divided among the five cities. Given the small sample size, the sample is by no means an accurate representation of the population in the five cities. To achieve a reasonable representation, the sampling process combines random sampling with quota targets in terms of gender, age-group and occupationalgroup representations so that they are roughly in accordance with the adult population statistics in these cities. The sample, however, is skewed towards highly educated workers. The variables we use for our analysis include residential location classifications, location preferences, household characteristics, housing types, preferences for urban amenities, and ownership of consumer durables. The definition and summary statistics of these variables are provided Table 1 Selected urban economic characteristics (year 2001) City

Beijing Shanghai Guangzhou Wuhan Chongqing

Built-up urban area (km2 ) Urban population (thousand) Per capita disposable income (RMB yuan) Tertiary employment share in urban area (% total employment) Per capita living area (m2 ) Per capita road area (m2 )

780 9880 11,578 63.16 13.76 6.11

550 12,620 12,883 48.88 12.5 10.62

526 5770 14,694 59.61 13.87 10.22

212 7580 7305 54.23 9.7 2.35

268 8960 6721 48.42 11.47 4.43

Sources: China Statistical Yearbook [30], and China Urban Statistical Yearbook [29].

12 Even for quasi-public goods, such as outdoor recreation facilities, air and water quality, and work-related health risks, studies have documented evidence of convergence between stated willingness to pay from CV survey and the revealed preferences from observed choices (see, e.g., Carson et al. [8]).

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in Appendix A. The number of valid observations varies across these variables due to missing values. We classify individuals’ residential location into one of the three contiguous zones in their city in descending order of accessibility, namely the central zone (denoted by LOCAT = 3), the intermediate zone (LOCAT = 2), and the fringe area (LOCAT = 1). Arguably there is no perfect demarcation about these zones. The accessibility is not always determined by the distance to city center alone, although it is expected to be highly correlated with the distance in Chinese cities. The accessibility would depend also on the spatial distribution of employment opportunities and consumer amenities in the city (thus 20 minutes west of city center could be regarded more accessible than 10 minutes east of city center if the west side provide more consumer amenities and job opportunities). Instead of using arbitrary boundaries, the survey relied on the respondents to indicate the zone where their home located according to their perception. We corroborate the indicated LOCAT with the data of the travel time from home to city center, TCC, reported by the survey respondents. Table 2 shows the distribution of our observations and the mean TCC by LOCAT classification and the cities. We find the location classification and the mean travel time to city center highly correlated in each of the cities, which gives us some assurance that LOCAT is overall a sensible location classification. There are possible misclassifications at the margin. In order to check whether our results are sensitive to possible misclassifications, we identify and reclassify some marginal cases and denote the reclassified location as LOCAT1, as follows. For those indicating a central zone (LOCAT = 3) but a TCC greater than 30 minutes, we reclassify them into the intermediate zone (LOCAT1 = 2), whereas those indicating LOCAT = 2 and a TCC smaller than 10 minutes are reclassified into the central zone (LOCAT1 = 3). In addition, we reclassify into the fringe area (LOCAT1 = 1) those who indicated an intermediate zone (LOCAT = 2) but reported a TCC exceeding 60 minutes; and those indicating LOCAT = 1 and a TCC no more than 20 minutes are reclassified into the intermediate zone (LOCAT1 = 2). The bottom part of Table 2 shows the number of reclassified cases. Individuals’ bid–rent gradient, BRG, as described in the previous section, is indicated by their willingness-to-pay for locating 15-minute nearer to city center. The 15-minute distance happens to be about the difference in average travel time to city center between two contiguous zones (see Table 2). On average, the respondents indicated a BRG of 12.5%, which appears consistent with

Table 2 Sample distribution by city and location (average travel time to city center, minutes) City

LOCAT = 3 Central zone

LOCAT = 2 Intermediate zone

LOCAT = 1 Fringe area

Number of observations

Beijing (BJ) Shanghai (SH) Guangzhou (GZ) Wuhan (WH) Chongqing (CQ)

28% (18.4) 26% (21.1) 44% (16.8) 52% (18.3) 42% (16.9)

59% (39.3) 63% (39.1) 43% (27.8) 39% (34.4) 47% (31.0)

14% (56.4) 10% (59.0) 11% (36.1) 8% (52.9) 11% (47.8)

206 248 207 235 220

Number of observations by location classification and reclassification LOCAT1 = 3 LOCAT1 = 2 LOCAT1 = 1 All

413 19 0 432

35 513 17 565

0 26 93 119

448 558 110 1116

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the perceived housing price gradient.13 The range of the observed BRG varies between −30% (only one observation has a negative value) and 100%. As common in CV-based studies, we trim the observations to limit the influence of possible gross outliers; observations of a negative BRG value or a value exceeding 40% (30 observations together) are excluded from our analysis. Appendix A shows that the average BRG (the trimmed sample) is about 11% and decreases monotonically from the central zone towards the fringe area. Several variables of household characteristics are employed in our analysis. The current annual income of the respondents (plus that of their spouse for married respondents), denoted by INCOME, is reported in 5 discrete levels from 1 (less than RMB50,000 yuan, or about USD6200) to 5 (more than RMB250,000 yuan or about USD31,000). About 62% reported an annual income of less than 80,000 yuan and 14%, above 150,000 yuan; the mean reported income level for the whole sample is 2.2. In addition to the current income, we estimate a permanent income for individual respondents based on a regression of the current income on the respondents’ education level, marriage status, job position, employer type, and ownership of consumer durables. Appendix B reports the income regression estimates and the computation of the permanent income measure P_INC. We also gauge the respondents’ confidence in their future personal economic condition (CONFID); the majority, about 74%, agreed or strongly agreed that their economic condition would improve in the next 5 years. In terms of demographic and employment characteristics, household size averages slightly over 3 people. We drop 8 observations where the household has more than 5 working adults, for they are of exceptional household structure. We estimate the number of working adults in the household (ADULTS) by subtracting 1 from the household size if a respondent was under 50-years old and lived with a child (most of urban families in China today would have no more than one child as a result of the family planning policy) or if the respondent was 50 or older and lived with parents. Respondents’ age (AGE) is recorded in five age groups; none in our sample is below 20 and about 9% are 50-year old or older. We have a number of binary indicators: EDU for those with tertiary education; MARRY, for being married; KID, for living with children; and OCCUP for working in the government, post and communication, education and research, or commerce and trading sector—jobs in these sectors tend to locate relatively close to city center in Chinese cities. About 72% of the respondents in our sample attained tertiary education, 68% were married, 30% had children in their household, and 29% were employed in the “central location” sectors. Furthermore, we have binary indicators LEADER for being an executive in a government office or in an enterprise, SOE for working in a state-owned or collective-owned enterprise, and HUKOU for having the official resident status (i.e. Hukou) in the city. These three binary variables reflect the individuals’ privilege in the previous work-unit based housing welfare system. Note that residents without Hukou are not illegal residents; they just have limited or no access to various public welfare benefits (e.g. state-provided housing and public schools) and consumer services (e.g. consumer loans) in the city. With respect to housing types, DW_WU is a binary indicator for living in a work-unit provided dwelling unit and DW_EC, for living in economy-housing projects (which were developed to house people displaced by urban redevelopment and for sale to low and middle income house13 We estimated a residential price gradient using a sample of 414 observations from our survey, where the individuals bought their home from the market after 1990. We regressed the logarithm of the reported purchase price on the logarithm of total living area, the location dummy variables and the joint fixed effects of city and the purchase year. We find a statistically significant price differential of 21.1% between the central zone and the intermediate zone and a statistically insignificant price differential of 8.8% between the intermediate zone and the fringe area.

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holds). 33% of the households in our sample were in the former dwelling type, whereas 11% were in the latter type. About 80% of those in DW_WU had purchased their home from their work unit (DW_WU×OWN). LAREA measures the total living area of the respondents’ home. The additional location-preference indicators include ENTMT, which expresses how important it is (ranked from 1 to 5) to the respondents to live near entertainment facilities; PARK, how important it is to live near outdoor recreational parks; and FARTH_2 and FARTH_1, binary variables indicating those who find it acceptable to live as far as in the intermediate zone and in the fringe area of the city, respectively. The last group of variables includes the respondents’ monthly spending on cell phone bill (CELL), ownership of a digital camera (DCAM), ownership of a private car (CAR), and commuting by car (TRANSP_CAR). 40% of the respondents spent more than RMB300 yuan per month on their cell phone bills, 42% owned a digital camera, 23% owned a car, and 18% commuted to work by car. 5. Empirical analysis and findings We present a threefold analysis. First, we examine the spatial structure of Chinese cities as reflected by our sample data. The present spatial structure of Chinese cities, after more than a decade of rapid urban expansion and transformation, has not been widely reported. A few observations of the important features of the current urban spatial structure would help to set the stage for our analysis of the location incentives and spatial equilibrium. Second, we examine the variations in location preferences across different population groups in our sample. We do so both to gain insights to individuals’ location incentives and to check the validity of our BRG measure. Third, we apply the ordered-location-choice model to investigate the effects of housing-market hindrances on urban spatial structure. 5.1. Urban spatial structure The summary statistics in Appendix A show several spatial features of our sample cities. First, we note that high-income residents appear to locate more centrally, as the average household income, in terms of either current INCOME or permanent P_INC, as well as the average economic confidence CONFID, decrease away from city center. This urban form seems more similar to European cities than to American cities (see, e.g., Brueckner, Thisse and Zenou [7]). The relative centralization of the high-income residents in Chinese cities is probably due to the concentration of high-paying jobs and cultural and consumer amenities. Table 3 shows the average commuting time to be generally longer in more distant zones (except Wuhan, where it varies little across the zones), indicating a greater concentration of employment than population in these Chinese cities. There also seem to be significant reverse commuting in the central zone, as the average commuting time in this zone, about 25 minutes, notably exceeds the average travel time to city center of about 18 minutes (see Table 2). The presence of reverse commuting shows the important influence of centralized urban amenities on residential location choices (Glaeser, Kolko and Saiz [20]), although part of the reverse commuting may reflect the violation against the AMM spatial order due to hindered housing choices. An absence of the transportation divide, à la Glaeser, Kahn and Rappaport [19], would also support the centralization of high-income residents. Indeed, those in our sample living in the central zone appear more likely to commute by car (see the summary statistics of TRANSP_CAR in Appendix A).

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Table 3 Average commuting time to work (minutes) by city and location (1043 observations) City

LOCAT = 3 Central zone

LOCAT = 2 Intermediate zone

LOCAT = 1 Fringe area

All locations

Beijing (BJ) Shanghai (SH) Guangzhou (GZ) Wuhan (WH) Chongqing (CQ) Average

25.7 27.0 19.6 30.7 20.4 24.8

35.5 36.9 28.9 27.9 24.0 31.5

36.0 57.9 29.7 28.9 36.8 38.6

32.8 36.6 24.8 29.4 23.7 29.7

It is interesting to note further in Appendix A that the average household size, in terms of ADULTS, is somewhat larger in the fringe area but the average area of living space somewhat larger in the central zone. Moreover, the proportion of respondents living in economy-housing projects is much lower in the central zone. These statistics reflect the fact that most of the new affordable residential projects are located relatively far away from city center. We also observe a positive correlation between the respondents’ home location and their job location, as those who work in “central location” sectors (OCCUP = 1) tend to live more centrally. Finally we find the more privileged population groups—the executives (LEADER = 1), highly educated (EDU = 1), and official residents (HUKOU = 1)—to be more likely in central locations. Those working for state-owned enterprises (SOE) or living in work-unit provided dwelling (DW_WU) are less likely to be found in the fringe area. 5.2. The determinants of the bid–rent gradient Here we have three objectives. First, we check whether the stated BRG varies across individuals consistently with their characteristics and other location-preference indicators. Second, we construct a predicted bid–rent gradient as an alternative measure of individuals’ willingness to pay to locate nearer to city center according to the systematic variation in the stated BRG across individuals. We denote this predicted bid–rent gradient BRG1 and use it to check the robustness of our spatial equilibrium analysis with respect to measurement errors in BRG. Third, we study how household income would affect BRG. As the literature has offered little direct evidence about the income effect on BRG, new evidence from this study would help our understanding of how income growth affects urban spatial structure. Income can affect individuals’ BRG in either direction. On the one hand, a higher income increases the demand for housing space, which according to the AMM model reduces BRG. On the other hand, a higher income means a higher opportunity cost of travel time, which increases BRG. In addition, when urban amenities are concentrated in central locations and the income elasticity of demand for these amenities is positive, a higher income would also raise BRG (see Brueckner, Thisse and Zenou [7]). Table 4 reports the results of the BRG regression. We include in Reg (1) the two income measures (INCOME and P_INC) and the measure of economic confidence (CONFID), in addition to the city fixed effects. We find the current income to have no effect on BRG but the permanent income and economic confidence to have positive and statistically significant effects on BRG. The result suggests a stronger demand for centrally-located urban amenities by households of a higher permanent income. The income effects on BRG do not change when we include the housing-space consumption, lifestyle and job-market needs in Reg (2). A larger housing space (LAREA) reduces BRG, as ex-

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Table 4 Regression estimates of the determinants of the bid–rent gradient Independent variable

Reg (1)

Reg (2)

Reg (3)

Reg (4)

Constant ln(INCOME) ln(P_INC) CONFID ln(LAREA) ADULTS MARRY × (1 − KID) AGE = 5 OCCUP DW_WU ENTMT PARK FARTH_2 FARTH_1 CELL > 1 LEADER SOE HUKOU SH GZ WH CQ R-squared No. of observations

3.377 (2.2)**

11.503 (3.4)***

13.230 (3.3)***

0.228 (0.3) 3.036 (2.3)** 0.904 (3.1)***

0.707 (0.8) 3.036 (2.2)** 0.681 (2.3)** −2.327 (3.0)*** 0.926 (2.6)*** 1.367 (1.9)* −2.568 (2.4)** 1.563 (2.1)** −1.582 (2.2)**

0.396 (0.5) 2.461 (1.8)* 0.780 (2.7)*** −1.902 (2.4)** 0.981 (2.7)*** 1.242 (1.8)* −2.080 (1.9)* 1.405 (1.9)* −1.574 (2.1)** 1.200 (3.5)*** −0.838 (2.0)** −3.459 (3.1)*** −5.226 (4.3)***

1.198 (1.2) 2.616 (2.5)** 4.570 (4.2)*** 1.444 (1.4) 0.056 797

1.204 (1.1) 1.936 (1.8)* 4.526 (3.8)*** 1.562 (1.4) 0.090 796

0.158 (0.1) 1.428 (1.3) 3.848 (3.2)*** 0.292 (0.3) 0.136 779

14.277 (3.4)*** 0.327 (0.4) 1.057 (0.6) 0.730 (2.5)** −1.896 (2.4)** 0.977 (2.7)*** 1.542 (2.1)** −2.005 (1.9)* 1.334 (1.7)* −1.491 (2.0)* 1.160 (3.4)*** −0.899 (2.2)** −3.426 (3.1)*** −5.216 (4.3)*** 1.707 (1.8)* −0.085 (0.1) 0.607 (0.9) −0.595 (0.7) 0.063 (0.1) 1.437 (1.3) 3.521 (2.8) −0.036 (0.0) 0.141 779

Notes. The dependent variable is BRG. The estimation is based on the trimmed sample. t -Statistics reported in parentheses are computed based on White heteroskedasticity-consistent standard errors and covariance. * Significance at the 10% level. ** Idem, 5%. *** Idem, 1%.

pected. Households with a larger number of working adults appear to have a stronger preference for central locations, as indicated by their higher BRG, perhaps because they would benefit more from a thicker job market offered by proximity to city center.14 Similarly, married couples not living with children (MARRY × (1 − KID)), perhaps more career oriented, appear to have a preference for proximity to city center. In contrast, senior workers over age 50 (AGE = 5) prefer less central locations, as indicated by their smaller BRG. Furthermore, as anticipated, those working in “central location” sectors (OCCUP = 1) have a higher BRG but those living in work-unit provided dwelling (DW_WU = 1), who would work in nearby work units, have a smaller BRG. We further examine in Reg (3) how the stated BRG correlates with other indicators of location preferences. We find that those with a strong preference for proximity to entertainment facilities have a higher BRG, as city centers tend to offer more attractive entertainment facilities; those with a strong preference for proximity to recreational parks, however, have a lower BRG, as large parks tend to be in less central locations. Moreover, those whose farthest acceptable residential location is in the intermediate zone have a significantly lower BRG than those who find only the 14 The same argument explains the fact that larger cities tend to attract disproportionately more dual career families (see Costa and Kahn [9]).

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central zone acceptable, whereas those who find the fringe area acceptable have an even lower BRG. We find the positive effect of permanent income P_INC to become weaker, although the positive effect of CONFID remains statistically significant, after accounting for these location preference variables. In Reg (4) we include cell phone usage (CELL > 1) as an additional regressor, to reflect individuals’ demand for communication with other people. Those who spend more than RMB300 yuan per month on cell phone bills (about 40% of the sample), other things being equal, have a marginally higher BRG than those who depend less on cell phone. We find that the positive effect of P_INC on BRG largely disappears after we account for the differential cell phone usage, which suggests that people with a high permanent income have a stronger preference for proximity to city center partly because of their greater demand for communication with other people.15 Included in Reg (4) as well are housing privilege variables (LEADER, SOE, and HUKOU); we find them to make no difference in individuals’ BRG. As an additional check, we also have experimented with including commuting mode choice (TRANSP_CAR) in the regression (not reported) and find it to make no difference to BRG (neither does it make any difference in the ordered-location-choice model to be presented shortly). Finally we take a look at the cross-city variation in BRG by calculating the correlation between the city fixed effects estimated in Reg (4) (Beijing, the default city, is given a fixed effect of zero) and each of the city economic characteristics reported in Table 1. Table 5 reports these pair-wise correlation coefficients, which appear consistent with the predictions of the AMM model. We find the average BRG in a city negatively correlated with the urban size, measured by either the built-up area or the urban population. The average BRG is negatively correlated with per capita disposable income, although the coefficient is relatively weak. A larger per capita living area (hence lower residential density) or a larger per capita road area in the city (hence less road congestion) lowers the average BRG in the city. A higher share of tertiary employment in the city (hence greater importance of the job market in the central area), on the other hand, increases the average BRG in the city. Overall, the results show that individuals’ stated BRG based on the CV method is broadly consistent with their intra-city location incentives. In particular, individuals’ preference for central proximity in Chinese cities, although uncorrelated with the individuals’ current income, tends to increase with their permanent income, reflecting a positive income elasticity of demand for concentrated amenities and social opportunities. We use Reg (4) to compute the predicted bid– rent gradient BRG1, whose summary statistics are found in Appendix A. BRG1 has a correlation Table 5 Pair-wise correlation between the city fixed effects of BRG in Reg (4) and selected urban economic characteristics Urban characteristics (reported in Table 1)

Correlation coefficient

Built-up urban area Urban population Per capita disposable income Per capita living area Per capita road area Tertiary employment share in urban area

−0.56 −0.58 −0.26 −0.62 −0.46 0.11

15 We thank one of the referees for suggesting this explanation.

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coefficient with BRG of 0.38, its average value decreases monotonically away from the central zone in each of the five cities, and it has a considerably smaller standard deviation than BRG does within each zone. 5.3. Efficiency of urban spatial structure Table 6 reports the estimates of the ordered-location-choice model described by Model (2). We interpret the estimates from the two perspectives discussed in Section 3. First, we examine how the AMM spatial order, representing the market force in urban spatial equilibrium, has influenced the spatial development of the Chinese cities since the housing market liberalization started a decade ago. Such influence would be shown by the explanatory power of our measures of individual bid–rent gradient in Model (2). Second, we examine the spatial distortion against the AMM spatial order, indicated by the spatial displacement ψ(Xj ), for various population groups based on our institutional knowledge of the potential hindrances to their housing-market participation. We focus on three possible sources of housing-market spatial distortion in recent urban development of old Chinese cities. The first one is the poor marketability and limited resale market for the pre-market-liberalization housing stock that were allocated through the previous housing welfare system and subsequently sold to the occupants via privatization schemes. The lack of marketability hinders the homeowners of such housing units from adjusting their residential location to cash out the “location bonus” in their home. The population groups who enjoy such “location bonus” would show a positive displacement towards city center, ψ(Xj ) > 0. The second possible source of spatial distortion arises from inadequate access to housing finance by some population groups, who, due to the liquidity constraints, would find it difficult to finance their BRG in their housing location choice. Those credit-constrained households would show a negative displacement relative to the AMM spatial order. The third possible source of spatial distortion is related to the supply of affordable housing spatially mismatched with job-market opportunities. As we noted in Section 2, job growth (in urban area) in many large Chinese cities in the past decade derived largely from a rapid growth in the tertiary sector. We note also that cities with a larger tertiary sector, other things been equal, tend to have a higher average BRG (see Table 5), which shows the importance of tertiary-sector job opportunities for residential location choices. However, the supply of affordable housing in central localities has been severely curtailed due to both the removal of old homes for new urban infrastructure, commercial real estate, and up-market housing projects and the meager resale market for the remaining old housing stock. Thus, we expect those households who value access to urban job market more highly or who live in economy-housing projects to show a negative displacement against the AMM spatial order. Ideally we would like to estimate Model (2) for each city individually, as the spatial distortions in different cities need not be the same; unfortunately, our sample size for the individual cities is too small for us to do so meaningfully. We believe, however, that the potential sources of spatial distortion discussed above are common to all the cities in our sample. Table 6 reports the estimates of Model (2) for the pooled sample of five cities. Ord (1) in Table 6 provides the basic results. First, we note that BRG is positively correlated with individuals’ intra-city spatial position as predicted by the AMM spatial sorting equilibrium. Moreover, across the different estimations in Table 6, from Ord (1) to Ord (6), BRG (or BRG1) is the single most significant predictor of the individuals’ spatial position. The finding suggests that

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Table 6 Ordered-probit estimates of spatial structure Model (2)

Dependent variable Independent variable BRG

Ord (1) LOCAT

Ord (2) LOCAT

0.021 (4.7)***

0.020 (4.3)***

BRG1 LEADER SOE

0.475 (3.3)*** 0.192 (2.1)**

0.474 (3.3)*** 0.175 (1.9)*

Ord (3) LOCAT

Ord (4) LOCAT

Ord (5) LOCAT1

Ord (6) LOCAT = 3

0.097 (6.5)*** 0.470 (3.5)*** 0.110 (1.4)

0.108 (7.0)*** 0.492 (3.6)***

0.103 (6.8)*** 0.460 (3.3)***

0.111 (6.5)*** 0.474 (3.1)***

0.265 (2.9)*** 0.201 (1.9)* 0.129 (2.9)***

0.129 (1.2) 0.251 (2.2)** 0.112 (2.2)**

DW_DU × OWN HUKOU INCOME P_INC AGE EDU ADULTS MARRY × (1 − KID) DW_EC

0.246 (2.2)** 0.142 (2.9)***

0.233 (2.0)** 0.132 (2.6)***

0.243 (2.3)** 0.123 (2.8)***

0.272 (3.0)*** 0.240 (2.3)** 0.131 (2.9)***

−0.048 (0.6) −0.113 (2.2)** 0.220

−0.030 (0.4) −0.110 (2.1)** 0.191

−0.187 (2.4)** 0.009 (0.2) 0.210

−0.208 (2.8)*** 0.008 (0.2) 0.218

−0.176 (2.4)** −0.014 (0.3) 0.240

−0.231 (2.7)*** −0.045 (0.8) 0.273

(2.0) ** −0.122 (2.7)*** −0.145 (1.7)* −0.248

(1.7)* −0.128 (2.8)*** −0.120 (1.4) −0.245

(2.2)** −0.142 (3.3)*** −0.163 (2.0)** −0.304

(2.3)** −0.159 (3.6)*** −0.180 (2.2)** −0.217

(2.6)*** −0.151 (3.5)*** −0.156 (1.9)* −0.212

(2.5)** −0.109 (2.4)** −0.232 (2.4)** −0.424

(2.1)**

(2.0)** 0.045 (1.0) −0.014 (0.2) 0.096

(2.9)***

(2.0)**

(1.9)*

(2.9)***

140.5 (15 df) 1004

147.3 (15 df) 1004

152.5 (15 df) 1004

146.8 (15 df) 1004

ENTMT PARK OCCUP Log likelihood ratio No. of observations

114.2 (15 df) 789

(1.0) 113.8 (18 df) 772

Notes. z-Statistics in parentheses are based on QML (Huber/White) standard errors and covariance. All equations control for city fixed effects, which, together with the estimates of the limit points, are not reported. * Significance at the 10% level. ** Idem, 5%. *** Idem, 1%.

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the market force in the housing market has had a significant influence on the spatial development in these Chinese cities. Examining the spatial distortion, we first note the positive spatial displacement for those likely enjoying the “location bonus”—those in executive positions in the government or enterprises (LEADER = 1), employed in publicly-owned enterprises (SOE = 1), or official residents in the city (HUKOU = 1). The coefficient estimates for these characteristics are positive and statistically significant, indicating that these individuals, having obtained their home from the previous housing welfare system (when homes were available mostly in what would be considered central localities relative to today’s urban boundaries), were hindered in their residential relocation choices due to the limited marketability of their homes. Next we inspect the spatial displacement possibly due to housing finance constraints. In the case of Chinese cities where consumer financing was still in an early stage of development, residents without Hukou, senior citizens, or those without a secure job would find it quite difficult to obtain adequate mortgage credit for home purchase.16 We have just noted the positive spatial displacement for those with Hukou (HUKOU = 1) relative to those without, possibly due to the differential privileges in the previous housing welfare system; the positive displacement, however, could also be due to the better access to housing finance a Hukou would provide. We note that AGE correlates with a negative spatial displacement, possibility due to the declining access to mortgage finance with age. The positive effect of education, EDU, appears consistent with the better access to mortgage finance afforded by the job security that highly educated workers would enjoy. Further evidence with respect to the influence of housing finance on the housing-market spatial structure can be seen from the different effects between the current income and permanent income. The permanent income P_INC has a negative effect (although statistically insignificant in Ord (1), it becomes statistically significant when BRG is replaced with BRG1), but the current income INCOME has a positive effect (it is statistically significant across the estimations). We know from the BRG regression that INCOME has no effect on individuals’ BRG and P_INC can have a positive effect. Thus the positive INCOME effect on individuals’ spatial position is likely to arise from the relaxation in household liquidity constraints, whereas the negative effect of P_INC, holding INCOME constant, from the increase in liquidity constraints. With respect to the possible spatial mismatch between the job-market and housing-market opportunities, we find the individuals who value the benefit of central job-market thickness more highly—those with a large number of working adults in the household (ADULTS) and those married not living with a child (MARRY × (1 − KID))—associated with a negative spatial displacement. The negative spatial displacement for those in economy-housing projects (DW_EC) shows further evidence supporting the spatial mismatch hypothesis. We check the robustness of the basic results in several ways. In Ord (2) we include some of the location preference indicators—ENTMT, PARK, and OCCUP. We find them to have no additional influence on individuals’ intra-city spatial position once BRG is taken into account. In other words, the positive and negative effects of ψ(Xj ) discussed above are unlikely due to deficiencies of the BRG measure employed to capture individuals’ location preferences. We further check the sensitivity of our findings to possible measurement errors in the BRG measure by replacing BRG with the predicted bid–rent gradient BRG1. The predicted BRG1 also gives us a larger sample size of 1004 observations. Under Ord (3) we find the coefficient estimate for BRG1 to be about 16 It was common at that time for banks to require a credit guarantee from borrowers’ work unit in order to approve the mortgage loan.

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5 times as large as those for BRG in Ord (1) and Ord (2) and its z statistic also to be much bigger.17 The greater magnitude of the BRG1 coefficient is probably due to the reduced noise in the predicted bid–rent gradient compared with the stated BRG. The estimates of the spatial displacement ψ(Xj ) remain largely unchanged except for the following: the effects of SOE and AGE become much weaker, the negative effect of P_INC becomes much bigger and statistically more significant, and the effects of spatial mismatch indicators ADULTS, MARRY × (1 − KID), and DW_EC, become more significant. In Ord (4), we replace SOE with DW_WU×OWN as an indicator of owning a poorly marketable home provided by the previous housing welfare system. DW_WU×OWN shows a positive and statistically significant displacement against the AMM spatial order. We further examine whether our results are sensitive to possible location misclassifications in Ord (5) and Ord (6). In Ord (5), the dependent variable is LOCAT1, where potentially misclassified cases in LOCAT are reclassified according to travel time to city center (see Table 2). The estimates remain largely unchanged, although the LR statistic of the estimation is improved, probably due to the reduced noise in LOCAT1 classification. In Ord (6), the dependent variable is the binary indicator of whether the individual is in the central zone (LOCAT = 3). Again, the estimates are not substantially changed except that the effect of DW_WU×OWN (or that of SOE) is weakened. Overall, our basic findings regarding the three possible sources of spatial distortion appear robust with respect to alternative measures of individuals’ bid–rent gradient and alternative location classifications. Finally, to check whether our results might be driven by the observations in a particular city instead of representing the common housing-market spatial distortions in our sample cities, we repeat estimation of Ord (4) by excluding one city from our sample for each of the five cities. These estimates are not reported, but we observe that the estimates are generally consistent across the (overlapping) sub-samples. For example, the coefficient estimate of BRG1 varies from 0.093 (z = 5.4) when SH is excluded to 0.127 (z = 7.3) when GZ is excluded and that of LEADER varies from 0.445 (z = 2.9) when CQ is excluded to 0.566 (z = 3.8) when GZ is excluded. More significant variations in spatial displacement appear to be with respect to MARRY × (1 − KID), whose coefficient estimate varies from −0.08 (z = 0.9) when BJ is excluded to −0.27 (z = 3.0) when CQ is excluded, and with respect to DW_EC, whose coefficient estimate varies from −0.17 (z = 1.3) when CQ is excluded to −0.26 (z = 2.3) when BJ is excluded. Thus, the degree of spatial displacement against the AMM spatial order does seem to vary across the cities; nevertheless, our findings regarding the three sources of spatial distortion also appear common across the Chinese urban housing markets. 6. Conclusions We have proposed a simple ordered-location-choice model to examine the spatial displacement in individuals’ residential location against the Alonso–Mills–Muth (AMM) spatial sorting equilibrium. Such spatial displacement can be sustained only when the housing choice of the affected population groups is hindered in the housing market. Our methodology thus enables us to show housing-choice hindrances that different population groups may face in a city. The key 17 It is not straightforward to compare the z statistics of the coefficient estimates for BRG and BRG1. Since BRG1 is a fitted regressor, the standard error of its coefficient estimate need to be adjusted upward to account for the measurement error in BRG1 (see Murphy and Topel [27]). However, BRG is subject to measurement error as well and it is not apparent which standard error, that of the BRG coefficient estimate or that of the BRG1, is more severely under estimated.

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to the identification of the spatial displacement is to have a measure of individuals’ willingness to pay to have their home nearer to city center (i.e. their bid–rent gradient, or BRG), which we obtain using a contingent-valuation (CV) method. We demonstrate the application of this methodology using matched location and locationpreference data from Chinese cities. Our empirical analysis identifies spatial displacement for individuals who may face hindrances to their housing choice in three respects: (1) the difficult to resell one’s home of old housing stock for its full market value due to deficient or ambiguous property rights, (2) inadequate access to mortgage finance, and (3) spatial mismatch between job-market and housing-market opportunities. These findings point to potential welfare improvements in Chinese urban housing markets when these hindrances are removed. Specifically, our findings suggest that many who inherited their home from the previous housing welfare system could be better off trading their relatively central homes with suburban dwellers, especially those with many working adults in the household and those married but not living with kids, who have a relatively high BRG due to their high value for access to thicker job markets, and those in economy-housing projects. Further liberalization in the housing market by improving the marketability of the property rights in previously state-provided homes and by increasing the availability of mortgage credit in the resale housing market and to low-income or elder home buyers, therefore, can effect a more efficient spatial structure in Chinese cities. Moreover, an increased supply of land for high-density low-income housing projects in more central urban localities can help to reduce the spatial mismatch between job-market and housing-market opportunities. Our analysis also demonstrates a useful role for the CV measure of location preferences in urban economic analysis. Despite the limitations of the CV method, our CV measure of BRG appears consistent with the individuals’ household characteristics, housing demand, and location preferences. We find households with a higher permanent income to have a higher BRG even after accounting for differences in their lifestyle and job-market needs. This finding, together with the observation of reverse commuting in central urban areas, suggests the important influence of central urban amenities and social opportunities on the spatial structure in Chinese cities. Given the limited sample size we have, the empirical results of this study should be viewed as tentative. Further studies with more adequate sampling and more careful design of CV measure of location preferences would be desirable to corroborate our findings. Such studies would be of particular relevance to transition-economy cities undergoing housing market liberalization. Acknowledgments We would like to thank for their helpful comments and suggestions Matthew Kahn, Jan Brueckner, the two anonymous referees and the seminar participants at the 2004 Rena Sivitanidou Research Symposium at the University of Southern California, the 2004 Summer Urban Economics Symposium at the University of British Columbia, and the 2005 AREUEA Conference in Philadelphia.

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Appendix A. Variable description and summary statistics Table A Variable

Description

BRG

Willingness to pay (% home price) for locating 15 min nearer to city center; excludes one obs with BRG < 0 and 29 obs with BRG > 40% Predicted BRG according to Reg (4) in Table 4

Sample mean (s.d.) by LOCAT All

BRG1 INCOME

P_INC CONFID

Annual gross earning, respondent and spouse (thousand yuan): 1 = 50 or less; 2 = 50∼80; 3 = 80∼150; 4 = 150∼250; 5 = 250 or above Estimated permanent income; see Appendix B

MARRY

Confidence in personal economic condition, from 1 = strongly disagree that the condition will improve in 5 years, to 5 = strongly agree Binary: being married

KID

Binary: living with children

ADULTS

Number of working adults in the household

AGE

Age of the respondent (years): 1 = below 20; 2 = 20∼29; 3 = 30∼39; 4 = 40∼49; 5 = 50 or above Binary: education attainment at or above tertiary education level Binary: work in the government, communication and post, education and research, or commerce sector Binary: having official resident status in the city

EDU

Central Interm. Fringe

No. of obs.

11.04 13.39 (9.36) (10.10)

9.75 (8.83)

9.30 (7.51)

832

11.16 (3.54) 2.23 (1.14)

12.30 (3.46) 2.38 (1.17)

10.50 (3.37) 2.17 (1.11)

10.08 1018 (3.48) 1.95 1098 (1.07)

2.07 (0.83) 3.90 (1.10)

2.15 (0.89) 4.06 (1.01)

2.04 (0.79) 3.84 (1.14)

1.96 1116 (0.79) 3.63 1114 (1.17)

0.68 (0.47) 0.30 (0.46) 2.77 (0.94) 2.95 (0.96)

0.63 (0.48) 0.28 (0.45) 2.74 (0.94) 2.83 (0.87)

0.73 (0.44) 0.33 (0.47) 2.74 (0.90) 3.07 (1.02)

0.57 (0.50) 0.22 (0.41) 3.03 (1.07) 2.80 (0.92)

1116 1116 1074 1110

0.72 0.78 0.69 0.66 1116 (0.45) (0.42) (0.46) (0.47) OCCUP 0.29 0.33 0.27 0.20 1116 (0.45) (0.47) (0.45) (0.40) HUKOU 0.80 0.83 0.80 0.70 1116 (0.40) (0.38) (0.40) (0.46) LEADER Binary: job position as a government or enterprise 0.11 0.17 0.08 0.03 1116 executive (0.31) (0.37) (0.28) (0.16) SOE Binary: working in a publicly owned enterprises 0.48 0.46 0.51 0.36 1116 (0.50) (0.50) (0.50) (0.48) DW_WU Binary: dwelling type being work-unit housing 0.33 0.30 0.37 0.25 1116 (0.47) (0.46) (0.48) (0.44) DW_WU × OWN Binary: dwelling type being work-unit housing 0.27 0.24 0.32 0.18 1057 purchased by the respondent (0.45) (0.43) (0.47) (0.39) DW_EC Binary: dwelling type being economy-housing 0.11 0.08 0.14 0.11 1057 project (0.32) (0.26) (0.35) (0.31) 93.85 97.36 91.85 90.82 1104 LAREA Home living area (m2 ) (49.99) (48.48) (52.56) (42.59) ENTMT Preference for living close to entertainment facilities: 3.63 3.70 3.62 3.48 1086 1 = not important, . . . , 5 = very important (0.95) (0.94) (0.97) (0.91) PARK Preference for living close to parks: 1 = not 4.29 4.21 4.37 4.26 1105 important, . . . , 5 = very important (0.83) (0.84) (0.79) (0.83) 0.37 1116 FARTH_2 Binary: the farthest acceptable residential location 0.59 0.55 0.67 (0.49) (0.50) (0.47) (0.48) being the intermediate zone FARTH_1 Binary: the farthest acceptable residential location 0.27 0.22 0.27 0.52 1116 being fringe area (0.45) (0.41) (0.44) (0.50) (continued on next page)

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Table A (continued) Variable

Description

Sample mean (s.d.) by LOCAT All

CELL

DCAM

Monthly expenditure on cell phone (yuan): 1 = below 300; 2 = 300∼500; 3 = 500∼1000; 4 = 1000∼2000; 5 = 2000 + Binary: owning a digital camera

CAR

Binary: owning a private car

TRANSP_CAR Binary: commuting by car

Fringe

No. of obs.

Central

Interm.

1.61 (0.87)

1.69 (0.91)

1.55 (0.83)

1.57 1116 (0.93)

0.42 (0.49) 0.23 (0.42) 0.18 (0.38)

0.48 (0.50) 0.25 (0.43) 0.20 (0.40)

0.39 (0.49) 0.21 (0.41) 0.16 (0.37)

0.33 1113 (0.47) 0.27 1113 (0.45) 0.17 1109 (0.38)

Appendix B. Permanent income estimation We regress the current income of the respondents (plus that of their spouse if married) on their education level, marriage status, job position, employer type, official residential status in Table B Sample statistics of the income determinants and the OLS coefficient estimates Variable

Description

Sample mean (s.d.)

Coeff. estimates (t-statistics)

INCOME

Annual gross earning, respondent and spouse (thousand yuan): 1 = 50 or less; 2 = 50∼80; 3 = 80∼150; 4 = 150∼250; 5 = 250 +

Before log 2.23 (1.14)

Dependent variable

Binary: person with high school education Binary: person with college or undergraduate education Binary: person with postgraduate education Binary: being married Binary: person being an executive of a government office or an enterprise Binary: person being an owner of a private business Binary: person being a sole proprietor Binary: working for a publicly owned enterprises Binary: no official resident status in the city CELL = monthly expenditure on cell phone (yuan): 1 = below 300; 2 = 300∼500; 3 = 500∼1000; 4 = 1000∼2000; 5 = 2000 + Binary: owning a digital camera Binary: owning a private car Fixed effect for Shanghai Fixed effect for Guangzhou Fixed effect for Wuhan Fixed effect for Chongqing

0.24 (0.43) 0.61 (0.49) 0.12 (0.32) 0.67 (0.47) 0.11 (0.31)

0.049 (0.6) 0.185 (2.5)** 0.373 (5.2)*** 0.469 (6.0)*** 0.267 (9.4)*** 0.134 (3.6)***

Constant SCHHG SCHUG SCHGD MARRY LEADER BIZ_OWNER SOLE_PROP SOE NO_HUKOU Ln(CELL)

DCAM CAR SH GZ WH CQ R-squared

0.05 (0.22) 0.04 (0.19) 0.48 (0.50) 0.15 (0.36) Before log 1.62 (0.88)

0.158 (2.5)** 0.114 (1.6) −0.073 (2.8)*** −0.071 (1.9)* 0.302 (9.5)***

0.42 (0.49) 0.23 (0.42)

0.176 (6.7)*** 0.213 (6.9)*** −0.114 (2.7)*** 0.099 (2.5)** −0.213 (5.6)*** −0.278 (6.9)*** 0.470

Note. The number of observations is 1091. The default city is Beijing and the default education category is middle school or below. * Significance at the 10% level. ** Idem, 5%. *** Idem, 1%.

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