Effects of Residential Relocation on Household and Commuting Expenditures in Shanghai, China

Effects of Residential Relocationon Household and CommutingExpenditures in Shanghai, China

JENNIFER DAY and ROBERT CERVERO

Abstractijur_916 762..788

Over the past three decades, China’s cities have undergone massive spatial restructuringin the wake of market reforms and economic growth. One consequence has been a rapidmigration of urban residents to the periphery. Some movers have been forced out eitherby rising urban rents or government reclamation of their residences. Others haverelocated willingly to modernized housing or for other lifestyle reasons. This articleexamines the effects of relocation to the urban edge on household well-being. It exploresthe factors underlying changes in housing and transportation costs as households moveto the periphery. The research also examines whether those who moved involuntarily areaffected differently from those who moved by choice. Results show that, relative to thosewho moved by choice, involuntary movers are disproportionately and adversely affectedin terms of job accessibility, commute time, housing consumption and disposable income.The findings also show that, compared with higher-income households, lower-incomegroups are disproportionately affected in relation to housing costs, accessibility losses,disposable income and household worker composition. These results indicate thatrelocation compensation for involuntarily relocated households should be expanded toinclude more than just housing value: it should encompass urban location changes,household needs and relocation costs.

IntroductionIn the past three decades, China’s cities have seen a large-scale restructuring of urbanspace. China’s growing prosperity has been highlighted in the media, focusing on itsrapid economic growth; however, the distribution of the impacts of these reforms at theurban scale has been largely unstudied. One product of China’s economic reforms — andparticularly housing-market reforms — has been a rapid migration of city residents to theurban periphery.

Throughout China, tens of millions of residents living in central cities have relocatedto the suburbs and exurbs, and a significant proportion of new housing development hasbeen on the urban periphery. Some have been forced out either by rising urban rents oreminent domain. Others have relocated willingly, to modernize and upgrade theirhousing, seek out a better urban environment or for other lifestyle reasons. Still othershave migrated from cities’ rural hinterlands, from other cities or from the countryside,

This research was support by grants from the Lincoln Institute of Land Policy and the Volvo Center forthe Future of Urban Transportation. Thanks also to Professor Pan Haixiao, Huang Zhaoxiong, and WangXiaobo in the Department of Urban Planning at Tongji University, whose cooperation was invaluable inthe survey design andadministration, and in other critical pieces of the research.

Volume 34.4 December 2010 762–88 International Journal of Urban and Regional ResearchDOI:10.1111/j.1468-2427.2010.00916.x

© 2010 The Authors. International Journal of Urban and Regional Research © 2010 Joint Editors and BlackwellPublishing Ltd. Published by Blackwell Publishing. 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St,Malden, MA 02148, USA

moving toward Shanghai’s opportunities and offerings, and to the urban edge for theaffordability and relative ease of housing acquisition.

Currently, Shanghai’s development policies encourage housing development at, andpopulation migration to, the urban edge. This includes not only housing for those thatmade a choice to move, but also housing designated as resettlement housing for thosehouseholds forced to move for redevelopment projects. Often, this housing is situated inlocations lacking convenient transportation options and other urban infrastructure. Rightnow, urban policy is being made and practiced in China that continues to resettleunwilling central-city denizens at the edges of the city, without an understanding of howthese policies affect the well-being of the resettled population. And although citizenprotest, media and some academic research have made clear the unwelcome nature ofthese dislocations, very little research focuses on the actual outcomes for displacedhouseholds. This research attempts to fill this knowledge gap. Given the current trendstoward suburbanization in Chinese and other developing-country cities, and given thegrowing social unrest in Chinese cities resulting from government land appropriation, astudy of suburban trends and differential welfare impacts is a timely and necessary topic.

In this research, household survey data is gathered and analyzed under the umbrella ofone overarching question: Is there differentiation in the well-being of various socialstrata as they relocate to the urban periphery? Here, this question is examined from theperspective of changes in household spending on housing and travel, wage increases,housing consumption and worker composition from before to after a suburban move.This research offers a household-level look at changing consumption and spendingpatterns at a time when most views of human well-being are measured at highlyaggregate levels (Starke, 2007). In China, this is largely due to a lack of publiclyavailable data suitable for micro-level analysis.

Literature reviewHousing market reforms and tenure changes

China’s land economy is in a state of transition, with vestiges of the old housing systemstill playing a major role in the provision of housing. From the establishment of thePeople’s Republic of China in 1949 to the present, virtually all urban land in the countryhas been owned by the government. Over the past 30 years, however, there have beenmarked changes in how land is used. Before 1978, state danwei (work units) providedhousing to virtually all employees, and since most urbanites worked for a state-ownedenterprise (SOE), virtually all urban Chinese households lived in danwei housing orother publicly provided housing. For households with no workers, city housing bureausgenerally provided apartments. In 1985, only around 10% of the housing stock in largeChinese cities was privately held (Li, 2003). With housing and workplaces located inclose proximity to each other, most large Chinese cities evolved during much of thetwentieth century with a compact, mixed-use form.

Beginning in 1978, China’s policymakers began liberalizing urban land markets (Li,2003). Among earlier reforms, a land-leasing system was adopted in 1988 that gavehouseholds permanent use rights to their unit, and the ability to profit by renting that unitto others or by selling use rights (Li, 1998). Thus commodity housing emerged. Asincomes rise and the housing supply increases, many Chinese families are opting forcommodity housing.

Forces driving suburbanization

Much of the urban redevelopment occurring in Chinese cities is occurring in oldneighborhoods with dense populations, small living spaces, and old, dilapidated housing(Huus, 1994; Li, 1998 citing Lu, 1993; 1994). In China, the initial aim of the programs

Effects of residential relocation in Shanghai 763

International Journal of Urban and Regional Research 34.4© 2010 The Authors. International Journal of Urban and Regional Research © 2010 Joint Editors and BlackwellPublishing Ltd.

was to modernize older housing in the central cities. A survey in 1985 showed that nearlyhalf of the city’s 1.8 million households were in overcrowded conditions — with 216,000households occupying an average per capita living space under 4 square meters and morethan 15,200 of those households living in fewer than 2 square meters per capita(UNESCO, n.d.). However, redevelopment projects ‘have quickly been transformed intoa large-scale speculative form of development involving massive demolition and ruthlessdisplacement’ (Zhang and Fang, 2004). Higher-value land uses, such as shopping mallsand high-rent housing have replaced old urban neighborhoods.

Property development today generates upwards of half of municipal revenues in someChinese cities (Zou, 1998). In Shanghai, 582 parcels of land (mostly in the old city) werereleased by the city for long-term leasing between 1992 and 1997 (Wu, 2000). Duringthis period, 1.5 million Shanghai households — one-seventh of the city’s population —were relocated, and 4.2 million square feet of housing were demolished. In order toprovide affordable units to relocated families, many developers built relocation housingon the periphery (Li, 1998).

Li and Siu (2001) examined the spatial patterns of intra-urban relocation inGuangzhou and found that more than 70% of movers were from the old city districts,36% moved from the central city to the new districts, and only 3% of moves were fromthe new districts to the central city. This, they conclude, reflects suburbanization forcesunleashed by the liberalization of land markets.

Other factors have given rise to suburbanization. Under China’s socialist land-allocation system, land was not allocated according to market value. Following landreforms, some work units found themselves sitting on valuable central-city land thatcould be sold at great profit to developers, and the housing for workers rebuilt elsewhere(Li, 2003). Li and Siu (2001) conclude that discontinuation of the danwei approach ofstate-provided housing has been the ‘primary driving force behind China’ssuburbanization’.

Relocation is not always attributable to dislocation. With rising incomes, moreChinese households are seeking residences on the fringes for lifestyle reasons, such asbetter environmental quality, less traffic, and the availability of larger, more modernhomes. Whatever its impetus, one effect of peripheral development has been the entry ofthe urban poor into the commodity housing market. Other impacts include the physicalseparation of workers from their jobs, the emergence of motorized commutes andcorresponding increases in travel costs (Shen, 1997).

Impacts of peripheral resettlement

Not all households benefit equally from relocating to the periphery. Wu (2004) notes that‘the effect of residential relocation on inequalities still waits to be studied’. In theliterature, it is generally assumed that the relocation of poor households reduces theirwelfare (Kapoor et al., 2004), however this remains largely unexamined in the case ofChinese cities.

Studies from other countries provide insights into this question. Kapoor et al. (ibid.)analyzed four relocation scenarios where slum dwellers in Pune, India, were moved tothe urban periphery. Their analysis gauged impacts of commute-cost changes andchanges in neighborhood caste composition, among other control variables. Theyconcluded that none of the scenarios increased household welfare, measured usingcompensating variation (CV). In situ service improvement was the only scenario thatactually improved the welfare of slum dwellers. In a similar study of Mumbai, Takeuchiet al. (2006) concluded that accessibility and neighborhood effects of relocation wereboth significant. Those who were moved farther away from their jobs lost welfare.

Other authors have focused on relocation impacts according to whether householdswillingly moved. Wu (2004) describes ‘voluntary’ and ‘involuntary’ relocation,concluding that an involuntary move is likely to be associated with less housing spaceconsumed after the move, and that traditional indicators of housing consumption in a

764 Jennifer Day and Robert Cervero


mature land economy (e.g. household life cycle) do not hold in the context of Shanghai.Yang (2006) includes an attitudinal variable of the ‘reluctant’ mover in a study of theeffects of urban relocation on travel time, and concludes that reluctant movers incur moretravel time than ‘affirmative’ and ‘neutral’ movers.

Involuntary peripheral resettlement and compensation structures

At the beginning of the land-reform process, relocation laws required that householdswere relocated on site, but subsequent legislation slowly relaxed these regulations, firstrequiring that households be offered the option of on-site relocation but requiring thatthey pay the cost differential for the increased square footage of the apartment (TheWorld Bank, 1993), and eventually eliminating the requirement of on-site relocationaltogether.

The mechanisms behind relocation and compensation have also changed in the pastseveral years. At the beginning of the wave of relocations, the city or district governmentswere responsible for relocating displaced residents. Over time, this responsibility wastransferred to developers (Li, 1998), who may further subcontract the job of relocation todemolition companies (Wu, 2004). This has led to the complication of relationshipsbetween relocated households, the local government which is responsible for socialstability and the public good, and developer/demolition companies who have profit astheir main motive.

Before a reform in November 2001, relocation compensation was based on thenumber of household members. After the implementation of the new Regulation ofUrban Housing Demolition, compensation structures were modified such that they arenow based on the assessed market value of the residence as it stood at the time ofrelocation — not taking urban location into account. This has meant that, for low-incomehouseholds living in low-quality housing in higher-priced areas, the compensationprovided is not enough to relocate in the same general area, and many households getpushed to the suburbs. The new regulation was designed to control cheating byhouseholds, who would register extended family members under the new regulation inorder to increase compensation. This has constrained the costs of relocation fordevelopers and, in this sense, has favored developers over relocated residents (Wu,2004).

Study focus, data and methodologiesThis article expands upon prior research on relocation in Chinese cities, focusing on theimpacts of peripheral moves on housing and transportation costs, and contrastingexperiences of choice versus non-choice movers, and lower-income versus higher-income movers. The city of Shanghai serves as a case context.

Study setting

Shanghai’s administrative area is a triangular region that covers 6,340 square kilometers,with an urbanized area covering 2,643 square kilometers, and the urban core locatedalong the Huangpu (Yellow) River (Figure 1). This study examines the housing andtravel expenditures of households living at the urban edge. For the purposes of this study,the ‘urban edge’ (i.e. urban periphery) is defined as the area at the edge of the built-upurban area (i.e. the darker-shaded areas of Figure 1). In general, residents of this arearoutinely access the urban core for employment and recreation, rather than the suburbantowns outside the central city.

Data and sampling frame

To carry out this research, a detailed survey was conducted of 900 households(containing 2,840 inhabitants) residing in housing developments in the four peripheraldistricts. Surveys were conducted by the municipality of Shanghai’s survey division



during a two-week period in October 2006. The survey was designed to gather currentand pre-move retrospective data on all household members’ travel patterns and monthlytravel costs, housing costs and apartment features, satisfaction with current and formerresidential location, neighborhood accessibility and neighborhood amenities. The surveyalso allowed choice and non-choice movers to be distinguished.

A stratified sampling approach was used to select households within the four districts.Six to seven developments were chosen in each district, with roughly 300 householdssurveyed in each district. In all, twenty housing developments situated at the peripheryof Shanghai were chosen, representing a variety of urban housing environments availableat the urban edge: high and low accessibility to rail transit, high and low bus-hubaccessibility, mixed and non-mixed land uses, housing costs per unit area, and variedper-apartment parking provisions. The selected neighborhoods also represented a varietyof resident demographics, including choice/non-choice mover status. It is noteworthythat although the selection of the developments was non-random, the selection of thehouseholds within each development was done quasi-randomly (one household waschosen from every third floor in each access stairwell). All sampled developments consistof twenty to forty mid- to high-rise residential buildings situated within a gated block.This development pattern was chosen because it is by far the most common residentialdevelopment style found on the periphery of Chinese cities, particularly Shanghai. Allhousehold members over the age of twelve years were asked to participate in the survey.

All housing developments in which surveying occurred were located no farther than1.5 kilometers from Shanghai’s Outer Ring Road — commonly used to demarcate theurban core from the outlying areas — and were no more than six years old. This meantthat the households surveyed were relatively new movers. It is noteworthy that, due to alack of available place-specific data on housing and population demographics, thehousing developments selected were the authors’ best guess at a representative sample ofthe peripheral population and new housing stock.

Figure 1 Shanghai administrative and urbanized area (urbanized area shaded)



In addition to the survey information, data were compiled on regional job accessibility(i.e. the number of jobs accessible to a household within one hour of network travel time,either via transit or auto modes). An accessibility index was computed using trafficanalysis zone (TAZ)-level data and the Shanghai street and transit network. GeographicInformation Systems (GIS) technology was used for the spatial computations. Fordetailed methodologies on the construction of the accessibility index, see Day (2009).

The survey response rate was quite high in large part because the survey was approvedand administered by the municipality of Shanghai. All households asked to participate inthe survey provided some responses, with complete information provided for between70% and 100% of questions asked.

Survey sites

Each of the four surveyed peripheral neighborhoods is briefly discussed below and itslocation within the Shanghai region is shown in Figure 2. Figure 3 shows the surveylocations relative to Shanghai’s Ring Roads.

Jiangqiao is a residential community that straddles the Outer Ring Road to thenorthwest of downtown. At the time of the survey, there was limited retail shopping andonly a few small grocery stores. The area is served by a freeway which links to the OuterRing Road, and has no metro service. Regular buses connect residents to the central city.

Served by the Metro rail network, Meilong and Xinzhuang are communities locatedat the southwest corner of the Outer Ring Road — Meilong inside it and Xinzhuang justoutside it. They are served by Metro Line 1 (the Red Line) and are primarily residentialcommunities with little relocation housing. Meilong is the older of the twoneighborhoods, and has high densities, retail uses and good cycling infrastructure.Xinzhuang is also a mixed-use neighborhood with somewhat lower densities.

Figure 2 Three surveyed neighborhoods on the periphery of Shanghai



A mixed-use suburban town, Sanlin is located in the Pudong area (east of the Huangpuriver). Over the past decade, the Shanghai government relocated over one million peoplewho previously occupied the riverside site of the 2010 World Expo, many of them toSanlin. With no Metro rail station, Sanlin residents rely mainly on bus transit formotorized travel.

Variables

This section describes some of the key variables used in the analysis. Tables 5, 6, and 7also provide variable descriptions.

Model dependent variablesThe outcomes to be examined are as follows (variable names are included in italics):

• Change in % of HH Income Spent on Housing (DPcntHHInc_Housing). This isthe percentage-point change in proportion of income spent on housing, from theprevious to the current residential locations. The metric was computed as follows:percent of household income spent on housing at the current residential location,minus the percent of household income spent on housing at the previous location. Thisparticular formulation was chosen as a second-best alternative to the percent increasein housing costs. The necessity of a substitute results from about one-third of thesample paying nothing for housing in the previous location, which makes thiscomputation impossible for that portion of the sample.

• Change in Commute Time (DWorkTTperWorker). This is the change in averagemonthly minutes of commute time per household worker. To compute this metric,survey respondents were asked to report the temporal length of their daily commuteand the weekly frequency of work trips, both for their current and previous residence.

Figure 3 Surveyed housing developments, relative to the Inner, Middle, and OuterRingroads



Total household monthly minutes of commute time were computed, then averaged perworker.

• Change in Regional Accessibility (DAI). The number of jobs available bycommuters’ reported commute mode (auto or transit) within 1 hour of travel timewere computed for previous and current residences. Commuters who reportedwalking or cycling to work were assumed to be transit commuters for this hypotheticalcase of longer distances. The difference was computed, then averaged for allhousehold adults, and reported here in units of 100,000.

• Net Impact. A measure of whether increases in household income are keeping pacewith increases in housing and out-of-pocket commuting costs. Two metrics of netimpact were explored. The first is in terms of absolute gain or loss of discretionaryincome1; the second examines gain or loss of discretionary income on a percentagebasis. The Net Impact metrics are computed as follows:

Net Impact = ( ) −ΔΔ

Annual Household Income Annual Household HHousing Costs Annual Work Out-of-Pocket Travel Costs

( ) −(Δ ))

Percent Net Impact =( ) −[ Post-Move Household IncomePost-Move Household Housing CostsPost-Move Work Out-of-Pocket T

( ) −rravel Costs

Post-Move Household IncomePre-Move House

( )]( ) −

hhold IncomePre-Move Household Housing CostsPre-Mov

( ) −[( ) −

ee Work Out-of-Pocket Travel CostsPre-Move Household In

( )]ccome( )

• Change in Housing Consumption (DHomeSize). A measure of the change in squaremeters of housing consumed from before to after the move.

• Change in Workers (DWorkers). The change in the number of workers a householdhas from before to after the move.

Choice and pre-move income variables

• Mover Choice Status (No Choice). A dummy variable indicating whether thehousehold relocated to the current location by choice, versus being involuntaryrelocated.

• Pre-Move Income. A set of categorical dummy variables indicating the income levelof the household at the time that it relocated to the current location. Thelowest-income group was defined as those households whose per-worker income wasless than half the mean per-worker income in the city of Shanghai in the year prior tothe move. This is an internationally recognized poverty benchmark (Saunders, 2006).The other three income categories are set in relation to the urban mean income.

It is important to consider these two sets of variables in tandem because, as will be seenin subsequent sections, they are highly correlated. Experience in Chinese cities teachesus that the lowest-income families are the most vulnerable to being involuntarilyrelocated — both because they are likely to live in the oldest housing which is thus more

1 Here, defined as the amount of income left over after household housing and commute expenses arepaid for.



likely to be the target of urban redevelopment projects, and because the poor have fewerresources with which to contest involuntary relocation. Simply examining the effects ofchoice status without considering pre-move income, then, would be likely to produceomitted-variable bias. Examining only the effects of income, on the other hand, wouldleave unexamined the question of the household’s free will to move or remain in itswelfare outcomes.

Why focus on the choice/non-choice dichotomy?

A natural question is, why focus on the distinction between choice and non-choicemovers, rather than other demographics such as income? Classic models of residentialmobility hypothesize that a move is a way for households to restore consumptionequilibrium, whether the adjustment they mean to make is for space, quality, location orother aspects of consumption (Brown and Moore, 1970; Hanushek and Quigley, 1978).Brown and Moore describe relocation as ‘a process of adjustment whereby one residenceis substituted for another in order to better satisfy the needs and desires of each intendedmigrant’. For those moving by choice, this is a reasonable assumption. But forhouseholds whose preference was not to move (non-choice movers), classic theories maynot apply. Thus, the comparison here is between households that acted rationally (bymoving) to maximize personal interest, and those households whose relocation was nota function of interest maximization. The choice variable is an expression of preference,and a comparison between choice and non-choice movers, then, is useful from a socialequity perspective because:

1 The outcomes for the choice-mover group provide a baseline for those changes (inexpenditure, satisfaction, etc.) which a typical household would likely regard astolerable. Costs incurred above this baseline could be viewed as welfarediscrepancies.

2 Concomitantly, the outcomes for the choice group indicate how non-choice moverhouseholds should be compensated to bring them to comparable levels of expenditureand satisfaction.

Structural equation models

Three models were developed to test the hypothesized relationships among theoutcome variables and the choice and income factors. Structural equation modeling(SEM) was used in order to control for potential endogeneity among model variables.There are strong theoretical reasons to believe that the direction of influence amongsome of the above variables is bi-directional — i.e. that outcome variables are bothinfluenced by and influence other outcome variables. The presence of bi-directionalityintroduces the potential for endogeneity bias in the models. Ordinary least squares(OLS) models (not included here) were also explored before the SEMs weredeveloped. A section later in this article discusses the improvements of the SEMs overthe OLS models.

SEM is a statistical approach for dealing with simultaneous equation systems usingcovariance (structure) analysis. This means that model parameters are identified such thatthe variance-covariance matrix predicted by the model is as similar as possible to thevariance-covariance matrix of the sample, while adhering to the constraints of the model(Golob, 2003). SEMs differ from conventional simultaneous equation models in theestimation method: conventional simultaneous equation models use joint sampleprobability while, as previously mentioned, SEM estimation analyzes covariant structure(Choo, 2004). The models specified here are:

1 Housing-Travel Trade-Offs Model2 Net Impact Model3 Percent Net Impact Model.



The models are expressed conceptually in Table 1. The following notes reflect the modelspecifications:

1 The model specification assumes that all exogenous variables in the model arecorrelated, and includes all of these correlations in the estimate.

2 Correlations among error terms were not specified in the models.3 A constant term was not included in any of the equations. This formulation improved

the model chi-squared fit statistic by more than an order of magnitude, to anappropriate level.

Models were estimated using maximum likelihood estimation (MLE). In the context ofSEM, MLE methods maximize the likelihood that the data (observed covariances) aredrawn from the observed population. MLE is a normal theory method, i.e. it assumesmultivariate normality for all endogenous variables (Kline, 2005), a structure to whichthe data conformed.

Empirical results and research findingsThis section describes the survey results, followed by predictive models that address thecore research questions posed.

Descriptive statistics

The key descriptive statistics findings from the survey are presented below. For moredetailed summary statistics, see Day (2009).Location of previous residenceFigure 4 maps the previous locations of the 900 sampled households residing in the threesurveyed neighborhoods. The three darkened lines encircling the central cityconcentrically are the Inner, Middle, and Outer Ring Roads. The largest share (32%)

Table 1 Conceptual models

Housing-Travel Trade-Offs Model

1) DPcntHHInc_Housing = f (C, I1, DLoc, DTen, HH, DWorkTTperWorker, DAI, DHomeSize, DWorkers)

2) DWorkTTperWorker = f (C, I1, DLoc, DTen, HH, DPcntHHInc_Housing, DHomeSize, DAI, DWorkers)

3) DAI = f (C, I1, DLoc, DTen, HH, DPcntHHInc_Housing, DHomeSize, DWorkers)

4) DHomeSize = f (C, I1, DLoc, DTen, HH, DPcntHHInc_Housing, DWorkTTperWorker, DAI,DWorkers)

5) DWorkers = f (C, I1, DLoc, DTen, HH, DAI, DHomeSize)

Percent Net Impact and Net Impact Models

1) (Percent)Net Impact = f (C, I1, DLoc, DTen, HH, DAI, DHomeSize, DWorkers)

2) DAI = f (C, I1, DLoc, DTen, HH, Pcnt_NetImpact, DHomeSize, DWorkers)

3) DHomeSize = f (C, I1, DLoc, DTen, HH, Pcnt_NetImpact, DAI, DWorkers)

4) DWorkers = f (C, I1, DLoc, DTen, HH, Pcnt_NetImpact, DAI, DHomeSize)

whereDPcntHHInc_Housing, DWorkTTperWorker, DAI, Percent Net Impact, Net Impact are as per section 3.4DHomeSize is the change in square meters of housing consumedDWorkers is the change in the number of household workersC is a dummy variable indicating the household’s choice statusI1 is a dummy variable indicating the household’s pre-move income levelDLoc are dummy variables indicating where in the urban region the household moved from, and to, and commutecharacteristics of the household workersDTen is a set of variables that describes various aspects of tenure, including type (own, mortgage, etc.) and length (atthe current and previous locations)HH is a set of variables that describes the household composition (family members, children, retirement status, etc.).



of surveyed households previously resided between the Inner and Middle Ring Roadsof Shanghai, followed by residency inside the Inner Ring Road (i.e. the historical citycore — 28%). Around a quarter of those surveyed previously lived outside the OuterRing Road and 14% resided between the Middle and Outer Rings.

Choice status and incomeTwo questions were asked in the survey to assign choice status. The first question askedwhy the household had left their previous location and why they moved to the currentlocation. The survey revealed that 51% of the sample left their previous residence bychoice, and chose their current residential location on their own volition. It also showedthat 15% were forced to leave their previous location, but freely chose the location oftheir new residence. The remaining 34% of the sample had no choice about leaving theirprevious residence, and had no other housing options aside from the one presented tothem at the time of relocation.

When asked a second question, about the reason for their move, nearly 42% attributedthis to involuntary relocation (Figure 5). More than a third moved to improve their livingenvironment. For purposes of our analysis, households were designated as ‘non-choice’movers if they answered affirmatively to either question — i.e. they indicated the impetusfor moving was not of their own volition, or if they specified ‘relocation’ as the reasonfor the move.

Mover status was associated with household income. Choice movers averaged anannual household income of 68,027 RMB compared to 39,715 for non-choice movers(based on stated incomes at their previous locations). These values are adjusted forinflation to 2006 RMB.

Tenure type and lengthAt the time of the surveys, three-quarters of the sampled households had lived in theircurrent residence for three or fewer years. This contrasts with their prior residence, wherethe median length of tenure was more than 15 years.

Figure 4 Location of previous residence in metropolitan Shanghai among 900 surveyedhouseholds



Table 2 shows the distribution of tenure status at the current and previous locations,along with the percentage-point change for each type of tenure from pre-moveto post-move. There was a sharp increase in outright ownership and mortgagedproperties — an indication of more residents buying into the commodity housingmarket — and a decrease in rental tenure, danwei tenure, and residence in other types ofpublic housing.

SEM fitting and interpretation

Once the initial hypothesized model was fitted, post-hoc model modifications wereperformed in all of the models with the aim of arriving at more parsimonious, better-fitting models. Highly insignificant relationships were constrained to be zero unless: (1)the relationship in question was between the choice and/or pre-move income variables,which being the relationships of primary interest, were retained regardless ofsignificance; (2) there was a strong theoretical reason to keep the path or; (3) theinsignificant relationship was a dummy variable in a set of related dummy variableswhere other relationships in the set were significant.

Table 3 presents fit statistics for the initial and final (post-hoc) models. Model fits aregenerally good, with all metrics within acceptable ranges (for interpretation of model fitstatistics, see Ullman, 1996; Schumacher and Lomax, 2004; Day, 2009).

A note is warranted about interpreting some of the model variables. All changevariables (e.g. change in urban location, change in tenure type) were computed bysubtracting the post-move status from the pre-move status. For instance, one common

Figure 5 Stated reasons for move among sampled households

Table 2 Tenure type at current and previous residential locations

Tenure % Current Location % Previous Location % Change

Own 59.9 32.3 27.6

Mortgage 25.9 4.4 21.4

Rent 3.2 15.4 -12.2

Danwei 0.2 10.3 -10.1

Gvt/Other Public 9.6 33.4 -23.9



variable used throughout the analyses is DTenure_Mortgage, a variable indicating thehousehold’s status as a mortgage holder. The following coding options are available forthis variable:

At time period t (the pre-move state), a household could either have a mortgage, ornot.Here, holding a mortgage is coded as a 1; zero otherwise. At time period t+1, the sameoptions are available. The change variable (t+1)-t reflects the difference of the twovalues. Thus, the set of coding options for mortgage status is (-1,0,1), with interpretationas described in Table 4.

SEM results

Tables 5, 6, and 7 show the estimates for the Housing-Travel Trade-Off model, the NetImpact model, and the Percent Net Impact model, respectively. This section summarizesthe model outputs for the choice and income variables. With a few exceptions, detailedinterpretation of model variables other than the choice and income factors is excludedhere for the sake of brevity. Day (2009) provides a detailed description of theserelationships. In Tables 5, 6, and 7, blank cells indicate that the relationship was notexamined; ‘zero’ indicates that the relationship was examined and then constrained tozero due to a lack of statistical significance.2

Housing-travel trade-offs modelContrary to expectations, Table 5 indicates that those households that are involuntarilydislocated are transferring a lower proportion of their incomes to accommodate risinghousing costs — more than 4% less than those who moved by choice. This is most likely

2 It is noteworthy that changes in satisfaction levels and tenure changes (i.e. switching to or from amortgage or ownership) were initially included in the models as dependent variables, as they wereconsidered to be important outcome variables, but were excluded in the final models due to a lackof effect.

Table 3 SEM fit statistics

Statistic

Housing-TravelTrade-Offs Model

Percent NetImpacts Model

Net ImpactsModel

Initial Model Final Model Initial Model Final Model Initial Model Final Model

DF 29 64 37 50 37 58c2 (N = 900) 62.2 119.9 83.81 120.05 83.81 176.13p 0.000 0.000 0.000 0.013 0.000 0.000CMIN/DF 2.145 1.873 2.265 2.401 2.265 3.037CFI 0.995 0.99 0.993 0.987 0.993 0.979RMSEA 0.036 0.031 0.038 0.039 0.038 0.048LO 90 0.023 0.022 0.027 0.03 0.027 0.04HI 90 0.048 0.04 0.048 0.049 0.048 0.056AIC 1194.2 803.9 1199.81 890.05 1199.81 930.13Stability index 0.227 0.25 0.227 0.25 0.227 0.25

Table 4 Variable coding example, mortgage status

T t + 1 (t + 1) - t Interpretation

1 1 0 Household had mortgage tenure in both locations1 0 -1 Household used to own through mortgage, now has another tenure type0 1 1 Household moved from another tenure type to a mortgage0 0 0 Household neither has a mortgage now, nor had one at the previous residence



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tHH

Inc_

Ho

usi

ng

DWo

rkT

Tp

erW

ork

er

DAI

DHo

me

Siz

eDW

ork

ers

Ch

oic

eS

tatu

s

No

Ch

oic

e:

HH

did

no

tm

ove

by

cho

ice

(1,0

)-4

.415

198

.77

9-1

.50

1-3

3.6

21

zero

(0.0

01)

***

(0.0

00

)***

(0.0

14)*

*(0

.00

0)*

**

Inco

me

HH

Inc1

_Le

ssT

han

Hal

fMe

an1 :

HH

inco

me

atp

revi

ou

slo

cati

on

less

than

hal

fth

ep

reva

ilin

gci

tym

ean

(1,0

)6

.80

2ze

ro-1

.28

7ze

ro0

.133

(0.0

00

)***

(0.0

62

)*(0

.00

0)*

**H

HIn

c1_L

ess

Th

anM

ean

:H

Hin

com

eat

pre

vio

us

loca

tio

nle

ssth

anth

ep

reva

ilin

gci

tym

ean

(1,0

)6

.53

6ze

ro-0

.88

8ze

ro0

.08

3(0

.00

0)*

**(0

.176

)(0

.014

)**

HH

Inc1

_Le

ssT

han

1.5

Me

an:

HH

inco

me

atp

revi

ou

slo

cati

on

less

than

1.5ti

mes

the

pre

vaili

ng

city

mea

n(1

,0)

7.6

29

zero

0.2

14ze

ro0

.03

6(0

.00

0)*

**(0

.76

)(0

.32

5)

DHH

Inc_

Pcn

t:P

erce

nt

chan

ge

inh

ou

seh

old

inco

me

-0.0

3ze

roze

roze

roze

ro(0

.00

8)*

**

Urb

anL

oca

tio

nan

dA

cce

ssib

ility

/Mo

bili

tyC

han

ge

s

DIn

ne

rRin

g:

Mo

ved

fro

min

ner

-rin

gzo

ne

(-1,0

)ze

roze

ro14

.911

-40

.37

2ze

ro(0

.00

0)*

**(0

.00

0)*

**DM

idd

leR

ing

:M

ove

dfr

om

mid

dle

-rin

gzo

ne

(-1,0

)ze

roze

ro10

.49

4-2

4.2

26

zero

(0.0

00

)***

(0.0

03

)***

DOu

tsid

eU

rban

:M

ove

dfr

om

ou

tsid

eth

eO

ute

rR

ing

Ro

ad(-

1,0)

zero

zero

-15

.157

-23

.00

8ze

ro(0

.00

0)*

**(0

.07

)*DM

eilo

ng

/Xin

zhu

ang

:M

ove

dto

rail-

serv

edT

OD

nei

gh

bo

rho

od

(1,0

)ze

ro-0

.37

9(0

.50

8)

DPu

xi:

Ch

ang

ein

Pu

xilo

cati

on

stat

us

(-1,0

,1)ze

ro6

.310

zero

(0.0

00

)***

Jo

bC

han

ge

_He

ad:

HH

hea

dch

ang

edjo

bs

(1,0

)-3

80

.83

4ze

ro(0

.00

0)*

**DA

uto

Co

mm

ute

rs:

Ch

ang

ein

nu

mb

ero

fw

ork

ers

com

mu

tin

gb

yau

to-9

7.0

75

3.3

63

(0.3

52

)(0

.00

0)*

**



Table

5C

on

tin

ued

DPcn

tHH

Inc_

Ho

usi

ng

DWo

rkT

Tp

erW

ork

er

DAI

DHo

me

Siz

eDW

ork

ers

Te

nu

rean

dH

ou

sin

gC

han

ge

s

DTe

nu

re_M

ort

gag

e2:

Ch

ang

ein

mo

rtg

age

ten

ure

stat

us

(-1,0

,1)2

3.8

07

-18

7.18

8ze

ro14

.815

-0.0

05

(0.0

00

)***

(0.0

09

)***

(0.0

01)

***

(0.8

66

)DT

en

ure

_Re

nt:

Ch

ang

ein

ren

ter

ten

ure

stat

us

(-1,0

,1)5

.85

2-1

85

.68

8ze

ro-1

7.7

88

-0.0

17(0

.00

0)*

**(0

.00

0)*

**(0

.00

0)*

**(0

.611

)DT

en

ure

_Dan

we

i:C

han

ge

inh

uko

ute

nu

rest

atu

s(-

1,0,1)

zero

zero

zero

-22

.63

8-0

.08

3(0

.00

0)*

**(0

.03

9)*

*DT

en

ure

_Gvt

Oth

er:

Ch

ang

ein

go

vtan

do

ther

ho

usi

ng

ten

ure

stat

us

(-1,0

,1)ze

roze

roze

ro2

7.7

86

0.0

17(0

.00

0)*

**(0

.48

4)

Mo

rtg

age

_No

Ch

ang

e:

No

chan

ge

inm

ort

gag

ete

nu

rest

atu

s(1

,0)

zero

zero

zero

zero

-0.0

74

(0.2

48

)R

en

t_N

oC

han

ge

:N

och

ang

ein

ren

ter

ten

ure

stat

us

(1,0

)ze

roze

roze

roze

roze

roD

anw

ei_

No

Ch

ang

e:

No

chan

ge

ind

anw

eite

nu

rest

atu

s(1

,0)

zero

zero

zero

zero

zero

Gvt

Oth

er_

No

Ch

ang

e:

No

chan

ge

inG

vtO

ther

ten

ure

stat

us

(1,0

)ze

ro4

.612

zero

zero

zero

(0.0

00

)***

DRe

loca

tio

nH

sng

:C

urr

ent

ho

me

isin

sub

sid

ized

relo

cati

on

ho

usi

ng

(1,0

)-2

.87

9ze

ro-2

.07

6ze

ro0

.07

5(0

.23

)(0

.02

5)*

*(0

.121)

†

Te

nu

reL

en

gth

Te

nu

re_L

en

gth

2:

Ten

ure

len

gth

atcu

rren

tre

sid

ence

(mo

nth

s)-0

.09

5ze

roze

ro0

.23

90

.00

1(0

.00

5)*

**(0

.02

3)*

*(0

.00

0)*

**T

en

ure

Le

ng

th1_

Pre

198

03:

Mo

ved

into

pre

vio

us

ho

usi

ng

bef

ore

198

0(1

,0)

7.19

5ze

roze

ro-1

7.8

72

zero

(0.0

00

)***

(0.0

08

)***

Te

nu

reL

en

gth

1_19

80

to19

90

:M

ove

din

top

revi

ou

sh

ou

sin

gb

etw

een

198

0an

d19

90

(1,0

)7

.53

4ze

roze

ro-2

0.8

86

zero

(0.0

00

)***

(0.0

02

)***

Te

nu

reL

en

gth

1_19

90

to2

00

0:

Mo

ved

into

pre

vio

us

ho

usi

ng

bet

wee

n19

90

and

20

00

(1,0

)7

.132

zero

zero

6.8

72

zero

(0.0

00

)***

(0.0

00

)***



Table

5C

on

tin

ued

DPcn

tHH

Inc_

Ho

usi

ng

DWo

rkT

Tp

erW

ork

er

DAI

DHo

me

Siz

eDW

ork

ers

Ho

use

ho

ldC

har

acte

rist

ics

Ch

ild:

Pre

sen

ceo

fch

ildin

ho

use

ho

ld(1

,0)

zero

zero

zero

Fam

ilyS

ize

:N

um

ber

of

HH

mem

ber

s-1

.010

zero

7.5

08

(0.0

83

)*(0

.00

0)*

**R

eti

red

HH

:H

Hco

nta

ins

no

wo

rkin

gad

ult

s(1

,0)

zero

-50

4.14

3-1

.99

5-0

.147

(0.0

00

)***

(0.0

09

)***

(0.0

00

)***

Po

ten

tial

En

do

ge

no

us

Re

lati

on

ship

s

DPcn

tHH

Inc_

Ho

usi

ng

:C

han

ge

inp

erce

nt

of

HH

inco

me

spen

to

nh

ou

sin

gN

Aze

ro0

.018

zero

(0.0

00

)***

DWo

rkT

Tp

erW

ork

er:

Ch

ang

ein

mo

nth

lyco

mm

ute

tim

e(m

in),

avg

per

HH

wo

rker

zero

NA

zero

zero

DAI:

Ch

ang

ein

reg

ion

alac

cess

(x10

0,0

00

job

sac

cess

ible

wit

hin

on

eh

ou

r)ze

ro-2

2.7

94

NA

-1.11

10

.00

3(0

.00

0)*

**(0

.06

7)*

(0.0

09

)***

DHo

me

Siz

e:

Ch

ang

ein

squ

are

met

ers

of

ho

usi

ng

con

sum

edze

ro0

.97

5-0

.00

9N

Aze

ro(0

.00

0)*

**(0

.23

8)

DWo

rke

rs:

Net

wo

rker

gai

no

rlo

ssw

ith

mo

veze

ro10

95

.62

8ze

roze

roN

A(0

.00

0)*

**

Bla

nkce

llsin

dica

teth

atth

ere

latio

nshi

pis

cons

trai

ned

tobe

zero

inth

em

odel

beca

use

the

rela

tions

hip

was

not

exam

ined

‘zer

o’in

dica

tes

that

the

rela

tions

hip

isco

nstr

aine

dto

beze

roin

the

mod

eldu

eto

stat

istic

alin

sign

ifica

nce

pva

lues

inpa

rent

hese

s†s

igni

fican

tat

15%

;*s

igni

fican

tat

10%

;**

sign

ifica

ntat

5%;

***s

igni

fican

tat

1%



Table 6 Unstandardized effects of endogenous and exogenous variables — percent netimpact model

Pcnt_NetImpact

DAI DHomeSize DWorkers

Choice Status

No Choice: HH did not move by choice(1,0)

-6.363 -1.384 -32.478 zero(0.047)** (0.008)*** (0.000)***

Income

HHInc1_LessThanHalfMean1: HHincome at previous location less thanhalf the prevailing city mean (1,0)

-8.502 -1.904 zero 0.113(0.008)*** (0.005)*** (0.002)***

HHInc1_LessThanMean: HH income atprevious location less than theprevailing city mean (1,0)

-5.411 -1.066 zero 0.078(0.068)* (0.093)* (0.02)**

HHInc1_LessThan1.5Mean: HH incomeat previous location less than 1.5times the prevailing city mean (1,0)

-7.611 0.003 zero 0.038(0.019)** (0.996) (0.307)

Urban Location and Accessibility/Mobility Changes

DInnerRing: Moved from inner-ringzone (-1,0)

-13.932 13.166 -22.563 -0.007(0.005)*** (0.000)*** (0.007)*** (0.864)

DMiddleRing: Moved from middle-ringzone (-1,0)

-6.409 10.695 -11.655 -0.068(0.068)* (0.000)*** (0.08)* (0.04)**

DOutsideUrban: Moved from outsidethe Outer Ring Road (-1,0)

-2.124 -15.476 -36.926 -0.029(0.637) (0.000)*** (0.001)*** (0.622)

DMeilong/Xinzhuang: Moved torail-served TOD neighborhood (1,0)

-0.139(0.81)

DPuxi: Change in Puxi location status(-1,0,1)

zero 5.75 zero zero(0.000)***

JobChange_Head: HH head changedjobs (1,0)

8.224(0.01)***

JobChange_A: Another adult workerchanged jobs (1,0)

-5.066(0.109)†

DAutoCommuters: Change in numberof workers commuting by auto

3.441(0.000)***

Tenure and Housing Changes

DTenure_Mortgage2: Change inmortgage tenure status (-1,0,1)

-22.403 -0.66 39.515 zero(0.000)*** (0.259) (0.000)***

DTenure_Rent: Change in rentertenure status (-1,0,1)

-16.293 0.69 -3.232 zero(0.000)*** (0.286) (0.674)

DTenure_Danwei: Change in hukoutenure status (-1,0,1)

zero -0.918 -22.895 -0.081(0.214) (0.002)*** (0.038)**

DTenure_GvtOther: Change in govtand other housing tenure status(-1,0,1)

4.464 0.007 31.697 zero

(0.147)† (0.988) (0.000)***

Mortgage_NoChange: No change inmortgage tenure status (1,0)

-7.794 zero 19.799 zero(0.163) (0.111)†

Rent_NoChange: No change in rentertenure status (1,0)

zero zero zero zero

Danwei_NoChange: No change indanwei tenure status (1,0)

zero zero zero zero

GvtOther_NoChange: No change inGvtOther tenure status (1,0)

zero 4.637 -19.86 zero(0.000)*** (0.044)**

DRelocationHsng: Current home is insubsidized relocation housing (1,0)

zero -2.119 zero zero(0.019)**



due to the availability of low-income and subsidized housing for forced movers. All otherrelationships, however, were as expected. The lowest-income households are transferringamong the highest proportion of income to cover new housing expenses — more than6.8% more than those in the highest-income category — though the difference in incometransferred to housing among the three bottom income groups is marginal.

Table 5 also indicates that non-choice movers see increased travel time relative tochoice movers (they increase their travel by about 200 minutes more per worker thanchoice movers), decreased accessibility to regional jobs relative to choice movers, andare seeing smaller increases in housing consumption than choice movers (almost 34fewer square meters consumed per household). The lowest-income movers, in addition,are the most adversely-affected income group with regard to accessibility and workercomposition. The lowest-income category is more likely to send a worker back to workafter the move — likely to make up for household budget shortfalls as housing and travelcosts increase.

Net impact modelsTables 6 and 7 indicate that choice status impacts the loss of income left over afterhousing and travel are paid for: in the Percent Net Impact and Net Impact models,respectively, non-choice movers are transferring more than 6% more of their income than

Table 6 Continued

Pcnt_NetImpact

DAI DHomeSize DWorkers

Tenure Length

Tenure_Length2: Tenure length atcurrent residence (months)

0.119 zero 0.148 0.002(0.055)* (0.228) (0.016)**

TenureLength1_Pre19803: Moved intoprevious housing before 1980 (1,0)

-6.582 -12.132 zero(0.044)** (0.108)†

TenureLength1_1980to1990: Movedinto previous housing between 1980and 1990 (1,0)

-8.944 -9.903 zero(0.007)*** (0.199)


zero 13.278 zero(0.053)*

Household Characteristics

FamilySize: Number of HH members zero 7.77(0.000)***

RetiredHH: HH contains no workingadults (1,0)

2.49 -1.498 zero(0.484) (0.045)**

Potential Endogenous Relationships

DPcnt_NetImpact: Change in percentof discretionary income

NA -0.032 0.931 zero(0.004)*** (0.005)***

DAI: Change in regional access(x100,000 jobs accessible withinone hour)

zero NA -2.164 0.004(0.000)*** (0.027)**

DHomeSize: Change in square metersof housing consumed

-0.235 zero NA zero(0.002)***

DWorkers: Net worker gain or losswith move

3.656 zero zero NA(0.202)

Blank cells indicate that the relationship is constrained to be zero in the model because the relationship was notexamined‘zero’ indicates that the relationship is constrained to be zero in the model due to statistical insignificancep values in parentheses†significant at 15%; *significant at 10%; **significant at 5%; ***significant at 1%



Table 7 Unstandardized effects of endogenous and exogenous variables – net impact model

NetImpact DAI DHomeSize DWorkers

Choice Status

No Choice: HH did not move by choice(1,0)

-7,605 -1.467 -29.15 zero(0.002)*** (0.005)*** (000)***

Income

HHInc1_LessThanHalfMean1: HHincome at previous location less thanhalf the prevailing city mean (1,0)

2,662 -1.545 zero 0.112(0.295) (0.02)** (000)***

HHInc1_LessThanMean: HH income atprevious location less than theprevailing city mean (1,0)

1,782 -0.799 zero 0.078(0.447) (0.204) (0.02)**

HHInc1_LessThan1.5Mean: HH incomeat previous location less than 1.5times the prevailing city mean (1,0)

-4,409 0.118 zero 0.038(0.084)* (0.862) (0.308)

Urban Location and Accessibility/Mobility Changes

DInnerRing: Moved from inner-ringzone (-1,0)

-7,208 13.14 -26.495 -0.005(000)*** (000)*** (0.000)*** (000)***

DMiddleRing: Moved from middle-ringzone (-1,0)

-1,586 10.675 -14.894 -0.066(000)*** (000)*** (0.008)*** (0.044)**

DOutsideUrban: Moved from outsidethe Outer Ring Road (-1,0)

1,854 -15.281 -35.77 -0.031(000)*** (0.000)*** (000)*** (0.6)

DMeilong/Xinzhuang: Moved torail-served TOD neighborhood (1,0)

0.177(0.76)

DPuxi: Change in Puxi location status(-1,0,1)

zero 5.7 zero zero(0.000)***

JobChange_Head: HH head changedjobs (1,0)

9,798(0.000)***

JobChange_A: Another adult workerchanged jobs (1,0)

-3,331(000)***

DAutoCommuters: Change in numberof workers commuting by auto

3.637(0.000)***

Tenure and Housing Changes

DTenure_Mortgage2: Change inmortgage tenure status (-1,0,1)

-8,698 -0.205 22.936 zero(0.000)*** (0.696) (0.000)***

DTenure_Rent: Change in rentertenure status (-1,0,1)

-6,826 0.918 -11.207 zero(0.004)*** (0.146)† (0.065)*

DTenure_Danwei: Change in hukoutenure status (-1,0,1)

zero -1.074 -18.801 -0.081(0.144)† (000)*** (000)***

DTenure_GvtOther: Change in govtand other housing tenure status(-1,0,1)

3,556 0.059 30.139 zero

(0.125)† (0.898) (0.000)***

Mortgage_NoChange: No change inmortgage tenure status (1,0)

-1,945 zero 12.142 zero(0.66) (0.273)

Rent_NoChange: No change in rentertenure status (1,0)

zero zero zero zero

Danwei_NoChange: No change indanwei tenure status (1,0)

zero zero zero zero

GvtOther_NoChange: No change inGvtOther tenure status (1,0)

zero 4.604 -19.936 zero(0.000)*** (0.03)**

DRelocationHsng: Current home is insubsidized relocation housing (1,0)

zero -2.206 zero zero(0.014)**



choice movers to cover housing and travel costs on a percentage basis, and more than7,600 RMB more on an absolute basis. This is a significant result: although Table 5indicates that non-choice movers are a bit better-off when it comes to housing outlay, thechoice metric in the Percent Net Impact and Net Impact models indicate that theadditional travel burdens and lack of wage adjustments are not offsetting increasedhousing and travel costs for non-choice movers.

Furthermore, as a percentage of income change, the lowest-income households aretransferring the highest amount to cover the increased costs for housing and travelassociated with a suburban relocation — more than 8.5% more than the highest-income(suppressed) category and about 1% more than the income class with the next highestimpact.

The variables related to changes in accessibility, home size and number of workersappear in all three models — and the significance, signs and magnitudes of thecoefficients in the Percent Net Impact and Net Impact models are consistent with thoseof the Housing-Travel Trade-Offs model.

It is notable that Tables 5, 6 and 7 indicate that location near a rail station (representedby the variable indicating a move to the rail-served Meilong and Xinzhuangcommunities) does not have a significant accessibility advantage associated with it. Itwas expected that this would be a significant relationship, and this unexpected finding islikely due to the strong significance of the variable DAI, which accounts for the commuteadvantage of living near the rail station.

Table 7 Continued

NetImpact DAI DHomeSize DWorkers

Tenure Length

Tenure_Length2: Tenure length atcurrent residence (months)

285 zero 0.057 0.002(0.000)*** (000)*** (0.016)**

TenureLength1_Pre19803: Moved intoprevious housing before 1980 (1,0)

-5,092 -10.338 zero(0.043)** (0.143)†


-6,687 -9.097 zero(0.008)*** (0.203)


zero 15.357 zero(0.016)**

Household Characteristics

FamilySize: Number of HH members zero 5.652(000)***

RetiredHH: HH contains no workingadults (1,0)

-6,829 -1.757 zero(0.015)** (0.019)**

Potential Endogenous Relationships

NetImpact: Change in discretionaryincome (RMB)

NA 0.001 0.001 zero(0.01)*** (0.018)**

DAI: Change in regional access(x100,000 jobs accessible withinone hour)

zero NA -1.899 0.004(0.000)*** (0.031)**

DHomeSize: Change in square metersof housing consumed

-112 zero NA zero(0.031)**

DWorkers: Net worker gain or losswith move

1,939 zero zero NA(0.389)

Blank cells indicate that the relationship is constrained to be zero in the model because the relationship was notexamined‘zero’ indicates that the relationship is constrained to be zero in the model due to statistical insignificancep values in parentheses†significant at 15%; *significant at 10%; **significant at 5%; ***significant at 1%



It is also notable that, with regards to the Net Impact and Percent Net Impact models,a potentially problematic bi-directional relationship exists between DPcnt_NetImpactand DHomeSize. The model coefficients indicate that an increase in home size implies aloss of net income (i.e. a larger portion of income is shifted to cover housing and travelexpenditures — a loss of 0.235% in DPcnt_NetImpact for each added square meter ofhousing consumed. This relationship is as predicted; however, the converse relationshipindicates that a 1% increase (or 1% smaller loss) in discretionary income implies an0.931 square-meter increase in home size in the Percent Net Impact model. In theory, weexpected the signs on the two relationships to match and be negative. However, the signon the DPercent Net Impact → DHomeSize is positive. The same relationship appears inthe Net Impact model (no figure presented).

This relationship is illustrated in Figure 6, which shows the relationships amongvariables considered to be potentially endogenous in the model. The solid arrowsindicate a significant relationship; the thin dashed arrows indicate that the relationshipwas constrained to zero in the model. The figure also summarizes the control variablesincluded in the models — those related to choice and income, tenure, urban location,commuting and household composition. These figures show standardized coefficients (inparentheses) in addition to the unstandardized estimates presented in Table 6. Therelationship in question here appears as a heavy arrow with a dashed line.

One potential explanation is that the indicator Net Impact is compound, reflecting bothchanges in income levels and changes in housing and travel expenditure — which canmake the relationships difficult to interpret. On one hand, we expect that increases inincome would increase housing consumption. On the other hand, increases in housingexpenditure that are unrelated to home size (e.g. location or quality upgrades) coulddecrease consumption. Further, increases in transport costs would tend to increaseconsumption. Depending on the relative weights of these impacts, the overall sign on therelationship could either be positive or negative.

The differing signs could also be related to multicollinearity. In structural equationmodels, multicollinearity can be problematic because it can inflate standard errors(Kline, 2005) and bias coefficient estimates, which is the problem we are seeing here. Incorresponding Ordinary Least Squares (OLS) regression models (see Day, 2009), thesigns in both directions are negative — indicating that multicollinearity is more likely thecause of the problem.

Figure 6 Unstandardized and standardized trade-off relationships in percent net impactmodel (solid arrows indicate significant relationship; thin dashed arrows indicate thatrelationship constrained to zero in the model; standardized coefficients in parentheses)



To identify multicollinearity here, we examine the regressors on DHomeSize to see ifthey are correlated with DPercentNetImpact or DNetImpact. Significant correlations herewould indicate the presence of multicollinearity that could be causing the problematicpositive sign. The analysis of Pearson correlations indicates a strong relationship(-0.3559) between the variable for a change to and from mortgage tenure, and PercentNet Impact. Likewise, there are significant correlations between Net Impact and themortgage variable.

One way to deal with multicollinearity is to eliminate one of the correlated regressorsfrom the system of equations. In this case, however, eliminating the problematicrelationships would result in empirical underidentification of the model (Kline, 2005),given the essential influence of home ownership in the computation of DHome Size.Instead, the correlated variables were allowed to remain, with the caveat thatmulticollinearity is present. Also, the formulation that includes the multicollinearrelationship also clears up some other logical inconsistencies in the models, and providessome information about causality among the dependent variables (see Day, 2009 formore detail).

Table 8 summarizes the results of the structural equation models presented in thischapter. The lowest-income group is either worse off or similarly affected for all of thewelfare indicators analyzed. On balance, then, this implies that this group is affectedmore adversely than those with higher incomes. With regard to the comparison of choiceand non-choice movers, the comparison is more ambiguous. Changes in proportionalhousing outlay favor those who did not move by choice, but travel time, accessibility, netimpact and housing-consumption changes favor choice movers.

Policy recommendationsReconsider compensation structures and public participation

As mentioned previously, compensation reform shifted compensation structures frombeing based on the number of family members in the household to the market value ofthe property as assessed at the time of the move.3 This kind of scheme does not take intoaccount the changes that have occurred in the housing system in the old and neweconomies in China, nor does it compensate households for increased travel for work,loss of job accessibility and therefore potential employment upgrades, loss of proximityto social support networks. Furthermore, this compensation scheme does not take urbanland values or location into account — that is, households are not given compensationthat reflects their location in the urban region.

Although this survey does not reveal how much compensation households are beinggiven for their move, it is reasonable to assume that households will generally userelocation compensation to offset the costs of their new housing and other costs — andtherefore, if relocation compensation were adequate, households would experience noincreases in outlay for these necessities. This is not the case for the sample of thepopulation surveyed here; current compensation structures are not working. According tothis analysis, for the most vulnerable households, budgets are not keeping pace with theneed for increased housing and travel expenditures when households relocate to theurban fringes. One policy remedy, then, would be to adjust housing subsidies andrelocation compensation packages to bring combined monthly housing and travel outlayto a level manageable within the household’s budget — until such a time that thehousehold’s wages catch up with the increased need for spending. In light of the scarcityof affordable housing and drastically changing travel needs when people move to theurban edge from elsewhere in the city, relocation compensation should be based on acomprehensive review of household finances and disposable income.

3 Almost all of the households participating in this study relocated after 2001.



Although spokespersons for government and development companies often reportsmooth resident relocations in which households are provided fair compensation, aretotally satisfied with the terms of relocation, and peacefully vacate the premises (CDT,2006; 2008), this is not the story that emerges time and time again from the perspectiveof those who are relocated. Often, movers are being relocated out of small, substandard,very old housing that sits on prime real estate in or nearby the urban center, or in someother place of high or potentially high value. Thus, if the quality of the housing only istaken into consideration, the compensation should be quite low. However, if the locationof the housing — not to mention the replacement value of housing in Shanghai — isconsidered, this would increase compensation levels considerably. Complicating theseconcerns is the fact that many relocatees were not owners, and even in use-rights-strongShanghai, their claims to the buildings that they occupy are questionable in the neweconomy that has left behind the principle of welfare housing.

Developers and government, then, have different ideas about fairness than thecommon citizen, because of their different interests and incentives. The problemcurrently is that only government and developers have a say in the process — the idea of

Table 8 Summary of SEM Results

Inquiry OutcomeChoice Income

1) How do different incomeand choice groupscompare with regards tochanges in housing outlayas a percent of income?

Non-choice movers aretransferring on averagemore than 4% less oftheir discretionary incometo housing, comparedwith choice movers

The lowest-income groupis among thosetransferring the highestamount of discretionaryincome to housing

2) How do different incomeand choice groupscompare when it comesto travel time changes?

Non-choice movers havethe larger traveltimegains – up to 200minutes per worker permonth more than choicemovers

No effect

3) How do different incomeand choice groupscompare when it comesto regional accessibilitychanges?

Non-choice movers havethe largest accessibilitylosses. In addition, thoseliving in relocationhousing have additionallosses of accessibility

The lowest-incomemovers suffer the highestaccessibility losses.

4) Are wages keeping upwith housing and travelcost increases?

Non-choice moversexperience a larger lossof discretionary income(both on an absolute andpercent basis) thanchoice movers

The lowest-income groupshas suffered the largestloss in discretionaryincome, on a percentbasis

5) How do home sizechanges vary acrosschoice and incomegroups?

Non-choice movers aregaining less housingspace than choicemovers, in terms ofabsolute space

No effect

6) Are choice andlower-income moversmore likely to increasethe number of householdworkers with the move?

No effect The lowest-incomehouseholds add moreworkers, on average, thanthose with higher incomes

7) Are households makinghousing-travel trade-offs?

Trade-off relationships are complicated bymulticollinearity issues.



public participation has not yet entered planning processes in China. The negotiation ofcompensation for relocation is one area where increased citizen participation in planningspecifically and government in general could help to represent the interests of all actorsin the relocation process, in effect facilitating relations between the developers, thegovernment and the public, and potentially prevent or dampen related social upheavalrelated to dissatisfaction with imposed relocation.

Other authors (for example Wu, 2004) have argued the exact opposite of the above:that housing compensation is skewed in favor of those who are relocated, because thehousing units with which they are compensated are worth far more than those they movefrom. Wu argues for a monetized compensation system, rather than an in-kindcompensation system, that compensates relocated households according to the value ofthe property they’ve given up. We assert that a more complete accounting of householdimpacts shows that compensation is still skewed against those who are relocated, and thatthese discrepancies should be factored into the compensation system. As this article goesto press, new regulations in Shanghai are being introduced to modify resettlementcompensation packages that more accurately reflect the market price of housing —though other effects such as induced travel costs do not appear to be addressed (ShanghaiMunicipal People’s Government, 2009). The impacts of these new policies will beimportant to observe in the coming years.

Provide affordable housing

There is a need in Shanghai and many Chinese cities to produce more affordable housing,particularly for households that are involuntarily uprooted from their original residences.In July 2006, the Ministry of Finance reported an alarming dearth of affordable housingin China’s major cities. At that time, nationally, less than $600,000,000 had been spenton low-rent housing since the early 1990s. Furthermore, 70 cities were providing nolow-rent housing at all (Bremner, 2006). In 2007, the central government expandedinvestment in low income housing, adding more than $9,000,000 in investment (Hu,2007).

This lack of affordable housing is problematic in current China because housingprices are increasing, incomes are not keeping up with rising costs for a large portion ofthe population, and suburbanization requires larger expenditures for travel. According tothe National Development and Reform Commission, average residential and commercialproperty prices were nearly 6% higher in June 2006 than they were in 2005 in China’s70 largest cities (Bremner, 2006). In January 2008, property prices in China’s majorcities rose 11.3% compared with the same period the previous year. Shanghai was up10.1% (Chinaview, 2008).

Planning of transit nodes and/or upgraded transport facilities

The results of this analysis indicate that those who live in highly accessible locales enjoycommute-time savings over those who do not — and relocated households enjoy lessaccessibility than their choice-mover counterparts. One way to mitigate this inequitywould be the placement of affordable housing nearer to regional transportation hubsand/or mandating that development companies pay for or partially fund high-qualitytransportation facilities (e.g. express bus services) that serve their relocation projectswith service quality similar to that of rail. Improved transportation services might alsoincrease household satisfaction with the move.

Hu (2007) cites the government-run Shanghai newspaper, the Jiefang Daily, asclaiming that peripheral relocation housing built in the future would be located inconvenient locations near transit hubs. Careful scrutiny by researchers and planners inthe coming years should hold Shanghai’s officials accountable to this statement.



Accessibility would be increased across the board if job development wereencouraged near suburban transit stations. While in large cities like Beijing andShanghai, planning strategies have included large industrial and technologicaldevelopment zones, suburban job development has not kept up with housingdevelopment in the suburbs. Consequently, many new suburban developments builtalong rail lines have become ‘typical bedroom communities’, as evidenced by heavyone-directional traffic during peak work-commute hours (Zhang, 2007). Transit-orienteddevelopments with mixed-income housing found on the outskirts of Scandinavian citieslike Stockholm and Copenhagen provide a potentially useful model for Chinese cities(Cervero, 1998).

Barriers to policy implementation

The policy interventions provided here would require marked increases in the costs ofrelocating households, and consequently in the costs of development projects. Given theimportance of property-led development in filling out cities’ operating budgets, it is inthe interest of cities to continue to allow housing developers the enormous profits theyare generating. The Chinese national government and city governments are currentlymaking decisions about the balance of overall economic growth versus distributionalequity, and by many accounts, the goal of the former significantly clouds the latter. Weobserve the dominance of the current model of urban development as a barrier toimplementing the reforms aforementioned.

Jennifer Day ([email protected]), Faculty of Architecture, Building and Planning,Architecture Building, The University of Melbourne, Parkville, VIC 3010, Australia andRobert Cervero ([email protected]), Department of City and Regional Planning, 228Wurster Hall #1850, University of California, Berkeley, CA 94720-1850, USA.

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RésuméAu cours des trois dernières décennies, les grandes villes chinoises ont connu une vasterestructuration spatiale à la suite des réformes du marché et de l’essor économique. Ilen a résulté notamment une rapide migration des habitants des villes vers la périphérie.Certains y ont été forcés à cause de l’augmentation des loyers urbains ou de larécupération de leur logement par le gouvernement. D’autres ont déménagévolontairement dans des logements modernisés ou pour d’autres motifs liés au mode devie. L’article examine les incidences des relogements en limite urbaine sur le bien-êtredu foyer. Il s’intéresse aux facteurs à la base des évolutions des frais de logement et detransport lors d’un déménagement à la périphérie. De plus, l’étude cherche si lesconséquences sont différentes selon que les habitants ont déménagé par obligation oupar choix: par rapport à ceux qui en ont fait le choix, les habitants contraints de partirsont affectés de manière disproportionnée et défavorable en termes d’accessibilité autravail, de temps de trajet, de consommation propre au logement et de revenu disponible.Les résultats montrent aussi que, comparés aux ménages aux revenus supérieurs, lesgroupes à plus faible revenu subissent un impact non proportionnel pour ce qui est desfrais de logement, des pertes d’accessibilité, du revenu disponible et du nombre d’actifsdans le ménage. L’indemnité de relogement attribuée aux ménages obligés de partirdevrait donc être augmentée afin de dépasser la seule valeur du logement: elle devraitcouvrir le changement d’implantation urbaine, les besoins du ménage et les coûts derelogement.



Effects of Residential Relocation on Household and Commuting Expenditures in Shanghai, China

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