Dynamic life course analysis on residential location choice

Transportation Research Part A xxx (2017) xxx–xxx

Contents lists available at ScienceDirect

Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

Dynamic life course analysis on residential location choice Biying Yu a,b,⇑, Junyi Zhang c, Xia Li b a

Center for Energy and Environment Policy Research, Beijing Institute of Technology, Beijing 100181, China School of Management and Economics, Beijing Institute of Technology, Beijing 100181, China Transportation Engineering Laboratory, Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima 739-8529, Japan b c

a r t i c l e

i n f o

Article history: Received 31 March 2016 Received in revised form 1 October 2016 Accepted 11 January 2017 Available online xxxx Keywords: Residential location choice Life course Intertemporal dependence Dynamic choice model Discounted utility Life domains

a b s t r a c t From a behavioral viewpoint, people choose where to live based on various factors, including their current situations, past experience, and plans for the future. Some aspects of residential preference might be constant over time, inherited from the initial stage of life, and other parts might be responses to residential biography or other biographical domains like household structure, employment/education, and travel. Capturing these intertemporal dependences needs a life course analysis of residential location choices. However, a serious methodological gap exists between the perceived importance of dynamic life course analyses and quantitative modeling approaches. This study developed a dynamic choice model with cross-sectional and longitudinal heterogeneities as well as discounted utility (called the DU-DCLH model) to describe the decision-making process for residential relocation by incorporating various intertemporal dependences over the life course. Model parameters were estimated using data collected from a life history survey conducted in Japan in 2010. The estimation results firstly confirm the effectiveness of the DU-DCLH model for portraying the dynamics of residential mobility over a life course. Next, it was found that previous experiences dominate decisions on residential location choice and can explain more than 75% of the total variations in choice. It was also revealed that as the mobility age increases, the influence of the past on their choices increases continuously. In contrast, the influence of the present situation is small and almost negligible. Furthermore, the study empirically confirmed not only the influence of time-constant and time-varying preference for residential neighborhoods but also the specific influence of household biography, employment/education biography, and travel biography. This study enriches the existing research by providing a systematic modeling framework incorporating broader behavioral mechanisms for residential location choice over the life course. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Urban transportation planning must incorporate various life needs, such as job, school, residence, social networks, daily activities, and travel behavior, into its decision-making processes. For example, the configuration of transportation facilities in residential neighborhoods is an important input into transportation planning. Inversely, planning may lead to changes in residential locations. In other words, changes in residential location are among the outcomes of planning. Planners therefore need to understand better people’s long-term decisions on various life choices (e.g., job, school, residence, and family ⇑ Corresponding author at: Center for Energy and Environment Policy Research, Beijing Institute of Technology, Beijing 100181, China. E-mail addresses: [email protected] (B. Yu), [email protected] (J. Zhang), [email protected] (X. Li). http://dx.doi.org/10.1016/j.tra.2017.01.009 0965-8564/Ó 2017 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

2

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

structure), which are an important part of a life-oriented approach to planning and policy (Zhang, 2015). As an aid to understanding the above long-term decisions, the life course approach in recent years has been attracting increasing attention in the field of transportation (Scheiner, 2006; Zhang et al., 2014). Within this approach, it is essential to capture mobilities in various life domains, not just travel mobility. Here, unless otherwise noted, the concept of mobility follows the general definition given by Hannam et al. (2006), which encompasses any changes in the various life domains. This study especially focuses on residential mobility, represented by residential location changes over the life course. Residential location change is a special biographical moment, in which familiar routines are likely broken (Scheiner, 2006). This may, consequently, result in changes in the accessibility of various daily facilities, such as workplace, school, bus stops and/ or train stations, shopping facilities, and leisure facilities. Multiple studies in transportation have analyzed the mechanisms of residential location choice and have concluded that land use policies, related to the residential environment at the neighborhood level, commonly lead to changes in people’s travel behavior (Bhat and Guo, 2007; Yu et al., 2015, 2012). Examining the state of the art, however, most of the existing studies are based on cross-sectional data and have neglected the temporal dynamics of decision-making over the life course (Oakil, 2013). From the behavioral viewpoint, households/individuals may choose their residential location based on not only their current situations but also their previous experiences and their plans for the future (Giele and Elder, 1998; López-Ospina et al., 2016). In this regard, even though, generally speaking, many human behaviors are forward looking (Chan and Stevens, 2004; Van der Klaauw, 2012; Van der Klaauw and Wolpin, 2008), relevant studies in transportation are very limited, as argued by Zhang et al. (2014) and Tran et al. (2015). In relation to residential mobility, some aspects of personal preference may be invariant over the life course, partially because of the influence of habit, norms, values, and/or the resulting lifestyle, while the other parts may vary, partially because of the influence of important life events. Furthermore, existing studies have shown that residential mobility is also affected by mobility in other life domains, such as household structure mobility, employment/education mobility, and travel mobility (Beige and Axhausen, 2012; Schoenduwe et al., 2015; Zhang et al., 2014), while residential mobility is also often accompanied by change in the other life domains (e.g., professional career, car ownership, and travel behavior) (Müggenburg et al., 2015; Oakil, 2013). Households may alter their lifestyles by collectively adjusting their behaviors in various domains in response to, or in anticipation of, mobilities within the household (Zhang et al., 2014). In other words, the influence of other domains on residential mobility cannot be ignored. Keeping the above concerns in mind, the life course approach seems to be promising for exploration of intertemporal mechanisms of correlated or interacting decisions on long-term choices, including residential mobility; however, there are serious methodological gaps between the perceived importance of life course analysis and the development of promising modeling frameworks, as argued by Zhang et al. (2014). To fill this research gap, this study develops a dynamic residential location choice model based on the concept of discounted utility over the life course, where mobilities in several other life domains, before and after any change of residence, are also incorporated. Additionally, both cross-sectional and longitudinal heterogeneities are jointly accommodated. Data were collected through a web-based retrospective life history survey, which was conducted in Japan in November 2010. In this survey, each respondent (i.e., one household member) was asked to report his/her mobilities in four life domains: residential mobility, household structure mobility, employment/education mobility, and car ownership mobility, since turning 18. As a result, 1000 respondents provided valid information. We start by interpreting the intertemporal decision-making mechanism for residential location choice over the life course, followed by a brief review of the existing research in Section 2. Section 3 introduces the methodology developed for depicting the residential location choice behavior over the life course. After the survey data are outlined in Section 4, model estimation results are analyzed and discussed in Section 5. The last section concludes this study along with some insights for future analysis.

2. Mechanisms of residential location choices Decisions on residential mobility are dynamic in nature (Oakil, 2013; Zhang et al., 2004). In early life, people may form their initial preference for residential location built on their needs, attitudes, sociodemographic and economic characteristics, and experiences while living under the care of their parents or other guardians, and so on. Some parts of the initial preference may remain unchanged through the whole life course, while other parts may vary over time because of the influence of various factors, including factors related to not only residential biography but also other biographies (i.e., external events) (Blossfeld and Blossfeld, 2015), such as income change, college degree, employment, marriage, parenthood, homeownership, travel mobility, and divorce. Meanwhile, people may adapt their preference according to their accumulated historical experience or future plans (Zhang et al., 2014). People finally decide where to reside based on their overall preference, which may include some time-constant parts inherited from the past, and time-variant parts, which may be endogenously modified. Such kinds of inheritance and covariation suggest that residential mobility is not an instantaneous event but a result of intertemporal choices. Keeping these issues in mind, it is necessary to conceptualize the residential location choices as a long-term decision-making process, where individual or household behaviors change with decisions in various life domains over the life course, while path-dependency and anticipation play an important role in this process as well. We simplify the above behavioral mechanisms, as shown in Fig. 1. Such mechanisms may be applicable to other mobility decisions (e.g., household mobility, employment mobility, and travel mobility) as well. Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

3

Fig. 1. Intertemporal decision-making process for residential location choice.

The concepts shown in Fig. 1 are supported by existing literature. First, the dynamic nature of human decisions was already well recognized in the 1980s (Heckman, 1979). This is especially true with respect to the influences of past choices and preferences on present behavior. Such backward-looking behavior may be decoded on the basis of reinforcements (and punishments) for past behavioral choices (Burke and Gray, 1999). Second, the influence of initial conditions on choice behaviors were revealed by panel surveys, which are used to capture behavioral changes over time, as early as the late 1990s (Ailawadi et al., 1999; Degeratu, 1999; Roy, 1998; Roy et al., 1996). This indicates that the start timing of panel surveys is important in representing dynamic human behavior (Zhang et al., 2004). Initial conditions are used to reflect the influence of previous preference and/or choice results that cannot be observed during panel surveys. Conceptually, the aforementioned initial preference and initial conditions are interchangeable. Third, many human decisions are forward looking (Burke and Gray, 1999; Chan and Stevens, 2004; Tran et al., 2015; Van der Klaauw and Wolpin, 2008). Burke and Gray (1999) argue that such forward-looking behavior may be made because of the existence of an internally held standard or goal. A considerable body of analyses of residential location choices is currently available (Bhat and Guo, 2007; Choudhury and Ayaz, 2015; Schoenduwe et al., 2015; Sener et al., 2011; Tillema et al., 2010). However, because cross-sectional data are mainly used, the learning effect of previous experiences and the influence of future anticipation and of key life events on residential mobility decisions have generally been disregarded. Such analyses have not extended our understanding of the dynamics of individual behavior over the life course. Recently, scholars have been increasingly treating residential mobility in a life course framework for providing a broader picture of individual mobility (Clark and Whiters, 2008; Coulter et al., 2015; Kulu, 2008; Lanzendorf, 2003; Scheiner and Holz-Rau, 2013). For instance, Warner and Sharp (2015) demonstrated, based on a random-effect discrete choice model using panel data, that residential mobility is strongly affected by unanticipated and disruptive life events (e.g., marriage, divorce, parenthood, homeownership, employment, college, and unemployment) in the United States. Chen et al. (2009) found that people become more tolerant to long commuting distances and regard residential environmental attributes like accessibility to retail, open space, and recreation opportunities as more valuable after having been exposed to them in the past, providing evidence that individuals adapt their preference to match their historical deposition. In addition, they also revealed that household events (e.g., parenthood) prevailed in decisions on residential location. Zhang et al. (2014) showed that past dependence and future expectations (not only short-term but also long-term) play a substantial role in explaining the occurrence and nonoccurrence of residential relocation and also confirmed significant interdependence between residential mobility and life events in household biography, employment/education biography, and car ownership biography. More examples can be found in Müggenburg et al. (2015). Even though the life course concept opens a new array of possibilities for mobility research, existing literature in the context of transportation has just scratched the surface in application to mobility problems. One of the main obstacles, blocking further progress, is the modeling approaches available for representing life course dynamics. Until now, two groups of methods have dominated mobility analysis in the context of individual/household life course. The first group is the logit-form models (e.g., logit model, mixed logit model, and logistic regression) for explaining the occurrence or nonoccurrence of the events. These methods treat the records of a respondent, in observed years, as independent, after controlling for individual-specific or time-specific effects (Beige and Axhausen, 2008, 2012; Clark et al., 2014; Oakil, 2013; Warner and Sharp, 2015). The second group includes the event history models (also known as survival-time analysis, duration models, or hazard models) for analyzing the timing or the length of time leading up to an event. These treat the episodes—i.e., the periods between consecutive mobilities—of the same respondent as independent, after controlling for individual-specific effects (Beige and Axhausen, 2008; Kulu, 2008; Kulu and Steele, 2013; Rashidi et al., 2011). The strategy of both of these model types is to simplify the description of the intertemporal choices by disassembling the events or trajectories in the individual’s or household’s life course into independent pieces. However, the inclusion of life course dynamics, hitherto poorly

Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

4

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

recognized, requires far more sophisticated methods. Moreover, prior studies mainly focused on the occurrence or timing of each mobility, while decisions on where to live have seldom been investigated in the context of life course. In reality, residential location choice is a household matter, with intrahousehold interactions and relative influences of different members affecting this decision (Borgers and Timmermans, 1993; Zhang and Fujiwara, 2009). However, tracing residential mobility over the life course at a household level is surely not an easy task. With this consideration, in this study, we implemented a life history survey by targeting just one member per household and asking him/her to report his/her mobility history and some household information. The second author of this paper has already developed a group discrete choice model in the context of residential location and commuting mode choices, where both the relative influence of different household members and intra-household interactions are explicitly incorporated, using the concept of multilinear utility (Zhang and Fujiwara, 2009). Even though we think that such an approach could be applied to this study, we have to ignore the above group decision-making mechanisms because of the difficulty of data collection. A lack of promising approaches in the literature for representing life course temporal dynamics encouraged us to perform this study. Extending our approach to the group decision context is left as an important research issue. 3. Methodology Following the behavioral mechanism shown in Fig. 1, this study attempts to represent the decision-making for residential location choice over the life course by treating individual mobilities in the same analysis framework, especially the influence of time-constant and time-varying preference, caused by life events in the residential and other domains, historical experience, and future expectations. To this end, a utility-based approach, named the dynamic discrete choice model with crosssectional and longitudinal heterogeneities (DCLH model) developed by Zhang et al. (2004), was first adopted to illustrate how individuals decide where to live by reflecting their various intertemporal dependences over the life course. We let i (i = 1, 2, . . ., I) denote individuals, j (j = 1, 2, . . ., J) refer to the residential neighborhood (i.e., the residential choice alternatives), and m (m = 1, 2, . . ., M) indicate the mth residential mobility over the life course. Note that M is the total number of residential movements over the observed life course and is different across individuals. With the above notations, the utility function of residential location choice related to each mobility can be written as follows:

U ijm ¼ uijm þ eijm ¼ him xijm þ eijm : neighborhood j is chosen if U ijm > max U ij0 m ðj ¼ 1; 2; . . . ; JÞ; 0 0

j –j

ð1Þ

where U ijm is the total utility comprised of the observed utility uijm , and the unobserved utility eijm ; xijm is a set of residential environment (RE) attributes for neighborhood j associated with individual i’s decision on residential location in response to the mth mobility. him is the coefficient vector depicting individual i’s or his/her household’s preference for RE attributes in xijm relevant to the mth mobility. To represent the intertemporal dependence of the preference, him is further decomposed as follows:

him ¼ him1 þ qim Dh:

ð2Þ

Here, him1 is the preference for RE attributes during the (m  1)th mobility, Dh is the preference change per unit of time, and qim is the heterogeneous influence of Dh through the time leading up to the mobility m for individual i. Taking the initial stage as the reference, Eq. (2) can be reformulated as: m X him ¼ h0 þ Dh qis ;

ð3Þ

s¼1

where h0 denotes the preference for RE attributes in the initial stage. Thus, the overall preference for a residential environment associated with the mth mobility (i.e., him ) consists of the prefP erence inherited from the beginning (i.e., h0 ) and the preference changing over time (i.e., Dh m s¼1 qis ), and heterogeneities across households are also jointly reflected in him . In summary, Eq. (3) is the mathematical form for portraying intertemporal dependencies derived from the time-constant and time-varying preferences shown in Fig. 1. The time-varying preference for residential location may be shaped partially by various life events not only in the residence domain but also in other domains (e.g., household structure, job, and education). The time elapsed since the previous mobility may also influence the time-varying preference, which may also vary across households. Accordingly, we define qim as follows, where both the above time-related effects and the influence of household heterogeneity are reflected:

qim ¼ sim  xim ; xim ¼ jZ im ; Z im ¼ fAim ; Ei;mt ; Ei;mþs g;

ð4Þ

where sim denotes the length of time between the mth mobility T im and its previous mobility T im1 , i.e., sim ¼ T im  T im1 (T im , T im1 : individual i’s ages at the mth and m  1th mobilities, respectively). Substituting Eq. (4) into Eq. (3), it is obvious that Dh  sim signifies the preference change over the period between Tim1 and Tim caused by the aforementioned time-related effects. This change, which may vary further across households, is captured by the vector xim (xim ¼ jZ im ). Z im includes individual or household attributes Aim at the mth mobility and life events at different time points over the life course (Ei;mt ; Ei;mþs : Ei;mt refers to life events that occurred during t years prior to the mth mobility, and Ei;mþs refers to those that occurred during s years after the mth mobility); and j is the parameter vector for Z im . Note that the above life events may include not only Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

5

those in the residence domain but also those in other domains. In this way, household-specific preferences for the residential environment attributes are incorporated. Integrating Eqs. (1)–(4), we can obtain the full description of the observed utility uijm in the DCLH model:

uijm ¼ him xijm ¼

h0 þ Dh

m X

!

sis  jZ im  xijm ¼ h0 þ

s¼1

m X

!

sis  j~ Z im

 xijm :

ð5Þ

s¼1

~ for Z im , indicating the influence of Z im For ease of model estimation, we combine Dh and j, resulting in a new parameter j on changing household preference. To incorporate further the influence of past experience and future expectations on present decision-making, the DCLH model is extended based on the concepts of discounted utility (Ida, 1998; Samuelson, 1937), within the framework of the DGEV (Dynamic Generalized Extreme Value) model (proposed by Swait et al. (2000)). The resulting new model is called the DU-DCLH model (DCLH model with Discounted Utility). The DU-DCLH model adopts the time-discounted utility structure, where the utility perceived in the present is discounted to the past and future. As an initial trial, we utilize a constant discount rate, following the form adopted by Samuelson (1937). Finally, individual i’s observed utility of residential location choice related to the mth mobility, which occurred at age T (i.e., uijm ðTÞ), over the life course can be reformulated as follows:

Z

T1

uijm ðTÞ ¼

eat uijm dt þ ebT uijm þ

0

¼ ½að1  eaðT1Þ Þ þ ebT þ ce where

R T1 0

Z

1

ect uijm dt

Tþ1 cðTþ1Þ

ð6Þ

  uijm ;

eat uijm dt refers to the past utility, ebT uijm to the present utility, and

R1 Tþ1

ect uijm dt to the future utility, respec-

tively; and a; b; c are their discount rates (or, the time preference rates). The terms að1  eaðT1Þ Þ, ebT and cecðTþ1Þ , derived from integral calculations, are the respective weights of past, present, and future utility for residential location choice. It is assumed that an individual attempts to maximize his/her total utility U ijm ðTÞ when choosing a residential location at the mobility age T over the life course, where the total utility is formulated as follows:

U ijm ðTÞ ¼ ½að1  eaðT1Þ Þ þ ebT þ cecðTþ1Þ   uijm þ eijm :

ð7Þ

Because decisions on residential mobilities that occur at different time points over the life course may not be independent, here, all the residential mobilities over the life course for each individual are jointly estimated in the same DUDCLH model. The probability function is specified by assuming that eijm follows an independent and identical distribution (see Eq. (8)). The maximum likelihood estimation method is adopted to estimate this model.

Pijm ðTÞ ¼

M Y

expð½að1  eaðT1Þ Þ þ ebT þ cecðTþ1Þ   uijm Þ P aðT1Þ Þ þ ebT þ cecðTþ1Þ   u 0 Þ ij m j0 expð½að1  e m¼1

ð8Þ

It is not difficult to discern that the DU-DCLH model is superior to the traditional static discrete choice models (e.g., multinomial logit model), because various intertemporal dependences over the life course are explicitly accommodated in Eq. (8). 4. Survey and data To fulfill the dynamic life course analysis on residential location choice in this study, longitudinal data are needed. Instead of conducting a time-consuming panel survey, we carried out a Web-based life story retrospective survey, covering each respondent from 18 years old to the time of the survey (November 2010), in major cities of Japan with the help of a major Web survey company in Japan (having more than 1.4 million registered panelists at the time of survey). These cities include the 23 wards of Tokyo, Saitama, Yokohama, Kawasaki, Chika, Sagamihara, Shizuoka, Hamamatsu, Nagoya, Osaka, Kyoto, Sakai, Kobe, Sapporo, Niigata, Okayama, Hiroshima, Kita-Kyusyu, and Fukuoka. The respondents (one member per household) were asked to report mobilities of residential location, household structure, education, job, and/or car ownership that continued for at least one year. Information about the four most recent changes, at most, is collected. In the end, 1000 respondents had provided valid information. The detailed survey contents are as follows.  Residential biography: residential mobility times, exact timing of each relocation, and the corresponding information for each episode between two consecutive mobilities, including residing location, income, house property, accessibility (here, refers to distance) to varied facilities (including railway; bus; primary, junior and high school; hospital; park; supermarket; and city hall).  Household structure biography: household structure mobility times, exact timing of each mobility, and the corresponding information for each episode, including household size, information for each household member in each episode (including age, gender, and relation with householder).  Employment/Education biography: employment/education mobility times, exact timing of each mobility, and the corresponding information for each episode, including job category, commute time to job/school, accessibility to job/school, and travel mode in each episode.

Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

6

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

 Car ownership biography: car-ownership mobility times, exact timing of each mobility, and the corresponding information about each episode, including car number, main user, car efficiency, purpose, and use frequency in each episode. The respondents were aged between 23 and 69 years. Considering that the time span was quite long for some individuals/households, we further assured the reliability of the data by checking the confidence levels (10-point scale: 0 means not confident at all; 10 means fully confident) of the answers reported by the respondents in the survey: the average confidence level was around 7 to 9 across different cohorts, suggesting an acceptable quality of the collected data. Fig. 2 shows the descriptive statistics of the collected data. Around 85% of the respondents experienced mobilities in the residential biography, household biography, and/or employment/education biography, with average mobility events over the life course being 2.3 times, 2.1 times, and 2.5 times, respectively. In contrast, car ownership mobility was less frequent: 36% of households had no experience of changing car ownership prior to the survey date, and nearly half of these had never owned a car. Out of the whole survey data, 795 households reporting at least one residential mobility event were extracted for this analysis. DCLH and DU-DCLH models were estimated by employing this rich longitudinal data. Based on the urbanization degree and access to a JR (Japan Railway) station reported by respondents, four typical residential neighborhoods were defined as the alternatives in the choice set for residential location choice: (1) urban neighborhood in the attachment area of a JR station (distance to city hall is <5 km, and distance to a JR station is <1 km), (2) urban neighborhood out of the attachment area of any JR station (distance to city hall is <5 km, and distance to a JR station is P1 km), (3) suburban neighborhood in the attachment area of a JR station (distance to city hall is P5 km, and distance to a JR station is <1 km), and (4) suburban neighborhood out of the attachment area of any JR station (distance to city hall is P5 km, and distance to a JR station is P1 km). It is worth mentioning that this choice set is exclusive and exhaustive. Choice (4) is regarded as the reference during estimation.

Fig. 2. Distribution of the mobility frequency for each birth cohort over the life course.

Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

7

5. Estimation results and discussion Dependent variables in the model are the residential neighborhoods that individuals selected at mobility age T (four residential mobilities for each household at most) over the life course. Regarding the explanatory variables, it is thought that individuals’ sensitivity to different RE attributes might be related to their specific characteristics at that time. For example, the sensitivity to distance to bus stop might depend on the number of cars in the household. Therefore, we defined interaction terms between RE attributes and household characteristics to capture the heterogeneous influences of RE attributes on different households’ residential mobility decisions. These interaction terms (xijm ) include distance to bus stop interacted with number of cars, distance to primary school interacted with number of children aged 0–12, distance to junior high school interacted with number of children aged 0–18, distance to hospital interacted with number of elderly (aged 65+), and distance to supermarket interacted with household size. As mentioned above, an individual’s preferences, reflected in xijm , might change over time because of the interdomain dependences between residential biography and others. Consequently, the time-varying preference for the xijm with respect to the mth mobility is depicted via Z im fAim ; Ei;mt ; Ei;mþs g as follows.  Aim : household income level (1–21, from lowest to highest) in the mobility year.  Ei;mt : occurrence of household structure mobility during T5 to T1 (the past 5 years) (abbreviated as HM_T–5toT–1), occurrence of job/education mobility during T5 to T1 (JM_T–5toT–1), and occurrence of car ownership mobility during T5 to T1 (CM_T–5toT–1).  Ei;mþs : occurrence of household structure mobility during T to T+4 (from mobility year to the 4th future year) (abbreviated as HM_TtoT+4), occurrence of job/education mobility during T to T+4 (JM_TtoT+4), and occurrence of car ownership mobility during T to T+4 (CM_TtoT+4). Note that if the life events in the other life domains occurred in the same year as the residential mobility, then their influence is included in Ei;mþs when modeling the choice probability of residential relocation at age T. In our survey, we requested mobility timing and information related to each episode between two consecutive mobilities in four life biographies. A total of 52 years (1959–2010) for respondents between 23 and 69 years old is covered in the data. To combine the information of different domains, we transformed the independent data in four biographies into the same time axis (1959–2010). Based on the mobility timing, we filled in the information related to each life biography for the years between 1959 and 2010, while for the attributes that change over time (e.g., age of each household member, and the resulting variables like presence of children or elders), we also made them evolve with the years moving forward. Finally, we derived a time series (1959–2010) for each individual with the attributes related to residential environment, household, employment/education, and car ownership, as well as the information of mobility or not in the four domains in each year. By doing this, we can easily know the occurrence of life events in all domains in the past and in the future for each year. Then, we define dummy variables indexing occurrence of mobility during T5 to T1 (Ei;mt ) and T to T+4 (Ei;mþs ). In addition to the variables present above, we tried different combinations between residential environment variables and sociodemographic attributes, and finally selected the model with the best performance. Estimation results of DCLH and DU-DCLH models are given in Table 1. 5.1. The effectiveness of the proposed model Regarding the model accuracy, the value of McFadden’s Rho-squared indicates that both DCLH and DU-DCLH models are acceptable. This confirms the validity of these two models in describing residential mobility over the life course, where both the intertemporal and interdomain influences are incorporated. Furthermore, the DU-DCLH model performs better than the DCLH model, because the Chi-squared value is 75.388, which is much larger than the critical value of 16.266 at the 99% confidence level. The statistical significance of the discount rates (aandc) further supports the effectiveness of the proposed DUDCLH model. All these results show that the DU-DCLH model is superior to the DCLH model because of the consideration of the influence of path-dependency and anticipation. Given the higher accuracy of the DU-DCLH model, we will focus on interpreting the estimation results from the DU-DCLH model in the subsequent sections. 5.2. Intertemporal dependence In the DU-DCLH model, the intertemporal dependence is embedded in two ways: (1) the influence of the past, present, and future utility on residential location choice; (2) the time-constant and time-varying preference for residential neighborhoods. (1) Influence of past, present, and future on residential location choice The parameters a; b; andc indicate the discount rates for the utility obtained from the past, present, and future. The estimation results show that the discount rates are statistically significant for the past and future but not for the present. Looking further at the weights of utility from the past, the mobility year, and future (Fig. 3), it is revealed that over the life course, Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

8

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

Table 1 Estimation results of DCLH and DU-DCLH models. Parameters for xijm

Constant1 (Preference for the neighborhood of urban area in the attachment area of JR station)

Constant2 (Preference for the neighborhood of urban area out of the attachment area of JR station)

Constant3 (Preference for the neighborhood of suburban area within the attachment area of JR station)

Distance to bus stop interacted with number of cars (bus_car)

Distance to primary school interacted with number of children aged 0–12 (primary_child0-12)

Distance to junior high school interacted with number of children aged 0 – 18 (junior_child0-18)

Distance to hospital interacted with number of elderly (aged 65+) (hospital_elder)

Distance to supermarket interacted with household size (supermarket_hhsize)

Time-constant preference (h0 ) and time-varying preference for Z im

DCLH Parameter

t-value

Sig.

Parameter

t-value

Sig.

h0 _constant1

1.039 0.001 0.037 0.013 0.082 0.040 0.023 0.092

6.235 0.586 2.344 0.862 4.790 2.214 1.338 4.544

***

12.190 0.022 0.050 0.204 0.652 0.295 0.093 0.340

19.636 2.211 0.341 1.427 3.612 1.759 0.588 1.849

***

0.088 0.015 0.085 0.100 0.091 0.156 0.078 0.195

0.058 1.745 0.611 0.714 0.609 0.990 0.503 1.129

7.379 0.003 0.019 0.138 0.446 0.353 0.057 0.001

8.444 0.360 0.122 0.910 2.481 1.890 0.342 0.005

***

**

j~ _income j~ _HM_TtoT+4 j~ _JM_TtoT+4 j~ _CM_TtoT+4 j~ _HM_T5toT1 j~ _JM_T5toT1 j~ _CM_T5toT1

DU-DCLH

**

*** **

***

h0 _constant2 j~ _income j~ _HM_TtoT+4 j~ _JM_TtoT+4 j~ _CM_TtoT+4 j~ _HM_T5toT1 j~ _JM_T5toT1 j~ _CM_T5toT1

0.396 0.002 0.004 0.003 0.030 0.015 0.021 0.054

2.518 2.239 0.274 0.197 2.022 0.960 1.324 2.962

**

h0 _constant3

0.586 0.001 0.006 0.016 0.066 0.040 0.033 0.052

3.597 0.829 0.381 1.018 3.682 2.078 1.871 2.477

***

h0 _bus_car

0.117 0.001 0.013 0.018 0.016 0.005 0.036 0.020

0.525 0.780 0.603 0.873 0.692 0.211 1.558 0.800

3.172 0.039 0.088 0.402 0.246 0.441 0.433 0.100

1.963 2.607 0.332 1.435 0.862 1.473 1.452 0.328

j~ _income j~ _HM_TtoT+4 j~ _JM_TtoT+4 j~ _CM_TtoT+4 j~ _HM_T5toT1 j~ _JM_T5toT1 j~ _CM_T5toT1

h0 _primary_child0-12

0.064 0.002 0.051 0.117 0.047 0.037 0.095 0.014

0.140 0.754 1.333 3.546 1.400 1.168 3.024 0.362

2.004 0.005 0.143 0.780 0.292 0.030 0.624 0.088

0.670 0.210 0.334 2.003 0.704 0.078 1.599 0.186

j~ _junior_child0-18 j~ _income j~ _HM_TtoT+4 j~ _JM_TtoT+4 j~ _CM_TtoT+4 j~ _HM_T5toT1 j~ _JM_T5toT1 j~ _CM_T5toT1

0.022 0.000 0.005 0.039 0.004 0.003 0.031 0.007

0.090 0.149 0.293 2.114 0.228 0.145 1.762 0.363

0.158 0.003 0.173 0.859 0.169 0.031 0.638 0.156

2.093 0.255 0.740 3.196 0.761 0.139 2.609 0.646

h0 _hospital_elder

0.155 0.001 0.006 0.020 0.004 0.014 0.002 0.006

0.740 0.992 0.322 0.993 0.208 0.667 0.076 0.247

1.265 0.012 0.269 0.277 0.018 0.147 0.061 0.172

0.723 1.089 1.302 1.179 0.074 0.560 0.244 0.596

h0 _supermarket_hhsize

0.026 0.000 0.012 0.001 0.001 0.006 0.002

0.515 1.375 2.930 0.233 0.208 1.380 0.415

0.543 0.006 0.014 0.105 0.046 0.125 0.090

1.590 2.069 0.271 1.943 0.787 1.981 1.504

j~ _income j~ _HM_TtoT+4 j~ _JM_TtoT+4 j~ _CM_TtoT+4 j~ _HM_T5toT1 j~ _JM_T5toT1 j~ _CM_T5toT1 j~ _income j~ _HM_TtoT+4 j~ _JM_TtoT+4 j~ _CM_TtoT+4 j~ _HM_T5toT1 j~ _JM_T5toT1 j~ _CM_T5toT1

j~ _income j~ _HM_TtoT+4 j~ _JM_TtoT+4 j~ _CM_TtoT+4 j~ _HM_T5toT1 j~ _JM_T5toT1 j~ _CM_T5toT1 j~ _income j~ _HM_TtoT+4 j~ _JM_TtoT+4 j~ _CM_TtoT+4 j~ _HM_T5toT1 j~ _JM_T5toT1

**

**

***

*** ** * **

***

***

**

*

***

**

*** *

*

*

** *

***

**

**

***

***

**

*

**

Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

9

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx Table 1 (continued) Parameters for xijm

Discount rates

Time-constant preference (h0 ) and time-varying preference for Z im

DCLH Parameter

t-value

j~ _CM_T5toT1

0.004

0.855

Alpha (a) Beta (b) Gamma (c)

0.090 7.532 0.309 1.500 0.039 1.714 3124.4496 3124.4496 2751.2034 2713.5093 0.1195 0.1315 0.1067 0.1187 75.388 (4degrees of freedom: 3) 795

Initial log-likelihood Converged log-likelihood McFadden’s Rho-squared Adjusted Rho-squared Chi-squared Number of observations

DU-DCLH Sig.

Parameter

t-value

0.048

0.721

Sig.

***

*

Note: HM: Household structure mobility; JM: Job/education mobility; CM: Car ownership mobility. *** Significant at the 99% confidence level. ** Significant at the 95% level. * Significant at the 90% level.

Weight of utility in the mobility year

Weight of utility from the past 0.095 0.09 0.085 0.08 0.075 0.07

15

25

35

45

55

65

75

0.0045 0.004 0.0035 0.003 0.0025 0.002 0.0015 0.001 0.0005 0 15

25

Respondent's mobility age

35

45

55

65

Weight of utility from the future 0.02 0.018 0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 0 15 75

25

Respondent's mobility age

(a)

35

45

55

65

75

Respondent's mobility age

(b)

(c)

Fig. 3. Weights of utility from the past, present, and future.

when residential mobility occurs, the older the respondents are, the larger the influence from the past on their location choice, but the smaller the influence from the present and future. In addition, the marginal effects of past experience diminish with increasing mobility age, especially after the age of 55 years. The weight of the present utility declines dramatically to nearly zero if the relocation occurs before respondents are 30 years old. In contrast, the influence from the future gradually decreases as people move on over the life course. These results indicate that the influences from the past, present, and future on residential location choice are dynamic with mobility age. Fig. 4 portrays the proportion of contribution as the respondents grow older. Utility from the past explained more than 75% of the total variance of the utility of residential mobility over the whole life course, while the present utility accounted for 0–5% and the future less than 20%. This implies that respondents choose their residential neighborhood mainly based on their amassed historical experience, followed by anticipation of the future, while consideration of the current situation is the least influential and can be almost ignored if Contribution to the utility from the past, present, and future 100% 90% 80% 70% 60%

Past

50% Present

40% Future

30% 20% 10% 0% 15

25

35

45

55

65

75

Respondent's mobility age Fig. 4. The partial utility from the past, present, and future in the total utility.

Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

10

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

the mobility occurs when the respondents are more than 25 years old. This finding casts doubt on the rationality of existing research on residential location choice that only considers current utility in the analysis. (2) Time-constant and time-varying preferences for RE attributes Parameters marked with h0 in the DU-DCLH model in Table 1 indicate the time-constant but household-specific prefer~ shape the time-varying preference caused by the ence for RE attributes over the life course, and parameters marked with j ~ change of household characteristics or the occurrence of life events in other domains. It can be seen that many h0 and j parameters are statistically significant. This supports the validity of the assumed mechanism in Fig. 1, arguing that households’ preference for residential location includes a time-constant part established in the beginning and time-varying part changing over the life course. Regarding the time-constant preference, it is revealed that every time when households decide where to reside, they will always consider the distance to a bus stop and a junior high school from their residence, implying an insufficient supply of bus lines and junior high schools in Japan. Households with more cars are less likely to choose to live in neighborhoods with good accessibility to a bus stop, and households with many children aged between 0–18 years prefer to live in neighborhoods near junior and high schools. In addition to the observed time-constant preference, households also have an unobserved constant preference for neighborhoods in urban areas or in the attachment area of a JR station when compared with suburban areas out of the attachment area of a JR station. This is evident from the significantly positive alternativespecific constant terms in the DU-DCLH model (Constant1, Constant2, and Constant3). These phenomena can be regarded as the results of ‘‘self-selection effects” caused by the household-specific attributes, and ignoring such self-selection effects will lead to the spurious evaluation of policy effects in practice (Yu et al., 2012). Given individuals’ significant time-constant preference for residential neighborhoods, it is evident that early publication and wide propagation of a blueprint detailing the mid- and long-term city planning strategy—for instance, a compact city development plan with clear configuration of bus stops, junior high schools, JR stations and urbanization—will be helpful for guiding people’s residential location choices. Although the above preferences will not change over the life course, the overall preference for residential neighborhoods will be modified by time-varying attribute preferences. The results show that as income increases, households are more likely to move to urban areas with good accessibility to bus stops and supermarkets. With economic development, the configuration of public transport and supermarkets in residential neighborhoods becomes increasingly important. The occurrence or potential occurrence of life events in other domains, related to household structure, job/education, and car ownership, has a significant effect on changing households’ preferences for the RE attributes over the life course. More to the point, mobilities in other life domains influence household or individual preferences for residential neighborhoods. The concrete effects of such external events are introduced in Section 5.3. 5.3. Influence of mobilities in other life domains The influence of mobilities in other life domains was explicitly determined by introducing a set of dummy variables, which indicate the occurrence of household structure mobility, job/education mobility, or car ownership mobility with respect to different time scales: from the near past before the residential mobility year (past five years) to the near future after the residential mobility year (future four years). The estimation results of the DU-DCLH model show that the events in the other domains will trigger changes to household or individual preferences for RE attributes. For instance, households who experienced a household structure mobility in the past five years are less likely to choose to live in a neighborhood within the attachment area of a JR station but more likely to live near a supermarket, especially for large households. This is probably because of high land price and high density in the attachment area of a JR station, while larger households have greater need for purchasing food or everyday articles in the supermarket. Household members (respondents) who plan to change their job or school in the near future prefer to reside in a place close to a primary school and a supermarket. This might be related to the expectation of having a baby after graduation or a job change. Our survey data shows that there is a peak period of mobility lying between 20 and 30 years of age for employment/education biography (more than 55% of respondents). Household members are more likely to have babies during this period or a little later. It is more likely for households to live in an urban area near a JR station if car ownership mobility occurred in the past five years but less likely for them to move to a neighborhood within the attachment area of a JR station if they intend to change cars in the next four years. All these findings confirm that individual residential location choices over the life course are dependent on mobilities in other domains in a complicated and dynamic way. Policies aiming to trigger the occurrence of events in any of these domains (e.g., measures to encourage childbirth, subsidies for purchasing vehicles, fuel taxes, industrial redistribution, etc.) will have derivative effects on the residential mobility of households, which will subsequently induce other long-term impacts over their life course. 5.4. Other parameters Most of the RE attributes included in the model have significant effects on residential location choice except the distance to hospital. The insignificance of the distance to hospital is probably due to the widespread distribution of private clinics in Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

11

these major Japanese cities. Parameters related to the occurrence or potential occurrence of events in the other domains are all not significant for explaining the influence of distance to a bus stop on residential location choice. Other than the constant terms, which indicate preference of neighborhood type, some of the life event parameters are statistically significant. This suggests that the occurrence or potential occurrence of events in the other domains makes individuals more sensitive to whether the neighborhood is near a JR station or not, compared with the case for a bus stop. This might be related to the intensive railway network and the dominant role of JR in the transportation system in Japan.

6. Conclusion There are some other studies on residential location choice in the context of life course in the literature; however, temporal dynamics have been poorly represented. This paper aims to provide an additional research method for describing the decision-making processes in relation to household residential mobility, where both of the pertinent intertemporal dynamics over the life course are accommodated. To this end, the DU-DCLH model has been developed. In contrast with the traditional discrete choice models (e.g., MNL), the parameters associated with the residential characteristics in the utility function of the DU-DCLH model are decomposed into time-constant and time-varying parts, and observed heterogeneities across individuals are also incorporated. The DU-DCLH model further reflects the influence of past experience and future expectation by introducing discounted utility. The model estimation results first confirm the effectiveness of the proposed DU-DCLH model for describing residential mobility over the life course. In this study, taking residential mobility as an example, we have demonstrated how the proposed dynamic life course model works. Theoretically, the DU-DCLH framework can be applied to represent other mobilities over the life course as well, such as car mobility, household mobility, and employment mobility. Secondly, the clear influence of past experiences and future expectations on residential location choice is identified in the sense that the DU-DCLH model outperforms the DCLH model, and parameters of discount rates (Alpha and Gamma) are both statistically significant. It is further shown that as the mobility age increases, the utility derived from past experience will contribute more to total utility, but an opposite trend is observed with respect to the utility derived from future expectations. The utility obtained from the present (mobility year) contributes the least to the total, and its contribution can be ignored. This finding suggests that existing research on residential location choice, which only considers the current utility, should be reconsidered. Ignoring the influence of past experience and future expectation on residential location choice might lead to biased estimation of housing demand at different locations and subsequent issues for urban planning and transportation infrastructure development, etc. Needless to say, this argument should be supported by more empirical studies in not only Japan but also other countries. Thirdly, the proposed mechanism, through which people determine their residential locations based on a time-constant preference inherited from the beginning of the life course as well as a time-varying preference shaped by interdomain influences, is verified. Last but not least, the occurrence of key events in household biography, employment/education biography, and travel biography is strongly associated with changes of residential location at different time points over the life course. In summary, the findings of this study highlight the necessity and importance of analyzing residential mobility within a life course context, together with life events that occur in other life domains. Having summarized our research findings, it is also necessary to mention the limitations of our analysis. Because respondents in the life story survey were asked to list only four changes at most, the influence of the unreported mobilities has been ignored. To address this limitation, we need to find a way to collect reliable biographical information of not only single household members but also multiple members over the whole life course in a cost-effective way. Concerning the modeling approach adopted in this analysis, constant discount rates are assumed in the DU-DCLH model. In spite of good performance and reasonable results from the DU-DCLH model, it is still necessary to try more flexible structures. In the case of multiperson households, it is further worth exploring the role of intrahousehold interactions. In particular, considering that members in a household change over time, it is also crucial to capture such changes over the life course. Examining life course residential location choice behavior has important long-term policy implications (e.g., policy design and evaluation); however, policy analysis is beyond the scope of this study. In the future, for example, insights into compact city development can be derived from analysis of residential locations defined by distances to stations and central urban areas; it is also possible to explore how to reduce car dependence by focusing on the car ownership mobility associated with other mobilities. As residential location and travel choices may be interdependent, the above policy analysis examples should emphasize crosssectoral features. To support cross-sectoral policy decisions, the methodology adopted in this study should be extended to represent mobility biographies covering two or more life domains simultaneously by explicitly incorporating the influence of not only within-domain interdependencies but also cross-domain interdependencies.

Acknowledgements The authors acknowledge financial support received through China’s National Key R&D Program (2016YFA0602603), and from the National Natural Science Foundation of China (No. 71521002, No. 71603020 and No. 71401012), and acknowledge the support from the Joint Development Program of Beijing Municipal Commission of Education.

Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009

12

B. Yu et al. / Transportation Research Part A xxx (2017) xxx–xxx

References Ailawadi, K.L., Gedenk, K., Neslin, S.A., 1999. Heterogeneity and purchase event feedback in choice models: an empirical analysis with implications for model building. Int. J. Res. Mark. 16, 177–198. Beige, S., Axhausen, K.W., 2008. Long-term and mid-term mobility decisions during the life course: experiences with a Retrospective Survey. IATSS Res. 32, 16–33. Beige, S., Axhausen, K.W., 2012. Interdependencies between turning points in life and long-term mobility decisions. Transportation 39, 857–872. Bhat, C.R., Guo, J.Y., 2007. A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transp. Res. Part B: Methodol. 41, 506–526. Blossfeld, H.-P., Blossfeld, G.J., 2015. Life course and event history analysis A2 - Wright. In: James, D. (Ed.), International Encyclopedia of the Social & Behavioral Sciences. second ed. Elsevier, Oxford, pp. 51–58. Borgers, A., Timmermans, H., 1993. Transport facilities and residential choice behavior: a model of multi-person choice processes. Papers Reg. Sci. 72, 45–61. Burke, P.J., Gray, L.N., 1999. Where forward-looking and backward-looking models meet. Comput. Math. Org. Theory 5, 75–95. Chan, S., Stevens, A.H., 2004. Do changes in pension incentives affect retirement? A longitudinal study of subjective retirement expectations. J. Public Econ. 88, 1307–1333. Chen, C., Chen, J., Timmermans, H., 2009. Historical deposition influence in residential location decisions: a distance-based GEV model for spatial correlation. Environ. Plan. A 41, 2760–2777. Choudhury, C.F., Ayaz, S.B., 2015. Why live far?—Insights from modeling residential location choice in Bangladesh. J. Transp. Geogr. 48, 1–9. Clark, B., Chatterjee, K., Melia, S., Knies, G., Laurie, H., 2014. Examining the Relationship between Life Transitions and Travel Behaviour Change: New Insights from the UK Household Longitudinal Study. Clark, W., Whiters, S., 2008. Family migration and mobility sequences in the United States: spatial mobility in the context of the life course. Demogr. Res. 17, 591–622. Coulter, R., Van Ham, M., Findlay, A.M., 2015. Re-thinking residential mobility Linking lives through time and space. Progr. Human Geogr., 0309132515575417. Degeratu, A.M., 1999. Estimation bias in choice models with last choice feedback. Int. J. Res. Mark. 16, 285–306. Giele, J.Z., Elder, G.H., 1998. Methods of Life Course Research: Qualitative and Quantitative Approaches. Sage Publications. Hannam, K., Sheller, M., Urry, J., 2006. Editorial: Mobilities, immobilities and moorings. Mobilities 1, 1–22. Heckman, J.J., 1979. Statistical models for discrete panel data. Department of Economics and Graduate School of Business. Ida, T., 1998. Uncertainty and Decision-making Economics. Nippon Hyoron Sha Co., Ltd. (in Japanese). Kulu, H., 2008. Fertility and spatial mobility in the life course: evidence from Austria. Environ. Plan. A 40, 632–652. Kulu, H., Steele, F., 2013. Interrelationships between childbearing and housing transitions in the family life course. Demography 50, 1687–1714. López-Ospina, H.A., Martínez, F.J., Cortés, C.E., 2016. Microeconomic model of residential location incorporating life cycle and social expectations. Comput. Environ. Urban Syst. 55, 33–43. Lanzendorf, M., 2003. Mobility biographies. A new perspective for understanding travel behaviour. In: Proceedings of 10th international conference on travel behaviour research (IATBR), Lucerne. Müggenburg, H., Busch-Geertsema, A., Lanzendorf, M., 2015. Mobility biographies: a review of achievements and challenges of the mobility biographies approach and a framework for further research. J. Transp. Geogr. 46, 151–163. Oakil, A., 2013. Temporal dependence in life trajectories and mobility decisions. Rashidi, T.H., Mohammadian, A., Koppelman, F.S., 2011. Modeling interdependencies between vehicle transaction, residential relocation and job change. Transportation 38, 909–932. Roy, A., 1998. An error components approach to segmentation and modelling brand choice dynamics. J. Econ. Psychol. 19, 463–484. Roy, R., Chintagunta, P.K., Haldar, S., 1996. A framework for investigating habits, ‘‘The Hand of the Past”, and heterogeneity in dynamic brand choice. Market. Sci. 15, 280–299. Samuelson, P.A., 1937. A note on measurement of utility. Rev. Econ. Stud. 4, 155–161. Scheiner, J., 2006. Housing mobility and travel behaviour: a process-oriented approach to spatial mobility: evidence from a new research field in Germany. J. Transp. Geogr. 14, 287–298. Scheiner, J., Holz-Rau, C., 2013. A comprehensive study of life course, cohort, and period effects on changes in travel mode use. Transp. Res. Part A: Policy Pract. 47, 167–181. Schoenduwe, R., Mueller, M.G., Peters, A., Lanzendorf, M., 2015. Analysing mobility biographies with the life course calendar: a retrospective survey methodology for longitudinal data collection. J. Transp. Geogr. 42, 98–109. Sener, I.N., Pendyala, R.M., Bhat, C.R., 2011. Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior. J. Transp. Geogr. 19, 294–303. Swait, J.D., Adamowicz, W.L., van Bueren, M., 2000. Choice and Temporal Welfare Impacts: Dynamic GEV Discrete Choice Models. Department of Rural Economy, Faculty of Agriculture, Forestry, and Home Economics, University of Alberta. Tillema, T., van Wee, B., Ettema, D., 2010. The influence of (toll-related) travel costs in residential location decisions of households: a stated choice approach. Transp. Res. Part A: Policy Pract. 44, 785–796. Tran, M.T., Zhang, J., Fujiwara, A., 2015. Interdependences between current choices and future expectations in the context of Hanoians’ residential location choices. Transp. B: Transp. Dyn., 1–18 Van der Klaauw, W., 2012. On the use of expectations data in estimating structural dynamic choice models. J. Labor Econ. 30, 521–554. Van der Klaauw, W., Wolpin, K.I., 2008. Social security and the retirement and savings behavior of low-income households. J. Econ. 145, 21–42. Warner, C., Sharp, G., 2015. The short-and long-term effects of life events on residential mobility. Adv. Life Course Res. Yu, B., Tian, Y., Zhang, J., 2015. A dynamic active energy demand management system for evaluating the effect of policy scheme on household energy consumption behavior. Energy. http://dx.doi.org/10.1016/j.energy.2015.1007.1131. Yu, B., Zhang, J., Fujiwara, A., 2012. Analysis of the residential location choice and household energy consumption behavior by incorporating multiple selfselection effects. Energy Policy 46, 319–334. Zhang, J., 2015. The life-oriented approach and travel behavior research. In: A discussion paper for the Workshop ‘‘Life-Oriented Approach for Transportation Studies” at the 14th International Conference on Travel Behaviour Research, Windsor, UK, July 19–23 (to be included in a book published by the IATBR: forthcoming). Zhang, J., Fujiwara, A., 2009. Intrahousehold interaction in transit-oriented residential choice behavior represented in stated preference approach. Transp. Res. Rec.: J. Transp. Res. Board 73–81. Zhang, J., Sugie, Y., Fujiwara, A., 2004. Representing travel choice behavior dynamics based on cross-sectional and longitudinal heterogeneity. Doboku Gakkai Ronbunshu 2004, 3–15 (in Japanese). Zhang, J., Yu, B., Chikaraishi, M., 2014. Interdependences between household residential and car ownership behavior: a life history analysis. J. Transp. Geogr. 34, 165–174.

Please cite this article in press as: Yu, B., et al. Dynamic life course analysis on residential location choice. Transport. Res. Part A (2017), http://dx.doi.org/10.1016/j.tra.2017.01.009