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Residential Mobility in the Post-Great Recession of Los Angeles: Sociodemographic Variations

Xuetao Huang and Guangqing Chi*

Department of Agricultural Economics, Sociology, and Education, Population Research Institute, and Social Science Research Institute

The Pennsylvania State University 112E Armsby

University Park, PA 16802, USA

Corresponding author: gchi@psu.edu; +1 814 865 5553

Funding Sources This research was supported in part by the National Science Foundation (Award # 1541136) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Award # P2C HD041025-16).

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Residential Mobility in the Post-Great Recession of Los Angeles: Sociodemographic Variations Abstract Residential mobility has been extensively studied in existing literature, especially the variables that trigger residential moves. However, it is unclear how the associations vary by demographic characteristics. Further, the potential impacts of the recent Great Recession on residential mobility are unknown. This research investigates the sociodemographic variations of residential mobility in the Great Recession of Los Angeles using data from the American Housing Survey of 2011 and the American Community Survey of 2007–2011. The mobility outcomes are examined by housing tenure, marital, gender, racial/ethnic, and educational attainments from July 2010 to December 2011, which is right after the Great Recession in the United States ended. One important finding is that young householders and households with larger sizes are more likely to move. Also, renters have a greater tendency to move. This study has important implications for addressing racial residential segregation and rent subsidies. Keywords: Residential mobility, Relocation, Sociodemographic variations, Great recession, American Housing Survey 1. Introduction For the past several years, the mover rate in the United States has remained between 11.5% and 12.5%, according to 2014 statistics released by the U.S. Census Bureau (U.S. Census Bureau, 2015). The mover rate between 2013 and 2014 was 11.5%, or 35.7 million people aged 1 year and over. When migration information started to be collected in 1948, about one in five people moved over a one-year period; this number has fallen to about one in nine today. In particular, 24.5% of all people living in renter-occupied housing units lived elsewhere one year prior. The mover rate of all people living in owner-occupied housing units was 5.0%. The study of residential mobility has a long tradition: geographers, sociologists, economists, and psychologists have contributed extensively to the literature on the residential mobility process and its relationship to changes in the urban environment (Dieleman, 2001). Economists have studied migration of households in response to both economic and non-economic pressures (Weinberg, 1979). The research on migration has been on intra-urban mobility, migration between urban areas or between rural and urban areas. This study also uses data of intra-urban mobility and focuses on the importance of both the sociological and economic determinants of residential mobility. Its contribution is to analyze the residential mobility of different sociodemographic groups right after the Great Recession. The rest of this paper is organized as follows: Section 2 briefly discusses the past literature on theories of residential mobility and determinants of residential mobility. Section 3 discusses the data used in this study—the 2011 American Housing Survey data of Los Angeles county—and the empirical methodology. Section 4 consists of the empirical results of the research and the interpretation. Finally, Section 5 includes the concluding remarks.

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2. Literature Review 2.1 Theoretical Studies Interregional residence relocation is correlated with the size of the urban area, whereas intraregional residence relocation is correlated with the structure of the urban area. Mills (1967) proposed an aggregative model to explain the sizes and structures of urban areas. The general idea is that the size of urban areas responses to income and employment opportunities while the structure of urban areas is characterized by factor substitution of land and capital. The variation in the relative price factors used to produce housing is more evident than that in the relative price of housing. For example, in the central area of an urban area, the price of land is comparatively high, so skyscrapers and high-rise apartments are built, but in the suburbs, single-story factories and single family homes are built because land price is relatively low. Therefore, the variation in urban structures is a market response to the variations in relative factor prices. Mills’ classic paper assumes monocentricity, which means a central business district (CBD) surrounded by a ring of residences, and that households choose where to live by weighing CBD accessibility against residential space. However, this assumption has drawbacks both theoretically and empirically. In theory, the locations of households and firms are assumed a priori; a more satisfactory model should be able to explain and endogenously determine the location of monocentricity. In reality, a monocentric location of firms and households seems implausible. An increasing trend of decentralization and the consequent declining tendency of CBDs being the center of employment indicate the untenability of monocentricity. Therefore, the concept of a monocentric city may not be a persuasive model of certain modern cities. One example of a non-monocentric model of urban land use has been developed by Fujita and Ogawa (1982). This model is based on a one-dimensional city with a fixed population assumption. Households and firms compete for space at different locations for residential and production use. The model identifies the within-sector interactions that are among business firms and generate productivity through agglomeration effects. It also depends on the between-sector interaction that is between business firms and households: households supply labor to business firms and business firms pay wages to households. The spatial configuration of the city favors concentration between firms by external effects and favors households following the employment distribution to reduce costs of commuting. Fujita and Ogawa construct a series of examples of monocentric, non-monocentric, and multicentric urban configurations. As commuting costs increase, areas of mixed use can emerge in equilibrium. Another paper that deduces internal structures of cities is by Lucas and Rossi-Hansberg (2002). This paper assumes a circular city, and the model is based on a competitive market theory of land use that is similar to the idea of Fujita and Ogawa (1982). In this spatial model, production requires both land and labor, and people consume goods and residential land. Production takes place in the cities instead of the suburbs because of the externality effect: the productivity of firms is determined by the distance at which other firms locate. Moreover, workers choose to live close to workplaces, and those who live far from their workplaces lose some of their labor endowment getting to and from work. These two forces bring together employment and residential places, closer to the city’s center, but the needs for space for production and residences keep the city from collapsing onto a point. Glaeser (2007) summarizes the economic approach to cities as a spatial equilibrium for workers, employers, and builders. The theoretical pillar of urban economics is the concept of no arbitrage equilibrium, which assumes that there are no free lunches to be gained by changing location. So individuals are indifferent across space, which means that the flow of wages plus amenities minus

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housing costs and transportation costs is roughly equal in every location. Within metropolitan areas, the Alonso-Muth-Mills model assumes that income is constant and is used to compare housing costs with amenities and transport costs (Alonso, 1964; Mills, 1967; Muth, 1969). Related to the analysis of this paper, the model is particularly effective at analyzing the location decisions of different population groups, such as the rich and the poor. The rich have higher travel costs because they are more likely to live in suburbs and it seems that they should pay more for low commuting costs at the city center, but the fact about American cities is that poverty rates are much higher in city centers than in the suburbs of town. This could be explained by the desire for land of the rich and the different transportation technologies used by the rich and the poor. These theoretical papers lay some important foundations for the analysis of the relationship between residence location and job location and also journey to work; for example, job location and residence location may not form a monocentric setup, and commuting time is related to income, housing cost, and amenities. The following section discusses some empirical evidence on the determinants of housing preference, e.g., income, family size, stage of family life cycle, social class, race, etc.; the effects of residence relocation on commuting time; the effects of commuting time on residence relocation; and so on. 2.2 Empirical Studies The movement of a household’s residence is usually accompanied by the movement of a worker’s workplace for long-distance migration across metropolitan areas. In contrast, either the household’s residence or its members’ workplaces may be retained when one of the locations changes in short-distance migration within a metropolitan area. Furthermore, long-distance moves are more likely to be for job-related reasons, while short-distance moves are more likely to be driven by housing-related reasons (U.S. Census Bureau, 2005). Sociologists and economists have different understandings of the effect of commuting time on residence relocation. Economists model the households to maximize their utility by adjusting their residence location, subject to a budget constraint including travel costs. So journey-to-work affects travel cost and further affects residential mobility. Sociologists use the term dissatisfaction to explain the workplace location effect that the household becomes dissatisfied with its residence after a workplace move and attempts to improve its satisfaction through a residence location change. These two aspects of interpretation are not necessarily inconsistent (Weinberg, 1979). Many sociologists and economists have discussed the socioeconomic and demographic determinants of the residential mobility decision (e.g., Clark and Onaka, 1983; Lee, 1966; Shuttleworth and Gould, 2010). The changes in housing demand that accompany life-cycle changes are generated by shifts in family composition, household size, the race of the household, the sex of the head of the household, income, housing tenure, education, occupation, historical mobility behavior, transportation cost, level of public goods and services provided, neighborhood quality, and housing market tightness. Age, as a life-cycle change variable, has been found to influence moving behavior. The expected direction of influence of the age variables for residence movement is negative, meaning that older households are less mobile. Abraham and Hunt (1997) show that young adults in their twenties and thirties are the segment of people with the highest mobility. Weinberg (1979) finds that White male subpopulations follow this pattern; however, for several other subpopulations, the most mobile age group may be older. Clark and Huang (2003) show that older and married individuals and households

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with a birth or marital status change are more mobile. There is also evidence that several non-White subpopulations have more residential mobility as they get older. The U.S. Census Bureau (2005) shows that differences occur across age and ethnic groups, that young adults have the highest moving rates, and that Blacks and Asians show higher residential mobility than Whites. Family size has been found to be an important determinant of mobility. Lee and Waddell (2010) observe that households with children or workers are less likely to move than households with no kids or no workers. Clark and Huang (2003) find that households that have size stress (either too much or too little room) tend to move. Clark and Dieleman (1996) show that the mobility rates are lower for households in larger homes. Weinberg (1979) shows that when the family size either increases or decreases there is often a residence move. Weinberg also finds that Black males appear to be very sensitive to changes in family size. The probability of moving for renters with a decrease in family size is four times as much as the probability of moving for renters when there is an increase in family size. Income has a critical effect on the propensity to move. Both increases and decreases in income increase the probability of a move. Loss of income might easily cause a family’s current housing to be too expensive and force a move, whereas increases in income may make previously too-expensive housing within reach. Weinberg (1979) shows an inverted U-shape of the coefficient for income dummy variables, with the highest mobility belonging to the middle-class income group. Level of education is found to be directly related to mobility. This may be because it is easier for those with more education to obtain and process information on housing opportunities. Education also represents knowledge of the housing market, the ability to compare between units and to bargain for favorable rates. Studies show that individuals with higher education levels favor residential locations in high density neighborhoods, suggesting that they are interested in urban lifestyles (Paleti et al., 2013). Changes in education and work opportunities very often coincide with changes in residence (Clark and Huang, 2003; Li and Wu, 2004; Prillwitz et al., 2007). Housing tenure has also been suggested as a possible variable explaining differences in residential mobility. Sommers and Rowell (1992) find that the length of residency and home ownership are two critical determinants of mobility decisions, with both variables leading to a lower likelihood to move. Social ties also play a key role in affecting residential mobility. Those with strong social ties tend to be more stable and less likely to move. Also, households that rent dwellings are more mobile simply because their moving costs are much lower than those for home-owning households (Clark and Huang, 2003; Clark and Dieleman, 1996; Clark and Onaka, 1983). Besides the socioeconomic and demographic variables discussed above, there are exogenously determined variables such as transportation cost or time and neighborhood quality. A change in transportation cost or time influences the choice of housing location through the change of household’s budget constraint via the journey to work. Studies show that longer commuting distances are associated with a higher probability for one to accept a job offer or a residential offer that would reduce the commuting distance (Clark et al. 2003). Locations with lower cost and higher ease of access to work are more attractive, and for households with children, a neighborhood with other families with children and a single-family residential home are more preferable. Another important determinant is neighborhood quality. People may move if they are dissatisfied with their present neighborhood for its housing disamenities or the character of the area’s residents. On the contrary, residential mobility is likely to be reduced if neighborhood quality improves; however, an

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improvement in neighborhood quality is likely to drive housing prices up, which may lead to mobility. For owners, the housing price increase contributes to a gain in capital that would offset the price increase partially. Studies have shown that the neighborhood attributes (e.g., population density, proportions of household with children or young adults) and accessibility measures (e.g. general accessibility to regional work opportunities or local shopping activities) were found to influence the mobility-location choices significantly (Lee and Waddell, 2010). With regard to the relationship between job and residential mobility, the residence may not change when the job location changes, or the residence may change more than once, given the appropriate job or residence offer. This indicates that workers choose the optimal location not due to current utility but due to lifetime utility, including moving costs and uncertainty about future relocations. Workers mainly consider the commuting time, which would be changed by residence or job relocation. Job and residential mobility increase with commuting distance. Kim et al. (2005) find that transport factors are important, with increases in commuting time associated with an increase in the probability of moving. Others examined commuting factors and the relations between residence and workplace locations (Clark and Withers, 1999). Assuming that the sole link between job and residential mobility are commuting costs, job and residential mobility are unrelated conditioning on commuting costs. So a change in workplace that does not change commuting time has no influence on the change in residential place and vice versa (van Ommeren et al., 1999). Some papers discuss the reasons for residential movement. The U.S. Census Bureau (2005) shows that most moves are driven by housing-related reasons such as the desire to own a home, upgrade to a nicer home or neighborhood, or have a home of a more appropriate size. There is also a correlation between significant life-course events and household moves. Household formulation and dissolution and changes in education and work opportunities often coincide with changes in residence (Clark and Huang, 2003; Prillwitz et al., 2007). In general, the more educated, those with higher income, and employed individuals are likely to move for job-related reasons. Eluru et al. (2009) simultaneously modeled reasons to move and duration of stay at a location between moves, treating the reason for relocation itself as an endogenous variable. They used data from a retrospective survey of households in Zurich, Switzerland, to study the influence on reasons to move and duration of stay over a 20-year period. Two features differentiate this study from previous studies. First, the reason to move is treated as an endogenous variable in a multinomial choice model as opposed to a binary choice decision (move/no-move) framework. Second, the duration of stay is modeled as a grouped choice variable that is in terms of approximate time-period ranges as opposed to exact continuous durations. 3. Data and Methodology 3.1 American Housing Survey This paper utilizes data from the American Housing Survey (AHS) in 2011. The survey is sponsored by the Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau. The American Housing Survey began in 1973 as the Annual Housing Survey. From 1981 to 2007, the AHS was two surveys conducted independently of one another. The U.S. Census Bureau conducted the national survey every odd-numbered year and the metropolitan survey every even-numbered year on a rotating basis. The 1984 metropolitan survey was renamed the American Housing Survey (HUD, 2004). Starting in 2007, the national and metropolitan surveys were conducted during the same time period to reduce costs. Although the responses were collected simultaneously, the resulting data were not pooled to produce a single set of estimates. The national data were used for regional- and national-level

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estimates, while the metropolitan data were used for local estimates. There was no Metropolitan sample in the 2011 AHS survey. Instead, a supplementary sample to the national survey of housing units was selected for 29 metropolitan areas. This supplemental sample was combined with the national sample in these areas in order to produce metropolitan estimates using the national survey (HUD, 2013). The American Housing Survey is a longitudinal survey of housing units. The sample design for 2011 includes housing units selected in 1985 and has been supplemented with new housing units over time to take into account new construction, survey attrition, and oversampling of selected populations. This sample launched in 1985 evolved into a thirty-year panel, with final interviews in 2013. A new sample was drawn for the 2015 AHS. While some surveys conducted by the U.S. Census Bureau focus on the householder, the AHS focuses on the housing unit. When a householder in an AHS housing unit moves, the next AHS survey interviews the in-movers to the housing unit. Out-movers from the housing units are not interviewed (Warner and Dajani, 2011). One special design used by the American Housing Survey metropolitan surveys (AHS-MS) and supplementary metropolitan samples is the concept of zones. As with all surveys conducted under U.S. Code Title 13, the Census Bureau is required to protect the identities of the AHS respondents. Because the supplementary metropolitan samples are relatively small (around 4,000 cases per metropolitan area), this requirement limits the geographic information that may be disclosed in the microdata. As a result, the conventional census tracts and blocks are not available on the AHS metropolitan supplementary files. In order to provide as much geographic information as possible while still meeting the legal requirement, HUD and the Census Bureau invented the concept of zones. Zones are collections of census tracts within a metropolitan area. One rigid requirement is that the population of each zone must be at least 100,000 persons. This ensures that the Census Bureau’s disclosure prevention requirement is met. In order to be useful analytical units, zones should be internally homogeneous in their demographic and housing characteristics. Since we often want to examine outcomes based on differences in government policies, to the extent possible, zones should not mix political jurisdictions. Similarly, zones should be contiguous and compact geographically. Therefore, the division of zones should respect political boundaries and keep geographical contiguity and homogeneity (Vandenbroucke, 2005). This paper analyzes the county of Los Angeles (LA). One reason for this choice is that LA is one of the six largest metropolitan areas that are surveyed as an enhanced sample of every other national survey. The other five areas are Chicago, Detroit, New York, Northern New Jersey, and Philadelphia. Note that only the national survey of 2011 contains the variable of “Zone.” Los Angeles has 74 zones, 34 in the central cities of Los Angeles, Long Beach, Pasadena, Pomona, and Burbank cities, and 40 in the suburban areas.

The 2011 AHS contains the data on apartments, single-family homes, manufactured/mobile homes, and vacant housing units. The information on the housing units includes household composition, income, housing and neighborhood quality, housing costs, equipment and fuels, and the size of the housing unit. It also presents data on mortgages, rent control, rent subsidies, previous unit of recent movers, reasons for moving, and so on. We used the data to generate the dependent variable: a dummy of recent move-in or not, and the independent variables (demographics, household characteristics, household economic factors, geographic variables, and transportation characteristics).

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3.2 American Community Survey The American Community Survey (ACS) is a household survey developed by the Census Bureau to replace the long form of the decennial census program. Since 2005, the ACS has produced social, housing, and economic characteristic data for demographic groups in areas with populations of 65,000 or more. The ACS also accumulates samples over three-year and five-year intervals to produce estimates for smaller geographic areas, including census tracts and block groups. Three-year estimates are available for areas of population of 20,000 or more, and five-year estimates are available for all areas. The survey for this study is the 2007–2011 five-year ACS at the census tract level. This time period is selected so that explanatory variables in the ACS happened before the moving behavior surveyed in 2011. The ACS has comprehensive coverage of the census long-form sample data. Estimates are produced for demographic characteristics, social characteristics, economic characteristics, and housing characteristics. We use the ACS as a supplementary data source in addition to the AHS. The main variables we obtain from the ACS are from the “journey to work” section. Commuting time aggregated at the census tract level is the main variable of interest for this paper. Also obtained is the use of public and private transportation of each census tract. In order to merge ACS data with AHS data, we use the spatial join function in ArcGIS (®ESRI) to match census tracts with the zone that contains the centroid of them. The journey to work information combined in the AHS dataset is at the zone level. 3.3 Data Description and Empirical Issue The description of each residence is at the household level and includes the type of housing tenure (ownership versus rental), the rating of neighborhood as place to live, and the age of structure. The geographic information is at the zone level and contains the dummy for central city, the mean use of public and private transportation, and the share of commuting time less than 30 minutes. Finally, for household head the record includes demographic information such as the person’s age, gender, race, marital status, dummy for kids under 18, education received, and household’s combined income. The dependent variable is a dummy of having moved or not last year. Additional regressions are based on stratification of the sample by housing tenure (renter or owner), marital status (married with kids under 18, married with no kids or with kids over 18, in marriage dissolution, or never married), sex of the head, householder’s race (White, Black, Asian, and others), and education of the head (lower than high school, high school, and bachelor’s or beyond). This paper contributes to the direct analysis of moving determinants by different demographic characteristics.

The dependent variables (MOVEDUM) are estimated for all respondents, for different housing tenures (in two categories), for different marital statuses (in four categories), for different gender groups (in two categories), for different races (in four categories), for different educational levels (in three categories) and also for different move-in years (2009–2011). The major econometric issue is that the dependent variables are dummy variables. The dependent variable can take on only two possible values, move or no move. Because the linear-probability function predicts out of range, we use the alternative of logistic models to estimate.

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4. Estimation Results 4.1 All Respondents Tables 2–7 show the results of logit models of residential mobility that we ran on all populations and subgroups depending on demographic attributes. These models are at the individual level of the householders. Table 2 presents that young unmarried household heads or young parents with children under 18 years old are more likely to move to the destination of a center city. Household heads with at least a high school degree are more likely to move, and renters are more likely to move than homeowners. Young people tend to migrate for both economic and demographic reasons. Single young people are at the stage of job starter and family formation. Also, they incur less physical and emotional costs of moving because of their lower opportunity costs. Some of them move out of their parents’ home for the first time. It is wise for them to rent a home because of the job change or family enlargement they may later face. Young parents are more likely to move, maybe because they are choosing a better school district for their children. The quality of the neighborhood is capitalized into the housing rent or price, so they may move for a larger or smaller home. Another possibility is that the living expenditure increases to bear and rear children, which makes residence relocation necessary. Table 2 shows that the parameters for the dummy of young people and the dummy of households with children under 18 are significantly positive, while that for married household is significantly negative. People with at least a high school degree are more likely to move than those without. Higher-educated people are more mobile geographically. In Table 2, the effect of the dummy of high school graduates is significantly positive, but that of the dummy of college graduates is not significant. This may be because people with bachelor’s degrees are more likely to have a “permanent” job, whereas those with high school degrees only have low-end manual labor jobs that are temporary. For those without high school diplomas, it may have difficult to find jobs again if they are unemployed. Therefore, there is no need of work-oriented migration for high-school dropouts and bachelor’s degree holders. Only those with high school degrees are most likely to rent and move when they change jobs. Another finding is that homeowners are less likely than renters to move. In Table 2, the dummy of homeowners has a significantly negative effect on migration. One reason is that renters are less likely than homeowners to engage in residential adaptation to meet their needs since it would mean putting time and money into the property of others. Hence, mobility would be the typical adjustment behavior open to renters, while some homeowners would be able to increase space or quality by building an addition or remodeling. In addition, mobility for owners is somewhat more difficult, since finding a buyer is not as readily doable as giving notice to a landlord (Morris et al., 1976). This is especially evident during recessions, since the value of the house may be low because of the economic downturn. If a home purchase loan has a higher balance than the free-market value of the home, this situation prevents the homeowner from selling the home unless s/he has cash to pay the loss out of pocket. This makes migration more difficult for homeowners during a recession. Finally, there are significantly more people choosing a central city as a migration destination; this is shown in Table 2, where the parameter for “cencty” is significantly positive. This may be related to the composition and life cycle of the migrants. Young single renters live disproportionately in the densest urban areas. A natural explanation of this phenomenon is that the crowding makes meeting other single people easier and facilitates the operation of the marriage market (Costa and Kahn, 2000). Alternatively,

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married people live disproportionately in suburbs possibly so that they can have access to more land. For households with young children, a central city may be more attractive if it has good schools. If education creates further growth, there will be multiplier effects on these amenities (Glaeser et al., 1995). Aggregated results indicate that homeowners are much less likely than renters to migrate. But the level of aggregation in the data does not allow us to categorically make that claim. In order to better understand the migration propensities we estimate mobile probabilities conditional on attributes. 4.2 Renters vs. Homeowners Table 3 shows the logit models for renters and homeowners. The parameters for renter regression are quite similar to that of all respondents in Table 2, whereas none of the independent variables for owner regression are significant. This illustrates our main finding that renters move more frequently than owners. Because independent variables are all insignificant in owner regression, the owners’ likelihood to move does not change depending on those sociodemographic variables. 4.3 Marriage Status Table 4 shows the regression results for household head with different marriage statuses. For married household head with children under 18, young age does not have significant effect, whereas for the married with no children (or older children), for those in marriage dissolution, and for those never married, the dummy of young age means more probability to move, especially for marriage dissolved household head. The households with young children may depend on their network or social support structures (e.g., siblings or parents) to help with childcare, household duties, or emotional support, and therefore are less likely to move. On the contrary, those in a marriage dissolution are not in a settled relationship and tend to move more when they are young, because they have less of a family tie to a location. With respect to gender, and in spite of the ever-increasing importance of women in the labor market due in part to the growth of female-headed households, the decision to migrate for household heads in different marriage statuses is still found to be a function of gender. Because men in general have more stable job prospects and women are more likely to be unemployed in a recession, we expect the coefficient for the dummy of male to be negative to indicate a lower probability of migration for men except for men in marriage dissolution. At the same time, household heads in marriage dissolution who have young children to raise may be more willing to take lower-paying positions after being laid off or terminated, and therefore are more likely to have work-related movement. Higher-educated households are more likely to move, especially when they have no children at home (never married or married but with no children or grown-up children). For never married household heads, having a high school diploma makes a difference, whereas for married households with no young children, having a bachelor’s degree matters. Similar as before, home ownership significantly inhibits migration for all four family structures. Household heads in marriage dissolution are most unlikely to move once they own the home. This may be because household heads have children they do not want to uproot from their homes. The heads have the option to sell the home and move out, but may find themselves bogged down trying to find a suitable buyer, especially in a recession. For these reasons, household heads in marriage dissolution with home ownership tend to stay.

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4.4 Male-Headed vs. Female-Headed Households Table 5 shows the logit regression for male- and female-headed households. Most signs and significance of the independent variables agree with the expectation. One note here is that the effect of having young children is significantly positive for female-headed households but is neutral for male-headed households. This could be interpreted as female-headed households with young children are more unstable than male-headed ones, with more migration and job changes, whereas male-headed households seem to be immune from such transitions. 4.5 Race/Ethnicity Table 6 shows the results for different ethnicities and races. Young Whites and young Asians are more likely to relocate, whereas age does not have much effect on the mobility of young Blacks. This is to say, when the economy is in recession, young Blacks are less likely to make a work-oriented move than young Whites and Asians are, which would help explain the large disparity in the unemployment rates of Blacks versus Whites and Asians. Marital status is expected to affect both the costs and benefits of moving. For married individuals, labor migration is a family decision with consequences for all family members, where some gain and some do not. Unfortunately, this means the signs of the coefficients are ambiguous. Therefore, it is not uncommon to have the coefficient of the dummy of married for Whites significantly negative, that for Blacks significantly positive, and that for Asians and others not significant. Whites and Asians with high school diplomas are more likely to move and Asians with bachelor’s degrees are further more likely to move, while neither high school nor college education plays a role in Blacks’ mobility. This finding seems to indicate that for Asians, the expectation to be mobile is having a college degree and beyond. This explains the higher incentive for Whites and Asians with higher education to move around and hunt for job opportunities during a recession, whereas the Blacks who have lower employment prospects are likely to adopt a wait-and-see attitude towards migrating. Other effects include the positive effect of the presence of young children on the mobility of White and Black households, whereas the effect on Asian households is nonsignificant. This may have something to do with Asians with children being more reliant on ethnic enclaves than Whites and Blacks. The effects of homeownership on mobility are consistent with previous models. 4.6 Educational Attainment Table 7 shows that for householders with all levels of education, young age plays a positive effect on tendency to move. The reason of movement for different education levels may be different, though. Less-educated people may be forced to move due to job displacement, whereas higher-educated people may move for better improvement and job promotion. Racial effects on the movement of less-educated households and higher-educated households are quite similar, although only the coefficient of Asians on movement of households with bachelor’s degree or beyond is significant. All of White, Black, and Asian groups are less likely to move if they are less-educated households, whereas all are more likely to move if they are higher-educated households. The effect of the Asian group is the largest of all.

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Owners of all levels of education tend to relocate less, especially householders with bachelor’s degrees or beyond. Married householders with low education are less likely to move, whereas only those with high school diploma is significant. For households with young kids, the effect is significantly positive if education is high school or lower, whereas it is negative but insignificant if education is bachelor’s or beyond. 5. Concluding Remarks In this study a logit model is estimated on a coverage of total population and sample subpopulations (stratified by housing tenure, marriage status, gender, race/ethnicity, and education of the household head). Some important results are as follows:

(1) Residential mobility generally decreases with age. (2) Residential mobility increases with family size (i.e., the presence of children under 18). (3) Home owners are less likely to move. (4) Residential mobility is not affected by household income. (But in a separate analysis, income has

a negative effect on job-related movement.) (5) Higher-educated households are more likely to move (i.e., with a high school diploma or higher). (6) The mobility behavior of each non-White group differs from the others. (7) The only locational variable that is significant is the dummy of central city. (8) Transportation accessibility does not affect residential mobility in general.

The results of this research lend credibility to the general argument that demographic changes underlie much of the residential mobility. In particular, the powerful role of housing tenure change provides a basis for the explanation that households are sensitive to the basic housing process of adjusting their housing consumption. These forces are working to predict the probability of mobility in the U.S. housing market. Typically, residential mobility in the United States has been a pathway to economic opportunity: people move because they are taking a new job, or they move into a bigger home or a better neighborhood. What was special about the Great Recession is that a large fraction of the movement was characterized by downward economic mobility. People were moving out of homes they owned because they lost their jobs or to more affordable or less desirable neighborhoods. A major difference between this study and prior ones is that the existing literature suggests that transportation accessibility has an impact on residential relocation. For example, Eluru et al. (2009) finds that compared to those who commute by car, those who use public transit are more likely to move for education/employment reasons. This study finds almost no association between transportation accessibility and mobility, probably because the accessibility variables are aggregated at the zone level instead of disaggregated at the household level. The commuting variable is available at the household level in some other years of the AHS (e.g., 2009), which could be used in future studies. Another future research direction is to apply multilevel analysis. The structure of this dataset is naturally multileveled with higher zonal level and lower household level. By allowing a multilevel structure, we could better capture the relationship between zonal groups. Also spatial analysis may be attempted with the application of the shape file of the zone map.

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15

Table 1 Sample Characteristics

Variable Variable description Sample size Sample share

Dependent variable

MOVEDUM Residence move 3,426 16.0%

Independent dummy variables

hhage1824 Age of householder 18–24 3,426 3.9%

hhmale Male householder 3,426 52.8%

hhwhite White householder 3,426 72.6%

hhblack Black householder 3,426 9.9%

hhasian Asian householder 3,426 15.4%

hhother Other householder 3,426 2.2%

hhmarried Married householder 3,426 47.9%

hhkidu18dum Householder with kids under 18 3,426 30.8%

hhhs Householder with high school diploma 3,426 78.3%

hhbach Householder with bachelor's degree 3,426 34.1%

owner Householder is owner of the house 4,072 44.5%

cencty Central city 4,463 53.7%

Independent numeric variables Mean

hcond Rating of neighborhood as place to live 3,302 8.10

hhinc Household income 3,426 63717.99

builtyr Building year 3,708 49.54

mean_priv Use of private transportation 4,463 0.86

mean_public Use of public transportation 4,463 0.07

mean_less30 Share of commuting time less than 30 minutes 4,463 0.21

16

Table 2 Logit models of residential mobility for all respondents

Model 1 Model 2

Full logit Reduced logit

hhage1824 1.492*** (0.20) 1.533*** (0.19)

hhmale -0.083 (0.11) -0.085 (0.11)

hhwhite 0.055 (0.36) 0.042 (0.36)

hhblack -0.108 (0.40) -0.101 (0.39)

hhasian 0.334 (0.38) 0.303 (0.37)

hhmarried -0.223* (0.13) -0.220* (0.13)

hhkidu18dum 0.311** (0.12) 0.302** (0.12)

hhhs 0.467*** (0.15) 0.466*** (0.14)

hhbach 0.149 (0.13) 0.165 (0.13)

owner -2.098*** (0.17) -2.114*** (0.16)

hcond -0.005 (0.03) -0.007 (0.03)

hhinc 3.28E-07 (0.00) 4.21E-07 (0.00)

builtyr -0.002 (0.00) -0.001 (0.00)

cencty 0.259** (0.12) / mean_public -1.251 (2.08) / mean_priv -0.581 (2.09) / mean_less30 -0.751 (1.35) / Constant -0.934 (2.10) -1.523*** (0.47)

N 3302 3302

pseudo R2 0.152

0.150 chi-square 299.203

293.966

p 0.000

0.000

Notes: *** p≤0.001, **p≤0.01, *p≤0.05; standard errors are in parentheses.

17

Table 3 Logit models of residential mobility for owners and renters

Model 3 Model 4

Owner Renter

hhage1824 0.892 (1.10) 1.514*** (0.20)

hhmale 0.086 (0.31) -0.111 (0.12)

hhwhite -0.061 (1.00) 0.077 (0.39)

hhblack 0.441 (1.09) -0.172 (0.43)

hhasian 0.218 (1.04) 0.351 (0.41)

hhmarried 0.131 (0.39) -0.283** (0.14)

hhkidu18dum 0.494 (0.33) 0.294** (0.13)

hhhs 0.691 (0.55) 0.450*** (0.15)

hhbach -0.260 (0.33) 0.213 (0.14)

hcond -0.103 (0.10) 0.005 (0.03)

hhinc 1.96E-07 (0.00) 2.81E-07 (0.00)

builtyr -0.001 (0.01) -0.002 (0.00)

cencty 0.196 (0.34) 0.262** (0.13)

mean_public -3.812 (5.61) -1.023 (2.24)

mean_priv -5.828 (6.06) 0.050 (2.22)

mean_less30 1.758 (3.19) -1.378 (1.50)

Constant 1.732 (6.01) -1.415 (2.24)

N 1374 1928

pseudo R2 0.025

0.050 chi-square 16.138

101.363

p 0.443

0.000

Notes: *** p≤0.001, **p≤0.01, *p≤0.05; standard errors are in parentheses.

18

Table 4 Logit models of residential mobility for different marriage groups

Model 5 Model 6 Model 7 Model 8

Married w/kids under 18

Married w/no kids or w/kids over 18

Marriage dissolution Never married

hhage1824 -0.340 (0.78) 1.156* (0.69) 1.910** (0.84) 1.534*** (0.24)

hhmale -0.417* (0.24) -0.034 (0.27) 0.440* (0.24) -0.354** (0.18)

hhwhite -0.971 (0.66) -0.262 (0.83) 0.070 (0.88) 0.589 (0.64)

hhblack -0.156 (0.78) 0.545 (0.99) -0.133 (0.90) -0.129 (0.69)

hhasian -0.435 (0.70) -0.155 (0.85) 0.118 (0.93) 0.949 (0.67)

hhhs 0.467 (0.28) 0.468 (0.40) 0.266 (0.31) 0.474* (0.26)

hhbach -0.255 (0.30) 0.896*** (0.30) 0.027 (0.34) -0.130 (0.20)

owner -1.964*** (0.35) -1.895*** (0.30) -2.598*** (0.48) -2.148*** (0.34)

hcond 0.117* (0.06) 0.054 (0.08) -0.093 (0.06) -0.045 (0.05)

hhinc -2.87E-07 (0.00) -2.17E-06 (0.00) 5.77E-06 (0.00) 4.88E-07 (0.00)

builtyr -0.004 (0.01) -0.005 (0.01) -0.003 (0.01) 0.002 (0.00)

cencty 0.324 (0.26) 0.516* (0.31) 0.223 (0.28) 0.191 (0.20)

mean_public -3.134 (5.13) 1.275 (5.06) 4.218 (5.06) -3.156 (3.20)

mean_priv -3.060 (5.34) 0.609 (4.72) 4.924 (5.06) -0.863 (3.19)

mean_less30 -2.938 (2.65) 2.578 (3.55) 0.204 (3.23) -3.078 (2.30)

hhkidu18dum /

/

0.515* (0.30) -0.031 (0.25)

Constant 2.323 (5.25) -3.523 (5.09) -6.064 (5.08) 0.159 (3.24)

N 707

870

798

927 pseudo R2 0.127

0.159

0.162

0.139

chi-square 59.287

78.495

68.406

105.988 p 0.000

0.000

0.000

0.000

Notes: *** p≤0.001, **p≤0.01, *p≤0.05; standard errors are in parentheses.

19

Table 5 Logit models of residential mobility for different gender groups

Model 9 Model 10

Male Female

hhage1824 1.624*** (0.29) 1.343*** (0.27)

hhwhite 0.383 (0.55) -0.184 (0.51)

hhblack 0.617 (0.61) -0.652 (0.54)

hhasian 0.630 (0.57) 0.160 (0.53)

hhmarried -0.272 (0.18) -0.064 (0.19)

hhkidu18dum 0.184 (0.18) 0.428** (0.17)

hhhs 0.368* (0.21) 0.590*** (0.21)

hhbach 0.204 (0.17) 0.128 (0.19)

owner -2.033*** (0.22) -2.177*** (0.25)

hcond 0.010 (0.05) -0.014 (0.04)

hhinc 8.07E-07 (0.00) -1.41E-06 (0.00)

builtyr -0.001 (0.00) -0.003 (0.00)

cencty 0.212 (0.17) 0.309* (0.17)

mean_public -3.987 (2.86) 1.526 (3.07)

mean_priv -2.320 (2.88) 1.434 (3.05)

mean_less30 -0.818 (1.94) -0.917 (1.93)

Constant 0.309 (2.92) -2.595 (3.07)

N 1747

1555 pseudo R2 0.149

0.164

chi-square 157.647

148.348 p 0.000

0.000

Notes: *** p≤0.001, **p≤0.01, *p≤0.05; standard errors are in parentheses.

20

Table 6 Logit models of residential mobility for different racial groups

Model 11 Model 12 Model 13 Model 14

White Black Asian Other

hhage1824 1.540*** (0.22) 0.530 (0.69) 2.217*** (0.70) 1.610 (1.45)

hhmale -0.096 (0.13) 0.562 (0.36) -0.244 (0.29) -0.835 (0.86)

hhmarried -0.360** (0.15) 0.684* (0.39) -0.182 (0.31) 1.140 (0.85)

hhkidu18dum 0.303** (0.15) 0.715* (0.37) 0.259 (0.32) 0.990 (0.78)

hhhs 0.436*** (0.16) 0.153 (0.46) 2.242** (1.04) -0.488 (0.88)

hhbach 0.060 (0.15) -0.074 (0.49) 0.536* (0.30) -0.899 (0.90)

owner -2.104*** (0.20) -1.767*** (0.46) -2.221*** (0.42) -2.038* (1.09)

hcond -0.027 (0.04) 0.064 (0.10) 0.075 (0.09) -0.000 (0.23)

hhinc 1.10E-06 (0.00) -3.02E-06 (0.00) -1.95E-06 (0.00) -1.35E-06 (0.00)

builtyr -0.002 (0.00) -0.006 (0.01) 0.004 (0.01) -0.007 (0.02)

cencty 0.288** (0.14) 0.154 (0.43) 0.493 (0.33) -0.585 (1.11)

mean_public -1.184 (2.46) -1.859 (9.55) 1.977 (5.68) 10.419 (24.62)

mean_priv -1.262 (2.42) -0.274 (10.26) 5.692 (5.88) -7.049 (24.65)

mean_less30 0.224 (1.70) -6.727** (3.29) -0.680 (3.45) 11.875 (16.11)

Constant -0.245 (2.43) -0.546 (9.53) -9.003 (5.72) 2.441 (25.65)

N 2416

321

492

73 pseudo R2 0.163

0.141

0.198

0.220

chi-square 229.258

38.747

63.300

22.102 p 0.000

0.000

0.000

0.077

Notes: *** p≤0.001, **p≤0.01, *p≤0.05; standard errors are in parentheses.

21

Table 7 Logit models of residential mobility for different educational groups

Model 15 Model 16 Model 17

Lower than HS HS Bachelor’s and beyond

hhage1824 2.243*** (0.63) 1.228*** (0.24) 1.941*** (0.43)

hhmale -0.143 (0.26) -0.132 (0.16) 0.057 (0.19)

hhwhite -0.556 (0.77) -0.118 (0.52) 0.783 (0.72)

hhblack -0.322 (0.85) -0.271 (0.56) 0.498 (0.82)

hhasian -2.014 (1.28) 0.043 (0.55) 1.218* (0.74)

hhmarried -0.299 (0.30) -0.396** (0.19) 0.156 (0.23)

hhkidu18dum 0.529* (0.27) 0.464*** (0.17) -0.168 (0.26)

owner -2.238*** (0.54) -1.870*** (0.24) -2.276*** (0.25)

hcond 0.123 (0.08) -0.039 (0.04) -0.015 (0.06)

hhinc 2.88E-06 (0.00) 9.56E-07 (0.00) -4.37E-07 (0.00)

builtyr -0.003 (0.01) -0.004 (0.00) 0.002 (0.00)

cencty 0.412 (0.28) -0.003 (0.17) 0.557** (0.22)

mean_public 2.184 (6.30) -3.771 (2.76) 1.044 (3.67)

mean_priv 5.984 (6.28) -4.954* (2.91) 1.948 (3.65)

mean_less30 -4.298 (3.21) 0.576 (1.87) 0.542 (2.49)

Constant -6.802 (6.57) 3.944 (2.86) -3.999 (3.58)

N 722

1467

1113 pseudo R2 0.149

0.143

0.196

chi-square 51.027

137.031

145.572 p 0.000

0.000

0.000

Notes: *** p≤0.001, **p≤0.01, *p≤0.05; standard errors are in parentheses.