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Explaining the “immigrant effect” on auto use: the influences of neighborhoods and preferences

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Abstract

Since immigrants will account for most urban growth in the United States for the foreseeable future, better understanding their travel patterns is a critical task for transportation and land use planners. Immigrants initially travel in personal vehicles far less than the US-born, even when controlling for demographics, but their reliance on autos increases the longer they live in the US. Cultural or habitual differences, followed by assimilation to auto use, could partly explain this pattern; and it may also be partly due to changes in locations and characteristics of home and work neighborhoods. Previous studies have rarely investigated non-work travel, and have not tested workplace land use measures, compared the relative influences of enclave and home neighborhood measures, or looked at the role of culturally-bound residential preferences or motivations for migration. This study relies on a unique and rich dataset consisting of a survey of US residents born in South Asia, Latin America, and the US, joined to spatial information in a GIS. I find that the home built environment is the most consistently influential factor in explaining the lower auto use of both recent and settled Latin American immigrants. Indian immigrants use autos less than would be expected given their home and work neighborhoods. There is little evidence that either ethnic enclaves, or cultural differences, play a role in lower auto use by immigrants. These results suggest there may be a role for neighborhood built environment policies in delaying immigrant assimilation to auto use in the US.

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Notes

  1. The LEHD is known to have rare issues with reporting locations that may be administrative rather than establishment, or at central locations for firms with multiple work sites (Spear 2011). Other known issues with the LEHD accuracy have to do with correct reporting of school, state government, and postal employment in some states. This could increase random error and therefore reduce the statistical significance of the retail and total employment density measures.

  2. I also tested 5-, 10-, and 20-year indicator variables to define “recent” and “settled” and found the most statistically significant differences in initial regressions to come with the 15-year versions. This was in part because the before-and-after division yields a roughly equivalent number, maximizing the statistical power of the test. I also tested a number of other time-in-US variables, including linear and spline forms. These variables were insignificant when the 15-year indicator variables were included.

  3. Tobit is a maximum likelihood method that accounts for the fact that a significant share of respondents did not make any auto trips for groceries, meals out, or visits. To remove these households from analysis would truncate the sample and bias the results. The Tobit model estimates coefficients that treat zeroes as part of an unobserved distribution that includes negative numbers, and so negative coefficients can be interpreted as reductions from the mean, subject to the constraint that net negative trip predictions are interpreted as zeroes.

  4. I discuss statistically insignificant results when large in magnitude because the data set is relatively small.

  5. The negative and positive percentages in this table are derived by subtracting one from the logit odds ratios.

  6. One would expect that same-country immigrant concentration would have the strongest negative correlation with auto use, consistent with Smart (2012). Due to collinearity, it was not possible to estimate well-functioning models using a variable interacting Latin American immigrant density with Latin American immigrant status and using the full sample. However, as noted below, estimating models on just the subset of immigrants revealed that population density was significant and immigrant density was not.

  7. Only statistically significant variables are shown in Tables 4 and 5.

  8. Variance inflation factors for this model peaked at 7.2 for logged Latin American population density; but effects on the remaining variables were roughly the same in magnitude and the same in significance when it and other collinear variables were removed so that the largest variance inflation factor was 4.

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Acknowledgments

The New Jersey Department of Transportation provided the funding to design and conduct the survey. Marc Weiner of the Bloustein Center for Survey Research and Chintan Turakhia of SRBI (now ABT/SRBI) assisted with survey design and sampling design. Chintan Turakhia, David Ciemnecki, and numerous telephone interviewers in the SRBI call center implemented the survey. Stephanie DiPetrillo was the project manager. Nick Klein created the GIS measures of population and employment density and transit access; Matt Brill and Timmy Bolton did the same for grocery stores and restaurants, as well as enclave measures at the Census tract level using ACS data. Dan Tischler provided research assistance in occupation recoding. Timmy Bolton helped with data cleaning and recoding, and making tables. The article analyzes data that were originally described in a report co-authored with DiPetrillo and Klein. Many thanks for helpful comments from several readers including Nick Klein, Mike Manville, Joan Walker, Randy Crane, Bob Noland, anonymous reviewers from the Transportation Research Board’s Travel Behavior and Values Committee (ADB10), and three anonymous reviewers for Transportation.

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  1. Department of City and Regional Planning, University of California, Berkeley, Berkeley, CA, USA

    Daniel G. Chatman

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Correspondence to Daniel G. Chatman.

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Chatman, D.G. Explaining the “immigrant effect” on auto use: the influences of neighborhoods and preferences. Transportation 41, 441–461 (2014). https://doi.org/10.1007/s11116-013-9475-4

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