Abstract
Sharp differences in time use by nativity emerge when activities are distinguished by incidence and intensity in recent US data. A model with daily fixed costs for assimilating activities predicts that immigrants are less likely than natives to undertake such activities on a given day; but those who do will spend relatively more time on them. Activities such as purchasing, education, and market work conform to the model. Other results suggest that fixed costs for assimilating activities are higher for immigrants with poor English proficiency or who originate in less developed countries. An analysis of comparable Australian data yields similar results.
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Notes
The role of learning and time use in assimilation has been recognized in a song: Leonard Bernstein, Candide, “I am so easily assimilated,..., It’s easy, it’s ever so easy! Do like the natives do.”
While all the results reflect population-based sampling weights in the ATUS, one might be concerned about unit nonresponse. It is true (Abraham et al. 2006) that in terms of observables, this is not a problem in the ATUS, but perhaps the sample is nonrepresentative along nonobservable dimensions. We obviously cannot account for this potential difficulty; but, if it exists, one would think that those immigrants who, other things equal, are less likely to have completed time diaries are those who are most different from natives. That being the case, the results here will understate the true immigrant–native differences.
One might be concerned that these activities include travel time. To exclude travel time would exclude something that is clearly endogenous. Nonetheless, we reestimated the basic models reported in this study with travel time excluded, a respecification whose results altered none of our conclusions qualitatively.
The time diary method requires total times to exhaust the day—1,440 min. Because a few categories could not, however, be coded, the sums of these averages do not quite exhaust the total: Among immigrants, they total 1,422 min; among natives, 1,419 min.
In the long-run problem described by Eq.1, \(a^{\ast }_{1}\) was normalized to represent the fraction of time spent on the assimilating activity in the initial period. For the short-term problem in Eq. 3, \(a^{\ast }_{1}\) is renormalized to represent the total amount of time (in days) spent on the assimilating activity in the initial period.
As before, for simplicity, we ignore any variable costs associated with engaging in the assimilating activity.
We thank a referee for suggesting this extension of the theoretical model.
This is easily accomplished in STATA using the routine “craggit” created by Burke (2009). The error terms in each probit and associated truncated regression are assumed independent. Since there are different numbers of observations in the two, no test of this standard assumption is possible.
This result is driven by purchasing of goods (see the Appendix), which accounts for slightly more than half of total time in this category. Immigrant–native differences in travel time, which are arguably less likely to be assimilating, are much smaller.
Although we cannot know the nativity status of an ATUS respondent’s fellow workers, it might be that those who are in blue-collar positions (CPS occupation code ≥ 4,000) are more exposed to the native culture than other workers. Dividing the sample by this criterion, we do find that, conditional on positive work time on the diary day, and holding all the controls constant, blue-collar immigrants do work longer than blue-collar natives.
A problem might arise (Stewart 2009) with this method if those who engage in the activity on a particular day are nonrandomly selected from the subsample that ever engages in the activity. Only a tiny fraction of people are likely to engage in educational activities during a year and nearly everyone does some purchasing, but only part of the sample works during the year. To examine this concern, we reestimate the models for work in Table 2 including only those respondents who stated that they usually have positive work hours in a week. The results, particularly the immigrant–native differences, are essentially the same as those presented in the table.
The results look very similar when we reestimate all equations separately for individuals younger or older than 40 years of age. The impact of immigrant status is nearly identical regardless of the age of the individual.
Another possibility is that immigrant–native differences differ by marital status, but that possibility too is not apparent in the data, nor do the differences result from immigrants’ much greater concentration in metropolitan areas: When rural residents are deleted, the results are nearly the same as in Tables 2 and 3, except that the immigrant–native differences in Table 2 are slightly more pronounced.
Farley and Alba (2002) report similar patterns with respect to the relative size and composition of the second-generation population in the USA.
The conclusions do not change if we interact the proxies for English-language knowledge with the individual’s educational attainment, nor do they change if we restrict the sample to Hispanics. Reestimating the equations in Tables 2, 3, and 5 including only the sample of Hispanics, both natives and immigrants, we find that immigrants as a group have a lower incidence of these activities than natives. Conditional on engaging in them, however, the intensity is greater. Moreover, the immigrant–native differences are entirely due to differences in language knowledge.
For most of the countries of origin, we use data for 2008 from the World Development Indicators of the World Bank. For a few others for which these were unavailable in that database, we obtained the information from the World Economic Outlook database of the IMF. GDP is converted to US dollars using the exchange rate against the dollar.
Adding interactions of home-country GDP with the language categorizations adds nothing to these equations—the effects are apparently independent. We also experimented with other proxies for cultural differences, including dominant Christian religion or Asian. These are so highly collinear with the variables English-language background and home-country GDP per capita that we cannot draw inferences about their possible independent effects.
We thank Brian Duncan for having supplied his tabulations from the Census 2000.
We exclude the few respondents over age 85 and thus outside the age range reported in the ATUS. Also, household residents in the Australian data are recorded as children only if they are under age 15, and their categorization by age differs slightly from that in the ATUS. Finally, the categories of educational attainment necessarily differ from those in the USA. We include as low-educated respondents those with secondary or lesser qualifications, as middle educated those with trade qualifications or a certificate or diploma, and as highly educated those with a bachelor’s degree or higher. We dropped the 5 % of respondents who were still attending school.
Among those who engaged in the same assimilating activity on both diary days, the within-person correlations of the residuals are 0.21, 0.30, and 0.32 for purchasing, education/training, and work respectively.
The ulpan is designed to teach adult immigrants to Israel the basic language skills of conversation, writing, and comprehension. Its primary purpose is to help new citizens to be integrated as quickly and as easily as possible into their new country.
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Acknowledgements
We thank Jenna Kawalsky for inspiring our interest in this topic, and we are grateful for the comments from Sandra Black, George Borjas, Deborah Cobb-Clark, Stephen Donald, Jonathan Gershuny, David Jaeger, Jay Stewart, participants in seminars at several universities, and the editor and three referees. We also thank Sarah Flood for the help with the ATUS data, Bob Gregory for the aid in obtaining the Australian data, and Holly Monti for her research assistance.
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Responsible editor: Klaus F. Zimmermann
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Hamermesh, D.S., Trejo, S.J. How do immigrants spend their time? The process of assimilation. J Popul Econ 26, 507–530 (2013). https://doi.org/10.1007/s00148-012-0440-x
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DOI: https://doi.org/10.1007/s00148-012-0440-x