I include county-year fixed effects that control for all observable and unobservable time-variant area-specific factors. Finally, social and cultural assimilation affect transportation assimilation. However, surveys generally do not ask related questions. In Table 1 , I find that The rate of public transit ridership among immigrant commuters decreases over time— This might be closely related to the trend of length of stay in the U.
Note that options for the question of the length of stay were not given as exact years; only year intervals e. Hence, I am only able to assign the average length of stay in the year interval to individual immigrants in the sample. However, given these intervals are fairly small within several years , I assume that the within-interval length of stay follows some symmetric distribution e. The place of work plays a crucial role in determining travel behaviors. Some central cities provide good public transit systems; moreover, parking and congestion could be difficult for drivers.
On the other hand, many non-metropolitan areas do not have any public transit system at all. There are more female commuters in , possibly due to the increasing female employment rate. The average age in the sample is close to 40 years old.
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More than half of Hispanic immigrants self-identify as white. On the contrary, the proportion of Asian immigrants has increased since The average number of children is about 1. Finally, the average years of schooling are Both years of schooling and personal income have increased over time among immigrant commuters. In the following Figs 1 — 3 , I descriptively present the public transit rate among U.
Figs 2 and 3 show the public transit rate as the function of length of stay based on each sub-sample. Graphically, the linear regression line appears to be relatively steeper in the and sample. This could suggest that the rate of transportation assimilation decreases over time.
Memorandum, Nisei Assimilation, July 21, Papers of Philleo Nash. | Harry S. Truman
In other words, the public transit rate among new immigrants upon arrival becomes relatively low in recent decades. This might partially account for the decrease in the rate of transportation assimilation. Again, Fig 2 is partially affected by data limitations that only year intervals of the length of stay rather than exact years were asked in the and census, but the general shape and qualitative pattern of transportation assimilation shown in Fig 2 are very similar to those shown in Figs 1 and 3.
Consider an immigrant i originally from country k and live in county c in the U. Denote P ijkc as the binary indicator of public transit use and I run a baseline linear probability model LPM to examine the rate of transportation assimilation: 1 where l ijkc is the length of stay in the U. Including year fixed effects helps control for year-specific factors, such as that the public transit ridership rate among newcomers might be different in difference census years due to year-specific characteristics.
With this specification, it is easy to establish similar logit or probit models. In the empirical analysis I will report odds ratios of logit regressions. While not presented in this paper, the marginal effects of logit models are numerically very similar to the OLS coefficients, and the results of this paper are not driven by the statistical models. The above equation produces the general estimation. To examine transportation assimilation by year, I further run four regressions based on sub-samples that contain individuals taking the survey in specific years.
These regression models estimate the assimilation rate in each survey year, i. Table 1 suggests the rate of public transit ridership among U.
Has the rate of transportation assimilation also changed during the past decades? To examine the trend in transportation assimilation, I further pool the data set and employ the fixed effect models to compare coefficients of length of stay l i by census year.
By observing the regression results of these models one can check whether the decreasing trend of transportation assimilation is statistically significant. I now report the empirical results of this paper. In Table 2 , I report odds ratios of simple logit models, in which I regress public transit ridership only on the length of stay in the U. Column 1 presents evidence of transportation assimilation: public transit ridership decreases as the length of residency in the U. Logit models in Column 2, 3, 4, and 5 further show that the rate of transportation assimilation could change over time: the assimilation rate appears to be relatively lower after In Table 3 I rerun the five regressions, but now including demographic and socioeconomic variables.
Results shows the same qualitative pattern: transportation assimilation does occur among immigrants; moreover, the assimilation rate decreases over time. Compared with findings of this table, the assimilation rate is underestimated in Table 2 where demographic and socioeconomic characteristics are included. Table 3 shows that female immigrants and older immigrants are more likely to commuter by public transit, while married immigrants and immigrants with higher income are less likely to ride public transit, and the effect of education appears to be minor. The number of children is negatively correlated with public transit ridership, and the place of work indeed has great effects on travel behaviors, in the sense that those working in central metropolitan areas are much more likely to take public transit, and those working outside metropolitan areas are less likely to be public transit riders.
In Table 4 I add another important determinant of transportation mode choices in the models: the geographic factors. Table 3 has presented the positive effect of working in central metropolitan areas and the negative effect of working outside metropolitan areas on public transit use. Table 4 further takes county information into account, in order to control for the possibility that different counties provide different levels of public transit services for travelers. Compared with Tables 3 and 4 shows that the assimilation rate is indeed underestimated without controlling for the country of residence.
This suggests that the uneven distribution of public transit systems in the U. Previous tables show some evidence that the rate of transportation assimilation has decreased in the past few decades. To further examine this, In Table 5 I pool the data and construct variables that present cross-sectional differences in the assimilation rate. To do so, I conduct pairwise comparisons of coefficients of length of stay between two sub-samples. In Column 1 I run the regression of transportation assimilation based on the and sample. Results show that the assimilation rate estimated in the sample is not significantly different from that estimated in the sample.
Column 2 suggests that the assimilation rate has steadily decreased in the s. Similarly, Column 3 shows that immigrants of the cohort could assimilate faster than those of the cohort. In Column 4 I split the full sample by year as there are four sub-samples in total. This final regression shows that the rate of transportation assimilation among U. Note that results in Table 5 —regarding trends in the rate of transportation assimilation over time—need to be interpreted with caution. This is a general statistical issue for individual-level datasets, as it is difficult to identify comparable individuals in the control group i.
Consistent with earlier findings [ 13 ], the results of this paper suggest transportation assimilation among immigrants in the U. Based on repeated cross-sectional data, this paper further contributes to the literature by pointing out the rate of transportation assimilation has decreased over time. This section first discusses possible mechanisms behind this phenomenon, and then discusses policy implications and limitations of the empirical analysis.
There are several possible reasons that could explain this phenomenon. This, however, could not fully explain the findings of this paper. On one hand, year fixed effects could partially control for this factor. On the other hand, this can be analyzed by running regressions of transportation assimilation based on immigrants who have just arrived in the U. Table 6 presents results of these regressions. Results of Column 3 and 4 suggest that the assimilation rate actually appears to be even slightly larger among new immigrants in and Second, the U. Hence, different cohorts of immigrants are exposed to different public transit systems upon arrival, which could affect the rate of transportation assimilation.
This, however, has been captured in the regression models by controlling for geographic factors such as county and the location of workplace.
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This could be seen from the coefficients of workplace locations in Tables 3 and 4 , as well as the change in the assimilation rate estimated in Tables 3 and 4 , before and after county fixed effects are included. Indeed, immigrants does not socially or economically assimilate into the U. Table 6 also suggests that newcomers generally assimilate faster. Hence, I extend the statistical model by introducing a quadratic term of the length of stay in Table 7.
Results show that the coefficient of this quadratic term is positive and statistically significant. Hence, the rate of transportation assimilation should be higher in a sample that contains more new immigrants. As the average length of stay has steadily increased reported in Table 1 , many immigrants in recent surveys have stayed long enough to assimilate into the U. Hence, Tables 6 and 7 suggest that the decreasing trend of transportation assimilation over time can be explained by the changing demographics of U. Another factor of the demographics of U.
In the context of this paper, The number of white immigrants—especially non-Hispanic whites—has greatly decreased over the past decades. The proportion of Hispanic immigrants has fluctuated, as seen in Table 1 and related research [ 28 ], but the Asian population in the U. Compared with non-Hispanic white immigrants in Table 3 , Hispanic immigrants are generally not significantly different but Asian immigrants are significantly likely to take public transit. Hence, the trend of transportation assimilation might be affected by different demographic backgrounds of immigrants of different cohorts.
Table 8 focuses on transportation assimilation using the Hispanic and Asian sample in two panels. Table 8 shows that in the full sample as well as sub-samples of specific survey years, Asian immigrants appear to assimilate at slower rates in terms of public transit ridership. In other words, many Asian immigrants might still take public transit even after staying in the U. While not reported here, I find similar results of transportation assimilation among Asian immigrants and other immigrants upon arrival.
As the Asian population has steadily grown in the U. To further study this, I compare the Hispanic and Asian immigrants in Table 9. Two immigrant populations have similar average years of stay and, among newcomers who have stayed in the U. There are at least two possible reasons behind how two the Asian and Hispanic population assimilate differently afterwards. It also allows you to accept potential citations to this item that we are uncertain about.
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Mental health assimilation of Australian immigrants. Mental diseases are a widespread phenomenon and trigger massive direct and indirect costs. Using Australian household survey data this study analyzes assimilation of immigrants' mental health over time. Therefore, this study contributes to the literature since previous literature has focused primarily on the assimilation of immigrants' physical health status. However, assimilation over other regions and instruments mostly shows that first-order improvements stem from assimilation in general, while bias corrections result in second-order changes.
Both the temporally averaged and time-varying mean differences of Figs. This reflects the importance of observations near the winter poles in the absence of heterogeneous chemistry in LINOZ. The impact of bias correction on the standard deviations of forecasts is not very significant. The drift of the mean biases in time in the absence of assimilation, as seen in Fig. For the GEM-LINOZ model, this results in a long spin-up period in which the ozone field moves from its initial state, based on an earlier assimilation, toward the ozone model equilibrium state.
Beginning with an initial ozone field at the model equilibrium state would have increased the mean observation-minus-forecast differences and would likely not have improved the ACC of the control case, as implied by Fig. Also from Fig. This limited deterioration would not deter, for example, in properly forecasting the movement of low total column ozone regions during these periods. This occurs even though the OMI-TOMS data set is larger by factors of about 6 to 12 than the individual thinned data sets of the other sources.
The ACC also demonstrates a more marked improvement in multiple sensor assimilation in the tropical region as compared to OMI-TOMS assimilation alone, which is not well seen in the mean differences. Multiple sensor assimilation with bias correction even further increases the ACC and thus the quality of the pattern and variation of the forecast fields. Bias correction of total column ozone data from satellite instruments was performed using three different approaches.
Two of the methods parameterized the bias estimation as a function of latitude, solar zenith angle, and time, while the other method used the ozone effective temperature in place of latitude and time. These approaches consisted of using observation colocation between satellite-borne instruments and a reference, referred to in this paper as the anchor. One approach also involved differences between observations and short-term forecasts.
The 2-month time-averaged bias estimates from the ozone effective temperature parameterization were similar to those from the other approaches. The anchor used in the bias-estimation schemes was chosen as the OMI-TOMS data product, due to its wide coverage in both time and space and its good agreement with ground-based instruments.
It was demonstrated that the assimilation of total column ozone observations that include bias corrections as derived in this study can improve the agreement between short-term forecasts and ground-based measurements. The benefit of including total column satellite data, even without bias correction, was most notable in the tropics, in addition to the polar vortex region. For the assimilation of two or more satellite sensors, while it is possible that the cancellation of errors from different instruments could reduce forecast biases, harmonizing the different data sets through bias correction better ensures that target reductions in residual biases are achieved.
The assimilation of bias-corrected observations from multiple sensors does not notably reduce the mean differences as compared to the assimilation of individual bias-corrected sensors. However, a notable improvement in multiple sensor assimilation was seen in the tropical region as compared to OMI-TOMS assimilation alone in the anomaly correlation coefficient metric.
This improvement implies an increase in the quality of the pattern and variation of the forecast fields. The observations can be obtained from the different centres identified in the text and the acknowledgements section below. The assimilation and forecasting system relies on ECCC computing environment tools and file conventions. Also, the computing hardware used for these assimilation cycles has since been replaced at ECCC with accompanying changes to the cycling package.
References of the system components are provided in this paper. The large sets of model analyses and forecasts, and the observation-minus-forecast data sets, are saved with an in-house binary file format. Subsets could potentially be made available by the authors upon request.
YJR directed and supervised this study, conducted some of the assimilation experiments and most of the final analyses, and is responsible for most of the manuscript text.
MS significantly contributed to the manuscript text, wrote the colocation and bias-estimation and bias-correction software, conducted many of the assimilation experiments and produced the figures and the data for the tables other than for Sect. Both MS and YJR contributed to the observation acquisition and preparation, and the setup of the assimilation experiments.
All the authors contributed to reviewing and revising the manuscript. The authors wish to thank Lawrence E. Also, we are very appreciative of the contributions by the two referees toward improving the quality of this paper. This paper was edited by Ken Carslaw and reviewed by Maria-Elissavet Koukouli and one anonymous referee.
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