IIMMLA Research Project 2019
OUR RESEARCH
Introduction
The United States has been home to immigrants from all over the world for centuries for people in search of making a better life for themselves and their family. All throughout American history, views of immigration have fluctuated and are reflected in immigration policies. At the beginning of the twentieth century, over a million people immigrated into the United States easily with little to no limitations. A couple decades later, xenophobia becomes more apparent in the Immigration Act of 1924 that implemented a new quota system that only allowed a certain number of people into the United States per nation but there was a clear preference for European countries. This is just a couple of policies in regards to immigration in the last century. Many immigrants aspired to achieve “The American Dream” of achieving success through hard work. However, this turned out to be harder than once before due to the huge influx of immigrants throughout the century, resulting in less jobs available than previously before. California is home to the most immigrants in the United States, and the reception has been well until recent political administration. In 2000, about 62 percent of residents in the Los Angeles area were born in a foreign country, or their parents were, making this area critical source to conduct a study from (Rumbaut et al., 2008). The IIMMLA study compared six groups in terms of many different circumstances and demographics, but we chose to focus on generation, level of educational attainment, ethnicity, occupation, and income, specifically.
Others who have studied the topic of immigration and intergenerational mobility have seen patterns of upward mobility depending on various factors such as path dependence (meaning that the outcome of the first generation affected the paths of the second generation) and location. Immigrants have generally shown an increase in rates of upward mobility, contradicting the general previous notion that most immigrants had lower socioeconomic status (Zhou et al. 2008). There is also evidence that the more years spent in the U.S., also shown through age of migration, is significantly correlated with acquiring greater human capital such as education and language fluency, and thus the reason for greater occupational mobility in certain people (Toussiant-Comeau, 2006). However, there are also studies that show education does not influence impact of space and race on the likelihood of working (Painter et al. 2007) and that the integrational progress is much slower for children due to race and ethnicity regardless of location settled by parents and education levels (Ellis, 2006). Despite these contradictions, scholars generally agree that immigrants show success in assimilating, closing the gaps in education and income with native-born whites, children of immigrants “do better” than their parents, as well as seeing an increase in socioeconomic mobility, slowly but surely. We also noticed a specific focus on 1.5 and 2nd generations in multiple studies and a lack of knowledge and analysis on the 3rd generation and beyond. The question that still remains is whether the progression of socioeconomic mobility throughout immigrant generations will be persistent throughout the years and future generations.
Research and Analysis
This project is important because it helps us understand immigrants in ways that the U.S. census doesn’t provide. Research calls for more details to be gathered in order to fully understand the immigrant experience (Duncan, Brian, & Stephen J. Trejo, 2014), and this particular dataset can fill the information gaps. With this data, we were able to find more information about the generation the respondent was, such as ethnicity, level of income, education level, and job occupation, and found relationships between these factors. We identified correlations between these factors to help others understand the disparities that still exist across generational cohorts and ethnicities, despite the emerging narrative that immigrants have shown steady upward mobility. Our research and analysis helps us illuminate any significant differences between the variables of education, income, and occupation across generations and ethnic groups. We believe this research is important because it addresses the gaps in mobility for third generation and future generations, which is lacking in current academia, as well as ethnic minorities. This can hopefully shift the conversation and begin a focus on later generations, working towards providing more resources for them the way they are provided for the first generation. In addition, we hope to cast light on the third and future generations of immigrants and make sure that their identity and narrative is not erased or forgotten.
First, we explored the relationship between the level of educational attainment and personal income of respondents. There is a general positive correlation between the level of education and total personal income as seen in incomes of $30,000 and higher. Most whose incomes are $30,000 or higher achieved a bachelor’s degree or an advanced degree. For example, of the respondents that answered $50,000-$69,999, 39% had a bachelor’s degree and 21% did advanced schooling. As percentages for these education levels increased, salary increased as well.
This trend confirmed our prediction that higher educational attainment typically leads to an increase in personal income. However, there were some disparities in income in relation to education levels. Out of all the respondents, the highest concentration of respondents had an income of $12,000 or less despite the variety of different education levels attained. This was unexpected as higher education is typically associated with higher levels of income.
Furthermore, the one dimensional bubble plot showed positive correlations between higher levels of education with an increase in white-collar jobs. Of respondents who did not complete high school were split, more had a blue-collar job than had a white collar-job. Respondents that attained higher education beyond high school had white-collar jobs associated with higher levels of income compared to blue-collar jobs. This demonstrates that higher education levels are positively associated with an increase in white-collar jobs and higher income levels.
Education attainment levels vary across all ethnicity groups due to differences in cultural expectations and translates over to how individuals perform in society. “Asian Americans have higher educational expectations for their children than all other racial group” (Kao and Tienda 1998; Goyette
and Xie 1999). These expectations are reflected in our data in that Asian Americans (specifically Chinese, followed closely by Koreans) have the highest percentage of bachelor’s degrees and advanced schooling compared to other ethnicities. Additionally, percentages of Asian Americans with bachelor’s degrees surpassed that of non-Hispanic whites. This can be attributed to the fact that “Asians and Asian Americans are more likely to adopt growth mindsets or the belief that intellect can be cultivated through effort as opposed to being inborn and fixed”
like non-Hispanic whites tend to do, due to white privilege (Stevenson and Stigler 1992; Li 2004). As a result, this drives Asian American youth to develop a stronger work ethic than their white peers (Hsin and Xie 2014). Mexican-Americans have the highest percentage of not graduating high school compared to other ethnicities and the Hispanic ethnic group had the lowest percentages of bachelor’s degrees out of all ethnicities. This can be supported by a study conducted by Sarah Gallo that showed amongst the lower performing ethnic groups, specifically Mexican immigrants which is the population she focused on, there is a significant amount of the criminalization of immigrants, which negatively impacts their children and their schooling (Gallo, 2014).
Income data across these ethnic groups correlates with the education data. The Chinese ethnic group, specifically, has a considerably larger percentage of the $30,000+ income range than the percentage in the Mexican population. The disparities in education and income across Asian and Mexican groups can be contextualized by a study published in The ANNALS of the American Academy of Political and Social Science. It was found that Chinese immigrant parents have the highest level of English-language proficiency, educational
attainment, and rates of home ownership when compared to Mexican groups. The offspring of Chinese immigrants are also more likely to grow up in two-parent, married household as compared to Mexican populations (Zhou et al., 2008). These differences can help explain the variations in these socioeconomic markers and mobility between these two ethnic populations. Looking at occupations specifically, despite the wide range of educational attainment across different ethnicities, there was a concentration of the same occupations across all ethnicities. Highest concentration for all ethnicities had the occupation of office, administrative support, sales and related, and education. Asians and non-Hispanic Whites had the highest percentage of white-collar jobs compared to other ethnic groups.
Across the board, highest percentage of education attained for each generation is associate degree and 1 to 2 years of college. Across all generations, the highest proportion of white collar workers had the occupations of office, administrative support, sales and related, education, and more. Across all generations, blue-collar workers worked at the same jobs previous generations had worked at before as well. The highest percentage of bachelor's degrees obtained is exhibited by the 1.5 generation, and the lowest is 3rd generation. This was unexpected as a study from The International Migration Review argues that Hispanic immigrants who immigrated at an earlier age and resided in the U.S. for a longer period of time are more likely to acquire U.S. education and language and achieve occupational status comparable to non-Hispanic whites (Toussaint-Comeau, 2006). This is similar to the idea that
the more years spent in the U.S., the more social and human capital would be acquired, leading to better integration in the U.S. education and labor market. as generations went on, it would be better because they had more years to assimilate, had more human capital. However, Rumbaut addresses the lower education level obtained by the 3rd generation due to an increase in dropout rates in both high school and college. This points to the third generation not identifying the same way as earlier generations; the need to be successful academically and economically is not a priority, although it is one for their parents. Moreover, total personal income across generations does not show any significant differences between income ranges. Generation is just one dimension success can vary across. Additionally, we found that location can also be a predictor of academic and economic success.
Many ethnic groups comprise metropolitan Los Angeles, some of which are represented in the IIMMLA dataset and in this map. The IIMMLA dataset contained geographic data about the following ethnic groups: Asian, Latin American, non-Hispanic Black, and non-Hispanic White. We noticed a clustering of Asian populations in the Anaheim/Orange County area and the Pomona/West Covina area. We also noticed a clustering of Latin American populations in the San Fernando Valley.
This map is not entirely representative of all the respondents in this dataset; the color each city on the map represents its largest ethnic population. Furthermore, 3,570 of the 4,656 (77.7%) data entries did not contain any specific information cities. Participants’ responses were either ‘Don’t Know’ ‘Not Applicable’ ‘Refused’ or ‘Other’ and are therefore not represented in the map.
Since our main focus is education, we mapped education across the different cities, similarly to the way we mapped ethnic groups, to visualize how education levels were distributed across metropolitan Los Angeles. Like the previous map, colors correlate to the highest percentage of
education level attained represented in each city. However, we did notice general higher percentages of college graduate education level in the Anaheim/Orange County area where we noticed Asian clustering.
Comparing this map to the “Ethnic Groups in Different Cities” map, we generally noticed more evenly distributed education levels across the map, as there was less obvious
clustering present. However, we did notice general higher percentages of college graduate education level in the Anaheim/Orange County area where we noticed Asian clustering. We also noticed some clustering in the Central/East Los Angeles area of people that did not complete high school. When checked across the Ethnic Groups map, we noticed that these cities were predominantly Latin American populations. This corroborates our data regarding education and ethnicities–Mexican-Americans have the highest percentage of not graduating high school compared to other ethnicities, and Hispanic groups had the lowest percentages of bachelors degrees across all ethnicities.
Conclusion
The United States of America has the largest immigrant population out of all countries worldwide. In 2017, there were 44.4 million immigrants in the U.S., accounting for 13.6% of the nation’s population (Media Inquiries). Immigrants have always played an important role in the American society and have contributed to many of the nation’s successes throughout history. Our research shows that the immigrant experience is far from homogeneous. The immigrant experience is diverse, with some being able to fulfill the American Dream more completely than others. With our research, we hope to illuminate the inequalities still present between ethnic groups and generations in terms of education, income, and occupations and how these inequalities affect their livelihoods. We also hope that we filled an information gap with the lack of studies focusing on 3rd generations and beyond. We hope to inform future research that can pinpoint causal factors to these disparities.