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Education Across Borders: The Relationship Between Age at Migration and Educational Attainment for Mexico-U.S. Child Migrants


by Karyn Miller - 2016

Background/Context: The flow of people, including children, across international borders is a growing trend. While research has emphasized the relationship between parental migration and children’s educational outcomes, little is known about how child migration itself influences educational attainment.

Purpose: To examine the relationship between Mexico-U.S. child migration and (a) completed years of schooling and (b) likelihood of dropping out of school.

Subjects. 33,705 Mexican-born individuals between 7 and 22 years old.

Research Design: Secondary data analysis.

Data Collection and Analysis. Using data from the Mexican Migration Project (MMP134), pooled OLS and logistic regression models were used to examine the relationship between Mexico-U.S. child migration and (a) completed years of schooling and (b) likelihood of dropping out of school. The sample was split into three groups representing age at first migration (0–6, 7–12, 13–15), allowing for investigation of age-specific incentives and barriers to investment in education. Further descriptive analysis explored what children who drop out of school do instead.

Findings: Mexican-born children who first migrate to the United States between the ages of 0 and 6 may have an educational advantage relative to their peers who stay behind, while those who migrate between the ages of 13 and 15 have an educational disadvantage. Specifically, migration in early childhood is related to more years of schooling and increased persistence in school for compulsory school-age children; migration in later childhood is associated with an increased likelihood of dropping out of school. Parental education and household wealth are strong, positive predictors of educational attainment, while being from a community with high migration rates is related to fewer years of schooling and a higher likelihood of dropping out. Of those who drop out, the majority of females are engaged in housework while the majority of males are employed as unskilled workers. Further, migrant students who drop out of school in the United States are more likely to be poor, male, members of large families, and have parents with low levels of education.

Conclusions: This study suggests that educational policy regarding migrant students cannot be divorced from the larger, national immigration debate. It also identifies key characteristics of migrant students who drop out of school in the United States, which has implications for practice. Schools and support services can target this vulnerable population and the specific challenges to educational attainment it encounters.



INTRODUCTION


What does it mean to educate children in an increasingly globalized world? Economic integration, global modes of communication, and instantaneous access to information have changed the nature and goals of education systems around the world. Education is more closely dictated by market needs than ever before (Stromquist, 2002); technology has made it possible for students to collaborate with peers around the world. While the international flows of goods, services, and ideas are changing the landscape of education systems worldwide, it is less clear how education systems are responding to another aspect of globalization—the flow of people, particularly school-age children, across international borders.

In 2010, 3.1% of the world’s population was comprised of international migrants, foreign-born individuals who moved to another country, often unauthorized, for a myriad of economic, familial, or cultural reasons (United Nations, 2011). This number reflects a trend in the last decade of increasing flows of people across international borders (OECD, 2012).  Evidence suggests that these flows are not one directional. In many cases, there is a high degree of fluidity across borders; international migrants often return to their home country after a temporary time abroad (Dustmann & Weiss, 2007; Reyes, 1997). Increasingly, youth make up a fair share of the international migrant community. Recent estimates suggest that one-third of migrants from developing countries are young people (World Bank, 2006).


These young people, international migrants who may travel back and forth across borders and generally maintain connections to their home countries while establishing new ties to their destination country, are the embodiment of a process anthropologists term “transnational migration” (Schiller, Basch, & Blanc, 1995), and increasingly constitute a new student population—the transnational student. Historically, education systems around the world have been national projects that reflect the ideals, norms, and aspirations of a country. Success in these systems depends, in part, on students’ ability to continue through the educational process with relatively little disruption, familiarity with the home culture, and knowledge of the home language. In many ways, the underlying structure and requirements of national education systems are at odds with the experiences of transnational students who may navigate multiple education systems and lack cultural knowledge and language fluency in the destination country. Although considerable research has been conducted on how parental migration affects the educational outcomes of their children who stay behind in their home country (see, for example, Antman, 2011; Halpern-Manners, 2011; Hanson & Woodruff, 2003; McKenzie & Rapoport, 2011; Nobles, 2011, for evidence from Mexico), relatively little is known about how the movement of children themselves across international borders influences their educational attainment (for existing research on the topic, see Kandel, 2003; Kandel & Kao, 2001).


This study begins to address this gap by examining the interesting case of child migration from Mexico to the United States. A child migrant is defined as any individual who migrated to the United States from Mexico for the first time before the age of 16, the approximate age at which compulsory schooling ends in both countries. This study presents the argument that child migrants face incentives and barriers to investment in education and that these differ by age at first migration; hence, age at migration is a critical factor in determining the educational attainment of transnational students from Mexico relative to their peers who stay behind.1 Specifically, it highlights the age-specific influence of economic opportunities in Mexico and the United States, ties to broader migrant networks, educational aspirations, and language acquisition on the educational attainment of migrant students.


This case warrants research attention for three reasons. First, the movement of children across the Mexico–U.S. border is a growing trend. As of 2010, 20% of all international migrants were residing in the United States alone (United Nations, 2011). Data from the Pew Hispanic Center indicate that, of the 40 million foreign-born immigrants in the United States, nearly 12 million are Mexican (Patten, 2012), slightly more than half of whom are unauthorized (Passel & Cohn, 2011). The magnitude of movement across the Mexico–U.S. border indicates a sizable population of transnational migrants, many of whom are children. According to recent results from the American Community Survey, 7.9% of Mexican-born migrants in the United States are under the age of 18 (Grieco et al., 2012). This amounts to approximately 945,000 children. Further, the trend of young people crossing the border is increasing. From late fall 2013 to midsummer 2014, the number of unaccompanied and unauthorized minors detained at the Mexico–U.S. border rose to 63,000, a 100% increase from the previous year (Park, 2014).2


Second, this is a politically salient issue. Not only is the flow of children into the United States from Mexico intensifying, but so too is the national debate in the United States about the rights and privileges of migrants, particularly those who arrive in the country as undocumented children. Recent legislation, such as 2011’s California DREAM Act and the Deferred Action for Childhood Arrivals (DACA) initiative, has increased access to higher education and opportunities for legal employment for unauthorized young people who moved to the United States as children. These laws may presage a new direction for immigration policy in the United States and increased educational and work opportunities for young migrants.


Lastly, the nature of transnational migration is two-directional. A substantial body of research has examined the educational attainment of first generation immigrant students in the United States (see Crosnoe & Lopez-Turley, 2011, for a review). Although evidence suggests these students may experience an educational advantage relative to U.S.-born children, this advantage becomes less clear when data is disaggregated by country of origin. Results from one study indicate that foreign-born students from Europe, East Asia, China, and the former Soviet Union strongly outperform U.S.-born students on math and reading achievement tests, while students from Latin America and the Caribbean underperform relative to their native-born peers (Conger, Schwartz, & Stiefel, 2007).


These findings are consistent with other research highlighting the low academic achievement and subpar schooling outcomes of Latino students in the United States relative to their native-born peers (Gandara & Contreras, 2009). Mexican-born students, in particular, encounter less quality educational opportunities and have lower educational attainment (Alba & Silberman, 2009; Glick & Hohmann-Marriott, 2007; Suárez-Orozco, Suárez-Orozco, & Todorova, 2008), are more likely to drop out (Crosnoe & Lopez-Turley, 2011; Ruiz-de-Velasco & Fix, 2000), and are less likely to enroll in U.S. schools (Oropesa & Landale, 2009). Compared to other Hispanic populations in the United States, Mexicans rank among the lowest in terms of educational attainment. For 26% of U.S.-based Mexicans aged 25 and older, a high school diploma is the highest level of education acquired; only 9% hold a degree from a 4-year university (Motel & Patten, 2012).


Educational outcomes in the United States have long been tied to employment prospects and social mobility; more than ever technology demands require highly skilled workers (Goldin & Katz, 2008). Undoubtedly, concern about the educational outcomes of Mexican-born students is linked to concern about their capacity for employment and economic prosperity in the United States (Gandara & Contreras, 2009).


While the research emphasis on the educational attainment of foreign-born students in relation to their U.S.-born peers can provide necessary insight into the educational outcomes of migrant children in the context of their destination country, it only illuminates one aspect of the transnational student experience. Given the likelihood that many child migrants will acquire only a share of their education in one country and may eventually enter the labor market in their origin country, it is imperative to also understand how the educational attainment of migrant children compares to their origin-country peers. This study complements the existing literature that suggests Mexican students have an educational disadvantage relative to native-born children in the United States by considering whether they may, in fact, have an educational advantage relative to their counterparts in Mexico.


The study’s sample of international migrant children is split into three distinct groups, those who first migrated between the ages of 0–6, 7–12, and 13–15. This three-level approach to classifying students by age at first migration allows for a clear differentiation between migrant students who enter the U.S. education system before reaching school age (0–6) and those who arrive in the United States approaching a working age (13–15). These two groups, in particular, likely encounter significantly different incentives and barriers to investment in education. This study specifically examines the relationship between Mexico–U.S. child migration and children’s (a) completed years of schooling and (b) likelihood of dropping out of school, relative to Mexican children who never migrate to the United States. Further investigation considers, of those children who drop out of school, what do they do instead?


Results suggest that there is an educational advantage for compulsory school-age individuals who first migrate to the Unites States between the ages of 0–6 relative to their nonmigrant Mexican counterparts; conversely, students who first migrate to the United States between the ages of 13 and 15 have a significant educational disadvantage relative to their nonmigrant peers in Mexico. Although consideration of age at first migration sheds light on transnational students’ educational outcomes, it certainly does not provide a complete picture. Independently of age, results show that a host of household, child, and community characteristics further illuminate students’ persistence in attaining a formal education. Lastly, there are strong gender differences in educational attainment as well as employment among young people who have dropped out of school in both Mexico and the United States.


This article is organized in five sections. The first section provides context for migrant education in the United States and describes the Mexican education system in order to establish a point of comparison for understanding the educational attainment of transnational students relative to their origin-country peers. The second section presents an argument for why age at migration matters and reviews a number of incentives and barriers to educational investment for migrant children. The third section describes the data and methods used to pursue the research questions. The following section presents and discusses results of the empirical analyses modeling the relationship between child migration and (a) years of completed schooling and (b) dropping out of school. The final section concludes with implications for policy and practice.


BACKGROUND


POLITICAL CONTEXT FOR MIGRANT EDUCATION IN THE UNITED STATES


Legislative history in the United States reflects a contentious debate about the rights of migrant children, particularly the undocumented majority. In the 1982 case of Plyler v. Doe, the U.S. Supreme Court ruled against the state of Texas’s attempt to deny undocumented children a free public education by charging them K–12 school tuition. This ruling has not prevented states from attempting to limit educational access to undocumented children. Most notably, California’s 1994 Proposition 187, a ballot initiative passed by voters, restricted access to public services, including education, to unauthorized migrants. Although this proposition was ruled unconstitutional by the federal district court, it reflects a suspicion of, and hostility to, non-citizens (Lopez, 2011; Petronicolos & New, 1999). This debate played out on the national stage when, in 1996, the House of Representatives passed the Gallegly Amendment to the Illegal Immigration Reform and Immigrant Responsibility Act (IIRIRA). Although this amendment was excluded from the final bill, it essentially overturned the Supreme Court’s Plyler v. Doe decision and empowered states to charge undocumented students school tuition or completely deny them access to public education (Green, 2003).


In spite of the national debate, federal government has long funded programs, namely the Migrant Education Program (MEP), to promote the educational attainment of migrant children. Established in 1966, MEP provides a range of services designed to support the unique needs of migrant students, specifically the children of seasonal agricultural and fishery workers. Evidence indicates that the government’s attention to this population has favorable consequences. Findings from a longitudinal study in California suggest that MEP support services boost high school persistence and the academic success rates of migrant Mexican children, particularly in relation to their U.S.-born Mexican peers (Gibson & Bejinex, 2002; Gibson & Hidalgo, 2009).


One current focus of the public discourse about immigration is a consideration of the rights to which young people who migrated to the United States as children are entitled. The DREAM Act, which would grant provisional legal status to several million undocumented youth who arrived in the United States before the age of 16, has been voted down by Congress twice, most recently in 2010. In response, President Barack Obama introduced the Deferred Action for Childhood Arrivals (DACA) initiative in 2011, intended to defer deportation for 2 years for undocumented young people who came to the United States as children and have resided in the U.S. for at least 5 continuous years while allowing them to work legally.3 Also in 2011, California’s governor, Jerry Brown, signed the California DREAM Act into law, allowing undocumented immigrants who came to the United States before the age of 16 to apply for student financial aid known as Cal Grants and ultimately increasing access to higher education. At the start of the 2012–2013 academic year, 289 private and public colleges, universities, and technical schools in California were eligible to receive these grants.4 The passage of this law may be suggestive of a shifting perspective in the United States regarding the country’s responsibility to provide access to education and encourage educational attainment for foreign-born individuals who come to the country without proper documentation as children.


THE MEXICAN EDUCATION SYSTEM AND EDUCATIONAL ATTAINMENT


Despite the potentially changing political landscape regarding the educational rights of migrant children, research indicates that Mexican students in the United States perform lower than their native-peers. However, it is less clear whether Mexican child migrants to the United States have an educational advantage relative to their origin-country peers. In order to pursue this research focus, it is necessary to understand the basic structure and outcomes of the Mexican education system.


The Mexican and U.S. education systems are similarly organized into four levels. In Mexico, primary school (primaria) includes grades 1–6. Students are expected to enroll in first grade at the age of six. The system is then divided into lower secondary school (secundaria), grades 7–9, and upper secondary school (preparatoria), grades 10–12. Students are eligible to enter higher education after completing the 12th grade. The government is responsible for the provision of compulsory basic education, grades 1–9, although it is also involved in the provision of preschool, upper secondary school, and higher education (Santibañez, Vernez, & Razquin, 2005).


Despite improvements over the past five decades, average educational attainment in Mexico remains relatively low (OECD, 2011). Although primary school enrollment is nearly universal and 86% of eligible students are enrolled in lower secondary schools, the enrollment rate at the upper secondary school level is only 51%, which may in part be driven by the government’s poor provision of upper secondary schools (Santibañez, Vernez, & Razquin, 2005). Low enrollment, coupled with high dropout rates, has resulted in low educational attainment among adults. Of 25–34 year olds in Mexico, 42% have a high school education (OECD, 2011); approximately 8% of the 18 and older population has a bachelor’s degree (Santibañez et al., 2005).


Understanding the educational outcomes of migrant compared to nonmigrant Mexican youth requires consideration of (a) the factors that drive child migration and subsequent investment in education, and (b) the barriers to educational success that hinder migrant children in the United States. Building on existing research that indicates age at migration to the United States is an important determinant of educational attainment relative to a native-born sample (Chiswick & DebBurman, 2004), the following section presents an argument for the incentives and barriers to educational investment that age at first migration might represent.


WHY AGE AT MIGRATION MATTERS


FORCES THAT DRIVE TRANSNATIONAL MIGRATION


This section introduces a number of factors that may drive individuals’ decisions to migrate to the United States, including labor market considerations, ties to existing migratory networks, and future aspirations. Further, it considers the relationship between each driving force and incentives to invest in education. Lastly, it examines the intersection between each factor, age at migration, and educational attainment.


Labor Market Considerations


Decisions about investing in certain resources, such as an educational credential, may depend in part on an individual’s anticipated economic value of that resource in the future. Human capital theory (Becker, 1967) proposes that labor market demands for particular skills incentivize investments in schooling and drive educational attainment. In the case of transnational students, the value of an educational credential must be considered in the context of the origin as well as the destination country.


Educational credentials obtained in a migrant’s home country often do not yield comparable economic returns in a destination country (Chiswick, 1978). Recent research indicates that the purchasing power of a Mexican high school diploma, for example, is higher in Mexico than in the United States; it will elicit better employment opportunities at home than abroad (Chiquiar & Hanson, 2005). Considering that U.S. labor market opportunities for immigrants, particularly undocumented migrant workers, emphasize low-wage and low-skill positions (Passel, Capps, & Fix, 2004), it is hardly surprising that young people who anticipate migrating to the United States are less likely to invest in their education in Mexico (Kandel & Massey, 2002; McKenzie & Rapoport, 2011).


Employment prospects for migrants in the United States further discourage investment in education once in the United States. An absence of educational credentials does not prohibit entry into the agricultural and service jobs typically available to migrant workers. Despite low educational attainment, research indicates that it is relatively easy, at least for foreign-born men, to find low-skill employment in the United States (Duncan & Trejo, 2012). Unless labor market restrictions are relaxed, there seems to be little economic incentive for labor migrants to invest in education once in the United States.


However, the degree to which this factor drives transnational students’ investment in education likely depends on age. Young people who migrate to the United States as they are approaching the completion of compulsory schooling (13–15 year olds in this study) are likely more aware of and sensitive to labor market entry requirements than children who are not yet or just barely old enough to enter the formal education system (0–6 year olds in this study). While these requirements may drive older students to disinvest in education, they likely do not influence young children, just entering school, in the same way. In lieu of precise data on young people’s employment prospects, economic necessity and responsibility, and career aspirations, age can serve as a proxy for the influence of labor market considerations in whether transnational students persist in attaining a formal education.


Ties to Migrant Networks


For young Mexicans, migration decisions may not depend solely on anticipated employment prospects but family and community ties to migration. Kandel and Massey (2002) find that Mexican children growing up in highly migratory families and/or communities develop increased aspirations to work in the United States as well. They further find that the increased access to information and networks among migrants eases the challenges of moving across borders, normalizes the process of doing so, and disincentivizes investment in education. Although this disinvestment may in part be driven by the utility of a Mexican education in the United States as well as by labor market opportunities, it is also a byproduct of young people’s migratory aspirations.


Children brought up in this “culture of migration” are more likely to disinvest in resources, like education, that could promote social mobility in their home country in anticipation of future migration (Kandel & Massey, 2002). McKenzie and Rapoport (2011) find that living in a migrant household not only increases the migration probability of boys ages 13–18, but also decreases the likelihood that both boys and girls will complete high school. The negative effect of parental migration is further evidenced by increased work hours and reduced study hours for 12–15 year old boys in Mexico (Antman, 2011), the decreased probability of transitioning from primary to lower secondary school as well as from lower to upper secondary school for 15–18 year olds in Mexico (Halpern-Manner, 2011), and fewer completed years of education for 15–18 year old and an increased probability of experiencing schooling disruption for 15–20 year olds in Peru (Robles & Oropesa, 2011). This research suggests that Mexican-born children with connections to migratory networks through family and friends are less inclined to pursue education in Mexico, whether or not they ultimately migrate themselves.


Human capital theory, in conjunction with researchers’ understanding of the “culture of migration,” suggests that the actual or anticipated U.S. migration of young people should ultimately have deleterious effects on their educational attainment. However, this framework assumes that young people, rather than their parents, are responsible for making decisions about whether or not to migrate. Yet, individuals of or approaching the legal working age who migrate (or anticipate migrating) to the United States for work constitute a very different transnational student population from children who first migrate when they are very young. Parents who choose to migrate to the United States with their young children may highly value an American education and thus encourage investment in the formal system.


Parental Aspirations


Migrant parents often have high aspirations for their children’s education (Goldenberg, Gallimore, Reese, & Garnier, 2001); these aspirations reflect the assumption that education provides a pathway to a better life and that schooling and economic opportunities are intrinsically linked. In a longitudinal study of Latino immigrant parents in the United States, Goldenberg and colleagues find that 90% have college aspirations for their children (Goldenberg et al., 2001). This finding suggests migrant parents perceive that educational opportunities are available to their children in the United States. Moreover, 25% of the children in the study were born in Mexico, again suggesting that educational considerations may drive parents’ decisions to migrate with their children, particularly when they are very young.


Further research finds that migrant parents in the United States often choose to send for their young children in the hope of providing them a better future, despite the costs and dangers associated with crossing the border (Orellana, Thorne, Chee, & Lam, 2001; Suárez-Orozco et al., 2008). Many parents who migrate with their young children are at least partially driven by their aspirations for their children’s futures. Particularly when parents migrate to the United States with their young children (0–6 year olds), parents’ investment in their children’s education may be driven by the knowledge that a U.S. education will be valuable whether a child ultimately ends up working in the United States or Mexico. Although this logic can similarly be applied to children who migrate when they are older, the oldest group in this study (13–15 year olds) is already approaching the completion of compulsory education when they migrate and are significantly more autonomous than young children. If the aspirations of this group to immediately follow the path of family and friends into the U.S. labor market are high (Kandel & Massey, 2002), they may be less likely to invest in their U.S. education regardless of parental aspirations. Again, in the absence of data on parental aspirations and involvement, a child’s age at the time of first migration may serve as an appropriate proxy.


BARRIERS TO EDUCATIONAL ATTAINMENT IN THE UNITED STATES


Labor market demands, the existence of migratory networks, and parental aspirations might drive both migration and educational investment decisions. However, educational attainment is also influenced by barriers to access or advancement. Upon arrival in a new destination country, many transnational students face language obstacles, cultural barriers, and school quality issues which may hinder educational access and success in school.


The effects of certain barriers on educational attainment for child migrants, particularly limited English-language proficiency and cultural differences, may differ by age at migration. Research shows a strong link between age and language acquisition; very early exposure to a new language is associated with peak proficiency (see Newport, 2002, for a review), in part because the exposure period is typically longer compared to someone who is first introduced to a new language at an older age (Singleton, 1995). While age may be one important predictor, it is certainly not the sole determining factor in foreign language acquisition. Existing first language skills, a strong origin-country educational background, family SES, parental education, and literary practices at home also play an important role in academic second language acquisition (Dixon, Zhao, Shin, Wu, Su, Burgess-Brigham, Gezer, & Snow, 2012).


Further, teacher effectiveness and school environment can foster language proficiency for migrant students. Regardless of students’ age, teachers who promote self-confidence, reduce students’ anxiety about learning a new language, increase motivation, and actively engage students in learning encourage second language acquisition (Dornyei, 1994; Noels, Clement, & Pelletier, 1999). Schools and classrooms that devote considerable time to language instruction, set clear and attainable goals, and implement programs specifically designed for second language learners positively affect students’ language proficiency skills (Dixon et al., 2012).


The existing research indicates that although early and prolonged exposure to a second language maximizes a child’s ability to develop native-like fluency, effective and experienced teachers and schools can promote language proficiency for older students. If migrant children typically ended up in high-quality schools for a sustained period of time, the argument that age matters in this case might not be appropriate. However, despite the apparent successes of targeted MEP services, migrant children continue to face daily barriers to educational quality and success.


Foreign-born children in the United States frequently end up in the lowest quality public schools where the environment is often dangerous and where the staff is ill-prepared to address the particular cultural and language needs of migrant students (Suárez-Orozco, Suárez-Orozco, & Todorova, 2008). Mexican students, in particular, are often enrolled in lower track classes (Alba & Silberman, 2009). Educational advancement is often complicated by fears of deportation, living in impoverished conditions, and, particularly for the children of agricultural workers, frequent movement across state lines and schooling disruption (Lopez, 2011; Prewitt, Trotter, & Rivera, 1990).  Further, work and family responsibilities often lead to high rates of absenteeism among migrant students (Gibson & Bejinez, 2002).


Given the reality of migrant students’ experiences, age may be an important factor in how well students are able to gain language skills and experience academic success. Relative to their nonmigratory peers in Mexico, children who migrate to the United States in early adolescence face barriers to language proficiency due to school quality issues, schooling disruption, and absenteeism. This suggests that younger migrants (ages 0–6), particularly those who spend an extended period of time in the United States, may acquire English skills more quickly and easily. Earlier integration into the U.S. system may also set students on an educational trajectory where 75.5% of all students graduate high school (Balfanz, Bridgeland, Bruce, & Fox, 2012), a much higher rate than Mexico’s 45% (OECD, 2011).


THE STUDY


The driving research question is: What is the relationship between Mexico–U.S. child migration and children’s (a) completed years of schooling and (b) likelihood of dropping out of school, relative to Mexican children who never migrate to the United States?


The context for migrant education in the United States is complicated. Many parents have high hopes for their children’s education and futures, federal funds provide increased opportunities for migrant children, yet there is not a national consensus about the rights of undocumented children. As such, many transnational students may face discrimination, inadequate school quality, and responsibilities that compel them to leave school. Age at migration may serve as a proxy for other incentives and barriers to educational attainment such as labor market considerations, parental aspirations, and language skills.


Due to the different incentives to invest in education, as well as barriers to educational attainment, the primary hypothesis is that age at migration is an important factor in transnational students’ educational outcomes. Based on findings from the research literature, early migration to the United States is hypothesized to be associated with higher educational attainment relative to students who stay behind in Mexico; alternatively, children who first migrate to the United States at an older age likely have a more difficult time integrating into the U.S. system and may be motivated to enter into the labor market rather than acquire additional years of education.


The age at which children first migrate to the United States may shed light on the educational outcomes of migrant students, but it cannot tell the entire story. The educational attainment of migrant students is certainly also influenced by a slew of personal, family, and community characteristics. A number of these characteristics will also be analyzed to provide a more comprehensive understanding of the factors that promote or hinder educational attainment for Mexican-born students.


DESCRIPTION OF DATA


Chiswick and DebBurman’s (2004) study relies on U.S. census data to examine the relationship between age at migration and educational attainment for foreign-born men in the United States. This study improves upon this method by using a more nuanced data source. One limitation of census data is that it undercounts undocumented individuals who are generally hesitant to identify themselves (Lopez, 2011; Massey & Capoferro, 2004). As such, these findings may underrepresent the experiences of the undocumented population in the United States, which includes nearly 6.5 million Mexicans (Passel & Cohn, 2011). This study defines migration more comprehensively and uses household survey data from the Mexican Migration Project (MMP) that accounts for both documented and undocumented migration to the United States.


The MMP is a collaborative research project based at Princeton University and the University of Guadalajara.5 Since 1982, researchers have surveyed households in migrant-sending communities in Mexico, collecting demographic and migration data on each member of the household, as well as more detailed migration histories from the household head and the head’s spouse. With few exceptions, researchers have surveyed several new communities annually since the project’s inception. These communities are specifically chosen because they have some degree of out-migration to the United States, although there is significant variation in the prevalence of migration across communities in the sample, and represent various levels of urbanization. Researchers have purposively included communities that represent four sizes, ranging from ranchos (population less than 2,500) to larger urban cities (population greater than 100,000). This study specifically uses the MMP134, which provides data on 144,258 people, 22,541 households, and 134 communities.  


Sampling of communities for inclusion in the MMP dataset is not random. As such, MMP data is representative at the community, rather than national, level. However, multiple studies indicate that, although MMP data is geographically skewed, it provides a good representation of documented and undocumented migration to the United States (Massey & Zenteno, 2000; Orreniusa & Zavodny, 2005). For this study, it can therefore be assumed that the characteristics of migrants from the MMP communities do not differ remarkably from characteristics of migrants from other parts of Mexico, and that results are not driven by community idiosyncrasies.


SAMPLE IDENTIFICATION


The study’s sample is derived from repeated cross-sectional survey data collected from 1987–2011. The main purpose of this study is to examine the relationship between children’s U.S. migration and educational attainment. Accordingly, the sample includes individuals between the 7 and 22 years old. The lower bound represents the age at which children would be expected to have completed one year of education under the Mexican system. The upper bound is meant to allow time for students to clear the educational system. The sample was further identified using the following criteria: (1) relationship to household head, (2) birth place, (3) child’s age at first U.S. migration, and (4) presence of education data.


The sample includes individuals identified as members of a household and not as the head of their household. Moreover, individuals were only included in the sample if they identified as the son/daughter, stepson/daughter, or the adopted child of the household head. Of the sample, 99.63% is described as the son/daughter of the household head. In addition to surveying households in Mexico, MMP researchers also survey migrants from the same communities who have moved permanently to the United States. In order to maintain a fair comparison group, 1,015 cases were excluded where a child from such a household was born and raised in the United States. All individuals in the sample were born in Mexico. Due to the opportunities presented to children of legal working age (16), 1,449 cases were excluded where children migrated to the U.S. for the first time after 15 years of age. It is likely that individuals migrating to the United States after the age of 15 do so primarily for economic reasons and are qualitatively different from children who migrate at a younger age. Seventy-three percent of the children with migration experience first entered the U.S. without documentation. Lastly, 106 cases missing any education data were excluded. The resulting final sample includes 33,705 individuals.


Table 1. Description of Variables


Dependent variables

 

Completed years of education

=count of total completed years of education

Child drops out of school

=1 if child’s principal occupation at the time of survey is not “student” or “student and worker”

Independent variables

 

Migration experience

 

Child migration to U.S.

 

    No migration experience

=1 if child has never migrated to United States (U.S).

    0–6 years old

=1 if child migrated for the first time before age 7

    7–12 years old

=1 if child migrated for the first time between the ages of 7-12

    13–15 years old

=1 if child migrated for the first time between the ages of 13-15

Time spent in U.S.

= percentage of child’s life spent in the U.S.

HH ever migrated to U.S.

=1 if the household head (HH) has migrated to the U.S. at least once

HH trips to U.S. during child’s school years

 

    0 trips

=1 if HH made no trips to the U.S. since child turned 6 years old

    1–2 trips

=1 if HH made 1-2 trips to the U.S. since child turned 6 years old

    3+ trips

=1 if HH made 3 or more trips to the U.S. since child turned 6 years old

Household characteristics

 

Parent education

 

    0–5 years

=1 if highest education level of HH or spouse is 0-5 years of schooling  

    6–8 years

=1 if highest education level of HH or spouse is 6-8 years of schooling  

    9+ years

=1 if highest education level of HH or spouse is 9 or more years of schooling  

Household size

= number of people living in a household

Family asset index

= sum of household assets (land, house, water, electric, sewer, stove, refrigerator, washing machine, sewing machine, radio, television, stereo, phone, cellular phone, computer, internet)

Child characteristics

 

Age

Child’s age at time of survey

Female

=1 if child is female

Preschool

=1 if completed years of education included at least one year of preschool

Oldest sibling

=1 if child is the oldest sibling in the family

Community characteristics

 

%  migrant adults

= average share of adults in community with migration experience, 1980–2010

Locale

 

    Urban

=1 if community population is >100,000

    Small town/city

=1 if community population is 2,500–100,000

    Rural

=1 if community population is <2,500


DEPENDENT VARIABLES


Dependent and independent variables used in the analysis are displayed in Table 1. The two primary educational outcome variables are completed years of education and whether an individual dropped out of school. A child was coded as having dropped out if his/her primary occupation was not “student” or “student and worker” (for a similar approach, see Bredl, 2011). One limitation of this method is that it may overestimate the dropout rate by not accounting for the fact that older individuals may be high school graduates who are not pursuing higher education at the time of the survey. Such individuals would not appear in the dataset as “student” and could therefore erroneously be coded as having dropped out of school. To account for this, all individuals 18 years or older were recorded as non-dropouts if they completed 12 or more years of education. All individuals coded as dropping out completed at least one year of school. This adds validity to the identification method, ensuring that the phenomenon of dropping out of school, rather than never enrolling in school, is being captured.


PRIMARY INDEPENDENT VARIABLES


The independent variables capture child and parental migration experience, as well as household, child, and community characteristics.


Child Migration


Limited research focuses on the effect of childhood migration on educational attainment, although findings indicate positive associations between Mexican children’s U.S. experiences and educational aspirations (Kandel & Kao, 2001) and the decreased probability of dropping out of school (Kandel, 2003). These results seem to be driven by the type of travel undertaken to the United States. Kandel and Kao (2001) find that Mexican children who traveled to the U.S. on a tourist visa are more likely to have higher university aspirations, although results are not significant for children with more extended travel to the United States. Kandel (2003) does not control for length of time spent in the United States or age at migration and therefore cannot adequately explain the relationship between a child’s U.S. experience and decreased probability of leaving school. However, the author infers that the finding is likely reflective of either a child’s early exposure to the United States as a tourist, and is therefore from a family who can afford to vacation internationally, or migration to the United States at a young age, allowing for time to develop strong English language skills.


I improve upon this model by explicitly accounting for age at migration and duration of an individual’s life spent in the United States. Four dummy variables identify children with no migration experience and those who first migrated to the United States between the ages of 0–6, 7–12, and 13–15. A continuous variable represents the percentage of a child’s life spent in the United States. This variable reflects the stability of a child’s migratory experience and accounts for exposure to culture, the English language, and educational opportunities in the United States.


Table 2 provides information on the migration experiences of the children included in the sample. In general, it shows a pattern of migration to the United States for an extended period of time. The majority of individuals make only one United States trip and stay for at least a year. The length of time spent in the United States is strongly determined by age at migration. Among compulsory school-age children (7–15), those who migrated between 0–6 years old have spent, on average, 5.6 years in the United States compared to 2.2 years for those who migrated between 7–12 years old and .75 years for those who first migrated when they were 13–15. The difference is more striking among elective school-age individuals (16–22). For this group, the average number of years in the United States is 11.1 years for those who migrated between 0–6 years old, 6.5 years for the 7–12 group and 4.1 years for those who first migrated between 13–15 years old. These numbers clearly indicate that children who migrate between 0–6 years old, on average, have prolonged, uninterrupted exposure to the United States and its educational system. Children who migrate when they are 13–15 years old have less exposure to the United States and likely navigate the education systems in both Mexico and the United States.


Table 2. Duration of Time Spent in United States by Children's Age at First Migration

Age at migration

N

At least one year in U.S.

(%)

Only one U.S. trip

(%)

Average years spent in U.S.

7–15 year olds

Average years spent in U.S.

16–22 year olds

0–6 years old

300

86

82

5.6

11.1

7–12 years old

245

81

80

2.2

6.5

13–15 years old

382

80

71

.75

4.1


Parental Migration

Parental migration may impact children’s educational attainment regardless of whether children themselves migrate. Parental migration is controlled for in two ways. First, a dummy variable indicates whether the household head ever migrated to the United States. In 90.9% of all cases in the sample, the household head represents an individual’s father. In the remaining 9.1% of cases, the household head is the individual’s mother. In addition, a set of three mutually exclusive dummy variables measures the extent of the head’s U.S. migration during an individual’s school years, or once a child turned 6 years old. It is possible that the extent of migration during a child’s school years may have a more direct relationship to educational outcomes than simply whether or not the household head has ever migrated.  


Control Variables


Additional control variables measure a slew of household, child, and community background characteristics.


Household characteristics. Parent education, household size, and family wealth indicators provide important information about each child’s household and are common controls in statistical models of children’s educational attainment.


Parent education, measured as three dummy variables, captures the highest level of education completed by either parent in the household. The three categories align with the years of education associated with primary (0–5 years), lower secondary (6–8 years), and upper secondary school (9 or more years) in Mexico. There are a number of reasons for creating a single variable that reflects the highest combined education of both parents. Mother and father education levels are typically highly correlated; in this sample, r =.67. Moreover, due to the presence of single parent households in the sample, 3,502 cases are excluded when both mother and father education levels are controlled for. Creating one parent education variable maintains the sample size and provides a valid representation of household education levels (Bredl, 2011; Edwards & Ureta, 2003).


Household size may promote or hinder children’s educational opportunities. In smaller families with one or two children, parents may be able to allocate more resources to their children’s education. Larger families may face more financial constraints, children may need to work to supplement income, and there may be less investment in formal schooling. A continuous variable captures the number of people living in each individual’s household.


It is well documented that household wealth is a strong predictor of children’s educational attainment (e.g., Filmer & Pritchett, 1999). However, there is considerable debate in the literature regarding how to best measure family wealth. One popular approach is to create an asset index that captures household ownership of durable goods and housing characteristics. Arguably, such an index is indicative of a household’s long-run economic status and provides a more stable representation of economic well-being than a measure such as current income (Filmer & Pritchett, 2001; McKenzie, 2005). This approach is particularly useful when working with a dataset that includes migrant households where income may significantly fluctuate by season. Although there are various ways to build an asset index, a common approach is to create a simple sum of equally weighted items (e.g., Case, Paxson, & Ableidinger, 2004; Nobles, 2011). Despite the availability of more sophisticated statistical techniques, evidence suggests that the count approach yields a good proxy for wealth. For example, Bollen, Glanville, and Stecklov (2002) find that asset indices created using a principal component approach as well as the simple count method both perform well as predictors of fertility in developing countries. For simplicity, this study uses the count method for the construction of a 16-item asset index.


Individual characteristics. Individuals’ age, gender, and sibling rank are also common control variables. There is a strong correlation, particularly when children are young and eligible for compulsory school, between a child’s age and the amount of schooling attained. In many developing countries, girls face more challenges to educational access and attainment (UNESCO, 2003). In Mexico, Parker and Pederzini (2000) find a higher dropout rate after primary school for girls, although this difference does not tend to show up in overall educational attainment until after age 20. In part, they surmise this may be due to boys’ increased likelihood of falling behind in school. Further, this study controls for whether an individual is the oldest sibling. Families may be inclined to invest more in the education of their firstborn. Conversely, firstborn children may feel pressure to join the labor market at an early age or leave school to help at home, particularly in lower-income households.


The final control for child characteristics is a dummy variable indicating whether an individual likely included years of preschool when providing information on completed years of education. Children in Mexico enter primary school at 6 years old and are expected to complete their first year of schooling by age 7. Therefore, the expected difference between a child’s age and the number of completed years of education should be at least six. However, the MMP data contains a number of cases where the difference is less than six. It is likely that some survey participants included up to three possible years of preschool, in Mexico known as K1, K2, and K3, in the final count of years of education. Following Bredl (2011), the preschool dummy variable represents cases where the difference between an individual’s age and years of education was less than six but greater than or equal to three. Two-hundred thirty-three cases were excluded where the difference was less than three, and therefore could not be accounted for by the preschool dummy variable. It is important to note that this variable likely does not capture the preschool experiences of everyone in the sample and therefore cannot be interpreted as a “preschool effect.”


Community characteristics. The remaining control variables capture community characteristics that may impact children’s educational attainment. The first is the extent of community migration to the United States, measured as the percentage of adults with migration experience. Children in communities with high migration rates may encounter opportunities, resources, and networks that make it easier to migrate as well. Migration experience may lead to increased educational opportunities in the United States or a departure from school and entrance into the labor market. The community migration variable represents the average share of the adult population with migration experience from 1980–2010. Finally, this study controls for locale. It is well documented that students from rural areas face more challenges to educational access and attainment, including fewer resources and increased distance to school (UNESCO, 2007). Three dummy variables reflect the different degrees of urbanization represented by the communities in the sample.


Table 3 provides the means and standard deviations for all dependent and independent variables.  The average completed years of schooling is 6.5 while the dropout rate is 35.3%. Approximately one-third of the sample has a household head with U.S. migration experience. Three percent of children have migrated to the United States themselves.


Table 3. Descriptive Statistics

 

Mean

SD

Dependent Variables

  

Completed years of education

6.540

3.462

Child drops out of school

0.353

0.478

   

Independent variables

  

Migration experience

  

Child migration to U.S.

  

    No migration experience

0.972

0.164

    0­–6 years old

0.009

0.094

    7–12 years old

0.007

0.085

    13–15 years old

0.011

0.105

Time spent in U.S

0.879

6.957

HH ever migrated to U.S.

0.331

0.470

HH trips to U.S. during child’s school years

  

    0 trips

0.875

0.331

    1–2 trips

0.103

0.303

    3+ trips

0.023

0.149

Household characteristics

  

Parent education

  

    0–5 years

0.380

0.488

    6–8 years

0.312

0.463

    9+ years

0.299

0.458

Household size

7.754

3.128

Family asset index

9.023

2.484

Child characteristics

  

Age

14.632

4.472

Female

0.509

0.500

Preschool

0.168

0.374

Oldest sibling

0.220

0.414

Community characteristics

  

% migrant adults

17.550

15.818

Locale

  

    Urban

0.213

0.401

    Small town/city

0.571

0.495

    Rural

0.216

0.411

   

Observations

33,700

 


EMPIRICAL APPROACH


Repeated cross-section pooled OLS and logistic regression models are used to examine the relationship between U.S. migration experience and educational outcomes. The basic empirical model underlying the analysis can be written as follows:

[39_18239.htm_g/00002.jpg]


Where y represents one of two educational outcomes: completed years of schooling or whether a child dropped out, [39_18239.htm_g/00004.jpg]-[39_18239.htm_g/00006.jpg] represents the vectors of migration, household, child, and community characteristics, respectively, and [39_18239.htm_g/00008.jpg] is the error term. Pooled OLS regression is used to estimate the association between U.S. migration-related and control variables on individuals’ completed years of education. Pooled logistic regression is used to estimate the association between U.S. migration-related and control variables and whether or not a child drops out of school.6 This model predicts the odds of an event (coded as a binary variable) occurring, in this case the odds of dropping out of school.


To account for the repeated cross-sectional nature of the data, individual year dummies were included for all but the first year in the sample (Wooldridge, 2009). Additionally, robust standard errors clustered at the household level were employed. Doing so takes into account that educational outcomes are not completely independent from one person to the next and that outcomes of children from the same families are likely similar to each other. Accounting for other household head characteristics, namely gender and marital status, did not result in significant findings. The inclusion of these variables also did not change the coefficients or significance levels of other variables in the model, and thus are excluded from the analysis. For both outcomes, additional analyses by gender and compulsory/elective schooling age (7–15 and 16–22, respectively) were conducted.


DATA LIMITATIONS


The repeated cross-sectional nature of the data is a result of researchers implementing the same survey annually but with different communities and households. Therefore, it is not possible to track the migratory patterns and educational progress of the same children over time. The data provides a snapshot of each individual’s educational attainment at the time of the survey and does not allow for modeling the effect of migration in light of each individual’s entire educational lifecycle. Due to this limitation, results must be viewed as associative rather than causal.


As previously discussed, age might represent other incentives and barriers to educational investment that cannot be explicitly accounted for due to data limitations. The MMP does not collect data on labor market demands and/or plans, educational aspirations, and a child’s premigration academic performance and first language proficiency. Apart from hypothesizing how each of these factors might intersect with age at migration, it is not possible to parcel out a precise estimate of the relationship between each of these factors and educational attainment.


RESULTS


This section is organized into three parts. First, OLS regression results where the outcome of interest is completed years of education are presented, followed by logistic regression results where the outcome is whether a child dropped out of school. Lastly, descriptive statistics provide a closer examination of who drops out and what these children do instead of attending school.


MEXICO-U.S. MIGRATION AND COMPLETED YEARS OF EDUCATION


Table 4 displays OLS regression results. Column 1 provides estimates for the full sample, ages 7–22, and shows a strong relationship between children’s U.S. migration experience and completed years of school. As hypothesized, the direction of the relationship depends on a child’s age at first migration. Relative to Mexican children who never migrate to the U.S., individuals who migrate for the first time between 0–6 years old acquire .41 additional years of education. This finding suggests that, on average, children who first migrate to the U.S. before they are of primary school age have an educational advantage over their Mexican peers who stay behind.7 This finding is particularly pronounced for the compulsory school-age sample. Relative to their nonmigrant counterparts, Mexican children of compulsory school age who migrate between 0–6 years old acquire .28 additional years of education.


Conversely, children who first migrate between the ages of 13–15 complete approximately .70 fewer years of education than students who never migrate from Mexico. These results support the hypothesis that children who migrate between 0–6 years old may have more incentive to invest in the U.S. education system and encounter fewer barriers to doing so than children who migrate between the ages of 13–15. For the most part, any relationship between child migration for those who migrated between the ages of 7–12 and completed years of schooling are statistically insignificant. However, the purpose of the analysis is really to distinguish between individuals who migrate when they are younger (0–6 years old) versus older (13–15 years old). Controlling for age at migration, the proportion of an individual’s life spent in the United States is statistically insignificant. It is important here to remember that the majority of child migrants in the sample have spent an extended period of time in the United States.  


Columns 2 and 3 report results for males and females, respectively. The final two columns (4 and 5), provide estimates for children of compulsory school age and older individuals for whom school is elective. The relationship between migrating at 0–6 years of age and completed years of education is not statistically significant by gender. Boys in Mexico, in particular, experience grade delays at high rates; by the age of 10, 20% of boys in the Mexican education system have already fallen behind (Parker & Pederzini, 2000). Early migration to the United States does not seem to mitigate the likelihood that males 7–22 years old will experience problems with normal grade progression.

 

Table 4. OLS Regression Results Where Outcome is Completed Years of Schooling

Dependent Variable: Completed years of schooling

 

(1)

(2)

(3)

(4)

(5)

 

7–22

7–22

7–22

7–15

16–22

Independent Variables

All

Male

Female

All

All

Migration experience

     

Child migration to U.S.

     

    No migration (reference)

---

---

---

---

---

    0–6 years old

0.407+

0.429

0.319

0.282**

-0.264

 

(0.212)

(0.278)

(0.305)

(0.109)

(0.434)

    7–12 years old

-0.162

-0.289

-0.051

-0.149

-0.660*

 

(0.187)

(0.232)

(0.276)

(0.134)

(0.294)

    13–15 years old

-0.703***

-1.005***

-0.194

-0.510*

-0.812***

 

(0.138)

(0.167)

(0.221)

(0.208)

(0.169)

Time spent in U.S

-0.004

-0.004

-0.003

-0.003

0.014*

 

(0.003)

(0.004)

(0.005)

(0.002)

(0.006)

HH ever migrated to U.S.

-0.037

-0.003

-0.071

-0.007

0.024

 

(0.038)

(0.049)

(0.049)

(0.025)

(0.072)

HH trips to U.S. during child’s school years

     

    0 trips (reference)

---

---

---

---

---

    1–2 trips

0.052

0.027

0.075

0.050

-0.188*

 

(0.055)

(0.070)

(0.072)

(0.035)

(0.095)

    3+ trips

-0.189

0.102

-0.435**

-0.065

-0.471**

 

(0.122)

(0.144)

(0.156)

(0.109)

(0.164)

Household characteristics

     

Parent education

     

    0–5 years (reference)

---

---

---

---

---

    6–8 years

0.813***

0.702***

0.911***

0.345***

1.192***

 

(0.040)

(0.052)

(0.050)

(0.028)

(0.067)

    9+ years

1.018***

0.949***

1.077***

0.401***

1.894***

 

(0.045)

(0.058)

(0.057)

(0.031)

(0.078)

HH size

-0.067***

-0.082***

-0.052***

-0.019***

-0.116***

 

(0.007)

(0.008)

(0.008)

(0.005)

(0.010)

Family asset index

0.207***

0.205***

0.210***

0.076***

0.347***

 

(0.007)

(0.010)

(0.009)

(0.005)

(0.012)

Child characteristics

     

Age

0.598***

0.605***

0.593***

0.859***

0.228***

 

(0.004)

(0.005)

(0.005)

(0.004)

(0.010)

Female

0.069**

  

0.067***

0.144***

 

(0.024)

  

(0.017)

(0.042)

Preschool

2.267***

2.262***

2.270***

1.991***

3.701***

 

(0.030)

(0.039)

(0.040)

(0.017)

(0.071)

Oldest sibling

0.016

0.044

-0.013

0.028

0.076

 

(0.029)

(0.041)

(0.042)

(0.020)

(0.050)

      

Table 4 (cont’d)

Community characteristics

     

% migrant adults

-0.008***

-0.008***

-0.007***

-0.003**

-0.013***

 

(0.001)

(0.002)

(0.002)

(0.001)

(0.002)

Locale

     

    Urban (reference)

---

---

---

---

---

    Small town/city

-0.074+

-0.017

-0.130*

0.038

-0.138*

 

(0.042)

(0.054)

(0.054)

(0.028)

(0.070)

    Rural

-0.175**

-0.085

-0.263***

-0.032

-0.327***

 

(0.055)

(0.069)

(0.070)

(0.037)

(0.091)

Time dummies8

Yes

Yes

Yes

Yes

Yes

Constant

-4.606***

-4.487***

-4.688***

-6.132***

0.800**

 

(0.144)

(0.183)

(0.182)

(0.101)

(0.298)

      

Observations

33,700

16,557

17,143

18,659

15,041

R-squared

0.629

0.636

0.625

0.798

0.429


Note: Robust standard errors, clustered at the household, in parentheses

*** p < 0.001, ** p < 0.01, * p < 0.05, + p <0.10


The significant negative association between migration at 13–15 years old and completed years of education is found for all subsamples, with the exception of females. The magnitude of the effect is relatively large for boys, 1.0 fewer years of education, and individuals of elective school age, .81 fewer years of education. These results are consistent with the culture of migration argument that adolescent migrant males have economic incentives to disinvest in the U.S. education system. Duration of time spent in the United States is not significantly related to completed years of education for any of the subsamples, with the exception of the elective school age grouping (Column 5). Even so, for this subsample the effect size of .01 is extremely modest.


Across all models, whether or not the household head has ever migrated to the United States is not statistically associated with children’s completed years of school. However, the extent of parental migration during a child’s school-age years is negatively related to completed years of education for girls and elective school-age individuals. Relative to girls whose household head never migrated to the United States during their school age years, girls whose household head made three or more trips complete .44 fewer years of education. Elective school-age individuals acquire .19 and .47 fewer years of school depending on whether their household head made 1–2 or more than three trips to the United States, respectively. These findings suggest that girls bear the brunt of the household head’s absence and may attend to household responsibilities in place of completing school. For elective school-age individuals, having a household head with extensive U.S. migration experience suggests family integration into the migrant culture, which may reduce educational attainment as young people anticipate their own migration. Both findings are consistent with prior research from Mexico indicating that living in a migrant household increases housework for girls and the likelihood of migration, particularly for 16–18 year old boys, at the expense of school participation (McKenzie & Rapoport, 2011).


Consistent with other findings in the literature, parent education level is highly associated with children’s educational attainment. Children of parents with 6–8 years of education complete .81 additional years of school relative to children of parents with 0–5 years of education. Children of parents with 9 or more years of school complete a full year of education more than the reference group. These findings are consistent across all subsamples, although parental education has a particularly pronounced effect on elective school-age individuals’ completed years of school.


Household size is also significantly related to children’s completed years of education. The household size coefficient suggests that in a household with four more people, a child is estimated to receive about .067(4) =.27 fewer years of education. Household wealth is substantively and statistically significantly related to completed years of education. Relative to children at the bottom of the asset index distribution, children of families with the highest asset index value complete .207(16) = 3.30 more years of education. Household wealth appears to have an even stronger influence on education attainment for individuals of elective school age. Moving from the lowest end of the asset index distribution to the highest is associated with a .347(16) = 5.55 year increase in educational attainment. This effect, although statistically significant, is much smaller for children of compulsory school age, 0.076(16) = 1.22 year increase. This finding suggests that poorer families may struggle to send their children to school beyond a publicly provided basic education.


Of the child characteristics, child age, gender, and whether a child attended preschool are statistically significantly related to completed years of education. Unsurprisingly, the coefficients on age and preschool are high. If fact, in a scenario where all children enter school at the expected age and progress through school at the expected rate, the projected age coefficient would be one, meaning that as a child gains a year in age they also gain a year in education. In this case, the preschool coefficient can be interpreted as the average years of preschool completed by individuals who included preschool in their count of completed years of education. There seems to be a slight increase in completed years of education for female. Although this relationship is statistically significant at the .01 level, the magnitude is small. On average, girls complete .07 additional years of schooling than boys.


The relationship between coming from a community with higher levels of out-migration to the United States and completed years of education is negative. In the sample, the share of community adults with U.S. migration experience ranges from .5% to 86%. Relative to the least migratory community, a child coming from the most migratory community is predicted to complete about .008(85.5) =.68 less years of schooling. The effect of coming from a heavily migrant community is larger for elective school age individuals who complete 0.013(85.5) = 1.11 fewer years of education relative to the least migratory. In comparison to children in urban areas, rural students acquire .18 fewer years of education. This effect is amplified for females and individuals of elective school age.


MEXICO-U.S. MIGRATION AND DROPPING OUT OF SCHOOL


Table 5 provides dropout rates for the sample by age group and U.S. migration experience. The dropout rate for 7–12 year olds with U.S. migration experience is slightly lower than those without U.S. experience. However, 13–15 year olds, as well as individuals over 16 years of age, with U.S. migration experience have higher dropout rates than their nonmigrant counterparts. Dropout rates for Mexican children with no U.S. migration experience reflect the relatively low levels of educational attainment in Mexico.


Table 5. Share of Children Dropped Out of School by U.S. Migration Experience

Age Group

Children w/ U.S. migration experience

(%)

Children w/ no U.S. migration experience

(%)

7–12 years old

4.92

6.37

13–15 years old

33.54

27.30

16+ years old

73.47

61.10


Table 6 reports logistic regression results for the likelihood of dropping out of school. Results are reported as odds ratios. Column 1 displays findings for the full sample. Once again, age at migration has a differential effect on the likelihood that a child drops out of school. U.S. migration between the ages of 0 and 6 neither increases nor decreases the likelihood of dropping out relative to Mexican children who stay behind. There is no statistical difference between the two groups. However, migration between the ages of 13 and 15 has a strong statistical negative association with leaving school. The odds of dropping out increase by 4.23 times for children who migrate between 7–15 years old relative to those who never migrate. The direction and significance of this finding is consistent for all subsamples save the compulsory school-age group. The odds of dropping out of school decrease for children who have spent a greater percentage of their lives in the United States. This finding is consistent in magnitude for all subsamples. Interestingly, while when children migrate to the United States is associated with an increased likelihood of dropping out, the duration of time spent decreases the odds of doing so.

 

Table 6. Logistic Regression Results, Displayed as Odds Ratios, Where Outcome is the Likelihood of Dropping Out

Dependent Variable: Whether a child drops out of school

 

(1)

(2)

(3)

(4)

(5)

 

7-22

7-22

7-22

7-15

16-22

Independent Variables

All

Male

Female

All

All

Migration experience

     

Child migration to U.S.

     

    No migration experience

---

---

---

---

---

    0–6 years old

1.260

1.364

1.188

1.431

1.251

 

(0.374)

(0.569)

(0.525)

(0.637)

(0.532)

    7–12 years old

1.455

1.718+

1.152

0.600

1.877*

 

(0.336)

(0.543)

(0.337)

(0.290)

(0.574)

    13–15 years old

4.227***

5.454***

2.730***

1.918+

5.605***

 

(0.799)

(1.362)

(0.700)

(0.651)

(1.346)

Time spent in U.S.

0.984**

0.988+

0.979**

0.990

0.982**

 

(0.005)

(0.007)

(0.007)

(0.010)

(0.006)

HH ever migrated to U.S.

1.072

1.125+

1.037

1.122

1.045

 

(0.056)

(0.078)

(0.068)

(0.082)

(0.071)

HH trips to U.S. during child’s school years

     

    0 trips

---

---

---

---

---

    1–2 trips

1.166*

1.112

1.223*

1.031

1.204*

 

(0.081)

(0.103)

(0.108)

(0.101)

(0.113)

    3+ trips

1.265*

1.087

1.445*

0.963

1.436*

 

(0.144)

(0.167)

(0.209)

(0.160)

(0.236)

Household characteristics

     

Parent education

     

    0–5 years

---

---

---

---

---

    6–8 years

0.622***

0.683***

0.572***

0.673***

0.568***

 

(0.028)

(0.041)

(0.032)

(0.047)

(0.033)

    9+ years

0.298***

0.321***

0.277***

0.400***

0.257***

 

(0.018)

(0.025)

(0.022)

(0.038)

(0.018)

HH size

1.079***

1.086***

1.072***

1.065***

1.094***

 

(0.008)

(0.010)

(0.010)

(0.011)

(0.011)

Family asset index

0.851***

0.855***

0.844***

0.890***

0.808***

 

(0.007)

(0.010)

(0.009)

(0.012)

(0.009)

Child characteristics

     

Age

1.392***

1.398***

1.388***

1.503***

1.119***

 

(0.007)

(0.010)

(0.010)

(0.021)

(0.012)

Female

1.048

  

1.157**

0.982

 

(0.032)

  

(0.056)

(0.040)

Preschool

0.291***

0.296***

0.286***

0.553***

0.084***

 

(0.021)

(0.029)

(0.027)

(0.043)

(0.011)


     

Table 6 (cont’d)

     

Oldest sibling

0.932+

0.957

0.898+

0.907

0.962

 

(0.037)

(0.054)

(0.051)

(0.062)

(0.048)

Community characteristics

     

% migrant adults

1.014***

1.015***

1.013***

1.014***

1.015***

 

(0.002)

(0.002)

(0.002)

(0.002)

(0.003)

Locale

     

    Urban (reference)

---

---

---

---

---

    Small town/city

1.353***

1.321***

1.392***

1.660***

1.128+

 

(0.077)

(0.097)

(0.101)

(0.163)

(0.076)

    Rural

1.306***

1.191*

1.435***

1.429**

1.178+

 

(0.089)

(0.106)

(0.125)

(0.163)

(0.101)

Time dummies

Yes

Yes

Yes

Yes

Yes

Constant

0.007***

0.005***

0.010***

0.001***

0.756

 

(0.001)

(0.001)

(0.002)

(0.000)

(0.221)

      

Observations

33,082

16,239

16,843

18,249

14,833

Pseudo R-squared

0.350

0.347

0.357

0.226

0.223

Note: Robust standard errors, clustered at the household, in parentheses

*** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.10


Odds ratios can be difficult to interpret in a substantively meaningful way and it can be more useful to examine logistic regression results in terms of the probability that an event, in this case dropping out, will occur (Osborne, 2006). Based on the same models displayed in Table 6, Table 7 presents the results as percent changes in the probability of dropping out. These findings can be interpreted as the change in the probability of dropping out given a minimum to maximum value change in an independent variable, holding all other variables constant at their means. To improve readability, Table 7 includes only significant relationships at p < .05.


Table 7 provides a more intuitive interpretation of the relationship between child migration at 13–15 years of age and children’s increased likelihood of dropping out. Results from the full sample, Column 1, indicate that children who migrate between 13–15 years of age are 33.5% more likely to drop out of school relative to their peers who stay behind. This effect is slightly larger for males who are 39% more likely to drop out compared to their nonmigrants counterparts, and still high for the elective school-age sample at 27.8%. Similar to the OLS regression findings, these results are likely indicative of U.S. labor market incentives for male migrants and older students to leave school in the pursuit of employment prospects. The probability of dropping out for females increases by 23.1% for girls who migrate to the United States between 13–15 years old.9


Overall, Mexican-born children who have spent their entire lives in the United States compared to those with no migration experience are 19.3% less likely to drop out of school. Duration of time spent in the United States is particularly significant for females and the elective school-age sample. The probability of dropping out of school for females who have spent their entire lives in the United States is 22.7% lower than for females with no migration experience, while in the elective school-age sample the decreased probability of dropping out for migrants is even more striking at 42.1%. This finding is consistent with Kandel’s (2003) inference that prolonged exposure to the U.S. education system, culture, and English language is positively linked to migrant children’s persistence in school.


Table 7. Percentage Changes in the Probability of Dropping Out, Calculated from Logistic Regression Results10

 

(1)

(2)

(3)

(4)

(5)

Independent Variables

7–22

All

7–22

Male

7–22 Female

7–15

All

16–22

All

Migration experience

     

Child migrated to U.S. for first time between 7–12 years old compared to children who never migrated

NS

NS

NS

NS

.131*

Child migrated to U.S. for first time between 13–15 years old compared to children who never migrated

.335***

.390***

.231***

NS

.278***

Time spent in U.S. (ex: 100% of one’s life compared to 0%)

-.193**

NS

-.227**

NS

-.421**

HH made 1–2 trips to U.S. since child entered primary school age compared to no U.S. trips since child entered primary school age

.030*

NS

.041*

NS

.042*

HH made 3+ trips to U.S. since child entered primary school age compared to no U.S trips since child entered primary school age

.047*

NS

.078*

NS

.080*

Household characteristics

     

Highest level of parental education is 6–8 years compared to 0-5 years

-.085***

-.066***

-.104***

-.026***

-.134***

Highest level of parental education is 9+ years compared to 0–5 years

-.200***

-.182***

-.218***

-.058***

-.324***

Children from largest household (23 people) compared to smallest households (2 people)

.339***

.361***

.314***

.125***

.370***

Having all asset indicators compared to no asset indicators

-.494***

-.465***

-.528***

-.151***

-.631***

Child characteristics

     

Oldest children compared to youngest (ex: full sample, 22 to 7 year olds)

.766***

.766***

.767***

.260***

.155***

Female children compared to male children

NS

---

---

.010**

NS

Children with preschool experience compared to no preschool experience

-.186***

-.174***

-.197***

-.037***

-.525***

Community characteristics

     

Children from most migrant-sending community (86%) compared to least migrant-sending community (.5%)

.257***

.266***

.241***

.116***

.253***

Children from small town/city compared to children from urban area

.056***

.050***

.064***

.034***

NS

Children from rural area compared to children from urban area

.052***

.032*

.074***

.027**

NS


Note: Table 7 only displays variables with at least one significant finding across models. Significant findings at p <.1 are not included. NS indicates “not significant”. Percentage change calculated using “prchange” in Stata/IC 12.1 (Long & Freese, 2001).

*** p < 0.001, ** p < 0.01, * p < 0.05


The extent of a household head’s migration to the United States during a child’s school years is significantly related to the probability of dropping out for the full sample, although the effect sizes are small. Column 1 indicates that, relative to children whose household head never migrated to the United States during school-age years, children whose head took 1–2 trips to the United States are 3% more likely to drop out. The probability of dropping out increases by 4.7% for children whose household heads took three or more trips to the United States. There are also slight effects for some subgroups. In the subsample of females, displayed in Column 3, children whose household head traveled to the United States three or more times during their school-age years are 7.9% more likely to drop out of school. Column 5 shows that individuals of elective school age are 8% more likely to leave school if their household head frequently traveled to the United States during their school years. These findings are consistent with the OLS results, and again suggest that (a) females may take on additional household responsibilities in the absence of the household head, and (b) adolescents with access to migrant networks through household head migration may be more inclined to migrate themselves and enter a U.S. labor market that does not reward additional years of schooling.


The percentage change in the probability of dropping out further allows for a more precise estimate of the other household, child, and community control variables. The probability of leaving school decreases by 8.5% for students whose parents have 6–8 years of education relative to 0–5 years. More strikingly, children whose parents have completed 9 or more years of education are 20% less likely to drop out. Parental education seems to be particularly important for the elective school-age sample. While compulsory school-age children whose parents completed 9 or more years of experience are 5.8% less likely to drop out than children whose parents acquired 0–5 years of education, the analogous comparison for the elective school-age sample shows a 32.4% decrease in the probability of leaving school.


A similar pattern can be observed with household wealth. Column 1 indicates that, overall, children from households with all asset indicators are nearly 50% less likely to drop out than children from the poorest households, which reported no asset indicators. The influence of household wealth, like parent education, is particularly strong for individuals of elective school age. Using the same household wealth comparison group, students of compulsory school age from the wealthiest households are 15.1% less likely to drop out, while elective school age individuals from the wealthiest households are 63.1% less likely to leave school. Similar to findings from the OLS regressions, these results suggest that household wealth is particularly important in promoting educational outcomes for elective school-age students who persist in their studies beyond a basic education.


As to be expected, the oldest individuals in the sample have a much higher probability of dropping out, 76.6%, compared to the youngest individuals. Although the estimates for preschool are significant across all models, and particularly large in magnitude for the elective school-age sample, these findings should be interpreted with caution. As discussed previously, the preschool variable likely does not represent preschool attainment for all individuals in the sample and is included in the model to control for a subset of survey respondents who may have included years of preschool in their total count of completed years of education. Therefore, it is inappropriate to make inferences about the relationship between attending preschool and the probability of leaving school.


Lastly, Table 7 also shows significant relationships between community characteristics and the probability of dropping out, particularly for the full sample. Column 1 indicates that children from the highest migrant-sending communities are 25.7% more likely to drop out than children from the least migrant-sending communities. This estimate is relatively stable across subsample models, although it is smaller for the compulsory school-age group (11.6%). These findings provide further support for the culture of migration argument. Children from small towns or cities are 5.6% more likely to drop out than children from urban areas, while rural children are 5.2% more likely to drop out of school.


A CLOSER DESCRIPTIVE LOOK AT WHO DROPS OUT


This section takes two approaches to better understand who drops out of school. First, it examines what children are doing if they are not attending school. One advantage of the MMP data is that it includes occupation information for all household members and allows for an examination of what children who leave school do instead. Second, it narrows the focus to only those children with U.S. migration experience. This approach compares the backgrounds of children who drop out with those who remain in school and identifies significant differences in household, child, and community characteristics.  


Table 8 reports the percentage of out of school children who work in various fields: housework, agriculture, mechanical/repair skilled and unskilled positions, sales, and services. In general, young people with U.S. migration experience are more likely to be employed as unskilled workers in manufacturing/repair and service workers than young people with no U.S. experience. The majority of girls who drop out of school are engaged in housework. The percentage doing so does not differ dramatically by U.S. migration experience; 55% of females with U.S. migration experience who have left school help around the house compared to 59% of nonmigrants. The largest share of male drop outs, for migrants and nonmigrants, is employed in agriculture, 30% and 32%, respectively.  Compulsory school-age children with U.S. migration experience are more likely to work as unskilled and skilled workers in manufacturing/repair than nonmigrants. Nonmigrants in this subsample are more likely to be engaged in housework and agriculture. Among the elective school-age sample, U.S. migrants are more likely to be agricultural workers, unskilled workers, and service workers relative to nonmigrants. U.S. migrants are less likely to be unemployed, although unemployment rates are quite low overall.



Table 8. Occupation of Children Who Have Dropped Out of School by U.S. Migration Experience

 

7–22

All

7–22

Male

7–22

Female

7–15

All

16–22

All

 

A

(%)

B

(%)

A

(%)

B

(%)

A

(%)

B

(%)

A

(%)

B

(%)

A

(%)

B

(%)

Unemployed (seeking work)

2

5

3

6

1

4

0

5

2

5

Helps around the house11

18

32

0

0

55

59

19

27

17

34

Agricultural worker

19

14

30

32

3

1

0

13

22

15

Manufacturing/repair skilled worker12

11

10

14

15

0

5

12

5

10

10

Manufacturing/repair unskilled worker13

18

10

21

18

9

4

13

7

17

9

Sales worker

6

8

6

8

7

7

9

5

8

9

Service worker15

14

4

15

2

13

7

7

3

16

4

Unspecified

3

6

    

20

27

  

Note: All ‘A’ columns indicate children with U.S. migration experience. All ‘B’ columns include children with no U.S. migration experience. Percentages are rounded to nearest whole number.


These findings are consistent with OLS and logistic regression results in two key ways. First, they support the conclusion that household responsibilities disproportionately affect girls’ educational attainment. Second, the low unemployment rates provide evidence that young people who drop out of school do so for economic reasons. The very low unemployment rate of 2% for those who migrate suggests that employment opportunities in the United States, which primarily consist of unskilled positions, provide a disincentive to invest in further schooling.


Table 9 compares the background characteristics of children with U.S. migration experience by whether or not they dropped out of school. T test results indicate that all household, child, and community mean differences are statistically significant. On average, children with U.S. migration experience who drop out have less educated parents than those who do not leave school early. In 63% of the drop out cases, the highest level of parental education is 0–5 years. Children who drop out also, on average, come from larger families and poorer households. They are more likely to be male, less likely to be the oldest sibling, and come from slightly more heavily migrant communities in Mexico. Relative to children with U.S. migration experience who do not drop out, those who do are more likely to come from rural Mexico.


Table 9. T Test Results Comparing Mean Background Characteristics of Children with U.S. Migration Experience by Dropout Status


 

No Dropout

Dropout

t test

Household characteristics

   

Parent education

   

    0–5 years

0.261

0.630

***

    6–8 years

0.375

0.275

***

    9 years

0.364

0.095

***

Household size

7.018

9.293

***

Family asset index

10.211

9.566

***

Child characteristics

   

Age

14.053

18.507

***

Female

0.506

0.313

***

Preschool

0.188

0.026

***

Oldest sibling

0.334

0.135

***

Community characteristics

   

% migrant adults

24.788

26.837

**

Locale

   

    Urban

0.156

0.085

**

    Small town/city

0.634

0.564

**

    Rural

0.211

0.352

***

*** p < 0.001, ** p < 0.01, * p < 0.05


CONCLUSION


In light of the increasing movement of people, particularly children, across international borders, and the burgeoning transnational student population, this study explored the educational attainment of transnational students using the particular case of Mexican-born children with U.S. migration experience. This study contributes to the existing literature in two primary ways. First, it focuses on an under-researched dimension of migration, children’s own migration, in relation to children’s educational attainment. In addition, it offers insight into how transnational students fare relative to their origin-country peers. This is a marked shift away from the literature, which generally emphasizes the educational attainment of migrants in comparison to their destination-country counterparts.


It was hypothesized that age at first migration has a differential influence on children’s educational attainment. Pooled OLS and logistic regression findings support this hypothesis. Mexican children, particularly those of compulsory school age, who migrate to the United States between the ages of 0–6 have an educational advantage relative to their peers who stay behind, while those who migrate between the ages of 13–15 have an educational disadvantage. These findings may be indicative of the incentives and barriers to educational investment that young migrants.


Mexican children who first migrate to the United States between 0 and 6 years of age acquire .41 additional years of schooling relative to those who stay behind. This positive relationship is most significant for compulsory school-age children. There is no significant difference in the likelihood of dropping out for children who first migrate between 0 and 6 years of age and those who never migrate from Mexico. In other words, children who migrate when very young are as likely to remain in school as Mexican children without migration experience, yet, on average, they acquire more years of education. These findings suggest that, for children who migrate between 0 and 6 years of age, migration to the United States is positively associated with grade progression. In Mexico, young boys are particularly at risk of falling behind and repeating grades. Results from this study suggest that this risk is mitigated for compulsory school-age children who first migrate to the United States when they are very young, although evidence does not suggest this trend continues past compulsory education.


Conversely, U.S. migration experience is negatively associated with educational outcomes for children who migrate between 13 and 15 years of age relative to those who stay behind. On average, these individuals acquire .70 fewer years of education and are 33.5% more likely to drop out of school relative to their nonmigrant peers in Mexico. Effect sizes are largest for males, who acquire 1.0 fewer years of schooling and are 39% more likely to drop out, and elective school-age individuals, who complete .81 fewer years of education and are 27.8% more likely to leave school. Childhood migration to the United States between 13–15 years of age increases the probability of dropping out for females by 23.1%. Individuals who migrate between the ages of 13–15 may be more likely to ultimately enter the U.S. labor market, with its demand for unskilled workers, and may also face more barriers to entry and advancement in the educational system relative to their origin-country counterparts.


The analysis also finds particularly large effect sizes for parental education, household wealth, and being from a community with high migration rates. Children with a highly educated parent acquire 1.02 more years of education and are 20% less likely to drop out than children in households with low parental education. These findings are amplified for elective school-age students who complete up to 1.89 additional years of school and are 32.4% less likely to leave school. Household wealth is also strongly associated with children’s positive educational outcomes. Compared to coming from a household at the lowest end of the wealth distribution, children who come from the wealthiest households acquire 3.3 additional years of education and are 49.5% less likely to leave school. Lastly, being from a community with high migration rates is associated with acquiring .69 fewer years of education and a 25.7% increase in the probability of dropping out of school.


Further investigation of what young people who drop out of school do instead reveals that these individuals are almost all engaged in some kind of work. However, there are strong gender differences in the type of work individuals partake in. The majority of migrant females who drop out are engaged in housework, although a slightly larger percentage are employed in unskilled or service jobs relative to nonmigrant females in Mexico. Males with U.S. migration experience are more likely to be agricultural, unskilled, or service workers compared to nonmigrant males in Mexico. These findings complement the regression results and support the implication that increased access to unskilled jobs in the United States may contribute to migrant students’ decisions to leave school.


This study has practical implications for Mexican migrant parents. For parents who are considering migrating with their children, this research suggests that early exposure to the United States and its educational system yields the most educational benefits. This research would support parents’ decisions to migrate with their children when they are young, 6 years old or younger, rather than waiting until they have already reached school age. At this older stage of development, children may encounter barriers to investment in education and are more likely to leave school prematurely.     


This study also has two primary implications for policy and practice. First, the findings suggest that human capital considerations motivate children with U.S. migration experience to leave school; this may be particularly true for individuals who migrate later in childhood and face increased barriers to integration in the U.S. education system. For this population of transnational students, the majority of whom are undocumented, there is strong evidence that educational attainment is intrinsically linked to employment opportunities. Therefore, education policy considerations cannot be divorced from the larger national debate regarding the rights of immigrants in the United States and opportunities to participate in the skilled-job labor market.


Recent legislation has begun to open pathways into higher education and legal labor market opportunities, particularly for those who entered the U.S. as unauthorized children. Migrant children’s incentives and barriers to acquiring additional years of schooling in the United States could well be altered if these political efforts gain traction and future legislation expands these opportunities. If labor market restrictions are relaxed, the economic incentives for young migrants to invest in education will change; higher skilled jobs require more training and offer a higher return to investment in additional education. In light of such an immigration policy incentivizing educational investment, education policy could seek to mitigate the barriers to educational advancement that transnational students face, particularly children who first migrate when they are between 13 and 15 years old and struggle to integrate into the U.S. education system, such as insufficient language skills, tracking into low quality schools, and ineffective teachers.


In the immediate future, this study has implications for school districts and programs like MEP that support the educational attainment of migrant children. Of Mexican children who migrate to the United States, those who drop out are more likely to be poor, male, members of large families, and have parents with low levels of education. Schools and support services can particularly target this vulnerable population and the specific challenges to educational attainment it encounters. Results from this study indicate that Mexican students who leave school do so for financial reasons or to attend to household responsibilities. This population may benefit from more alternative schooling options, such as night or weekend programs, where children can both attend school and work.


In addition to targeting student needs, schools and support services can also increase outreach to the families of migrant students. Considering that migrant students who drop out are less likely to have highly educated parents, parental outreach could include adult literacy and community education classes. Such classes may provide a way to not only help parents improve basic skills and language acquisition, but also integrate migrant parents into school communities.


There are several opportunities here for future research. Although the results presented in this essay are consistent with a number of other findings in the literature, the cross-sectional nature of the data requires an associative, rather than causal, explanation of the relationship between child migration and educational attainment. A longitudinal study of Mexican transnational students, that tracks migration status and educational attainment annually, could provide researchers with a more nuanced understanding of the causal effect of child migration on educational attainment. Further research is also needed on the distinct experiences of migrant children in the U.S. education system by age at migration. Specific research questions include: do schools provide differential treatment to migrant children who enter the U.S. education system at the beginning of their school careers compared to those who arrive in the United States with prior schooling experience in Mexico? In what ways do schools and support services help younger children integrate into the U.S. education system? How can these practices be adjusted and implemented for the population of children who migrate when they are older? Such a study could have significant education policy implications for improving the transition into the U.S. education system and the overall educational attainment for children who migrate to the United States between 13 and 15 years old.  Finally, this study focused on transnational students from Mexico. Future research could expand on this focus by investigating the educational outcomes of transnational students from other countries and regions of the world relative to their origin-country peers.


Notes


1. This is an extension of the argument that age at migration matters when comparing the educational attainment of foreign-born and native-born adults in the United States. Chiswick and DebBurman (2004) find that foreign-born adults who migrated to the United States between 0 and 4 and 5 and 12 years of age acquired more education than native-born adults. However, migrating at 13 years old or older was negatively related to educational attainment relative to the native-born adults.

2. While there was a steady increase in the number of Mexican-born children detained at the border between 2011–2013, this most recent data overwhelmingly reflects the detainment of children from El Salvador, Guatemala, and Honduras (Park, 2014).

3. http://www.immigrationpolicy.org/just-facts/deferred-action-childhood-arrivals-qa-guide-updated

4. http://www.csac.ca.gov/pubs/forms/grnt_frm/eligible_cal_grant_schools.pdf

5. mmp.opr.princeton.edu

6. In this model, y is estimated as prob(y = 1).

7. It is important to note that this finding is statistically significant at the 0.10 level of significance.

8. For estimates for each survey year dummy variable, see Appendix.

9. An alternative way to specify a child’s age at first migration is to use a linear term rather than the categorical variable used in the analyses reported in Tables 4 and 6. I reran the first model in both tables using the linear term instead of the categorical dummy variables. Results for the linear age at migration term indicate that a one year increase in age at migration is associated with -0.093 fewer years of education (p < 0.001). The odds of dropping out increase (OR = 1.13) for each one year increase in age at first migration (p < 0.001). These results fail to account for the non-linear relationship between age at first migration and educational outcomes accounted for by the mutually exclusive dummy variables for age at migration.

10. Percentage changes displayed as decimals.

11. Survey responses include “homemaker” and “helps around the house”.

12. Sample responses include “tailor,” “carpenter,” “house painter,” “plumber,” and “electrician.”

13. Sample responses include “construction unskilled workers,” “electrical equipment, electronics, and telecommunications installation and repair unskilled workers.”

14. Sample responses include “workers in retail establishments,” “insurance and real estate agents,” and “delivery workers.”

15. Category includes both “personal services workers in establishments” and “domestic service workers.” Sample responses include “gardener,” “doorman,” bartender,” and “clothes-cleaning service workers.”


References


Alba, R., & Silberman, R. (2009). The children of immigrants and host-society educational systems: Mexicans in the United States and North Africans in France. Teachers College Record, 111(6), 1444-1475.


Antman, F. M. (2011). The intergenerational effects of paternal migration on schooling and work: What can we learn from children's time allocations? Journal of Development Economics, 96(2), 200–208.


Balfanz, R., Bridgeland, J. M., Bruce, M., & Fox, J. H. (2012). Building a grad nation: Progress and challenge in ending the high school dropout epidemic. Annual update. Civic Enterprises, Everyone Graduates Center at Johns Hopkins University, America’s Promise Alliance, Alliance for Excellent Education.


Becker, G. (1967). Human capital: A theoretical and empirical analysis, with special reference to education (2nd ed.). New York, NY: National Bureau of Economic Research, Columbia University Press.


Bollen, K. A., Glanville, J. L., & Stecklov, G. (2002). Economic status proxies in studies of fertility in developing countries: Does the measure matter? Population Studies, 56(1), 81–96.


Bredl, S. (2011). Migration, remittances and educational outcomes: The case of Haiti. International Journal of Educational Development, 31(2), 162-168.


Case, A., Paxson, C., & Ableidinger, J. (2004). Orphans in Africa: Parental death, poverty, and school enrollment. Demography, 41(3), 483–508.


Chiquiar, D., & Hanson, G. H. (2005). International migration, self-selection, and the distribution of wages: Evidence from Mexico and the United States. Journal of Political Economy, 113(2), 239-281.


Chiswick, B. R. (1978). The effect of Americanization on the earnings of foreign-born men. Journal of Political Economy, 86(5), 897–921.


Chiswick, B. R., & DebBurman, N. (2004). Educational attainment: Analysis by immigrant generation. Economics of Education Review, 23(4), 361–379.


Conger, D., Schwartz, A. E., & Stiefel, L. (2007). Nativity differences in school stability and special education: Evidence from New York City. International Migration Review, 41(2), 403-432.


Crosnoe, R., & Lopez-Turley, R. (2011). The K-12 educational outcomes of immigrant youth. Future of Children, 21(1), 129–152.


Dixon, L. Q., Zhao, J., Shin, J.-Y., Wu, S., Su, J.-H., Burgess-Brigham, R., Gezer, M. U., & Snow, C. (2012). What we know about second language acquisition: A synthesis from four perspectives. Review of Educational Research, 82(1), 5–60.


Dörnyei, Z. (1994). Motivation and motivating in the foreign language classroom. The modern language journal, 78(3), 273–284.


Duncan, B., & Trejo, S. (2012). The employment of low-skilled immigrant men in the United States. The American Economic Review, 102(3), 549–554.


Dustmann, C., & Weiss, Y. (2007). Return migration: Theory and empirical evidence from the UK. British Journal of Industrial Relations, 45(2), 236–56.


Edwards, A. C., & Ureta, M. (2003). International migration, remittances, and schooling: Evidence from El Salvador. Journal of Development Economics, 72(2), 429–461.


Filmer, D., & Pritchett, L. (1999). The effect of household wealth on educational attainment: Evidence from 35 countries. Population and Development Review, 25(1), 85–120.


Filmer, D., & Pritchett, L. (2001). Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India. Demography, 38(1), 115–132.


Gandara, P., & Contreras, F. (2009). The Latino education crisis: The consequences of failed social policies. Cambridge, MA: Harvard University Press.


Gibson, M. A., & Bejinez, L. F. (2002). Dropout prevention: How migrant education supports Mexican youth. Journal of Latinos and Education, 1(3), 155–175.


Gibson, M. A., & Hidalgo, N. D. (2009). Bridges to success in high school for migrant youth. Teachers College Record, 111(3), 683–711.


Glick, J. E., & Hohmann-Marriott, B. (2007). Academic performance of young children in immigrant families: The significance of race, ethnicity, and national origins. The International Migration Review, 41(2), 371–402.


Goldenberg, C., Gallimore, R., Reese, L., & Garnier, H. (2001). Cause or effect? A longitudinal study of immigrant Latino parents' aspirations and expectations, and their children's school performance. American Educational Research Journal, 38(3), 547–582.


Goldin, C., & Katz, L. (2008). The race between education and technology. Cambridge, MA: Harvard University Press.


Green, P. E. (2003). The undocumented: Educating the children of migrant workers in America. Bilingual Research Journal, 27(1), 51–71.


Grieco, E., Acosta, Y., de la Cruz, G., Gambino, C., Gryn, T., Larsen, L., Trevelyan, E., & Walters, N. (2012). The foreign-born population in the United States: 2010. Washington, DC: U.S. Census Bureau.


Halpern-Manners, A. (2011). The effect of family member migration on education and work among nonmigrant youth in Mexico. Demography, 48(1), 73–99.


Hanson, G. H., & Woodruff, C. (2003). Emigration and educational attainment in Mexico. UCSD Working Paper.


Kandel, W. (2003). The impact of U.S. migration on Mexican children’s educational attainment. In M. Cosio, R. Marcoux, M. Pilon, & A. Quesnel (Eds.), Education, Family and Population Dynamics. Paris: CICRED.


Kandel, W., & Kao, G. (2001). The impact of temporary labor migration on Mexican children's educational aspirations and performance. International Migration Review, 35(4), 1205–1231.


Kandel, W., & Massey, D. S. (2002). The culture of Mexican migration: A theoretical and empirical analysis. Social Forces, 80(3), 981-1004.


Long, J. S., & Freese, J. (2001). Regression models for categorical dependent variables using Stata. College Station, TX: Stata Press.


Lopez, M. P. (2011). Reflections on educating Latino and Latina undocumented children: Beyond Plyler v. Doe. Seton Hall Law Review, 35(4), 1373-1406.


Massey, D. S., & Capoferro, C. (2004). Measuring undocumented migration. International Migration Review, 38(3), 1075–1102.


Massey, D. S., & Zenteno, R. (2000). A validation of the ethnosurvey: The case of Mexico-U.S. migration. International Migration Review, 34(3), 766–793.


McKenzie, D. (2005). Measuring inequality with asset indicators. Journal of Population Economics, 18(2), 229–260.


McKenzie, D., & Rapoport, H. (2011). Can migration reduce educational attainment? Evidence from Mexico. Journal of Population Economics, 24(4), 1331–1358.


Motel, S., & Patten, E. (2012). The 10 largest Hispanic origin groups: Characteristics, rankings, top counties. Washington, DC: Pew Hispanic Center.


Newport, E. L. (2002). Critical periods in language development. In L. Nadel (Ed.), Encyclopedia of cognitive science. London: Macmillan Publishing Ltd./Nature Publishing Group.


Nobles, J. (2011). Parenting from abroad: Migration, nonresident father involvement, and children's education in Mexico. Journal of Marriage and Family, 73(4), 729–746.


Noels, K., Clement, R., & Pelletier, L. (1999). Perceptions of teachers' communicative style and students' intrinsic and extrinsic motivation. The Modern Language Journal, 83(i), 23-34.


OECD. (2011). Education at a glance 2011: Country note-Mexico. Paris: OECD Publishing.


OECD. (2012). International migration outlook 2012. Paris: OECD Publishing.


Orellana, M., Thorne, B., Chee, A., & Lam, W. (2001). Transnational childhoods: The participation of children in processes of family migration. Social Problems, 48(4), 572–591.


Oropesa, R. S., & Landale, N. S. (2009). Why do immigrant youths who never enroll in U.S. schools matter? School enrollment among Mexicans and Non-Hispanic Whites. Sociology of Education, 82(3), 240–266.


Orreniusa, P. M., & Zavodny, M. (2005). Self-selection among undocumented immigrants from Mexico. Journal of Development Economics, 78(1), 215—240.


Osborne, J. W. (2006). Bringing balance and technical accuracy to reporting odds ratios and the results of logistic regression analyses. Practical Assessment, Research & Evaluation, 11(7), 1-6.


Park, H. (2014, August 7). Children at the border. The New York Times.  Retrieved from http://www.nytimes.com/interactive/2014/07/15/us/questions-about-the-border-kids.html?_r=0

 

Parker, S. W., & Pederzini, C. (2000). Gender differences in education in Mexico. World Bank Departmental Working Paper 21023. Washington, DC: The World Bank.


Passel, J. S., Capps, R., & Fix, M. (2004). Undocumented immigrants: Facts and figures. Washington, DC: The Urban Institute. Retrieved from http://www.urban.org/UploadedPDF/1000587_undoc_immigrants_facts.pdf


Passel, J., & Cohn, D. (2011). Unauthorized immigrant population: National and state trends, 2010. Washington, DC: Pew Hispanic Center.


Patten, E. (2012). Statistical portrait of the foreign-born population in the United States, 2010. Washington, DC: Pew Hispanic Center.


Petronicolos, L., & New, W. S. (1999). Anti-immigrant legislation, social justice, and the right to equal educational opportunity. American Educational Research Journal, 36(3), 373–408.


Prewitt, J., Trotter, R., & Rivera, V. (1990). The effects of migration on children: An ethnographic study. State College, PA: Centro de Estudios Sobre la Migracion.


Reyes, B. (1997). Dynamics of immigration: Return migration to western Mexico. San Francisco, CA: Public Policy Institute of California.


Robles, V. F., & Oropesa, R. S. (2011). International migration and the education of children: Evidence from Lima, Peru. Population Research and Policy Review, 30(4), 591-618.


Ruiz-de-Velasco, J., & Fix, M. (2000). Overlooked and undeserved: Immigrant students in U.S. secondary schools. Washington, DC: The Urban Institute.


Santibañez, L., Vernez, G., & Razquin, P. (2005). Education in Mexico: Challenges and opportunities. Santa Monica, CA: RAND Corporation. RAND/DB-480-HF


Schiller, N. G., Basch, L., & Blanc, C. S. (1995). From immigrant to transmigrant: Theorizing transnational migration. Anthropological Quarterly, 68(1), 48–63.


Singleton, D. (1995). Introduction: A critical look at the critical hypothesis in second language acquisition research. In D. Singleton & Z. Lengyel (Eds.), The age factor in second language acquisition (pp. 1–29). Bristol, PA: Multilingual Matters.


Stromquist, N. P. (1989). Determinants of educational participation and achievement of women in the third world: A review of the evidence and a theoretical critique. Review of Educational Research, 59(2), 143–183.


Suárez-Orozco, C., Suárez-Orozco, M., & Todorova, I. (2008). Learning a new land: Immigrant students in American society. Cambridge, MA: Harvard University Press.


UNESCO. (2003). EFA global monitoring report 2003/4. Gender and education for all: The leap to equality. Paris: UNESCO.


UNESCO. (2007). EFA global monitoring report 2008. Education for all by 2015: Will we make it?. Paris: UNESCO.


United Nations. (2011). International migration report 2009: A global assessment. New York, NY: Department of Economic and Social Affairs, Population Division.


Wooldridge, J. (2009). Introductory econometrics: A modern approach. (4th ed.). Mason, OH: South-Western CENGAGE Learning.


World Bank. (2006). World development report 2007: Development and the next generation. Washington, DC: The International Bank for Reconstruction and Development/The World Bank.


APPENDIX A


The following table provides the regression coefficients for each survey year included in the models reported in Column 1 in Tables 4 and 6. All estimates are relative to the omitted survey year 1987.

  

 

(1)

(2)

 

Years of education

Dropping out

(Odds ratio)

Survey Year

  
   

1988

0.177

1.082

 

(0.121)

(0.165)

1989

-0.018

1.091

 

(0.157)

(0.177)

1990

0.465***

0.813

 

(0.122)

(0.122)

1991

0.647***

1.326+

 

(0.117)

(0.199)

1992

0.200

1.105

 

(0.124)

(0.170)

1993

0.713***

0.536*

 

(0.161)

(0.131)

1994

0.517***

0.888

 

(0.116)

(0.133)

1995

0.667***

0.846

 

(0.127)

(0.132)

1996

0.633***

0.528***

 

(0.122)

(0.086)

1997

0.314*

1.037

 

(0.128)

(0.191)

1998

0.083

1.071

 

(0.115)

(0.162)

1999

0.383**

1.168

 

(0.122)

(0.193)

2000

0.009

4.227***

 

(0.129)

(0.787)

2001

-0.101

1.697***

 

(0.124)

(0.268)

2002

0.638***

0.820

 

(0.127)

(0.143)

2003

0.190

1.115

 

(0.138)

(0.187)

2004

-0.283*

0.859

 

(0.137)

(0.149)

2005

-0.002

1.542+

 

(0.216)

(0.367)

2006

0.274*

0.765

 

(0.136)

(0.136)

2007

0.578***

0.900

 

(0.130)

(0.160)

2008

0.071

1.821**

 

(0.134)

(0.334)

2009

0.297*

1.153

 

(0.130)

(0.214)

2010

0.557***

1.644*

 

(0.143)

(0.320)

2011

0.254+

1.601*

 

(0.136)

(0.332)

Robust standard errors in parentheses

*** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.10





Cite This Article as: Teachers College Record Volume 118 Number 1, 2016, p. 1-48
https://www.tcrecord.org ID Number: 18239, Date Accessed: 1/28/2022 11:20:04 PM

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  • Karyn Miller
    Texas A&M International University
    E-mail Author
    KARYN MILLER is an assistant professor in the Department of Professional Programs within the College of Education at Texas A&M International University. She is interested in issues of educational access and attainment for all children and her research focuses on both international and domestic education policy concerns.
 
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