Educational Opportunity and Immigration in México: Exploring the Individual and Systemic Relationships
by José Felipe Martínez, Lucrecia Santibanez & Edson E. Serván Mori - 2013
Background/Context: Much research has investigated the complex interplay between education and migration. Education has been alternatively conceptualized as playing an important role as motivator or deterrent of future migration. This relationship, however, is often investigated in terms of coarse indicators of educational attainment.
Purpose: In this paper we investigate a less commonly explored side of the link between education and immigration. Our study estimates the relationship between immigration and educational quality and opportunity for the case of Mexico and the United States. Using these indicators we are able to delve deeper into how education affects migration decisions. Studying the relationships between immigration rates and aggregate indicators of education quality and opportunity can shed light into the ways in which education systems and governmental structures may influence or react to immigration patterns among school-age children.
Research Design: Data for this study come from three different sources: The Mexican Family Life Survey (MXFLS), which contains information on individual migration decisions; the Oportunidades program, which contains extensive family socio-economic characteristics and school quality indicators; and data from the National Population Council of Mexico, which contains socio-economic and demographic information about communities. Taking advantage of this data, we use logistic and linear regression models to estimate the relationship between education quality and migration.
Results: Our results suggest significant relationships between individual decisions to migrate and indicators of educational access, quality, and opportunity, suggesting that the experiences and opportunities afforded to individuals and families in school throughout the years can be of consequence for explaining immigration decisions and patterns.
Conclusions/Recommendations: Our analyses raise questions for delineating a framework for studying the relationship between educational quality and immigration. Such a framework should consider that not only individuals may have a lower incentive to further their schooling, but communities and even authorities may also have a lower incentive to improve school quality and opportunity.
More than three quarters of the worlds 200 million international migrants move to a country with a higher level of human development than their country of origin. Indeed, history and contemporary evidence suggest that development and migration go hand in hand: The median rate of incoming migration in a country with low human development is below 4 percent, compared to more than 8 percent in countries with high levels of human development (United Nations Development Program, 2009). The effects of migration on the sending countries have been studied extensively and depend critically on the magnitudes, composition, and nature of the migration streams, and the specific context from which migrants are drawn. Migration can have positive effects on sending countries; it can operate as a leveling mechanism enabling greater wage and income equality between sending and receiving regions (Ozden and Schiff, 2006). It can also have an impact on the family, particularly for those members who stay behind. In the countries of origin, the impact of immigration can be felt in higher incomes and consumption, better education and improved health, as well as at a broader cultural and social level. Moving can bring benefits, most directly in the form of remittances sent to immediate family members who have remained in their country of origin. Financial advancement related to immigration can have a significant effect in reducing the level, depth, and severity of poverty (Adams, 2005), improving health indicators like birth weight and infant mortality (Amuedo-Dorantes & Pozo, 2008; Hildebrandt and McKenzie, 2005; Duryea, López-Córdova, & Olmedo, 2005), and education indicators like dropout rates and student achievement (McKenzie, 2006; Hanson & Woodruff, 2003).
In the 1980s and 90s, Mexico went through a series of economic crises and periods of slow growth that took a toll on real salaries and employment. As a result, families implemented a range of strategies to compensate for deteriorated economic circumstances: One resulting trend was the incorporation of household members that traditionally did not work (i.e., women and children) into the formal or informal work force; another was an increase in migration rates within and outside the country, in particular to the United States (Durand & Massey, 1992; Canales, 2002). Migration from Mexico to the United States is not only an economic phenomenon triggered by differences in income between the two countriessocial and human capital are also important predictors of first-time migration (Massey & Espinosa, 1997). Moreover, the decision to migrate for the first time is crucial in jumpstarting migration cycles that tend to be reinforced as migrants accumulate social capital for advancing in the U.S. (through family ties, friendships, relationships, social networks) as well as human capital (knowledge of English, additional education, knowledge of and experience in the U.S labor market), often attracting others to join them in the U.S (Bustamante, 2008).
Much research has investigated the complex interplay between education and migration in this process. Demographers have long been interested in differences in likelihood of migrating for people with different levels of education (Borjas, 1994; Chiquiar & Hanson, 2005; Caponi, 2006). Economists have investigated the role of increased education expenses related to immigration in helping keep children in school longer, reducing educational inequality, and the economic and social hazards of leaving school (Miranda, 2007; Cox, Ureta & Ureta ,2003; López-Córdova, 2004, Adams, 2005; McKenzie & Rapoport, 2004, 2006). In the host country research suggests a strong correlation between the skills of immigrants and the skills of the second-generation, suggesting that skill gaps in migrant groups carry over to their children (Borjas, 1994). Finally, the educational disadvantages faced by immigrant children in the receiving country have become one of the most pressing issues facing educational systems in developed countries in recent years (Cervantes & Hernandez, 2011; Passel, 2011). In the case of the United States in particular, increasing attention is being paid to the role of structural features of the education system, from the local school to the state and federal level, as determinants of educational opportunity, and how these structures can work to accentuate or alleviate the disadvantages faced by children of immigrant families (Oh & Cooc, 2009; Gandara & Rumberger, 2009; Holdaway & Alba, 2009). In this paper we investigate a less commonly explored facet of the link between education and immigration, namely educational quality and opportunity in the country of origin. While student attainment (as measured by level of education) is typically the focus of research and has been shown to predict likelihood of future migration, we argue that the role of educational opportunity and students experiences in schools should also be investigated. This includes direct links related to differences in access to schooling, and indirect links through variation in achievement tied to differences in quality and opportunity across schools. Finally, we investigate the potential of reciprocal relationships whereby immigration rates may in turn influence educational quality in the county or region of origin.
EDUCATION, EDUCATIONAL OPPORTUNITY, AND IMMIGRATION
While Mexican migration to the U.S. is not a new phenomenon, in recent years three pronounced trends have become noticeable (see, e.g., Canales, 2002). First, as a demographic phenomenon Mexican migration to the United States grew dramatically over the past two decades; by the mid-2000s over 8 percent of the Mexican population lived in the U.S., compared to fewer than 2 percent in the 1950s and 60s. Estimates have 400,000 Mexicans migrating to the U.S. every year for either temporary or permanent settling (Cornelius & Salehyan, 2007).1 According to the 2000 Mexican Census, 97 percent of people who reported they had migrated abroad between 1995 and 2000 had gone to the United States (see, e.g., Caponi, 2006). Second, migration increased in states and urban centers that traditionally had not seen intense migratory patterns. While migration used to be a predominantly rural phenomenon, currently half of migrants come from urban centers, and half from rural areas (Durand, Massey, Zenteno, 2001). Finally, the profile of the Mexican immigrant to the United States is changing and no longer reflects the prototype of middle aged fathers of poor families; todays immigrant is more likely to be younger (the average migrant in our data is 26 years old), single, and to have better education than the national average for the population. In addition, a growing proportion of women and children are migrating to the U.S. (Canales, 2002, argues this trend began in the 1990s). Women in particular now make up close to 20 percent of non-permanent (work-related) migration, while in the early 1990s they represented only 4 percent.
The changing profile of the Mexican immigrant means, among other things, that an increasing proportion of immigrants are still of school age. In this context the educational experiences in the country of origin can provide helpful context to understand not only subsequent patterns of school achievement for migrants and their children, but also potentially the initial decision to migrate (Marcelli & Cornelius, 2001). Moreover, research suggests that the relationship between immigration and education does not only benefit the immigrant. Immigration can generate economic benefits for families who stay behind, create positive social capital, and raise expectations of better opportunities overall (Massey & Espinosa, 1997). Thus, while education may help predict migration at the individual level, migration rates could also influence educational indicators and outcomes (on the aggregate or for individuals).
The link between education and immigration has been the focus of much research. Interestingly, education has been alternatively conceptualized as playing an important role as motivator or deterrent of future migration. Massey and Espinosa (1997), using data from household field surveys collected in five states in western Mexico between 1987-92 found that the probability of migrating to the U.S. decreases with additional years of schooling and attributed this to a negative rate of return to a Mexican education in the U.S.the benefits of migrating are comparatively lower for the more educated. Caponi (2006) notes that this disincentive could be particularly acute in the case of undocumented workers who are often forced to take on a similar range of jobs in the U.S. irrespective of their level of education. Moreover, Flores (2010), using data from the Latin American Migration Project finds that Mexicans are at a disadvantage relative to immigrants from Central American countries, such as Nicaragua, because of the difficulty of obtaining legal work documents. Massey and Espinosa (1997) also observed that community schooling infrastructure decreases the probability of first-time migration, which is consistent with the idea of lower expected benefits of migrating to the U.S for the better educated.
Alternatively, recent work by Caponi (2006) using data from the 2000 Mexican Census analyzed the relationship between level of education and migration choices among Mexicans, and found that the relationship is U-shaped, with the highest and lowest educated groups migrating at higher rates than the middle educated (those with six to nine years of schooling). As shown in Table 1, Mexican immigrants in the U.S. are more likely to be uneducated than the Mexican population in general, but they are also more likely to be highly educated (high school or more). At the lower end the author argues this relationship may emerge because uneducated Mexicans have little human capital to lose; however, the author does not articulate an explanation for the increased migration rates among people with high school degrees (and does not make a distinction between migrating with and without documents). The author finds that additional education not only tends to deter migration (up to the point of graduation from high school), but that there is a loss of human capital for people who migrate to the U.S (i.e., a negative interaction between education and immigration status). Thus, Caponi (2006) and others have suggested that increasing schooling opportunities in Mexico could bring more people to a point where migrating becomes less attractive as an alternative.
Table 1. Mexico Residents compared to Mexicans in the U.S., by gender (2000)
Source: Caponi, 2006 with data from the US Census (PUMS5), 2000 (U.S. Census Bureau), and the XII Censo General de Población y Vivienda, 2000 (INEGI).
Researchers have also sought to understand the potential for mutual effects between education and migration, both in the host country and in the country of origin. Miranda (2007) used longitudinal data from the Mexican Migration Project collected between 1982 and 2005 to examine if family and community migration patterns affect individual education outcomes. Miranda found that an extra migrant in the family decreases the likelihood of high school graduation by 2.4 percentage points, and suggested that individuals may behave in a forward-looking fashion: those with migrants in their family migrate at higher rates and they tend to drop out of school early to avoid the perceived opportunity cost of continuing in school. The findings also suggest that individuals who live far from traditional migrant areas (1,000 km or more) have a higher chance of graduating from high school (17.4 percent higher). Miranda (2007) seems to contradict studies discussed earlier (e.g., Caponi, 2006) that suggest that having more education increases the probability of migration. The paper speculates that families with higher income (though not necessarily wealthy) may be able to finance both education and migration choices, thus making it possible to create positive migrant selection (i.e., the migration of more educated individuals).
McKenzie and Rapoport (2006) explore the impact of migration on educational attainment in rural Mexico, using data from the 1997 National Survey of Demographic Dynamics (ENADID). Using historical migration rates by state to account for current migration, the authors find that there is a significant negative effect of migration on educational attainment (years of schooling) for youth ages 16-18. This effect is largest for males but is also present for females. Moreover, higher levels of maternal education increase this disincentive. Migration thus could seem to lower education inequality, but this effect appears to be driven by lowering educational attainment at the top of the education distribution, not by raising levels at the bottom.
Notably, most research investigating the relationship between immigration and education employs raw education outcomes such as years of schooling as proxies of educational attainment. At most, a few studies include standardized achievement tests as outcomes. Importantly, all of these studies take the features of the educational system as a factor that is either constant or unobservable. While student attainment and level of schooling is an important variable that has been shown to predict likelihood of future migration in multiple contexts, it is also important to consider the role of educational opportunity when considering relationships between education and immigration. However, the literature includes few examples of studies investigating the relationship between educational opportunity and immigration in the country of origin, before families or young people decide to migrate. One example is the study by Massey and Espinosa (1997) which reported a negative relationship between community schooling infrastructure and probability of first-time migration from Mexico to the United States: the authors found that people are less likely to migrate when a community has a high school (preparatoria). Using a different dataset, Martinez, Santibanez, and Servan-Mori (2008) reported that a range of indicators of educational opportunity were associated with a higher likelihood that students and young adults will consider migrating to the United States. In particular, being retained in grade, working for pay while attending school, and being forced to interrupt studies by lack of school, closed schools, or absent teachers, increased the probability of considering migration as an alternative for the future.
Thus, the findings reported in the literature are at best inconsistent, alternatively suggesting that immigration may hurt or improve education outcomes, or that more education increases or decreases likelihood of migration. This reflects in part a lack of conceptual clarity and consistency in the models and frameworks used to investigate the relationship between educational opportunity and immigration. Differences in educational opportunity may relate to immigration, for example by limiting access (e.g., not having a high school nearby), which directly alters the context of a decision to migrate. However, this relationship could also emerge through subtler differences in education quality across schools or regions (e.g., having better teachers, or a better equipped school), which could influence immigration decisions indirectly, mediated through intermediate outcomes like achievement. Moreover, education opportunity and immigration may exhibit different patterns of relationships at different levels of aggregation and for different indicators: While remittances from a migrant family member may decrease the chances of dropping out from high school, the opposite could be true in the aggregate if increases in remittances make migration an attractive alternative to continued schooling. Finally, relationships need not be unidirectional; for example, education opportunity may be related to migration rates, which in turn could also impact opportunity or quality for those left behind.
This study used multiple national datasets available in Mexico to investigate the links between educational opportunity and immigration to the United States from two complementary perspectives: First we investigate the link between immigration and the educational histories of Mexican students and the opportunities they are afforded by the education system. For this purpose, we distinguish between relationships in terms of individual expectations of migrating in the future, but also those involving the actual decisions to migrate to the United States later on. Second, we investigate the relationships between regional migration rates and aggregate indicators of educational quality and opportunity in the Mexican education system.
This paper addresses two main research questions: (a) are educational quality and opportunity related to individuals intentions and decisions to migrate, and (b) are rates of immigration related to aggregate measures of educational quality and opportunity? Below we describe the datasets and analytic methods used to address these research questions.
SAMPLE AND MEASURES
Data for this study come from three different sources. The first is The Mexican Family Life Survey (MxFLS), a 10-year longitudinal, nationally representative panel survey of individuals, households, communities and regions in Mexico2 developed by the Centro de Investigación y Docencia Económicas (CIDE), the Universidad Iberoamericana, and UCLA. The sample was collected using a probabilistic stratified design; the first wave, collected in 2002, includes about 36,000 subjects in 8,400 households distributed across 150 communities in 16 Mexican states, as well as their neighborhoods, communities, and schools (Rubalcava & Teruel, 2006).3 The design includes tracking of subjects over time regardless of migration decisions; the second wave, collected in 2006, achieved follow up rates over 90 percent for the sample, including people who had migrated to the United States since 2002.
The second source of data is the Encuesta de Caracteristicas Socioeconómicas de los Hogares (ENCASEH). The data were part of the evaluation of the Oportunidades program, one of the largest conditional cash transfer (CCT) programs in the world, reaching almost 25% of Mexicos population. Economically disadvantaged families receive a bimonthly stipend conditional on fulfilling a series of co-responsibilities designed to increase their health, nutrition, and education. The 2003 evaluation data includes 34,203 families in rural communities in 281 municipalities in 7 states (Guerrero, Hidalgo, Michoacán, Puebla, Querétaro, San Luis Potosí, and Veracruz). Within communities, households below certain levels of income were deemed eligible and invited to participate (Skoufias, Davis, & Behrman, 1999). In addition to extensive information about individuals, families, and communities, information was also collected about the schools attended by individuals in the sample. These variables were used to construct municipal aggregate indices to reflect school infrastructure, resources, and climate, teacher background and practices, and availability of support and materials for teachers and students.
Finally, immigration and marginality indices were obtained from the National Population Councils (CONAPO) dataset for all municipalities in the country. The intensity of immigration index is a composite of the percentage of households receiving remittances, percentage with members in the United States during past five and 10 years, and the number of circular migrants leaving and returning. The marginality index is a composite measure of social and economic wellbeing at the municipal level, based on the proportion of families with access to essential goods and services, including income, adequate housing, and education, among others.
Table 2 presents descriptive statistics for the variables from each dataset included in our analyses. Data collected at the individual level includes gender, age, ethnicity, rural location, family income, and receiving federal assistance (from the Oportunidades, which will be explained below in more detail). Individual-level education indicators include years of schooling, repeating grade, and working while in school, as well as being forced to interrupt school attendance because there was no school in the community or the school was closed. The reported intention to migrate was collected in 2002, and in 2005 whether or not the person had migrated; the data distinguish between internal and external (U.S.) migration.4 School-level indicators include availability of scholarships; free breakfast or lunch, or other support for students; access to books and materials; extra-curricular activities; vocational education or workshops; infrastructure; and parental involvement in efforts to improve academic outcomes. Also included are indicators of teacher education, absenteeism, participation in the federal teacher incentive program (Carrera Magisterial)5, and engagement in improvement efforts at the school. Factor analyses conducted on the school-level indicators led to the creation of two school quality indices.6 The first captures school resources available to students beyond the basic curriculum (e.g., extra curricular activities, workshops, administrative support). The second index reflects direct financial or nutritional support for students from federal or state programs. Teacher engagement, absenteeism, and participation in Carrera Magisterial did not relate to these two common factors and are considered individually.
Table 2. Sample descriptive statistics for individual, school, and regional variables.
Note: MxFLS: The Mexican Family Life Survey. ENCASEH: Encuesta de Caracteristicas Socioeconomicas de los Hogares. CONAPO: National Population Councils.
We use individual level data from MxFLS along with municipal aggregates from the CONAPO dataset to gain a better understanding of the relationship between education quality and migration.7 We first present descriptive statistics and breakdowns by intention to migrate in 2002 and migrant status in 2005. We then address the first research question (are educational quality and opportunity related with individual intention or decision to migrate?) estimating a series of probit regressions with the base form
where the dependent variables are the (log) likelihoods of reporting intention to migrate in 2002, and deciding to migrate by 2005; equation 1 is a cross-sectional model using only 2002 data, while equation 2 represents a longitudinal model using data from the 2002 and 2005 waves. In both cases X1 represents covariates tapping on family and community resources (e.g., family income, community marginality, rural location); X2 represents individual indicators of educational history and opportunity (e.g., grade retention, working while studying). In the case of Equation 2, X2 also includes the individuals intention to migrate as reported in 2002. X3 represents school-level indicators of education quality (e.g., schools in poor physical condition, teacher qualifications and absenteeism, financial support for students).
To address the second research question (the relationship between migration rates and educational quality and opportunity) we use linear regressions to relate aggregate municipal indices of educational quality from MxFLS and Oportunidades to municipal migration rates estimated after partialing out the potential effect of regional economic development. The models follow the general regression form
where the Ys are municipal aggregates of the various educational indicators, and X1 and X2 are the municipal indices of marginality and immigration respectively from the CONAPO dataset.
Importantly, the data does not permit the establishment of causal links between educational quality and opportunity and immigration in either direction. Our models cannot test the directionality of the relationships, and the standardized coefficients shown represent only partial correlations between migration and education quality, holding the level of economic development constant. As discussed earlier, different kinds of mechanisms of relationship could explain patterns observed when looking at individual level data, which may differ from the relationships at the school and system levels. Educational opportunity may decrease the likelihood of migrating for individuals by altering the rates of return to schooling and perceptions about future education and employment opportunities in Mexico. At the aggregate level an inverse relation could exist if, for example, education quality is negatively impacted in regions with high migration rates. Importantly, this descriptive view of the relationships in operation at the individual, school, and system levels can offer crucial insight for refining a conceptual framework to help guide and focus future research, by helping to generate testable hypotheses about effects and mechanisms at each level.
ANALYSIS OF INDIVIDUAL LEVEL DATA
We first present descriptive statistics for our sample: Table 3 shows breakdowns for individuals who reported they intended or did not intend to migrate in 2002, and for those who had migrated by the time data was collected for the second time in 2005. The results in the table suggest that the educational experiences of people who intended to migrate in 2002 were different from those of the general population represented in the sample, and those who did not intend to migrate. The group who intended to migrate internally was on average younger (27 years) and considerably better educated (9.5 years of schooling) than the general population.8 Those who reported that they intended to migrate to the United States were even younger (25.5 years) and also had above average education (8.40 years of schooling). Moreover, the educational experiences of this group seem to reflect particularly challenging schooling circumstances: they were more likely to work while in school (36% report working at some point during their education, compared to 26% of the general population) and to have been retained in grade (42% were retained at least once, compared to 34% in the population).
Table 3. Sample characteristics by Intention to migrate (2002) and migrant status (2005).
The differences appear more acute if we look at people who had actually migrated by 2005 using the data from the second wave of MxFLS. Compared to the general population, the three rightmost columns in Table 3 suggest that migrants to the United States are younger (26.3 years old), slightly less educated (7.8 years of schooling), and disproportionately male (68%). This group was also more likely to have experienced grade retention while in elementary school (35%), to work while in school (32%), and slightly more likely to report that closed or unstaffed schools had forced them to interrupt their studies at least once (7%).9
To address the first research question (whether educational quality and opportunity is related to individual intentions and decisions to migrate) we estimate a series of regressions modeling the relationship between intentions and decisions to migrate, and individual background, educational experiences, and school-level indicators of educational quality and opportunity. The results of these analyses are shown in Table 4: cross sectional models estimated with the 2002 data suggest that reported intention to migrate is related to individuals family background and educational history. Aside from regional development, people who are younger, more educated, and have middle or high income levels were more likely to report they intended to migrate in the future. In terms of educational experiences, on the other hand, working while in school, and being retained in grade is positively associated to the reported intention to migrate in the future, as is interrupting studies due to lack of school or teacher. Interestingly, living in a rural community, and receiving benefits from the federal Oportunidades program are negatively related to reported intention to migrate. Finally, significant parameters for a number of school-level indicators of educational opportunity in the model indicate that attending (or having attended) better quality schools (i.e., schools that have more resources, offer more support to students, and have better teachers as judged by membership in the Carrera Magisterial teacher incentive program) is negatively related to reported intention to migrate.
Table 4 also presents the results of longitudinal models examining the relationship between information on family background, educational history, and school quality collected in 2002 and the decision to migrate (i.e., migrant status in 2005). The results are presented separately for all migrants (e.g., internal, U.S., and other countries) and people who migrated the United States. The results for all migrants with respect to educational history mirror the patterns observed with intention to migrate: people who report having been retained in grade or needed to work while studying were more likely to migrate by 2005; this result is consistent with other studies suggesting that these two factors are related to the likelihood that students will decide to drop out of schools. Similarly indigenous status and receiving benefits of the Oportunidades program are negatively related to migrant status 2005. On the other hand the results with respect to age and years of schooling run contrary to the findings for intention to migrate: all else being equal, while younger people were more likely to report that they intended to migrate in 2002, the opposite is true of migrant status in 2005. Also, years of education was positively related with intention to migrate in 2002, but negatively related with migrant status in 2005. The results with respect to our indices of school quality (i.e., educational opportunity) on the other hand reveal no significant relationship to migrant status in 2005, considering all migrants (external and internal) together.
Table 4. Probit Regression. Intention to migrate (2002) and migrant status (2005) 1
Note: Estimates computed in STATA V. Significance levels based on robust standard errors with sample clustering and unequal probability weighting.
*** p<0.01, ** p<0.05, +p<0.10.
Importantly, the results for the more restricted sample of migrants to the United States (the lions share of Mexican international migrants) differ considerably from those for all migrants taken together. Migrants to the U.S. tend (again) to be younger and disproportionately male. In terms of educational history, grade retention and working while studying are not predictive of likelihood of migrating to the U.S.; on the other hand, people who reported that they had been forced to interrupt their studies at some point because of closed schools, lack of school, or absent teachers were more likely to migrate to the U.S. Clearly, these are external factors largely outside the control of the student and the family, and thus the results are best construed as reflective of the potential impact of the levels of educational access and opportunity offered by school systems on familial decision making when considering whether to migrate. Finally, net of family income and regional development, some indicators of school quality were also significantly related to U.S. migrant status. Specifically, people who attended schools with fewer resources, or that offered less support for students were more likely to migrate, and the same is true of people who attended schools with higher rates of teacher absenteeism and a less educated teaching staff.
ANALYSIS OF MUNICIPAL LEVEL INDICES
Next we investigate the relationship between municipal immigration rates and aggregates of our indicators of educational quality and opportunity. The results in Table 5 correspond to indices obtained from the Mexican Family Life Survey sample of 137 municipalities in 16 states. Each row in the table is a linear regression with the corresponding index as outcome, and the municipal immigration rate and marginality index as the predictors. The partial coefficients are shown standardized due to the differences in the scales of each index.
Table 5. Partial regression of indices of educational quality on immigration rates (holding municipal marginality constant).
Partialling out the level of economic development, in municipalities with higher migration rates, a larger proportion of respondents reported repeating grade at some point during their studies, or being forced to interrupt their studies because schools were not available, or had been closed in their community (a result consistent with Massey and Espinosa, 1997). At the level of the school, the results suggest that in municipalities with higher rates of immigration schools offer significantly fewer extra-curricular activities and vocational workshops or talleres. Similarly, even among municipalities with similar levels of economic development, migration rate is negatively related to school infrastructure, staffing, and access to materials. On the other hand, no relationship was observed at the municipal level between immigration rate and the availability of programs that support and encourage school attendance, either financial (e.g., scholarships) or in kind (e.g., breakfast).
Migration rates are also not significantly related with municipal aggregates of teacher education, absenteeism, or engagement. Interestingly, however, the results suggest that in municipalities with higher levels of immigration fewer teachers are enrolled in Carrera Magisterial the national incentive program tied to teacher performance. This contrasts with the individual level results in Table 3 suggesting that attending schools where more teachers participate in this program was negatively related to intention to migrate. Moreover, parental involvement with and community support for the school (as reported by teachers) are lower in municipalities with higher migration rates. Lastly, higher migration rates are positively associated to the number of teachers and principals who report that, migration adversely affects student achievement. While the nature and directionality of this relationship cannot be ascertained with the data available, one possibility is that higher migration influences achievement negatively by increasing student turnout and absenteeism and making the classroom a less stable environment. In addition, students may be disincentivated in relation to their schooling because of the possibility of becoming migrants in the future (this seems consistent with the results of McKenzie and Rapoport, 2006 who reported this type of disincentive effects for boys aged 16-19).
The findings from this study provide complementary evidence from quantitative analyses of three large-scale Mexican datasets that revealed a variety of interesting relationships between indices of school quality and educational opportunity, and individual intentions and decision to migrate, as well as municipal migration rates. Overall, the descriptive and correlational results we present suggest that a negative relationship exists between school quality as measured by various indices obtained from the MxFLS, and the likelihood of migrating (outside of communities of origin, and to the United States specifically). At the same time, the results also indicate that municipalities with higher rates of migration to the U.S. tend to have lower indices of school quality and opportunity as measured by the same indices. Municipalities with higher migration rates are more likely to not have schools available (particularly middle and high schools) and experience school closings more frequently. In addition, municipal migration rates are negatively associated with school infrastructure, staffing, and access to educational materials, and with availability of extra-curricular activities and vocational workshops. Moreover, higher immigration rates are also negatively associated with parental involvement and community support for schools as reported by teachers, and with perceptions in schools that immigration adversely affects student achievement.
The reach of the dataset and the types of analysis we conducted limit interpretation of the results in this paper. First, while the data cover more than half the states in the country, this is still not a nationally representative sample (although it covers most of the states with highest rates of immigration). Moreover, as mentioned earlier, our data and analyses are insufficient for establishing causal relationship between educational quality and immigration. In particular, limitations in our ability to merge the datasets prevented estimation of joint statistical models that could provide a more rigorous test of these relationships. Thus the coefficients reflect only partial bivariate correlations between migration and indices of education quality, holding regional economic development constant. We are not able to test for confounding factors or ascertain the directionality of the relationships: for example, since the marginality index includes information about school access among its components, by controlling for it in the analyses we could have eliminated variation associated to education quality. While the results may be indicative of true relationships, they are mute as to the directionality of these relationships; thus, the causal nature and the precise mechanisms through which any effects operate are not clear. It is possible for example, indeed likely, that the true relationship between education quality and immigration rates is bidirectional in nature. Mutually causal relationships could plausibly operate at different levels, with education quality and opportunity influencing individual migration decisions, and migration rates influencing education quality.
Nevertheless, the availability of a longitudinal dataset and use of adjustments for family income and regional development suggest the results reflect more than a mere association between economic disparity and immigration across regions. While the analyses are not conclusive in a causal sense, they point to interesting hypotheses delineating a framework for studying the relationship between educational quality and immigration. This evolving framework has important implications for researchers and policymakers in this area by highlighting potential differences in the relationships at play at the level of the individual (immigration may influence incentives and decisions to further schooling), compared to aggregate relationships (immigration rates may influence incentives to communities and authorities to improve school quality and opportunity.) Our results are consistent with the notion that an expectation to migrate lowers incentives for people to continue their schooling, and that living in a community with high immigration rates may impact educational attainment among children and youth (Massey & Espinosa, 1997; McKenzie & Rapoport, 2006; Miranda, 2007). However, the relationships also suggest that high migration municipalities may offer fewer schooling opportunities and lower quality education overall. This type of relationship has been reported in the past and is problematic from a policy perspective, as high migration rates should not in theory discourage public investment in education for the children who stay behind. In turn this raises questions about the degree to which cycles of migration in these communities may be reinforced because lower school quality lowers rates of return to schooling for individuals in those communities, as well as opportunities for future educational advancement. These communities could also have fewer incentives for engaging with schools and demanding improvements to school quality and opportunity from school personnel and education officials, while the latter may also see reduced incentives for improving education in communities that are experiencing high migration to the U.S.
The findings also suggest a promising line of future research exploring the links between educational opportunity in the home country and international migration through a social network lens (Massey et al., 1993). The data from the MxFLS includes information on all household members, including siblings of those who eventually migrated, and future waves (the third has been collected and is being prepared for publication) will include migration decisions by siblings of those who migrated in 2005. Social network analysis (Davis, Stecklov, & Winters, 2002; Snijders, 2001) provides a useful lens for examining how a critical mass of migrants may influence migration decisions in the household by affecting future educational opportunities, both real and perceived. Specifically, because young people will naturally see themselves reflected in those migrants close to them, these social network influences may make migration seem like a possible and desirable goal.10
Overall, the results are consistent with the idea of immigration as a self-reinforcing, dynamic cycle, and seem to specifically suggest that educational quality may play a key role in strengthening the cycles of immigration and disadvantage in communities. Nevertheless, additional research is needed using data from subsequent waves from the MxFLS and other available datasets in Mexico and the U.S., in order to continue to refine and test hypotheses involving the interrelationships between education and immigration. In this effort, researchers and policymakers will benefit from richer and more comprehensive definition of education and education quality to more fruitfully investigate these relationships. Studying the relationships between immigration rates and aggregate indicators of education quality and opportunity can shed light into the ways in which education systems and governmental structures may influence or react to immigration patterns among school age children.
. More recent data point to decreasing migration rates related to lower demand for labor as a result of the economic recession and stronger border security efforts in the USA, and improving economic conditions in Mexico (Cave, 2011).
2. Regional definitions are in accordance with the Mexican Federal Government National Development Plan 2000-2006.
3. The sample is representative at the national, urban-rural, and regional levels. Regional stratification maximized representation and allowed to better capture Mexican cultural and socioeconomic diversity.
4. Internal migration refers to people who move to another city and/or state within Mexico. External migration refers to people who leave Mexico to reside (for an extended period of time) in another country. In this case, we focus only on external migration to the United States.
5. Carrera Magisterial is the national teacher incentive program created in Mexico in 1993. Teachers who volunteer to be evaluated by the program receive initial benefits of around 25% of base wage. The evaluation has six factors: a standardized test of teacher knowledge, the average scores of the teachers' students on a standardized curriculum-based test, a peer review component, seniority, highest degree earned, and an indicator of professional development undertaken during the evaluation year. The program was reformed in 2011, and is pending the implementation of a new, revised evaluation system.
6. After a principal component analyses ruled out unidimensionality, factors were extracted via Principal Axis Factoring with Promax rotation for interpretability. The analyses were carried out in SPSS v.16.
7. It is not possible to match the datasets at the student level; the samples overlap only minimally, were collected in different years, and do not share individual identifiers.
8. The average age of respondents in Table 2 was 29 years, with a range from 15 to more than 60 years. In 2005, the average years of schooling among Mexicans aged 15 and older was 8.4. For those aged 25 and older the average was lower at 7.8 (source: The World Bank, World Data Bank. Available at databank.worldbank.org (accessed July 6, 2012).
9. Additional breakdowns by urban status not presented here suggest that individuals in rural areas face particularly challenging educational experiences.
10. We thank one anonymous reviewer for suggestions about links with existing literature in social network analysis, and potential future lines of research.
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