Many changes have occurred since the last survey of social mobility more than 40 years ago. These include profound changes in social institutions. Workshop presentations explored changes in family structures, education, and labor markets, giving further attention to the implications of these changes for developing accurate measures to be used in a new study of social mobility. U.S. demographics have also shifted markedly, due in part to immigration. Nuanced measures will be required to capture the experience of immigrants and their descendants. Patterns of political affiliation and participation may also be relevant to a new study of social mobility.
One of the key institutional changes that have occurred since the last major survey of social mobility is in family structure. Individuals are situated within families of origin that transfer resources from one generation to the next, conferring advantages and disadvantages. Yet the simple category “family of origin” has become a markedly fluid and complex construct, comprising a shifting cast of characters, including cohabiting unmarried partners, non-resident parents, blended and stepfamilies, grandparents, as well as extended kin and quasi-kin networks. Complex and changing family structures are also manifested at different rates across the socioeconomic strata.
Complex and Fluid Family Structures
What are the implications of these changes for measuring social mobility? This question received attention throughout the entire workshop and was the explicit focus of an in-depth presentation, “Social Mobility in an Era of Family Instability and Complexity,” by Laura Tach of the Department of Policy Analysis and Management at Cornell University.
Tach began by making the case for the imperative of updating the means to identify and measure family members. Survey questions based on an expected norm of a stable and continuous nuclear family with married parents would have generated some minor measurement problems and misclassifications in the mid-20th century. Today, they would result in major measurement errors and data distortions. Tach elaborated with evidence regarding new patterns of non-marital cohabitation, multiparous fertility, fluid unions, blended families, and other familial structures.
Non-marital cohabitation and fertility have increased markedly in the United States in the past 50 years. In 1960, about 5 percent of children were born outside of marriage. In 2009, about 40 percent of children were born outside of marriage. The massive overall increase masks another very important feature of this particular shift. While some other changes in family structure are more evenly distributed across the socioeconomic spectrum, non-marital fertility is sharply stratified by socioeconomic status. Almost all the increase in non-marital fertility is in the lower part of the socioeconomic distribution.
Tach immediately clarified that non-marital fertility and non-marital relationships are not the same thing as single parenting or parenting outside the context of romantic relationships. Rather, many parents, though unmarried, are nonetheless cohabiting. Tach cited findings of the Fragile Families and Child Wellbeing Study, which follows a birth cohort of children born in urban areas in the late 1990s. That study found that while a substantial number of children were born outside of marriage, more than half of them were born to parents who were cohabiting. Children and both parents were living together all or most of the time. Further, another third of children were born to parents who, although not married or living together, were nonetheless in ongoing romantic relationships with each other. Thus, Tach explained, only one in five children born outside of marriage could be considered children of a “single” mother—that is, someone who is parenting alone, outside of the context of a romantic relationship. The other four of the five non-marital children were born to unmarried parents who were in romantic relationships.
While these findings are from an urban birth cohort in the late 1990s, Tach confirmed that they are similar to nationally representative statistics from the National Survey of Family Growth and other surveys that indicate that about half of non-marital births are occurring to cohabiting
couples. Indeed, the rise in births to cohabiting couples accounts for almost all of the increase in non-marital childbearing that has happened in recent decades. National-level estimates from the 1990s also indicated that 40 percent of all children will live in a cohabiting household by age 16. This figure includes births to cohabiting couples, as well as children of divorced and never-married parents who enter cohabiting relationships. These figures, Tach noted, could well be higher now.
Fluidity is a significant feature of cohabiting relationships. According to data from the Fragile Families and Child Wellbeing Study, of all the non-marital cohabiting relationships that resulted in the birth of a child, 40 percent of those relationships have ended by the child’s first birthday, and 61 percent have ended by the child’s fifth birthday. By comparison, the same dataset indicates that 20 percent of formal marital unions have ended by the child’s fifth birthday. As Tach encapsulated these findings, “There is a lot of change going on pretty rapidly.”
Nationally representative data again mirror this evidence. By the time children reach age 15, 75 percent of children born to cohabiting parents experience the dissolution of their parents’ relationship while one-third of children born to married parents experience their parents’ divorce. Dissolution of the parents’ relationship, Tach observed, “is clearly a common experience for many groups and it is a modal experience for certain subgroups of the population as well.”
Family structures are changing in other ways. Serial partnerships have become more common. After the dissolution of a cohabiting relationship or a divorce, parents will quickly repartner. Children often stay with mothers when relationships or unions come to an end. New partners then enter the household—either a formally married stepfather or what is called a “social father” in a non-marital relationship with the mother. Data from the Fragile Families and Child Wellbeing Study indicate that among unmarried parents who end their relationship, 70 percent are involved in at least one new relationship by the child’s fifth birthday. About onethird of parents are becoming involved in multiple new relationships, as each new relationship is also unstable. More than half of these new relationships involve cohabitation. Thus, Tach reflected, by the child’s fifth birthday, the child experiences multiple adults moving in and out of the household.
National samples confirm these trends. Reviewing the nationally representative data, Tach noted that more than half of divorced women remarry within 5 years, and three-quarters do so within 10 years. As a result, 30 percent of all children spend time in a marital or non-marital step-family by age 18. Repartnering is thus not just an issue in the lower socioeconomic strata or non-marital relationships. Repartnering occurs relatively quickly and at high rates across different types of relationships.
Complexity of family structure has yet further aspects. New partners may bring their children from prior relationships into the household or family system as step-siblings. Remarriages and new non-marital partnerships may also produce new children who are half-siblings of the child of a previous union. In data from the Fragile Families and Child Wellbeing Study, 60 percent of children have a half-sibling (that is, another child to whom they are related by one parent but not both parents) by the time they reach age 5. A quarter have three or more half-siblings, either living in the same household or elsewhere. Tach suggested, “You can think of this as the modern day Brady Bunch. But while the Brady Bunch was a rare novelty, this experience is actually the modal experience for these fragile families.”
Tach reviewed nationally representative data on the same topic. Data from the National Longitudinal Surveys indicate that by midlife, 20 percent of women have had children with more than one partner. Another 2011 survey of a nationally representative sample found that 30 percent of adults reported having a step- or half-sibling. “Clearly,” Tach observed, “this is no longer a minor measurement issue, but something that is affecting a large portion of the population.”
Tach also briefly reviewed some other diverse family forms. The number of three-generational and custodial-grandparent households is increasing. The prevalence at a point in time (not over the course of childhood) of children living with their grandparents is as high if not higher than the prevalence of children at a point in time living in a cohabiting household. The number of children with gay and lesbian parents has also increased, although estimates vary from 500,000 to 2 million children.
Implications for Measuring Families
All of these various changes in family structure have profound implications for the study of social mobility. Despite myriad and shifting residential and biological ties, families remain a key unit of socialization and the locus of intergenerational transfers of resources. Tach suggested that the instability and complexity of contemporary families may require new ways to identify family members, measure their class positions, and study how they transmit resources to the next generation.
Tach reviewed the many ways that standard questions in earlier surveys might miss or misidentify members of contemporary families. For example, regarding cohabiting unmarried parents, Tach observed, “If you think about how these couples would be captured in our traditional mobility studies, they would either be excluded if the analysis was based on the marital status of the parents, or they would be pulled together with married parents if it was based on a child’s biological relationship.” An
accurate picture of mobility, however, will require distinguishing between married parents, cohabiting parents, single parents, and step-parents.
Tach immediately acknowledged that this “is a very complicated task”—both because some of the relevant constructs are difficult to measure and because of the noted fluidity of many households. As an example, Tach contrasted the challenges of identifying step-children and halfsiblings. Step-children may be identified by asking about step-parents. Half-siblings, however, are far more difficult to identify in household surveys because they are living in the same household with both of their biological parents and therefore appear like children in stable intact two-parent families. For a new social mobility survey, Tach asserted, “Thinking about how to measure and identify these half-siblings is really important.”
More nuanced questions are also necessary for gathering accurate data on gay and lesbian households. Questions about same-sex couples will yield data for families where both parents are living in the same household as the child, but miss gay and lesbian single parents. Questions will also need to identify bisexual and transgender parents. This “may be a small fraction of the population if you are asking children about their parents, but it is going to be a much larger fraction of the population if you are looking at a reference generation, often called Generation 0 or G0, and asking questions about their children. It is going to only grow in magnitude obviously given the social changes under way.”
For the study of social mobility, new survey questions are also crucial for identifying the correct class positions of families of origin and destination and considering how families—in whatever form—transmit class-specific resources to the next generation. As Tach traced new complex structures in families, she also considered some of the complex ways economic and cultural resources flow through or away from households.
Tach shared findings from the few mobility studies that have differentiated between resident and non-resident parents. Key among these is that intergenerational correlations between children and non-resident parents are greater than zero, “but they are also weaker than they are for the resident parents and their children.” As intergenerational associations between children and non-resident parents are greater than zero, then failure to include non-resident or unmarried parents in measures of social class position will bias both individual- and family-based measures of social mobility. Further, the finding presents a core puzzle of identifying “the underlying mobility process that is generating these weaker correlations. Can we get any more nuanced beyond that out of a new round of survey or data collection?”
If intergenerational transmission of resources is contingent on parentchild contact and interaction, then new family structures could have many
ramifications for social mobility. Tach noted that the investments cohabiting parents make in their children and households may differ from those made by married parents in ways that matter for mobility. For example, cohabiting fathers may provide less instrumental and social support to mothers, exercise weaker parenting control, or engage less in income pooling. Data indicate that teens report less attachment to cohabiting biological parents than to married biological parents.
To explore these issues in a new study of social mobility, Tach suggested several kinds of information to gather. One is the length of time that children have been living apart from their non-resident parent, as well as the timing of the separation. If a child is apart from a parent from early childhood onward, or if the separation does not occur until the child’s teen years, does this have an impact on the strength of intergenerational transfers and correlations?
Another relevant line of inquiry is the nature and extent of involvement between the child and non-resident parent. Tach noted the “great heterogeneity in the intensity and type of contact and involvement that non-resident parents have with their children.” In pursuing this, Tach surmised, “Of course, I think adults will not be able to retrospectively recount their father’s economic and child support payments or things like that, but they will probably be able to answer questions about how often they saw their non-resident parents at a particular point in time.”
Direct studies of intergenerational correlations between children and step-parents are lacking, but as with cohabiting unmarried parents, it is possible that those correlations might be weaker or different in some way for step-parents compared to married biological parents in intact families. Tach referred to data indicating that investments of resources, time, and money are very different for step-parents than they are for biological parents. For the study of social mobility, she explained, it would be imperative to have information on how long a child spent in a step-family and also on the level of investment or intensity of involvement of step-parents.
For blended families, Tach raised similar questions. Children in blended families may have access to different economic and cultural resources than their half- and step-siblings, despite living in the same household, because of their different biological, step-, and non-resident parents. Resources may flow across households differently, as parents in blended families may be sending resources to ex-partners and children in other households. The quality of parenting of biological children in blended families may also be affected by parents’ relations with previous partners, non-resident children, or step-children.
Tach provided a summary of what she termed as essential tasks for a new survey that could properly measure contemporary families and
fully consider the intergenerational correlations that are key to the study of social mobility:
• Cohabiting parents: identify presence and class position; distinguish from married parents and single parents.
• Non-resident parents: identify presence and class position; determine duration of non-residence; indicate level of involvement with children.
• Step-parents; identify presence and class position; determine duration of step-family; indicate level of involvement with children.
• Blended families: identify based on half-siblings or parent’s other children.
• Other families: identify presence and class position of grandparents; sex of partners/parents and perhaps sexual orientation.
In concluding, Tach drew attention to a valuable resource. The National Center for Family Marriage Research has created question crosswalks for all of the large national household surveys. They have compiled information on how each survey measures cohabitation, marital status, household rosters, union instability, and other indicators, and generated crosswalks between them. Tach affirmed that this “is a really good resource for getting existing survey measures.” Nonetheless, “none of these surveys do a very good job about asking about these issues for those respondents’ parents.”
Robert Mare of the University of California, Los Angeles, explored some related themes in his presentation, “Measuring Social Networks Beyond the Immediate Family.” Mare chose first to highlight a point that he noted he has been making for several years in this context. In Mare’s view, any inquiry into social mobility should approach “the relevant family forms as a subject of research itself rather than assuming always that we know whose characteristics it is that we should be correlating or associating.” Thus, rather than a plea to include specific kin forms in the investigation of social mobility, Mare made a plea that “we scratch our heads as we go into any particular study,” and consider that the unit of analysis in mobility studies may be created by processes related to mobility itself. Mare acknowledged, “I am just trying to be a little disquieting about focusing narrowly.”
Mare then directed attention to several aspects of multigenerational families and the role of grandparents in intergenerational transfers. Mare criticized the “customary two-generation scope,” suggesting it may be a
legacy of mobility studies in the 20th century but is inappropriate to current contexts. With increased longevity, grandparents are living longer and may remain relevant actors in the intergenerational transmission of resources. Mare noted that an increasing proportion of children at age 10 have all their grandparents alive, and these grandparents are “a potential supply of help in raising children.”
The overall picture of longevity, however, provides no information on the social stratification of grandparents. As Mare mused, “Some of us have grandparents with a lot of resources that can help us. Some of us have a lot of grandparents, and well—they are nice, but they cannot really do much for us.” If grandparents remain relevant to the transmission of resources, and if they themselves command very different resources, then this has implications for patterns of social mobility.
Mare also encouraged a perspective on families “as entities that exist over more than two generations,” and further attention to how advantages and disadvantages accumulate across multiple generations. He considered the possibility of deep and enduring legacies that remote ancestors may impart. As examples, Mare speculated on the impact of having a remote ancestor who went to Yale, or won a lottery, or was sold into slavery, or made a decision to immigrate, or failed to make that decision. Any of these, Mare suggested, would have “profound multigenerational consequences,” well beyond the immediate next generation. “Remote ancestral privilege”—or hardship—cannot be investigated in a mobility survey, yet Mare emphasized that “just identifying kin, measuring their characteristics and putting those in a regression equation does not fully capture what I mean by multigenerational effect.”
Demography is a further concern of Mare. Although social mobility depends on the intergenerational transmission of advantages and disadvantages, it also depends on basic demographic reproduction. Mare said he is curious about the connections between mobility and demographic effects. These may include differential fertility across socioeconomic strata, patterns of childlessness over time, trends in “whether, when, and whom we marry and where we live as a result of migration,” and a variety of other demographic trends.
Mare’s perspectives lead him to several considerations regarding the study of social mobility. One is the importance of gathering information on grandparents, if only to enable better interpretation of data. As Mare noted, “When we try to assess the effects of one kin member’s characteristics on another or when we try to study transfers or exchanges, the problematic thing is who is not in the data that are complicating our interpretations of the relationships that we have observed involving the people who are in the data.”
Another issue for Mare is the potential value of developing mobility
models that address more than two generations, to facilitate capturing enduring legacies. Perhaps, Mare suggested, the combined associations across three generations will prove stronger than two-generation associations, helping to explain the persistence of social inequality.
Education has also been changing in many ways over the past half century, including the types of institutions that provide it, the proliferating pathways that people follow to acquire it, and its value and impact on social mobility. These were some of the issues addressed by Chandra Muller of the Department of Sociology at the University of Texas at Austin, in her presentation, “Measuring Education, Skill, and Personality.”
Overall, increasing heterogeneity in educational experiences requires nuanced measurement of education as both an outcome of and an ingredient to social mobility. Types of educational institutions are increasingly varied. They include a plethora of licensure and certification programs, adult educational programs in correctional facilities, online courses and degree-granting programs, as well as charter schools and home schooling. Postsecondary institutions have diversified, ranging, she explained, from “a small liberal arts college or research university versus a community college that focuses in certain kinds of vocational areas.”
Conventional schools, still the most common setting for educational activities, are themselves vastly different in terms of quality, curricula, resources, cost, populations served, faculty and other professional staff, student achievement, and organization and policies. Significant disparities may exist within any school, offering “very different opportunities to learn.” Muller noted the reintroduction of student tracking. In course sequences that unfold over time, students are set on a path toward college or toward less skilled occupations. As Muller described it, “Once you are on a trajectory, it is not difficult to move down, but it is difficult to catch up and move up.” Course content and curricula may also vary markedly and warrant measurement, as “differences in curriculum exposure can be something that lasts over the lifetime and are clearly linked to future earnings and future well-being more generally.” Data on actual curricular exposure can be relatively difficult or costly to obtain, but, Muller noted, “if you want to understand the intergenerational transmission of advantages, then understanding the kinds of courses kids get into could be a very important mechanism.”
Another issue in education that compelled Muller’s attention is the
rise of non-normative pathways of educational attainment. These have implications both for the extent of students’ educational attainment and for the feasibility of measuring that attainment. Muller highlighted the increasing number of students enrolled in postsecondary education, yet the lack of a concomitant increase in the number actually completing degrees. Individuals who tend not to graduate also tend to exhibit what she termed “funky enrollment patterns, unstable enrollment patterns, part-time to full-time, backward movement from four-year to two-year enrollments. Enrollment patterns could be something that is quite telling about potential mechanisms and also will give you some information about parents supporting kids.” Disrupted or unstable pathways of educational attainment could indicate “risk factors or problems, sometimes with paying for college, maybe other factors.”
Other characteristics of students who enroll in postsecondary institutions but do not complete a degree are that they are disproportionately non-white and from lower socioeconomic strata. Conventional indicators of educational attainment, such as degree completion, will not satisfactorily convey their educational experiences in ways that are useful for the study of social mobility.
Available Data Sources
Muller identified many possible sources of useful data on educational experiences over the life course. High school and postsecondary transcripts are valuable, often detailed sources of information about courses enrolled in, credits attempted, credits earned, courses completed or withdrawn from, and grades earned. Transcripts tend to be fairly accurate, are held indefinitely, and are rich with information not only about an individual student’s academic experience, but also about school context.
Among other data sources Muller discussed are the U.S. Department of Education’s National Center for Education Statistics and Office for Civil Rights, which also have data collections useful for considering school contexts and quality. The American Council on Education keeps records on general educational development tests, although it is important to bear in mind that different states have different thresholds for a passing score. The National Student Clearinghouse tracks degrees earned at a large portion of colleges and universities. Muller has not found a comparable central data source addressing the certificates and professional licenses that people obtain by passing professional tests or attending specialized school and training programs. Such information, she noted, “is fragmented across the different licensees, sometimes in states, sometimes in local areas, sometimes nationally, but for a specific occupation. That is problematic if you are trying to be comprehensive.” A systematic
collection of data is also lacking for instruction that occurs in correctional facilities. The U.S. Department of Education reports that about one-third of adult education is occurring in correctional facilities, but “there is essentially no data collected on that. They really do not know how to do that,” according to Muller. All of these sources are complemented by selfreports and administrative data.
In concluding, Muller underscored the “increasing variability in educational experiences, particularly at the postsecondary level.” That heterogeneity requires insightful measurement, particularly for revealing experiences, patterns, and trends relevant to the study of social mobility. Muller also affirmed that “it is really worth thinking about using the vast data that we have in hand to start thinking about measuring some of the nuanced differences beyond degrees of attainment and years of schooling.”
Significant changes in labor markets are highly relevant to the study of social mobility and raise substantive issues about measurement including not only how but also when measurements are made across the life course. Bhash Mazumder of the Federal Reserve Bank of Chicago addressed these issues in his presentation, “Implications of Labor Market Complexities on Measuring Social Mobility.”
Labor Market Trends and Measurement Challenges
Mazumder began by identifying some fundamental changes in the economy and labor markets that could affect measures of social mobility. These include technological change, globalization, and outsourcing. All of these have exacerbated instability in employment, occupation, and earnings, resulting in “greater turbulence over the life course.” In an ever-changing economic environment, Mazumder suggested, standard measures of labor market involvement may not be adequate for “credible research on intergenerational mobility.”
Trends of concern to those interested in studying social mobility include income mobility and occupational mobility. Advances have been made in measuring intergenerational income mobility. While earlier studies used a single year of income for each generation, more recent studies average several years of income to better capture lifetime status or “permanent income.” Measuring income at several points in the life cycle can minimize bias. For example, adult children who will eventually have higher permanent incomes may have steeper earnings profiles and systematically lower income when younger. These issues are not resolved by
simple age controls. Longer time averages of parents’ income and adult children’s income have suggested stronger correlations across generations. Mazumder noted that consumption patterns might provide a better indicator of status than income; however, data on income are more readily available.
Occupational status is another labor market outcome that may be an indicator of intergenerational mobility. As with measurements of income, the timing and frequency of measurements of occupational status is significant. Some individuals will have many occupations across a career, and there may not be an optimal age for sampling, given the more volatile labor market. Mazumder noted evidence of the rise in occupation switching, as data show a substantial increase in the rate at which workers change occupations when they switch jobs. Mazumder also discussed the so-called polarization of jobs. In this pronounced labor market trend, occupations involving routinized skills that are relatively easy to replace through technology or outsourcing have declined substantially over time at an accelerating pace. These jobs tend to be in the middle of the occupational income distribution.
In addition to occupation switching and job polarization, Mazumder highlighted mass incarceration as a third significant trend affecting labor markets. Incarceration rates, particularly of African American men, have reached such levels as to influence measures of mobility based solely on those who are active in the labor market. In 2008, for example, more than a third of African American males lacking a high school diploma and between the ages of 20 and 34 were incarcerated, compared to just more than 10 percent in 1980. For this same group, their rates of incarceration were higher than their rate of employment. This situation underscores possible pitfalls of focusing on occupation measures as indicators of mobility for subgroups of whom many individuals will not be employed.
A fourth labor market trend that may have implications for measuring mobility is the declining labor force participation of younger workers as they delay labor market entry. Employment for high school–age youth has fallen nearly 20 percentage points over the last 25 years. Some of this decline can be attributed to rising education. The polarization of the adult labor market is also a factor, causing more adults to take jobs previously held by younger workers. This pattern may be particularly significant for research using first jobs as a measure. It underscores the importance of creating measures of intergenerational mobility with respect to joblessness.
Data Sources and Solutions
For meeting these various measurement challenges, Mazumder suggested several possible solutions, including administrative data, retro-
spective data, and new statistical methods. Administrative data are essential for measuring income mobility, particularly for measuring income over long enough time spans and over key portions of the life cycle. Data from the Internal Revenue Service and the Social Security Administration (SSA) might suffice for income mobility measures, although statistical adjustments would be necessary for real-time analysis. Administrative data might not suffice for studying occupational mobility, although records from unemployment insurance matched with data from firms could be helpful.
Retrospective data from asking more detailed retrospective questions to develop thorough labor market histories could be another important source in the study of mobility. Interviewers might use resumes or prior tax returns to help facilitate respondents’ recollections. Questions might address occupation upon initial labor market entry, at age 40, and at retirement.
New statistical methods will also advance the study of social mobility. Analogues for the statistical methods used for income mobility adjustments could potentially be applied to measures of occupation. Creative strategies—for example, the use of surnames as a grouping estimator to infer intergenerational persistence—have enabled researchers to gather more information from datasets. Detailed identifiers (such as exact names, date and location of birth, Social Security number) preserve the possibility of future linkages and creative approaches, so long as confidentiality can be ensured. Matched datasets have also been crucial to mobility research. Mazumder offered the example of Sweden, where linkages among population registers, health registers, crime registers, military registers, and earnings registers have facilitated the study of mobility. For the United States, Mazumder has used linkages between Survey of Income and Program Participation (SIPP) and SSA earnings data, both to estimate intergenerational earnings elasticities and to correct for problems with administrative data.
The historically important role of immigration in U.S. demographic growth has increased substantially in recent years. Immigrant flows have intensified and changed, with a larger share of new immigrants coming from developing countries and arriving with very low levels of schooling, English proficiency, and other skills. The U.S. context for those who are immigrants has shifted as well, including a labor market characterized by steep earnings inequality, with greater rewards to the education and skills that most immigrants lack. All these changes are subsequent to the last major survey of social mobility and require study.
Stephen J. Trejo of the Department of Economics at the University
of Texas at Austin explored many issues of immigration in his presentation, “Assessing the Socioeconomic Mobility and Integration of U.S. Immigrants and Their Descendants.” For the most part, Trejo observed, the same data and methods of analysis that are useful for studying social mobility in the native population are also useful for studying mobility in immigrants. Nonetheless, measuring mobility among the immigrant population and their descendants presents some unique challenges.
Intragenerational Mobility: First Generation
Trejo first addressed the social mobility of the immigrants themselves. This includes tracing their assimilation and integration in the United States and comparing their outcomes with their own, siblings’, or peers’ experience in the country of origin. Relevant outcomes include income, earnings, employment, occupational attainment, education, and language proficiency. Suitable timeframes for measuring include just after the immigrant arrives and then at intervals over the course of the post-arrival life.
Regarding intragenerational mobility, Trejo declared, “We know a lot about this. We have actually pretty good data.” Data sources well-suited for studying the U.S. experiences of immigrants include the Current Population Survey (CPS), American Community Survey (ACS), and SIPP. These surveys provide information on immigrants’ country of origin, time of arrival in the United States, and, to some extent via either synthetic cohorts or longitudinal data, ultimate outcomes. These datasets can also be matched with administrative data, such as SSA earnings records, to examine immigrant integration. Trejo noted that more information would be helpful, particularly further detail about initial and ongoing immigration status, refugee status, and legal status. This is especially so because much immigration to the United States is undocumented, and legal status may impact assimilation and integration. Thus, while much is known about immigrants’ intragenerational mobility, Trejo concluded that “there are ways we could improve on the data we currently collect in the CPS or the ACS.”
Intergenerational Mobility: The Second Generation
Trejo then turned to issues of intergenerational mobility, from the first to the second generation, or from immigrants themselves to their children. Historically, much of the mobility achieved by immigrant families has occurred across rather than within generations. Earlier waves of unskilled migrants in the late 19th century and early 20th century enjoyed substantial progress, enabling their descendants to join the economic mainstream within two or three generations. Trejo noted the considerable skepticism
regarding whether this pattern of assimilation and adaptation will operate similarly for more recent immigrants and their descendants.
Trejo first paused to comment on the relative difficulty of using the term “generation” for this population, as tidy distinctions can be blurred by interethnic and cross-generational unions as well as selective attrition of ethnic self-identification. As an example of the difficulty, Trejo posed a hypothetical marriage between a Mexican and a Salvadoran. Is their child a second-generation Mexican or a second-generation Salvadoran? Similarly, what if a new Mexican immigrant marries a second-generation Mexican? Selective ethnic attrition complicates the picture further, as selfidentification is subjective and the “ethnic leakage” differs across different subgroups.
Trejo observed that, given these challenges to even identifying the second generation, much less measuring their mobility; it would be useful to have better data. He recommended the increased collection of information that will allow more precise identification of the descendants of immigrants, such as the countries of birth not only of the respondent, but also of that individual’s parents and grandparents.
Trejo then focused on whether the second and even third generations are catching up with the native population. According to Trejo, “It is hard to say with the data that we have now. It is hard to measure that precisely.” He turned to available data on educational attainment, as educational attainment is a key determinant of economic success, health, and life opportunities.
Those data indicate that by the second generation, most contemporary immigrant groups meet or exceed the U.S. average educational attainment. As Trejo described the pattern, “They have caught up.” The notable exception to this pattern is several Hispanic groups: Central Americans (although not Cubans or South Americans), Dominicans, Mexicans, and Puerto Ricans. What are the sources of the pattern? Trejo noted that firstgeneration immigrants from these countries tend to have particularly low levels of education, so “it is not surprising that their kids have not completely caught up by the second generation.” Perhaps these groups will catch up in the third generation—but studying that would require data on the third generation, which involves other challenges.
Third Generation: Selective Ethnic Attrition
Identifying third-generation immigrants generally involves studying people born in the United States, with both parents born in the United States but who self-identify as being Asian, Hispanic, or whatever subgroup is being examined. In this endeavor, however, Trejos noted, “It turns out we are missing people. And if we are missing people in large
numbers and if the people we are missing have different education levels or different outcomes than the people we are not missing, then that causes problems.” Trejo suggested that this problem of selective and distorting gaps in data on third-generation immigrants is much like the selective and distorting gaps in data on African Americans, due to mass incarceration.
Trejo explored further the problems that arise from depending on subjective responses regarding ethnic identity. He shared data that Hispanic self-identification drops significantly from the first- to third-generation immigrants. Without the microdata to assess the ancestry of respondents, it is difficult to determine precisely how much ethnic attrition has occurred, or whether it is selective. Trejo noted that intermarriage is probably a fundamental source of ethnic attrition. For example, individuals with Hispanic ancestry from both parents are almost assured of identifying as Hispanic, while among individuals with Hispanic ancestry from just one parent, only 20 percent identify as Hispanic.
The selectivity of ethnic attrition is also important to study. To illustrate, Trejo shared data on educational attainment of second-generation adults by ethnic identification. For people with a parent born in India and who self-identify as Asian, the average educational attainment is an impressive 16.7 years of schooling. For people with a parent born in India who do not self-identify as Asian, the average educational attainment is a far lower 15.2 years of schooling. For studying mobility, Trejo affirmed, “That matters. It pulls down the overall average.”
The distortions of selective ethnic attrition are even greater in the third generation and with increasing intermarriage. Interestingly, Trejo noted, “The selection process works differently for Hispanics than for Asians.” Among Hispanics, higher educated and higher earning individuals are apt to intermarry. It is their children who cease to self-identify as Hispanic. Thus, Trejo noted, “It is their kids that are missing. In some sense, we are understating the attainment of the third generation for Hispanics because we are missing some of the kids from the advantaged families.” Among Asians, the pattern appears to be the opposite, as “it is the higher educated families that are able to transmit their ethnicity or their ethnic identification to their kids.” Difficulties in identifying the third generation, particularly in the face of selective ethnic attrition, may generate measurement biases that vary across national origin groups in direction as well as magnitude and distort inferences about mobility of immigrants’ descendants.
For meeting these challenges, Trejo suggested gathering more information to objectively identify the third generation by asking about the birth countries of grandparents. Oversampling of Hispanics is also advisable, as this is a particularly important group for understanding the longterm mobility of immigrant populations.
Henry Brady of the Goldman School of Public Policy at the University of California, Berkeley, was lead author on a paper prepared for the workshop that drew heavily on joint work with Kay Schlozman and Sidney Verba. Brady gave the workshop presentation, “Political Reproduction from Generation to Generation,” based on that paper, advocating that intergenerational patterns in political participation and party identification be included in the study of social mobility.
The question raised is whether and how intergenerational transfers of economic resources and social capital help to reproduce and perpetuate unequal patterns of political authority. Brady began by observing that many forms of political participation are stratified by socioeconomic standing. This includes giving campaign donations, attending campaign meetings, and doing campaign work. Offering data on political input from 1990, Brady observed that while the top quintile contributes 70 percent of campaign donations, conducts 30 percent of campaign hours worked, and casts 26 percent of votes, the lowest quintile contributes less than 1 percent of campaign donations, conducts 11 percent of campaign hours worked, and casts 14 percent of votes. “This just proves,” Brady observed, “we have inequality in the political realm.” Furthermore, presenting data back to 1960, Brady affirmed that stratification of political participation has remained persistent.
Brady suggested that political authority “should be measured by party involvement, political participation, civic engagement, and things like that.” He further argued that authority is an important dimension of stratification because socioeconomic rigidities “might persist partly because there are governmental structures in place which fail to ameliorate them. They fail to ameliorate them because political participation is highly stratified and therefore the folks who would benefit from amelioration are not participating in politics. And the folks who benefit from the status quo are participating in politics. That may be part of the problem.”
Brady acknowledged that political authority may be distinctive because it deals with public goods, rather than such private goods as income, occupation, prestige, and education. It nonetheless merits attention within the study of mobility. In Brady’s view, the intergenerational transmission of party identification and levels of political participation has been studied almost exclusively from a cultural perspective. According to Brady, “It is time we put that behind us and look at different ways of thinking about this problem.”
Brady identified two different dimensions worthy of study: content of political identification (designated by identification as Democrat, Independent, or Republican; or liberal or conservative) and intensity of political participation (measured by hours worked in a campaign, amount of
money donated, and frequency of voting). Both the direction of party identification and the intensity of political participation are relevant to Brady, as “it turns out it is the interaction of those two things that really have an impact on politics.”
For measuring intensity of political participation and civic engagement, Brady proposed four areas of inquiry: attitudes toward parties, the exercise of political voice via various acts, engagement in civil society, and possessing political social networks—that is, as Brady described it, “having the ability to influence politics because you have a social network that will get you to somebody important.”
Brady shared questions that have been used in surveys conducted by the American National Election Studies and in CPS supplements addressing these areas of inquiry. Questions regarding the exercise of political voice addressed voting, campaign work and contributions, serving on local boards, and protesting or demonstrating. Questions regarding civic engagement concerned volunteer activities and participation in organizations, such as school and community groups, neighborhood watch groups, civic organizations (such as American Legion or Lions Club), sports clubs, or religious organizations. Questions regarding political social networks inquired about personal acquaintance with various office holders and media actors.
Brady then noted findings that party identification remains highly stable across a lifetime, as do levels of political participation. Significant evidence also exists for intergenerational transmission of party identification and levels of political participation, although the exact size of the correlation and the degree to which it is mediated by socioeconomic status is unclear. For studying the intergenerational transmission of political identification and levels of participation, and how that transmission might strengthen patterns of unequal political authority, Brady said he would like further data on parents’ political identification and participation, as well as family and peer influences.