Robert D. Mare

Social Background and School Continuation Decisions

Abstract Logistic response models of the effects of parental socioeconomic characteristics and family structure on the probability of making selected school transitions for white American males are estimated by maximum likelihood using the 1973 Occupational Changes in a Generation Survey data. As a consequence of differential attrition patterns, parental socioeconomic effects decline sharply from the earliest school transitions to the latest. Estimated effects of parental income on grade progression decline by more than 50 percent between elementary school and college.

TRENDS IN EDUCATIONAL ASSORTATIVE MARRIAGE FROM 1940 TO 2003

This paper reports trends in educational assortative marriage from 1940 to 2003 in the United States. Analyses of census and Current Population Survey data show that educational homogamy decreased from 1940 to 1960 but increased from 1960 to 2003. From 1960 to the early 1970s, increases in educational homogamy were generated by decreasing intermarriage among groups of relatively well-educated persons. College graduates, in particular, were increasingly likely to marry each other rather than those with less education. Beginning in the early 1970s, however, continued increases in the odds of educational homogamy were generated by decreases in intermarriage at both ends of the education distribution. Most striking is the decline in the odds that those with very low levels of education marry up. Intermarriage between college graduates and those with “some college” continued to decline but at a more gradual pace. As intermarriage declined at the extremes of the education distribution, intermarriage among those in the middle portion of the distribution increased. These trends, which are similar for a broad cross section of married couples and for newlyweds, are consistent with a growing social divide between those with very low levels of education and those with more education in the United States.

Change and Stability in Educational Stratification.

The effects of parental socioeconomic characteristics on highest grade offormal school completed are stable over cohorts born during the first half of the twentieth century. Mathematical analysis and empirical findings based on the 1973 Occupational Changes in a Generation Survey show that linear models of the educational attainment process are stable over cohorts because their coefficients depend upon quantities which vary over time in offsetting directions. The coefficients are weighted sums of the associations between socioeconomic background and school continuation decisions where the weights are functions of the school continuation probabilities. Intercohort increases in school continuation rates by themselves imply declining background effects on educational attainment, but, over cohorts, the associations between background and continuation increase to offset the dampening effect of the changing marginal distribution of schooling. Stable linear model effects are the result.

Regression models with ordinal variables.

Most discussions of ordinal variables in the sociological literature debate the suitability of linear regression and structural equation methods when some variables are ordinal. Largely ignored in these discussions are methods for ordinal variables that are natural extensions of probit and logit models for dichotomous variables. If ordinal variables are discrete realizations of unmeasured continuous variables, these methods allow one to include ordinal dependent and independent variables into structural equation models in a way that (I) explicitly recognizes their ordinality, (2) avoids arbitrary assumptions about their scale, and (3) allows for analysis of continuous, dichotomous, and ordinal variables within a common statistical framework. These models rely on assumed probability distributions of the continuous variables that underly the observed ordinal variables, but these assumptions are testable. The models can be estimated using a number of commonly used statistical programs. As is illustrated by an empirical example, ordered probit and logit models, like their dichotomous counterparts, take account of the ceiling andfloor restrictions on models that include ordinal variables, whereas the linear regression model does not. Empirical social research has benefited during the past two decades from the application of structural equation models for statistical analysis and causal interpretation of multivariate relationships (e.g., Goldberger and Duncan, 1973; Bielby and Hauser, 1977). Structural equation methods have mainly been applied to problems in which variables are measured on a continuous scale, a reflection of the availability of the theories of multivariate analysis and general linear models for continuous variables. A recurring methodological issue has been how to treat variables measured on an ordinal scale when multiple regression and structural equation methods would otherwise be appropriate tools. Many articles have appeared in this journal (e.g., Bollen and Barb,

Secondary School Tracking and Educational Inequality: Compensation, Reinforcement, or Neutrality?

This article examines the effects of academic tracking in secondary schools on educational stratification and considers how that tracking may affect levels and dispersions of academic achievement and high school graduation rates among social groups. Data from the High School and Beyond survey of students who were sophomores in 1980 show that placement in the college track substantially benefits growth in mathematics achievement and the probability of high school graduation, even when measured and unmeasured sources of nonrandom assignment to tracks are taken into account. Track assignment reinforces preexisting inequalities in achievement among students from different socioeconomic backgrounds. However, track assignment and differential achievement in tracks partially compensate blacks and girls for their initial disadvantages and makes racial and sexual inequalities smaller than they may have otherwise been. The article provides qualified support for the view that students are assigned to the tracks that provide the greatest reward to their measured background characteristics.

NEIGHBORHOOD CHOICE AND NEIGHBORHOOD CHANGE

This article examines the relationships between the residential choices of individuals and aggregate segregation patterns. Analyses based on computational models show that high levels of segregation occur only when individuals’ preferences follow a threshold function. If individuals make finer‐grained distinctions among neighborhoods that vary in racial composition, preferences alone do not lead to segregation. Vignette data indicate that individuals respond in a continuous way to variations in the racial makeup of neighborhoods rather than to a threshold. Race preferences alone may be insufficient to account for the high levels of segregation observed in American cities.

A Multigenerational View of Inequality

The study of intergenerational mobility and most population research are governed by a two-generation (parent-to-offspring) view of intergenerational influence, to the neglect of the effects of grandparents and other ancestors and nonresident contemporary kin. While appropriate for some populations in some periods, this perspective may omit important sources of intergenerational continuity of family-based social inequality. Social institutions, which transcend individual lives, help support multigenerational influence, particularly at the extreme top and bottom of the social hierarchy, but to some extent in the middle as well. Multigenerational influence also works through demographic processes because families influence subsequent generations through differential fertility and survival, migration, and marriage patterns, as well as through direct transmission of socioeconomic rewards, statuses, and positions. Future research should attend more closely to multigenerational effects; to the tandem nature of demographic and socioeconomic reproduction; and to data, measures, and models that transcend coresident nuclear families.

Structural Equations and Path Analysis for Discrete Data

This article proposes a solution to the long-standing methodological problem of incorporating discrete variables inoto causal models of social phenomena. Only a subset of the variety of ways in which discrete data arise in empirical social research can be satisfactorily modeled by conventional log-linear or logit approaches. Drawing on the insights of several literatures, this article exposits a general approach to causal models in which some or all variables are discretely measured and shows that path analytic methods are available which permit quantification of causal relationships among variables with the same flexibility and power of interpretation as is feasible in models that include only continous variables. It presents methods of identifying and estimating these models and shows how the direct and indirect effects of independent varibles can be calculated by extensions of usual path analysis methods for continuos variables An important distincion developed here is that discrete variables can play two roles: (1) as measures of inherently discrete phenomena and (2) as indicators of underlying continous variables. The value of this distinction is shown in two empirical examples examined previously by other authors. In examining the effects of social background and parental enouragement on college plans of high school seniors, the article shows that modeling a discrete measure of encouragement as an indicator of a latent continous variable rather than as an inherently discrete variable (as has been done in previous analyses) provides a clearer interpretation and a superior fit to the data. In examining the effects of state Fair-Employment-Practices Legislation on black-white wage differentials, this study shows that two distinct effects on the relative wage can be detected: the direct ameliorative effect of the law itself and the effects of the popular progressive sentiment for racial equality of which the law is an indicator. The methods and models presented here are not only natural generalizations of structural equation and path analysis methods for continuous varibles to include discrete variables but also provide a means of investigating a richer variety of substantive hypotheses than is feasible with methods for discrete data commonly used in the sociological literature to date.

Employment During High School: Consequences for Students' Grades in Academic Courses:

High school students who work intensively at paid jobs tend to have lower grades in academic courses. Prior research has not properly tested theories about the source of the relationship between student employment and grades (or other outcomes), and has not explicitly modeled the potentially reciprocal nature of this relationship. We focus on both the short- and long-term effects of adolescent employment on grades in academic courses and simultaneously consider the extent to which grades may influence employment behaviors. We find no evidence that high school employment has either short-or long-term effects on grades in academic courses or that grades in these courses influence employment activities. Pre-existing differences between more and less intensively employed students fully account for the association between employment intensity and grades in academic courses.

Socioeconomic effects on child mortality in the United States.

Despite considerable reason for scholarly and policy interest in socioeconomic mortality differentials, socioeconomic effects on child and teenage mortality in the United States have been a neglected research topic because of several data limitations. Exploiting data obtained for other purposes, this paper reports socioeconomic effects on the mortality of children and teenagers. Socioeconomic mortality differentials among children are large--at least as large as those among adults. The major source of socioeconomic mortality differences among children is apparently differential risk to accidental death. Within the child population, the strength of socioeconomic effects varies directly with the relative importance of accidents as a component of overall mortality.