Jennie E. Brand

Who Benefits Most from College? Evidence for Negative Selection in Heterogeneous Economic Returns to Higher Education

In this article, we consider how the economic return to a college education varies across members of the U.S. population. Based on principles of comparative advantage, scholars commonly presume that positive selection is at work, that is, individuals who are most likely to select into college also benefit most from college. Net of observed economic and noneconomic factors influencing college attendance, we conjecture that individuals who are least likely to obtain a college education benefit the most from college. We call this theory the negative selection hypothesis. To adjudicate between the two hypotheses, we study the effects of completing college on earnings by propensity score strata using an innovative hierarchical linear model with data from the National Longitudinal Survey of Youth 1979 and the Wisconsin Longitudinal Study. For both cohorts, for both men and women, and for every observed stage of the life course, we find evidence suggesting negative selection. Results from auxiliary analyses lend further support to the negative selection hypothesis.

A General Panel Model with Random and Fixed Effects: A Structural Equations Approach

Fixed- and random-effects models for longitudinal data are common in sociology. Their primary advantage is that they control for time-invariant omitted variables. However, analysts face several issues when they employ these models. One is the choice of which to apply; another is that FEM and REM models as usually implemented might be insufficiently flexible. For example, the effects of variables, including the latent time-invariant variable, might change over time. The latent time-invariant variable might correlate with some variables and not others. Lagged endogenous variables might be necessary. Alternatives that move beyond the classic FEM and REM models are known, but they involve estimators and software that make these extended models difficult to implement and to compare. This article presents a general panel model that includes the standard FEM and REM as special cases. In addition, it provides a sequence of nested models that provide a richer range of models that researchers can easily compare with likelihood ratio tests and fit statistics. Furthermore, researchers can implement our general panel model and its special cases in widely available structural equation models software.

Estimating Heterogeneous Treatment Effects with Observational Data

Individuals differ not only in their background characteristics but also in how they respond to a particular treatment, intervention, or stimulation. In particular, treatment effects may vary systematically by the propensity for treatment. In this paper, we discuss a practical approach to studying heterogeneous treatment effects as a function of the treatment propensity, under the same assumption commonly underlying regression analysis: ignorability. We describe one parametric method and two nonparametric methods for estimating interactions between treatment and the propensity for treatment. For the first method, we begin by estimating propensity scores for the probability of treatment given a set of observed covariates for each unit and construct balanced propensity score strata; we then estimate propensity score stratum-specific average treatment effects and evaluate a trend across them. For the second method, we match control units to treated units based on the propensity score and transform the data into treatment-control comparisons at the most elementary level at which such comparisons can be constructed; we then estimate treatment effects as a function of the propensity score by fitting a nonparametric model as a smoothing device. For the third method, we first estimate nonparametric regressions of the outcome variable as a function of the propensity score separately for treated units and for control units and then take the difference between the two nonparametric regressions. We illustrate the application of these methods with an empirical example of the effects of college attendance on women’s fertility.

The Impact of College Education on Fertility: Evidence for Heterogeneous Effects

As college-going among women has increased, more women are going to college from backgrounds that previously would have precluded their attendance and completion. This affords us the opportunity and motivation to look at the effects of college on fertility across a range of social backgrounds and levels of early achievement. Despite a substantial literature on the effects of education on women’s fertility, researchers have not assessed variation in effects by selection into college. With data on U.S. women from the National Longitudinal Survey of Youth 1979, we examine effects of timely college attendance and completion on women’s fertility by the propensity to attend and complete college using multilevel Poisson and discrete-time event-history models. Disaggregating the effects of college by propensity score strata, we find that the fertility-decreasing college effect is concentrated among women from comparatively disadvantaged social backgrounds and low levels of early achievement. The effects of college on fertility attenuate as we observe women from backgrounds that are more predictive of college attendance and completion.

Effects of Layoffs and Plant Closings on Subsequent Depression Among Older Workers

Job displacement is widely considered a negative life event associated with subsequent economic decline and depression, as established by numerous prior studies. However, little is known about whether the form of job displacement (i.e., layoffs vs. plant closings) differentially affects depression. The authors assessed the effects of different ways in which workers are displaced on subsequent depression among U.S. men and women nearing retirement. They hypothesized that layoffs would be associated with larger effects on depression than plant closings, particularly among men. The findings generally support these hypotheses. The authors found that men had significant increases in depression as a result of layoffs, but not as a result of plant closings, whereas the reverse was true among women.

Civic Returns to Higher Education: A Note on Heterogeneous Effects

American educational leaders and philosophers have long valued schooling for its role in preparing the nations youth to be civically engaged citizens. Numerous studies have found a positive relationship between education and subsequent civic participation. However, little is known about possible variation in effects by selection into higher education, a critical omission considering educations expressed role as a key mechanism for integrating disadvantaged individuals into civic life. I disaggregate effects and examine whether civic returns to higher education are largest for disadvantaged low likelihood or advantaged high likelihood college goers. I find evidence for significant effect heterogeneity: civic returns to college are greatest among individuals who have a low likelihood for college completion. Returns decrease as the propensity for college increases.

Job Displacement and Social Participation over the Lifecourse: Findings for a Cohort of Joiners

We examine the effects of job displacement, an involuntary event associated with socioeconomic and psychological decline, on social participation. Using more than 45 years of panel data from the Wisconsin Longitudinal Study, we find that job displacement is associated with significant, long-term lower probabilities of subsequent involvement with various forms of social participation for workers displaced during their prime earnings years; displacement is not associated with lower probabilities of involvement for workers displaced in the years approaching retirement. We also find that post-displacement socioeconomic and psychological decline explain very little of the negative effect of job displacement on social participation, and that a single displacement event, rather than a series of multiple displacement events, is most strongly associated with lower probabilities of social involvement.

Causal Effect Heterogeneity

Individuals differ not only in background characteristics, often called “pretreatment heterogeneity,” but also in how they respond to a particular treatment, event, or intervention. A principal interaction of interest for questions of selection into treatment and causal inference in the social sciences is between the treatment and the propensity of treatment. Although the importance of “treatment-effect heterogeneity,” so defined, has been widely recognized in the causal inference literature, empirical quantitative social science research has not fully absorbed these lessons. In this chapter, we describe key estimation strategies for the study of heterogeneous treatment effects; we discuss recent research that attends to causal effect heterogeneity, with a focus on the study of effects of education, and what we gain from such attention; and we demonstrate the methods with an example of the effects of college on civic participation. The primary goal of this chapter is to encourage researchers to routinely examine treatment-effect heterogeneity with the same rigor they devote to pretreatment heterogeneity.

Job Displacement Among Single Mothers:: Effects on Children’s Outcomes in Young Adulthood*

Given the recent era of economic upheaval, studying the effects of job displacement has seldom been so timely and consequential. Despite a large literature associating displacement with worker well-being, relatively few studies focus on the effects of parental displacement on child well-being, and fewer still focus on implications for children of single-parent households. Moreover, notwithstanding a large literature on the relationship between single motherhood and children’s outcomes, research on intergenerational effects of involuntary employment separations among single mothers is limited. Using 30 years of nationally representative panel data and propensity score matching methods, the authors find significant negative effects of job displacement among single mothers on children’s educational attainment and social-psychological well-being in young adulthood. Effects are concentrated among older children and children whose mothers had a low likelihood of displacement, suggesting an important role for social stigma and relative deprivation in the effects of socioeconomic shocks on child well-being.

IDENTIFICATION AND ESTIMATION OF CAUSAL EFFECTS WITH TIME-VARYING TREATMENTS AND TIME-VARYING OUTCOMES*

We develop an approach to identifying and estimating causal effects in longitudinal settings with time-varying treatments and time-varying outcomes. The classic potential outcome approach to causal inference generally involves two time periods: units of analysis are exposed to one of two possible values of the causal variable, treatment or control, at a given point in time, and values for an outcome are assessed some time subsequent to exposure. In this paper, we develop a potential outcome approach for longitudinal situations in which both exposure to treatment and the effects of treatment are time-varying. In this longitudinal setting, the research interest centers not on only two potential outcomes, but on a whole matrix of potential outcomes, requiring a complicated conceptualization of many potential counterfactuals. Motivated by sociological applications, we develop a simplification scheme—a weighted composite causal effect that allows identification and estimation of effects with a number of possible solutions. Our approach is illustrated via an analysis of the effects of disability on subsequent employment status using panel data from the Wisconsin Longitudinal Study.