Life Course Risks, Mobility Regimes, and Mobility Consequences: A Comparison of Sweden, Germany and the U.S.

Intragenerational mobility has been a central concern in sociology, especially in the latter half of the 20th century. Most of this analysis has proceeded using measures of social position that are functions of an individual's occupation. This approach has been based on two primary justifications. First, occupational mobility is a key attribute of labor market structure, and the labor market, along with the educational system, is the principal institution responsible for a country's structure of inequality. Second, occupation is an income producing asset that provides an approximate measure of "permanent income" and standard of living. Occupation-based models of social mobility, however, have limitations that arguably have grown during the recent past. Meta-analysis of available evidence for Sweden, western Germany, and the United States concerning occupational mobility, household income mobility, job displacement, union dissolution, and poverty dynamics shows the limitations of the individual-level occupation-based careertrajectory approach to life course mobility. An alternative formulation at the household rather than the individual level is developed that focuses on cross-national variation in the extent to which institutions influence the rate of class-altering events, and the extent to which they mitigate the consequences of these events. The combination of these two institutional processes produces the distinctive characteristics of the mobility regimes of these three countries.

Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments

Propensity score matching provides an estimate of the effect of a treatment variable on an outcome variable that is largely free of bias arising from an association between treatment status and observable variables. However, matching methods are not robust against hidden bias arising from unobserved variables that simultaneously affect assignment to treatment and the outcome variable. One strategy for addressing this problem is the Rosenbaum bounds approach, which allows the analyst to determine how strongly an unmeasured confounding variable must affect selection into treatment in order to undermine the conclusions about causal effects from a matching analysis. Instrumental variables (IV) estimation provides an alternative strategy for the estimation of causal effects, but the method typically reduces the precision of the estimate and has an additional source of uncertainty that derives from the untestable nature of the assumptions of the IV approach. A method of assessing this additional uncertainty is proposed so that the total uncertainty of the IV approach can be compared with the Rosenbaum bounds approach to uncertainty using matching methods. Because the approaches rely on different information and different assumptions, they provide complementary information about causal relationships. The approach is illustrated via an analysis of the impact of unemployment insurance on the timing of reemployment, the postunemployment wage, and the probability of relocation, using data from several panels of the Survey of Income and Program Participation (SIPP). -- Propensity score matching ermöglicht die verzerrungsfreie Abschätzung der Kausalwirkung einer treatment-Variable auf eine Ergebnisvariable sofern Verzerrungen allein aus dem Zusammenhang zwischen Kausalfaktor und beobachteten Kovariaten resultieren. Matchingverfahren sind allerdings anfällig für Schätzverzerrungen aufgrund von hidden bias durch unbeobachtete Variablen, die sowohl die Zuweisung des Kausalfaktors als auch die Ergebnisvariable bestimmen. Im letzteren Fall besteht eine mögliche Strategie darin, mit Hilfe der Methode der sogenannten Rosenbaumschranken abzuschätzen, wie stark der Einfluss unbeobachteter Kovariaten auf die Zuweisung des Kausalstatus sein müsste, um die beabsichtigten Schlussfolgerungen im Hinblick auf den interessierenden kausalen Effekt qualitativ zu verändern. Instrumentalvariablenschätzer (IV) wären ein zweites Verfahren, um in dieser Situation kausale Effekte abschätzen zu können, allerdings führt das Verfahren in der Regel zu wenig präzisen Schätzungen und beinhaltet in der Anwendung zusätzliche Unsicherheiten aufgrund der empirisch nicht testbaren Annahmen des IV-Ansatzes. In diesem Aufsatz wird eine Methode zur Abschätzung dieser Unsicherheiten vorgeschlagen, wodurch die potentiellen Verzerrungen innerhalb einer IV-Schätzung mit den durch die Rosenbaumschranken abgeschätzten Verzerrungen innerhalb eines entsprechenden Matchingansatzes verglichen werden können. Da diesen Verfahren jeweils unterschiedliche Informationsgrundlage sowie unterschiedliche Annahmen zugrunde liegen, erbringen sie komplementäre Informationen über den Gehalt kausaler Beziehungen. Wir illustrieren die vorgeschlagene Vorgehensweise anhand einer Analyse des kausalen Effekts der Arbeitslosenversicherung auf die Dauer der Arbeitslosigkeit, den Lohn bei Wiederbeschäftigung sowie der Wahrscheinlichkeit geographischer Mobilität auf der Basis von Daten des amerikanischen Survey of Income and Program Participation (SIPP).

Estimating Causal Effects with Matching Methods in the Presence and Absence of Bias Cancellation

This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naïve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate the estimation of average causal effects in different ways, depending upon the nature of the missing mechanism. While - contrary to published assertions in the literature - bias cancellation does not generally occur when the multivariate distribution of the errors is symmetric, bias cancellation has been observed to occur for the case where selection into training is the treatment variable, and earnings is the outcome variable. A substantive rationale for bias cancellation is offered, which conceptualizes bias cancellation as the result of a mixture process based on two distinct individual-level decision-making models. While the general properties are unknown, the existence of bias cancellation appears to reduce the average bias in both OLS and matching methods relative to the symmetric distribution case. Analysis of simulated data under a set of difference scenarios suggests that matching methods do better than OLS in reducing that portion of bias that comes purely from the error distribution (i.e., from "selection on unobservables"). This advantage is often found also for the incomplete data case. Matching appears to offer no advantage over OLS in reducing the impact of bias due purely to selection on unobservable variables when the error variables are generated by standard multivariate normal distributions, which lack the bias-cancellation property.

High School Environments, STEM Orientations, and the Gender Gap in Science and Engineering Degrees

This study examines two important and related dimensions of the persisting gender gap in science, technology, engineering, and mathematics (STEM) bachelor degrees: First, the life-course timing of a stable gender gap in STEM orientation, and second, variations in the gender gap across high schools. We build on existing psychological and sociological gender theories to develop a theoretical argument about the development of STEM orientations during adolescence and the potential influence of the local high school environment on the formation of STEM orientations by females and males. Using the National Education Longitudinal Study (NELS), we then decompose the gender gap in STEM bachelor degrees and show that the solidification of the gender gap in STEM orientations is largely a process that occurs during the high school years. Far from being a fixed attribute of adolescent development, however, we find that the size of the gender gap in STEM orientation is quite sensitive to local high school influences; going to school at a high school that is supportive of a positive orientation by females towards math and science can reduce the gender gap in STEM bachelor degrees by 25% or more

School Context and the Gender Gap in Educational Achievement

Today, boys generally under-perform relative to girls in schools throughout the industrialized world. Building on theories about gender identity and reports from prior ethnographic classroom observations, we argue that the school environment channels the conception of masculinity in the peer culture, and thereby either fosters or inhibits the development of anti-school attitudes and behavior among boys. Girls' peer groups, in contrast, do not vary as strongly with the social environment in the extent to which school engagement is stigmatized as "un-feminine." As a consequence, boys are more sensitive to school resources that create a learning oriented environment than are girls. Our analyses use a quasi-experimental research design to estimate the gender difference in the causal effect on test scores, and focus on peer SES as an important school resource. We argue that assignment to 5th grade classrooms within Berlin schools is practically random, and we evaluate this selection process by an examination of Berlin's school regulations, by simulation analysis, and by qualitative interviews with school principles. Estimates of the effect of SES composition on male and female performance strongly support our central hypothesis, and other analyses support our proposed mechanism as the likely explanation of the gender differences in the causal effect

Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments

"Propensity score matching provides an estimate of the effect of a 'treatment' variable on an outcome variable that is largely free of bias arising from an association between treatment status and observable variables. However, matching methods are not robust against 'hidden bias' arising from unobserved variables that simultaneously affect assignment to treatment and the outcome variable. One strategy for addressing this problem is the Rosenbaum bounds approach, which allows the analyst to determine how strongly an unmeasured confounding variable must affect selection into treatment in order to undermine the conclusions about causal effects from a matching analysis. Instrumental variables (IV) estimation provides an alternative strategy for the estimation of causal effects, but the method typically reduces the precision of the estimate and has an additional source of uncertainty that derives from the untestable nature of the assumptions of the IV approach. A method of assessing this additional uncertainty is proposed so that the total uncertainty of the IV approach can be compared with the Rosenbaum bounds approach to uncertainty using matching methods. Because the approaches rely on different information and different assumptions, they provide complementary information about causal relationships. The approach is illustrated via an analysis of the impact of unemployment insurance on the timing of reemployment, the postunemployment wage, and the probability of relocation, using data from several panels of the Survey of Income and Program Participation (SIPP)." (author's abstract)"Propensity score matching ermöglicht die verzerrungsfreie Abschätzung der Kausalwirkung einer "treatment"-Variable auf eine Ergebnisvariable sofern Verzerrungen allein aus dem Zusammenhang zwischen Kausalfaktor und beobachteten Kovariaten resultieren. Matchingverfahren sind allerdings anfällig für Schätzverzerrungen aufgrund von "hidden bias" durch unbeobachtete Variablen, die sowohl die Zuweisung des Kausalfaktors als auch die Ergebnisvariable bestimmen. Im letzteren Fall besteht eine mögliche Strategie darin, mit Hilfe der Methode der sogenannten Rosenbaumschranken abzuschätzen, wie stark der Einfluss unbeobachteter Kovariaten auf die Zuweisung des Kausalstatus sein müsste, um die beabsichtigten Schlussfolgerungen im Hinblick auf den interessierenden kausalen Effekt qualitativ zu verändern. Instrumentalvariablenschätzer (IV) wären ein zweites Verfahren, um in dieser Situation kausale Effekte abschätzen zu können, allerdings führt das Verfahren in der Regel zu wenig präzisen Schätzungen und beinhaltet in der Anwendung zusätzliche Unsicherheiten aufgrund der empirisch nicht testbaren Annahmen des IV-Ansatzes. In diesem Aufsatz wird eine Methode zur Abschätzung dieser Unsicherheiten vorgeschlagen, wodurch die potentiellen Verzerrungen innerhalb einer IV-Schätzung mit den durch die Rosenbaumschranken abgeschätzten Verzerrungen innerhalb eines entsprechenden Matchingansatzes verglichen werden können. Da diesen Verfahren jeweils unterschiedliche Informationsgrundlage sowie unterschiedliche Annahmen zugrunde liegen, erbringen sie komplementäre Informationen über den Gehalt kausaler Beziehungen. Wir illustrieren die vorgeschlagene Vorgehensweise anhand einer Analyse des kausalen Effekts der Arbeitslosenversicherung auf die Dauer der Arbeitslosigkeit, den Lohn bei Wiederbeschäftigung sowie der Wahrscheinlichkeit geographischer Mobilität auf der Basis von Daten des amerikanischen Survey of Income and Program Participation (SIPP)." (Autorenreferat

Teacher effects on social/behavioral skills in early elementary school

Though many recognize that social and behavioral skills play an important role in educational stratification, no studies have attempted to estimate teachers’ effects on these outcomes. Using data from the Early Childhood Longitudinal Study–Kindergarten Cohort (ECLS–K), we estimate teacher effects on social and behavioral skills as well as on academic achievement. Teacher effects on social and behavioral skill development are sizeable, and are somewhat larger than teacher effects on academic development. Because—as we show—social and behavioral skills have a positive effect on the growth of academic skills in the early elementary grades, the teachers who are good at enhancing social and behavioral skills provide an additional indirect boost to academic skills in addition to their direct teaching of academic skills. Like previous studies we find that observable characteristics of teachers and the instructional approaches utilized in their classrooms are weak predictors of teacher effects. However, our results suggest that the teachers who produce better than average academic results are not always the same teachers who excel in enhancing social and behavioral skills

Teacher Effects on Academic and Social Outcomes in Elementary School

Numerous studies conclude that teacher effects on academic achievement are substantial in size. Education is about more than academic achievement, and we know very little about teachers' effectiveness in promoting students' social development. Using data from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS- K), we estimate teacher effects on social as well as academic outcomes. We find that teacher effects on social development are sizeable, and are approximately twice as large as teacher effects on academic development. We further determine that teachers who produce better than average academic results are not the same teachers who produce better than average social results. However, we find that observable characteristics of teachers and the instructional approaches utilized in their classrooms are weak predictors of teacher effects. Finally, we show that the development of social skills has a positive effect on the growth of academic skills, and therefore teachers who are good at teaching social skills provide an additional indirect boost to academic skills in addition to their direct teaching of academic skills. We conclude that current policy debates over what it means to be a "highly qualified teacher" should also take social development into account

Trends in Gender Segregation in the Choice of Science and Engineering Majors

Numerous theories have been put forward for the high and continuing levels of gender segregation, but research has not systematically examined the extent to which these theories for the gender gap are consistent with actual trends. Using both administrative data and three education panel datasets, we evaluate several prominent explanations for the persisting gender gap in STEM fields, and find that none of them are empirically satisfactory. Instead, the persisting gender gap in STEM fields is plausibly attributable to a females' greater preference relative to males for elite occupational careers that are less "vocationally oriented" in the undergraduate years and that permit greater flexibility in undergraduate. This hypothesis is supported by an analysis of gendered pathways to medical and law school

Kausalanalyse durch Matchingverfahren

Having close linkages with the counterfactual concept of causality, nonparametric matching estimators have recently gained in popularity in the statistical and econometric literature on causal analysis. Introducing key concepts of the Rubin causal model (RCM), the paper discusses the implementation of counterfactual analyses by propensity score matching methods. We emphasize the suitability of the counterfactual framework for sociological questions as well as the assumptions underlying matching methods relative to standard regression analysis. We then illustrate the application of matching estimators in an analysis of the causal effect of unemployment on workers' subsequent careers.Matching; Causality; Nonparametric estimators; Observational data; Rubin causal model; Counterfactual analysis