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.

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 career-trajectory 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

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 na?ve 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

The value of non-working time incorporated in quality of life comparisons: The case of the US vs. the Netherlands

Comparisons of well-being across societies depend both on the amount of inequality at the national level and also on the national average level of well-being. Comparisons between the U.S. and western Europe show that inequality is greater in the U.S. but that average GDP per capita is also greater in the U.S., and most Americans have higher standards of living than do western Europeans at comparable locations in their national income distributions. What is less wellknown is that (depending on the country) much or all of this gap arises from differences in the level of working hours in the U.S. and in western Europe. Cross-national comparisons of wellbeing have typically relied on the methodology of generalized Lorenz curves (GLC), but this approach privileges disposable income and cash transfers while ignoring other aspects of welfare state and labor market structure that potentially affect the distribution of well-being in a society. We take an alternative approach that focuses on the value of time use and the different distributions of work and family time that are generated by each country's labor market and social welfare institutions. In this empirical exercise involving the U.S. and the Netherlands, we show that reasonable estimates of the contribution to well-being from non-market activities such as the raising of children or longer vacations can overturn claims in the literature that the U.S. offers greater well-being to more of its citizens than do western European countries

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

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