Suchergebnisse

1 Impediments to the Advancement of Women in the Japanese Employment System: Theoretical Overview and the Purpose of this Book -- 2 Determinants of the Gender Gap in the Proportion of Managers Among White-Collar Regular Employees -- 3 Chapter 3 Causes and Effects of Gender Occupational Segregation: Overlooked Obstacles to Gender Equality -- 4 Gender Income Disparity Among White-Collar Regular Employees: Explaining the Causes Responsible for 80% of the Disparity and Its Mechanisms -- 5 Impacts of Companies' Promotion of Work–Life Balance and the Restrictive Regular Employment System on Gender Wage Gap -- 6 Empowerment of Women in the Workplace and Labor Productivity: Which Company Policies Are Effective and Why -- 7 Statistical and Indirect Discrimination: Revisiting the Incentive Problem -- 8 Gender Inequality and Its Irrationality: Implications from the Analytical Results -- Index

This article introduces a new causal analytic method for survival analysis that retains the framework of Rubin's causal model as an alternative to the marginal structural model (MSM). The major limitation of the MSM is a systematic bias in the effects of past treatments when the method is applied to the hazard rate analysis of nonrepeatable events in the presence of unobserved heterogeneity. This systematic bias is demonstrated in the article. The method introduced here assumes a semiparametric conditional-incidence-rate model and provides consistent estimates of the effects of present and past treatments on the conditional cumulative-incidence rate in the analysis of nonrepeatable events in the presence of unobserved heterogeneity. Unlike the MSM, which requires a sequential and cumulative use of the inverse-probability-of-treatment weighting many times for data with many time points, the new method uses the inverse-probability-of-treatment weighing only twice sequentially for estimation of the present and past treatment effects at each time of entry into treatment, and not cumulatively across different treatment entry times. Analysis of the conditional-incidence rate can also provide a more efficient parameter estimate for the treatment effect than the hazard rate model in cases where a majority of sample persons experience the event and thereby cease to be members of the risk set of the hazard rate during the period of observation. An application to an analysis of sexual initiation demonstrates that leaving home promotes sexual initiation, especially premarital sexual initiation, because it greatly increases the rate of premarital sexual initiation during the year after leaving home.

The author introduces methods for the decomposition analysis of multigroup segregation measured by the index of dissimilarity, the squared coefficient of variation, and Theil's entropy measure. Using a new causal framework, the author takes a unified approach to the decomposition analysis by specifying conditions that must be satisfied to decompose segregation into unexplained and explained components. Here, the unexplained component represents the direct effects of the group variable on the conditional probability of acquiring a social position—such as a residential district in an analysis of residential segregation or an occupation in an analysis of occupational segregation—and the explained component represents indirect effects of the group variable on the outcome through covariates. The major merit of this approach is its ability to control individual-level covariates for the decomposition analysis of segregation. Two methods, one for semiparametric outcome models with the identity link function and the other for semiparametric outcome models with the multinomial logit link function, are introduced in this unified framework. The application of these methods focuses on occupational segregation among racial/ethnic groups. Father's occupation, subject's educational attainment, and the region of interview are included as covariates, using data from the General Social Surveys.

Although substantive studies on segregation, such as residential or school segregation by race and occupational segregation by gender, are many in sociology, the analytical methodology is almost exclusively focused on measurement issues. The author introduces a set of two statistical models for the decomposition analysis of segregation. These models can be regarded as a tool to analyze whether one dimension of racial or gender inequality is related to another dimension of inequality, because they can assess, for example, the extent to which gender differences in human capital are related to gender segregation in occupation. One of the new models is a simple extension of the DiNardo-Fortin-Lemieux decomposition method of inequality, which implicitly assumes a supply-driven determination of positional status attainment, and another model modifies it to incorporate demand-based macrosocial size constraints on positional status attainment, but both models rely on Rubin's conception of modeling counterfactual outcomes and inverse-probability-of-treatment weighting on the basis of propensity score. An application focuses on gender segregation in occupation in Japan and leads to a paradoxical result: equalizing human capital and labor supply characteristics between men and women increases, rather than decreases, gender segregation in occupation. Although the underlying behavioral mechanism for gender differences in occupational choice remains to be investigated, the analysis clarifies at least demographically why segregation increases under the counterfactual situation.

This paper begins by clarifying that propensity-score weighting in the DiNardo-Fortin-Lemieux (DFL) decomposition analysis—unlike propensity-score weighting in Rubin's causal model, in which confounding covariates can be endogenous—may generate biased estimates for the decomposition of inequality into "direct" and "indirect" components when intervening variables are endogenous. The paper also clarifies that the Blinder-Oaxaca method confounds the modeling of two distinct counterfactual situations: one in which the covariate effects of the first group become equal to those of the second group and the other in which the covariate distribution of the second group becomes equal to that of the first group. The paper shows that the DFL method requires a distinct condition to provide an unbiased decomposition of inequality that remains under each counterfactual situation. The paper then introduces a combination of the DFL method with Heckman's two-step method as a way of testing and eliminating bias in the DFL estimate when some intervening covariates are endogenous. The paper also intends to bring gender and race back to the center of statistical causal analysis. An application focuses on the decomposition of gender inequality in earned income among white-collar regular employees in Japan.

This article describes (1) the survey methodological and statistical characteristics of the nonrandomized method for surveying sensitive questions for both cross-sectional and panel survey data and (2) the way to use the incompletely observed variable obtained from this survey method in logistic regression and in loglinear and log-multiplicative association models. The nonrandomized method, which was introduced by Yu, Tian, and Tang and Tian et al. for surveying sensitive questions, is much more efficient than randomized response methods, and unlike the latter, the former can be included in a mail survey. The method also has unique advantages compared with the randomized response method for the analysis of panel survey data. A simulation analysis presents how the relative efficiency of statistics in the analysis of data collected with this method changes as a function of mixing probability compared with a hypothetical situation of collecting responses of a sensitive question from every sample subject. The use of statistical software for the application of the logistic regression model, with an incompletely observed variable from cross-sectional and panel surveys, is also described.

This paper introduces some regression models for a categorical dependent variable with data from egocentric social networks as covariates to analyze the determinants of the outcome for the subject and the determinants of the extent of agreement in the outcome between the subject and the people socially connected to the subject. The merits of the models introduced in this paper are their wide applicability to surveys that collect data on egocentric social networks and their ability to identify the determinants of agreement between subjects' and their friends' attitudinal or behavioral outcomes, controlling for the tendency to agree because of homophily in the choice of friends. An illustrative application from the Japanese General Social Survey data shows that several substantively distinct characteristics of egocentric networks affect subjects' political party preferences and the extent of agreement between the political party preferences of subjects and their significant others.

Although log-linear and log-multiplicative analysis of association between variables is useful, its major limitation has been a lack of association with causal analysis. The objective of this article is to introduce a causal analysis on the basis of an adjusted cross-classified frequency table having such counterfactual odds ratios between a treatment variable X and its dependent variable Y that would be realized if the effects of confounding covariates on the association between X and Y were eliminated. The author first clarifies that the attainment of statistical independence between X and the covariates is not sufficient, because the issue of collapsibility remains even under completely randomized treatment allocation, and then proposes a specific standardization method for covariate states to solve this issue. By assuming semiparametric regression models, the author then introduces a new method of retaining the causal effects of X on Y, and the possible dependence of those effects on the third variable, in adjusted two-way or three-way contingency tables. As a result, the author also introduces a method for applying semiparametric logit and multinomial logit regression models that do not specify the effects of the confounding covariates V on Y. The estimation of standard errors for applying log-linear and log-multiplicative association models for the data of adjusted contingency tables is also described.

This paper introduces a new method for decomposing group differences in the mean of a variable into various within-group and between-group components with respect to group categories of intermediary variables. This is accomplished by considering counterfactual outcomes that would be realized by social interventions that change the relationship among variables. Because such a change does not by itself determine the counterfactual outcome, the paper introduces and juxtaposes two different mechanisms—the mechanism of realizing the counterfactual state that deviates least from the existing state, and the mechanism of holding relations among variables other than those that are modified by a given intervention unchanged—and demonstrates that despite the large difference in the mechanisms, they yield highly congruent outcomes. As an illustrative example, the paper analyzes gender inequality in hourly wages in Japan and thereby demonstrates the usefulness of the new method for deriving policy implications.

This paper describes and contrasts two useful ways to employ a latent class variable as a mixture variable in regression analyses of panel data with a categorical dependent variable. One way is to model unobserved heterogeneity in the trajectory, or change in the distribution, of the dependent variable. Two models that accomplish this are the latent trajectory model and latent growth curve model for a categorical dependent variable having ordered categories. Each latent class here represents a distinct trajectory of the dependent variable. The latent trajectory model introduces covariate effects on the composition of latent classes, while the latent growth curve model introduces covariate effects on both the "intercept" and the "slope" of growth in logit, which may vary among latent classes. The other useful way is to model unobserved heterogeneity in the state dependence of the dependent variable. Two models that accomplish this are introduced for a simultaneous analysis of response probability and response stability, and the latent class variable is employed to distinguish two latent populations that differ in the stability of responses over time. One of them is the switching multinomial logit model with a time-lagged dependent variable as its separation indicator, and the other is the mover-stayer regression model. By applying these four models to empirical data, this paper demonstrates the usefulness of these models for panel-data analyses. Example programs for specifying these models based on the LEM program are also provided.