Women's Opportunities under Different Family Policy Constellations: Gender, Class, and Inequality Tradeoffs in Western Countries Re-examined
TL;DR: This paper explored tradeoffs reflecting interaction effects between socioeconomic class and different types of family policies on gender inequalities in terms of agency and economic inequality in eighteen Organization for Economic and Cultural Development countries.
Abstract: This article explores tradeoffs reflecting interaction effects between socioeconomic class and different types of family policies on gender inequalities in terms of agency and economic inequality in eighteen Organization for Economic and Cultural Development countries We identify multiple dimensions in family policies, reflecting the extent to which legislation involves claim rights supporting mothers' paid work or supporting traditional homemaking We use constellations of multidimensional policies in combination with multilevel analysis to examine effects on class selectivity of women into employment and glass ceilings with respect to women's access to top wages and managerial positions Our results indicate that while major negative family policy effects for women with tertiary education are difficult to find in countries with well-developed policies supporting women's employment and work-family reconciliation, family policies clearly differ in the extent to which they improve opportunities for women without university education
Citations
More filters
TL;DR: Gornick and Meyers as discussed by the authors argue that a dual-earner-dual-carer society can be achieved through three key changes: shifting several hours per week of men's time from paid work to care for children, and a smaller number of women's hours from home to paid work.
Abstract: Families That Work: Policies for Reconciling Parenthood and Employment. Janet C. Gornick and Marcia K. Meyers. New York: Russell Sage Foundation. 2003. 392 pp. ISBN 0-87154-356-7. $39.95 (cloth). This book begins with the phrase "imagine a world," which aptly describes the authors' aim. The "world" that Janet Gornick and Marcia Meyers imagine is a "dual-earner-dual-carer" U.S. society, a "fully gender-egalitarian, economically secure, caring society" (p. 4). They acknowledge the enormous magnitude of transformation that would be required for such an end vision to be achieved, but point out that without a clear end vision, such a transformation would be impossible. Gornick and Meyers propose that a dual-earner-dual-carer society can be achieved through three key changes: 1) Shifting several hours per week of men's time from paid work to care for children, and a smaller number of women's hours from home to paid work 2) Creating new employment arrangements to allow men and women to take time for parenting without excessive financial or advancement penalties in the workplace 3) Reducing standard working hours and expanding paid family leave and public funding for child care Although not given a great deal of space in the book, I was intrigued by Gornick and Meyers's analysis of tension among three discourses about work and family. Advocates for children tend to favor policies such as child tax credits and maternity leaves that make it easier for mothers to stay at home with young children, temporarily opting out of the labor market. Discourse about work-family conflict tends to favor policies that make it easier for women to be workers and parents simultaneously, such as part-time work, job sharing, and flexible schedules. Feminists emphasize the pursuit of gender equality by improving access to high-quality nonparental care for children and access to high-quality jobs for women. The problem with each of these solutions, according to Gornick and Meyers, is that they pit gender equality against the interests of children, in each case sacrificing one interest in favor of the other, and in all cases sidestepping the need for change in men's behavior. The dual-earner-dual-carer vision is an effort to simultaneously pursue both goals by transforming both men's and women's roles. Much of the book is devoted to articulating shortcomings in U.S. policies relative to other industrialized nations in North America or Europe. While the theme is familiar, the book includes an expansive, data-driven, and clear articulation of the evidence, drawing on national data sets and the content of specific regulations in several countries. For example, the U.S. spends $650 per child and 0.5% of its GDP on children, less than half the amounts allocated by countries in Scandinavia and Western Europe. Policy data are juxtaposed with an embarrassing litany of poor outcomes for children in the United States relative to their peers in other countries: high rates of low birth weight and mortality among infants, low achievement scores in science and math and high levels of television watching among school children, and high rates of teen pregnancy. …
610 citations
TL;DR: In this article, the authors focus on the cross-national variation in the gap in employment participation and working hours between mothers and childless women and provide evidence that institutional and cultural contexts shape maternal employment.
Abstract: Existing research shows that women’s employment patterns are not driven so much by gender as by motherhood, with childless people and fathers employed at substantially higher levels than mothers in most countries. We focus on the cross-national variation in the gap in employment participation and working hours between mothers and childless women. Controlling for individual- and household-level factors, we provide evidence that institutional and cultural contexts shape maternal employment. Well-paid leaves, publicly supported childcare services for very young children, and cultural support for maternal employment predict smaller differences in employment participation and working hours between mothers and childless women. Yet, extended leave, notably when unpaid, is associated with larger motherhood employment gaps.
195 citations
Cites background from "Women's Opportunities under Differe..."
...…iu m d ue to la ck o f 1 99 4 da ta ) c 1 = y es , 0 = n o or n o be ne fit s av ai la bl e Following current practice (Gornick and Meyers 2003; Korpi, Ferrarini, and Englund 2013; Mandel and Semyonov 2006; Pettit and Hook 2009), our childcare measures include the percentage of children aged…...
[...]
...Thus, not all work-family policies support maternal employment equally (Korpi, Ferrarini, and Englund 2013; Lewis 2006)....
[...]
TL;DR: In this article, three dimensions of gender equality related to employment, financial resources, and family work are sketched, which incorporate this understanding: (1) the ability to maintain a household; (2) agency and the capability to choose; and (3) gender equity in household and care work.
Abstract: Does gender equality matter for fertility? Demographic findings on this issue are rather inconclusive We argue that one reason for this is that the complexity of the concept of gender equality has received insufficient attention Gender equality needs to be conceptualized in a manner that goes beyond perceiving it as mere “sameness of distribution” It needs to include notions of gender equity and thus to allow for distinguishing between gender difference and gender inequality We sketch three dimensions of gender equality related to employment, financial resources, and family work, which incorporate this understanding: (1) the ability to maintain a household; (2) agency and the capability to choose; and (3) gender equity in household and care work We explore their impact on childbearing intentions of women and men using the European Generations and Gender Surveys Our results confirm the need for a more nuanced notion of gender equality in studies on the relationship between gender equality on fertility They show that there is no uniform effect of gender equality on childbearing intentions, but that the impact varies by gender and by parity
147 citations
147 citations
TL;DR: This paper examined to what extent family policies differently affect poverty among single parent households and two-parent households and found that employment reduces poverty, particularly for parents in professional occupations and for coupled parents who are dual earners.
Abstract: This study examined to what extent family policies differently affect poverty among single-parent households and two-parent households. We distinguished between reconciliation policies (tested with parental leave and the proportion of unpaid leave) and financial support policies (tested with family allowances). We used data from the Luxembourg Income Study Database, covering 519,825 households in 18 OECD countries from 1978 to 2008, combined with data from the Comparative Family Policy Database. Single parents face higher poverty risks than coupled parents, and single mothers more so than single fathers. We found that employment reduces poverty, particularly for parents in professional occupations and for coupled parents who are dual earners. Longer parental leave, a smaller proportion of unpaid leave, and higher amounts of family allowances were associated with lower poverty among all households with children. Parental leave more effectively facilitated the employment of single mothers, thereby reducing ...
139 citations
References
More filters
Book•
01 Jan 1990
TL;DR: In this paper, Esping-Andersen distinguishes three major types of welfare state, connecting these with variations in the historical development of different Western countries, and argues that current economic processes such as those moving toward a post-industrial order are shaped not by autonomous market forces but by the nature of states and state differences.
Abstract: Few discussions in modern social science have occupied as much attention as the changing nature of welfare states in Western societies. Gosta Esping-Andersen, one of the foremost contributors to current debates on this issue, here provides a new analysis of the character and role of welfare states in the functioning of contemporary advanced Western societies. Esping-Andersen distinguishes three major types of welfare state, connecting these with variations in the historical development of different Western countries. He argues that current economic processes, such as those moving toward a postindustrial order, are shaped not by autonomous market forces but by the nature of states and state differences. Fully informed by comparative materials, this book will have great appeal to all those working on issues of economic development and postindustrialism. Its audience will include students of sociology, economics, and politics."
16,883 citations
01 Jan 1989
TL;DR: The authors argues that Black women are sometimes excluded from feminist theory and antiracist policy discourse because both are predicated on a discrete set of experiences that often does not accurately reflect the interaction of race and gender.
Abstract: This chapter examines how the tendency is perpetuated by a single-axis framework that is dominant in antidiscrimination law and that is also reflected in feminist theory and antiracist politics. It suggests that this single-axis framework erases Black women in the conceptualization, identification and remediation of race and sex discrimination by limiting inquiry to the experiences of otherwise-privileged members of the group. The chapter focuses on otherwise-privileged group members creates a distorted analysis of racism and sexism because the operative conceptions of race and sex become grounded in experiences that actually represent only a subset of a much more complex phenomenon. It argues that Black women are sometimes excluded from feminist theory and antiracist policy discourse because both are predicated on a discrete set of experiences that often does not accurately reflect the interaction of race and gender. The chapter discusses the feminist critique of rape and separate spheres ideology.
11,236 citations
Book•
01 Jan 1999
TL;DR: In this paper, the authors proposed a multilevel regression model to estimate within-and between-group correlations using a combination of within-group correlation and cross-group evidence.
Abstract: Preface second edition Preface to first edition Introduction Multilevel analysis Probability models This book Prerequisites Notation Multilevel Theories, Multi-Stage Sampling and Multilevel Models Dependence as a nuisance Dependence as an interesting phenomenon Macro-level, micro-level, and cross-level relations Glommary Statistical Treatment of Clustered Data Aggregation Disaggregation The intraclass correlation Within-group and between group variance Testing for group differences Design effects in two-stage samples Reliability of aggregated variables Within-and between group relations Regressions Correlations Estimation of within-and between-group correlations Combination of within-group evidence Glommary The Random Intercept Model Terminology and notation A regression model: fixed effects only Variable intercepts: fixed or random parameters? When to use random coefficient models Definition of the random intercept model More explanatory variables Within-and between-group regressions Parameter estimation 'Estimating' random group effects: posterior means Posterior confidence intervals Three-level random intercept models Glommary The Hierarchical Linear Model Random slopes Heteroscedasticity Do not force ?01 to be 0! Interpretation of random slope variances Explanation of random intercepts and slopes Cross-level interaction effects A general formulation of fixed and random parts Specification of random slope models Centering variables with random slopes? Estimation Three or more levels Glommary Testing and Model Specification Tests for fixed parameters Multiparameter tests for fixed effects Deviance tests More powerful tests for variance parameters Other tests for parameters in the random part Confidence intervals for parameters in the random part Model specification Working upward from level one Joint consideration of level-one and level-two variables Concluding remarks on model specification Glommary How Much Does the Model Explain? Explained variance Negative values of R2? Definition of the proportion of explained variance in two-level models Explained variance in three-level models Explained variance in models with random slopes Components of variance Random intercept models Random slope models Glommary Heteroscedasticity Heteroscedasticity at level one Linear variance functions Quadratic variance functions Heteroscedasticity at level two Glommary Missing Data General issues for missing data Implications for design Missing values of the dependent variable Full maximum likelihood Imputation The imputation method Putting together the multiple results Multiple imputations by chained equations Choice of the imputation model Glommary Assumptions of the Hierarchical Linear Model Assumptions of the hierarchical linear model Following the logic of the hierarchical linear model Include contextual effects Check whether variables have random effects Explained variance Specification of the fixed part Specification of the random part Testing for heteroscedasticity What to do in case of heteroscedasticity Inspection of level-one residuals Residuals at level two Influence of level-two units More general distributional assumptions Glommary Designing Multilevel Studies Some introductory notes on power Estimating a population mean Measurement of subjects Estimating association between variables Cross-level interaction effects Allocating treatment to groups or individuals Exploring the variance structure The intraclass correlation Variance parameters Glommary Other Methods and Models Bayesian inference Sandwich estimators for standard errors Latent class models Glommary Imperfect Hierarchies A two-level model with a crossed random factor Crossed random effects in three-level models Multiple membership models Multiple membership multiple classification models Glommary Survey Weights Model-based and design-based inference Descriptive and analytic use of surveys Two kinds of weights Choosing between model-based and design-based analysis Inclusion probabilities and two-level weights Exploring the informativeness of the sampling design Example: Metacognitive strategies as measured in the PISA study Sampling design Model-based analysis of data divided into parts Inclusion of weights in the model How to assign weights in multilevel models Appendix. Matrix expressions for the single-level estimators Glommary Longitudinal Data Fixed occasions The compound symmetry models Random slopes The fully multivariate model Multivariate regression analysis Explained variance Variable occasion designs Populations of curves Random functions Explaining the functions 27415.2.4 Changing covariates Autocorrelated residuals Glommary Multivariate Multilevel Models Why analyze multiple dependent variables simultaneously? The multivariate random intercept model Multivariate random slope models Glommary Discrete Dependent Variables Hierarchical generalized linear models Introduction to multilevel logistic regression Heterogeneous proportions The logit function: Log-odds The empty model The random intercept model Estimation Aggregation Further topics on multilevel logistic regression Random slope model Representation as a threshold model Residual intraclass correlation coefficient Explained variance Consequences of adding effects to the model Ordered categorical variables Multilevel event history analysis Multilevel Poisson regression Glommary Software Special software for multilevel modeling HLM MLwiN The MIXOR suite and SuperMix Modules in general-purpose software packages SAS procedures VARCOMP, MIXED, GLIMMIX, and NLMIXED R Stata SPSS, commands VARCOMP and MIXED Other multilevel software PinT Optimal Design MLPowSim Mplus Latent Gold REALCOM WinBUGS References Index
9,578 citations
Book•
01 Jan 1987TL;DR: In this article, the authors present a general classification notation for multilevel models and a discussion of the general structure and maximum likelihood estimation for a multi-level model, as well as the adequacy of Ordinary Least Squares estimates.
Abstract: Contents Dedication Preface Acknowledgements Notation A general classification notation and diagram Glossary Chapter 1 An introduction to multilevel models 1.1 Hierarchically structured data 1.2 School effectiveness 1.3 Sample survey methods 1.4 Repeated measures data 1.5 Event history and survival models 1.6 Discrete response data 1.7 Multivariate models 1.8 Nonlinear models 1.9 Measurement errors 1.10 Cross classifications and multiple membership structures. 1.11 Factor analysis and structural equation models 1.12 Levels of aggregation and ecological fallacies 1.13 Causality 1.14 The latent normal transformation and missing data 1.15 Other texts 1.16 A caveat Chapter 2 The 2-level model 2.1 Introduction 2.2 The 2-level model 2.3 Parameter estimation 2.4 Maximum likelihood estimation using Iterative Generalised Least Squares (IGLS) 2.5 Marginal models and Generalized Estimating Equations (GEE) 2.6 Residuals 2.7 The adequacy of Ordinary Least Squares estimates. 2.8 A 2-level example using longitudinal educational achievement data 2.9 General model diagnostics 2.10 Higher level explanatory variables and compositional effects 2.11 Transforming to normality 2.12 Hypothesis testing and confidence intervals 2.13 Bayesian estimation using Markov Chain Monte Carlo (MCMC) 2.14 Data augmentation Appendix 2.1 The general structure and maximum likelihood estimation for a multilevel model Appendix 2.2 Multilevel residuals estimation Appendix 2.3 Estimation using profile and extended likelihood Appendix 2.4 The EM algorithm Appendix 2.5 MCMC sampling Chapter 3. Three level models and more complex hierarchical structures. 3.1 Complex variance structures 3.2 A 3-level complex variation model example. 3.3 Parameter Constraints 3.4 Weighting units 3.5 Robust (Sandwich) Estimators and Jacknifing 3.6 The bootstrap 3.7 Aggregate level analyses 3.8 Meta analysis 3.9 Design issues Chapter 4. Multilevel Models for discrete response data 4.1 Generalised linear models 4.2 Proportions as responses 4.3 Examples 4.4 Models for multiple response categories 4.5 Models for counts 4.6 Mixed discrete - continuous response models 4.7 A latent normal model for binary responses 4.8 Partitioning variation in discrete response models Appendix 4.1. Generalised linear model estimation Appendix 4.2 Maximum likelihood estimation for generalised linear models Appendix 4.3 MCMC estimation for generalised linear models Appendix 4.4. Bootstrap estimation for generalised linear models Chapter 5. Models for repeated measures data 5.1 Repeated measures data 5.2 A 2-level repeated measures model 5.3 A polynomial model example for adolescent growth and the prediction of adult height 5.4 Modelling an autocorrelation structure at level 1. 5.5 A growth model with autocorrelated residuals 5.6 Multivariate repeated measures models 5.7 Scaling across time 5.8 Cross-over designs 5.9 Missing data 5.10 Longitudinal discrete response data Chapter 6. Multivariate multilevel data 6.1 Introduction 6.2 The basic 2-level multivariate model 6.3 Rotation Designs 6.4 A rotation design example using Science test scores 6.5 Informative response selection: subject choice in examinations 6.6 Multivariate structures at higher levels and future predictions 6.7 Multivariate responses at several levels 6.8 Principal Components analysis Appendix 6.1 MCMC algorithm for a multivariate normal response model with constraints Chapter 7. Latent normal models for multivariate data 7.1 The normal multilevel multivariate model 7.2 Sampling binary responses 7.3 Sampling ordered categorical responses 7.4 Sampling unordered categorical responses 7.5 Sampling count data 7.6 Sampling continuous non-normal data 7.7 Sampling the level 1 and level 2 covariance matrices 7.8 Model fit 7.9 Partially ordered data 7.10 Hybrid normal/ordered variables 7.11 Discussion Chapter 8. Multilevel factor analysis, structural equation and mixture models 8.1 A 2-stage 2-level factor model 8.2 A general multilevel factor model 8.3 MCMC estimation for the factor model 8.4 Structural equation models 8.5 Discrete response multilevel structural equation models 8.6 More complex hierarchical latent variable models 8.7 Multilevel mixture models Chapter 9. Nonlinear multilevel models 9.1 Introduction 9.2 Nonlinear functions of linear components 9.3 Estimating population means 9.4 Nonlinear functions for variances and covariances 9.5 Examples of nonlinear growth and nonlinear level 1 variance Appendix 9.1 Nonlinear model estimation Chapter 10. Multilevel modelling in sample surveys 10.1 Sample survey structures 10.2 Population structures 10.3 Small area estimation Chapter 11 Multilevel event history and survival models 11.1 Introduction 11.2 Censoring 11.3 Hazard and survival funtions 11.4 Parametric proportional hazard models 11.5 The semiparametric Cox model 11.6 Tied observations 11.7 Repeated events proportional hazard models 11.8 Example using birth interval data 11.9 Log duration models 11.10 Examples with birth interval data and children s activity episodes 11.11 The grouped discrete time hazards model 11.12 Discrete time latent normal event history models Chapter 12. Cross classified data structures 12.1 Random cross classifications 12.2 A basic cross classified model 12.3 Examination results for a cross classification of schools 12.4 Interactions in cross classifications 12.5 Cross classifications with one unit per cell 12.6 Multivariate cross classified models 12.7 A general notation for cross classifications 12.8 MCMC estimation in cross classified models Appendix 12.1 IGLS Estimation for cross classified data. Chapter 13 Multiple membership models 13.1 Multiple membership structures 13.2 Notation and classifications for multiple membership structures 13.3 An example of salmonella infection 13.4 A repeated measures multiple membership model 13.5 Individuals as higher level units 13.5.1 Example of research grant awards 13.6 Spatial models 13.7 Missing identification models Appendix 13.1 MCMC estimation for multiple membership models. Chapter 14 Measurement errors in multilevel models 14.1 A basic measurement error model 14.2 Moment based estimators 14.3 A 2-level example with measurement error at both levels. 14.4 Multivariate responses 14.5 Nonlinear models 14.6 Measurement errors for discrete explanatory variables 14.7 MCMC estimation for measurement error models Appendix 14.1 Measurement error estimation 14.2 MCMC estimation for measurement error models Chapter 15. Smoothing models for multilevel data. 15.1 Introduction 15.2. Smoothing estimators 15.3 Smoothing splines 15.4 Semi parametric smoothing models 15.5 Multilevel smoothing models 15.6 General multilevel semi-parametric smoothing models 15.7 Generalised linear models 15.8 An example Fixed Random 15.9 Conclusions Chapter 16. Missing data, partially observed data and multiple imputation 16.1 Creating a completed data set 16.2 Joint modelling for missing data 16.3 A two level model with responses of different types at both levels. 16.4 Multiple imputation 16.5 A simulation example of multiple imputation for missing data 16.6 Longitudinal data with attrition 16.7 Partially known data values 16.8 Conclusions Chapter 17 Multilevel models with correlated random effects 17.1 Non-independence of level 2 residuals 17.2 MCMC estimation for non-independent level 2 residuals 17.3 Adaptive proposal distributions in MCMC estimation 17.4 MCMC estimation for non-independent level 1 residuals 17.5 Modelling the level 1 variance as a function of explanatory variables with random effects 17.6 Discrete responses with correlated random effects 17.7 Calculating the DIC statistic 17.8 A growth data set 17.9 Conclusions Chapter 18. Software for multilevel modelling References Author index Subject index
5,839 citations
TL;DR: The authors argue that intersectionality is the most important theoretical contribution women's studies, in conjunction with related fields, has made so far, and they even say that intersectional is a central category of analysis in women’s studies, and that women are perhaps alone in the academy in the extent to which they have embraced intersectionality.
Abstract: Since critics first allegedthat feminism claimed tospeak universally for all
women, feminist researchers havebeen acutely aware ofthe limitations of
genderas a single analyticalcategory. In fact, feministsare perhaps alone in
the academy in theextent to which theyhave embraced intersectionality – the
relationshipsamong multiple dimensions andmodalities of social relations
and subject formations – as itselfa central category ofanalysis. One could
evensay that intersectionality isthe most important theoreticalcontribution
that women’s studies,in conjunction with relatedfields, has made sofar.1
4,744 citations
"Women's Opportunities under Differe..." refers background in this paper
...McCall (2005, 1788) notes that comparative research can use familiar statistical methods, such as interaction and multilevel analyses, to evaluate such inequalities....
[...]