Empowering Women: The Role of Emancipative Forces in Board Gender Diversity
TL;DR: In this paper, the authors investigate the effect of country-level emancipative forces on corporate gender diversity around the world and develop an emancipatory framework of board gender diversity that explains how action resources, emancipation values and civic entitlements enable, motivate and encourage women to take leadership roles on corporate boards.
Abstract: This study investigates the effect of country-level emancipative forces on corporate gender diversity around the world. Based on Welzel’s (Freedom rising: human empowerment and the quest for emancipation. Cambridge University Press, New York, 2013) theory of emancipation, we develop an emancipatory framework of board gender diversity that explains how action resources, emancipative values and civic entitlements enable, motivate and encourage women to take leadership roles on corporate boards. Using a sample of 6390 firms operating in 30 countries around the world, our results show positive single and combined effects of the framework components on board gender diversity. Our research adds to the existing literature in a twofold manner. First, our integrated framework offers a more encompassing, complete and theoretically richer picture of the key drivers of board gender diversity. Second, by testing the framework empirically, we extend the evidence on national drivers of board gender diversity.
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TL;DR: The authors explored how sub-national policies shape corporate board gender diversity of publicly traded firms and found that firms with progressive policies that protect women from discrimination and provide greater availability of emergency contraception and public funding for abortions have higher shares of women directors in their board of directors.
54 citations
TL;DR: In this paper, the authors examine industry sector and national institution drivers of the prevalence of women directors on supervisory and management boards in both public and private firms across 41 advanced and emerging European economies.
52 citations
13 May 2020
TL;DR: In this paper, an Employee Relations (ER) article was published in Employee Relations on 13 May 2020 (online), available at https://doi.org/10.1108/ER-10-2019-0385.
Abstract: Article published in Employee Relations on 13 May 2020 (online), available at: https://doi.org/10.1108/ER-10-2019-0385.
48 citations
TL;DR: In this paper, the authors use a mixed methods research design to investigate how national cultural forces may impede or enhance the positive impact of females' economic and political empowerment on increasing gender diversity of corporate boards using both a longitudinal correlation-based methodology and a configurational approach with fuzzy-set qualitative comparative analysis.
Abstract: In this study, we use a mixed methods research design to investigate how national cultural forces may impede or enhance the positive impact of females’ economic and political empowerment on increasing gender diversity of corporate boards. Using both a longitudinal correlation-based methodology and a configurational approach with fuzzy-set qualitative comparative analysis, we integrate theoretical mechanisms from gender schema and institutional theories to develop a mid-range theory about how female empowerment and national culture shape gender diversity on corporate boards around the world. With our configurational approach, we conceptually and empirically model the complexity that is associated with the simultaneous interdependencies, both complementary and substitutive ones, between female empowerment processes and various cultural dimensions. Our findings contribute unique insights to research focused on board gender diversity as well as provide information for firm decision makers and policymakers about possible solutions for addressing the continuing issue of the underrepresentation of women on corporate boards.
42 citations
TL;DR: The authors argue that institutions create gendered contexts in the Global South, where women's entrepreneurship is subjugated and treated as inferior and second class, and they conclude that entrepreneurship can empower but modestly and slowly.
Abstract: This paper addresses the following questions: Are women entrepreneurs empowered by entrepreneurship, and critically, does entrepreneurship offer emancipation? Our theoretical position is that entrepreneurship is socially embedded and must be recognized as a social process with economic outcomes. Accordingly, questions of empowerment must take full account of the context in which entrepreneurship takes place. We argue that institutions—formal and informal, cultural, social, and political—create gendered contexts in the Global South, where women’s entrepreneurship is subjugated and treated as inferior and second class. Our thematic review of a broad scope of the literature demonstrates that in different regions of the Global South, women entrepreneurs confront many impediments and that this shapes their practices. We show how the interplay of tradition, culture, and patriarchy seem to conspire to subordinate their efforts. Yet, we also recognize how entrepreneurial agency chips away and is beginning to erode these bastions, in particular, how role models establish examples that undermine patriarchy. We conclude that entrepreneurship can empower but modestly and slowly. Some independence is achieved, but emancipation is a long, slow game.
38 citations
References
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TL;DR: In this article, the authors synthesize these previously fragmented literatures around a more general "upper echelons perspective" and claim that organizational outcomes (strategic choices and performance levels) are partially predicted by managerial background characteristics.
Abstract: Theorists in various fields have discussed characteristics of top managers. This paper attempts to synthesize these previously fragmented literatures around a more general “upper echelons perspective.” The theory states that organizational outcomes—strategic choices and performance levels—are partially predicted by managerial background characteristics. Propositions and methodological suggestions are included.
11,022 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 Apr 2002
TL;DR: This work focuses on the development of a single model for Multilevel Regression, which has been shown to provide good predictive power in relation to both the number of cases and the severity of the cases.
Abstract: 1. Introduction to Multilevel Analysis. 2. The Basic Two-Level Regression Model. 3. Estimation and Hypothesis Testing in Multilevel Regression. 4. Some Important Methodological and Statistical Issues. 5. Analyzing Longitudinal Data. 6. The Multilevel Generalized Linear Model for Dichotomous Data and Proportions. 7. The Multilevel Generalized Linear Model for Categorical and Count Data. 8. Multilevel Survival Analysis. 9. Cross-classified Multilevel Models. 10. Multivariate Multilevel Regression Models. 11. The Multilevel Approach to Meta-Analysis. 12. Sample Sizes and Power Analysis in Multilevel Regression. 13. Advanced Issues in Estimation and Testing. 14. Multilevel Factor Models. 15. Multilevel Path Models. 16. Latent Curve Models.
5,395 citations
Book•
01 Jan 2011
TL;DR: In this paper, Glommary et al. proposed a multilevel regression model with a random intercept model to estimate within-and between-group regressions, which is based on a hierarchical linear model.
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 2741524 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
4,162 citations
Book•
01 Jan 1999
TL;DR: In this article, the authors present a survey of welfare regimes for a post-industrial era, including Wefare Regimes for a Post-Industrial Era Bibliography and the Structural Bases of Postindustrial Employment.
Abstract: 1. Introduction PART ONE: VARIETIES OF WELFARE CAPITALISM 2. The Democratic Class Struggle Revisited 3. Social Risks and Wefare States 4. The Household Economy 5. Comparative Welfare Regimes Re-examined PART TWO: THE NEW POLITICAL ECONOMY 6. The Structural Bases of Postindustrial Employment 7. Managing Divergent Employment Dilemmas PART THREE: WELFARE CAPITALISM RECAST? 8. New Social Risks in Old Welfare States 9. Recasting Wefare Regimes for a Postindustrial Era Bibliography
4,016 citations