手法を比較

	MAIHDA	Gender Gap Decomposition
分野	Gender Studies	Gender Studies
系統	Regression model	Regression model
提唱年	2018	1973
提唱者	Clare Evans & S. V. Subramanian (building on Juan Merlo)	Ronald Oaxaca & Alan Blinder
種類	Cross-classified random-effects multilevel model	Regression-based decomposition of a mean group difference
原典	Evans, C. R., Williams, D. R., Onnela, J.-P., & Subramanian, S. V. (2018). A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Social Science & Medicine, 203, 64–73.	Oaxaca, R. (1973). Male-female wage differentials in urban labor markets. International Economic Review, 14(3), 693–709.
別名	Intersectional MAIHDA, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy, Intersectional Multilevel Analysis	Oaxaca-Blinder Decomposition, Blinder-Oaxaca Decomposition, Wage Gap Decomposition
関連	3	3
概要	MAIHDA — Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy — is a quantitative method for studying intersectional inequalities. Introduced for intersectionality by Clare Evans and S. V. Subramanian in 2018, building on Juan Merlo's discriminatory-accuracy framework, it treats the many strata formed by crossing social categories (for example gender × race/ethnicity × education) as level-2 units in a multilevel model, then partitions outcome variation between and within those strata to assess how much intersectional position predicts the outcome.	Gender gap decomposition, most often implemented as the Oaxaca-Blinder decomposition, splits the mean difference in an outcome such as wages between men and women into a part explained by differences in measured characteristics (education, experience, occupation) and an unexplained residual part attributed to differences in how those characteristics are rewarded. Introduced independently by Ronald Oaxaca and Alan Blinder in 1973, it is the workhorse method for quantifying how much of the gender pay gap reflects composition versus differential treatment.