방법 비교

	MAIHDA	Gender Gap Decomposition
분야	Gender Studies	Gender Studies
계열	Regression model	Regression model
기원 연도	2018	1973
창시자	Clare Evans & S. V. Subramanian (building on Juan Merlo)	Ronald Oaxaca & Alan Blinder
유형	Cross-classified random-effects multilevel model	Regression-based decomposition of a mean group difference
원전	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.
별칭	Intersectional MAIHDA, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy, Intersectional Multilevel Analysis	Oaxaca-Blinder Decomposition, Blinder-Oaxaca Decomposition, Wage Gap Decomposition
관련	3	3
요약	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.