방법 비교

	Oaxaca-Blinder Health Decomposition	Life Expectancy Decomposition
분야	Social Epidemiology	Social Epidemiology
계열	Regression model	Process / pipeline
기원 연도	1973	1984
창시자	Ronald Oaxaca; Alan Blinder (health extension popularized by Fairlie and others)	Eduardo E. Arriaga; John H. Pollard
유형	Regression-based decomposition of a between-group mean gap in a health outcome	Demographic decomposition pipeline for differences in a summary measure
원전	Oaxaca, R. (1973). Male-Female Wage Differentials in Urban Labor Markets. International Economic Review, 14(3), 693-709.	Arriaga, E. E. (1984). Measuring and explaining the change in life expectancies. Demography, 21(1), 83-96.
별칭	Blinder-Oaxaca Decomposition for Health Inequalities, Threefold Decomposition of Health Disparities, Detailed Decomposition of Health Gaps, Nonlinear Oaxaca-Blinder for Binary Health Outcomes	Life Expectancy Decomposition Methods, Decomposition of Changes in Life Expectancy, Age and Cause Decomposition of Life Expectancy, Stepwise Life Expectancy Decomposition
관련	4	4
요약	The Oaxaca-Blinder decomposition partitions the mean difference in a health outcome between two groups into a portion explained by differences in their measured characteristics and a residual, unexplained portion attributed to differences in how those characteristics translate into health. Developed independently by Ronald Oaxaca (1973) and Alan Blinder (1973) to study labor-market wage gaps, the method was imported into social epidemiology to quantify, for example, how much of a Black-White, urban-rural, or rich-poor gap in self-rated health, BMI, hypertension, or mortality is accounted for by differences in socioeconomic exposures versus differences in returns to those exposures. Group-specific regressions are estimated, the gap in fitted means is written as a function of mean covariates and coefficients, and that gap is algebraically split into an explained (composition) component and an unexplained (coefficient) component, each of which can be further decomposed variable by variable.	Life-expectancy decomposition answers a question that a single number cannot: when life expectancy rises over time, or differs between two populations, exactly which ages and which causes of death are responsible? The family of methods takes two life tables and splits their gap in e0 (or ex at any age) into additive contributions from mortality differences in each age interval, with the contributions summing exactly to the total gap. Eduardo Arriaga's 1984 stepwise discrete method became the field standard because it is exact, intuitive, and easy to extend to a cause-of-death breakdown, separating a 'direct' effect of changed survival within an interval from an 'indirect plus interaction' effect that the change propagates to later ages. John Pollard's continuous formulation expresses the same decomposition as an integral of age-specific mortality differences weighted by their leverage on life expectancy, providing the theoretical underpinning and a cross-check. This page treats the general decomposition pipeline; the dedicated Arriaga and Pollard pages cover each estimator in depth.