The Kitagawa–Oaxaca–Blinder (KOB) decomposition, or simply Kitagawa decomposition or Blinder–Oaxaca decomposition (), is a statistical method that explains the difference in the means of a dependent variable between two groups by decomposing the gap into within-group and between-group differences in the effect of the explanatory variable. The method was originally invented by sociologist and demographer Evelyn M. Kitagawa in 1955. Ronald Oaxaca introduced this method in economics in his doctoral thesis at Princeton University and eventually published in 1973. The decomposition technique is also named after Alan Blinder who proposed a similar approach in the same year. Oaxaca's original research question was the wage differential between two different groups of workers (male vs. female), but the method has since been applied to numerous other topics.
Method The following three equations illustrate this decomposition. Estimate separate linear wage regressions for individuals i in groups A and B:
( 1 ) ln ( wages A i ) = X A i β A + μ A i ( 2 ) ln ( wages B i ) = X B i β B + μ B i {\displaystyle {\begin{aligned}(1)\qquad \ln({\text{wages}}_{A_{i}})&=X_{A_{i}}\beta _{A}+\mu _{A_{i}}\\(2)\qquad \ln({\text{wages}}_{B_{i}})&=X_{B_{i}}\beta _{B}+\mu _{B_{i}}\end{aligned}}}
where Χ is a vector of explanatory variables such as education, experience, industry, and occupation, βA and βB are vectors of coefficients and μ is an error term. Suppose we have the regression estimates β ^ A , β ^ B {\displaystyle {\hat {\beta }}_{A},{\hat {\beta }}_{B}} . Then, since the average value of residuals in a linear regression is zero, we have the KOB decomposition as:
( 3 ) E ( ln ( wages A ) ) − E ( ln ( wages B ) ) =
β ^ A E ( X A ) − β ^ B E ( X B ) =
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