The Blinder-Oaxaca decomposition for linear regression models

The counterfactual decomposition technique popularized by Blinder (1973, Journal of Human Resources, 436–455) and Oaxaca (1973, International Economic Review, 693–709) is widely used to study mean outcome differences between groups. For example, the technique is often used to analyze wage gaps by sex or race. This article summarizes the technique and addresses several complications, such as the identification of effects of categorical predictors in the detailed decomposition or the estimation of standard errors. A new command called oaxaca is introduced, and examples illustrating its usage are given.

Decomposition studies of group 6 hexacarbonyl complexes. Part 1: Production and decomposition of Mo(CO)6 and W(CO)6

Chemical studies of superheavy elements require fast and efficient techniques, due to short half-lives and low production rates of the investigated nuclides. Here, we advocate for using a tubular flow reactor for assessing the thermal stability of the Sg carbonyl complex – Sg(CO)6. The experimental setup was tested with Mo and W carbonyl complexes, as their properties are established and supported by theoretical predictions. The suggested approach proved to be effective in discriminating between the thermal stabilities of Mo(CO)6 and W(CO)6. Therefore, an experimental verification of the predicted Sg–CO bond dissociation energy seems to be feasible by applying this technique. By investigating the effect of 104,105Mo beta-decay on the formation of 104,105Tc carbonyl complex, we estimated the lower reaction time limit for the metal carbonyl synthesis in the gas phase to be more than 100 ms. We examined further the influence of the wall material of the recoil chamber, the carrier gas composition, the gas flow rate, and the pressure on the production yield of 104Mo(CO)6, so that the future stability tests with Sg(CO)6 can be optimized accordingly.

Standard Errors for the Blinder-Oaxaca Decomposition

The decomposition technique introduced by Blinder (1973) and Oaxaca (1973) is widely used to study outcome differences between groups. For example, the technique is commonly applied to the analysis of the gender wage gap. However, despite the procedure's frequent use, very little attention has been paid to the issue of estimating the sampling variances of the decomposition components. We therefore suggest an approach that introduces consistent variance estimators for several variants of the decomposition. The accuracy of the new estimators under ideal conditions is illustrated with the results of a Monte Carlo simulation. As a second check, the estimators are compared to bootstrap results obtained using real data. In contrast to previously proposed statistics, the new method takes into account the extra variation imposed by stochastic regressors.

OAXACA: Stata module to compute the Blinder-Oaxaca decomposition

-oaxaca- computes the so-called Blinder-Oaxaca decomposition, which is often used to analyze wage gaps by sex or race. Older versions of this routine are available as -oaxaca9- and -oaxaca8-.

FAIRLIE: Stata module to generate nonlinear decomposition of binary outcome differentials

fairlie computes the nonlinear decomposition of binary outcome differentials proposed by Fairlie (1999, 2003).

SMITHWELCH: Stata module to compute trend decomposition of outcome differentials

smithwelch computes decompositions of differences in mean outcome differentials. Smith and Welch (1989) used such decomposition techniques in their analysis of the change in the black-white wage differential over time. An alternative application would be the decomposition of country differences in the male-female wage gap.

JMPIERCE: Stata module to perform Juhn-Murphy-Pierce decomposition

jmpierce computes the decomposition of differences between two outcome distributions introduced by Juhn, Murphy and Pierce (1993). Examples are the decomposition of changes in the income distribution over time, the decomposition of male-female wage differentials, or the decomposition of wage inequality differences between countries. This routine was previously circulated as jmp.

JMPIERCE2: Stata module to compute trend decomposition of outcome differentials

jmpierce2 computes the decomposition of differences in mean outcome differentials proposed by Juhn, Murphy and Pierce (1991). An example is the decomposition of the change of the black-white or the male-female wage differential over time or the decomposition of differences in the male-female wage differential between countries. This routine was previously circulated as jmp2.

A Stata implementation of the Blinder-Oaxaca decomposition

The counterfactual decomposition technique popularized by Blinder (1973) and Oaxaca (1973) is widely used to study mean outcome differences between groups. For example, the technique is often used to analyze wage gaps by sex or race. The present paper summarizes the technique and addresses a number of complications such as the identification of effects of categorical predictors in the detailed decomposition or the estimation of standard errors. A new Stata command called -oaxaca- is introduced and examples illustrating its usage are given.