Decomposition Methods in Economics
Методы декомпозиции в экономике
2010-06-01
SCID: 54.1/fbsb87wy
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Gini coefficientOaxaca-Blinder decompositiondecomposition methodsdistributional statisticsquantile decomposition
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Abstract (AI)
This chapter provides a comprehensive overview of decomposition methods that have been developed since the seminal work of Oaxaca and Blinder in the early 1970s. These methods are used to decompose the difference in a distributional statistic between two groups, or its change over time, into various explanatory factors. While the original work of Oaxaca and Blinder considered the case of the mean, our main focus is on other distributional statistics besides the mean such as quantiles, the Gini coefficient or the variance. We discuss the assumptions required for identifying the different elements of the decomposition, as well as various estimation methods proposed in the literature. We also illustrate how these methods work in practice by discussing existing applications and working through a set of empirical examples throughout the paper.
Key Findings
1
It surveys estimation methods and demonstrates their practical use through existing applications and empirical examples.
2
Its primary focus extends beyond mean decompositions to quantiles, the Gini coefficient, variance, and other distributional statistics.
3
The chapter examines identification assumptions required to interpret different decomposition components.
4
The chapter reviews decomposition methods developed since Oaxaca–Blinder for explaining distributional differences between groups or changes over time.
Research Object
Distributional differences between two groups or changes over time
Research Subject
Decomposition of distributional statistics into explanatory factors, including quantiles, the Gini coefficient, and variance, with the associated identification assumptions and estimation methods
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2010-06-01
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