Joint hypothesis tests for multidimensional inequality indices

- We study attribute decomposable multidimensional inequality indices.
- We exploit their decomposition by attributes in several ways.
- We derive the asymptotic distribution of the vector of inequality measures.
- We derive joint hypotheses tests on the vector of inequality measures.
- We present Monte Carlo evidence on their finite sample behavior.

An inequality index over p dimensions of well-being is decomposable by attributes if it can expressed as a function of p unidimensional inequality indices and a measure of association between the various dimensions of well-being. We exploit this decomposition framework to derive joint hypothesis tests regarding the sources of multidimensional inequality, and present Monte Carlo evidence on their finite sample behavior.