I have data concerning the distribution of income, at the individual level, i.e. I have for each individual income and some socio-demographic descriptors. I have this kind of data for several years, but for unrelated individuals (it is not panel data). With this data I can calculate various inequality measures (e.g. Gini coefficient, D9/D1 ratio, relative poverty threshold (EU definition : 60% of the median income)).
My question is : how can I examine the effects of the sociodemographic determinants and more particularly the changes over time of these determinants on these indicators ? A question I would like to be able to answer is "what is the influence of the increasing share of single parents/increasing female participation in the workforce/the increase of the average schooling duration?" on these indexes of inequality.
One of the difficulty lies in the fact that these indexes depend on the whole distribution, not only on means. The most convincing literature I have found yet would be using unconditional quantile regressions / RIF regressions as descriped in unconditional quantile regressions and Decomposition methods in economics, but it seems a bit "heavy".
- Is this method actually making sense in my context ?
- Are there any other "obvious" methods I have missed ?