Wage Inequality Analysis with Quantile Regression

I am seeking a professional to explain the applications of various quantile regression methodologies for the analysis of wage and gender wage gap differentials based on previous studies. The review is to be done from latest to oldest, and thematically, with the main themes being: - Conditional Quantile Regression for wage or gender wage gap Analysis - Unconditional Quantile Regression (or Recentered Influence Function) for wage or gender wage gap analysis - Sample Selection Adjustment in both the conditional and unconditional quantile regression for wage or gender wage gap analysis - Comparison between the above methodologies - Quantile Regression-Based Decompositions, namely Machado-Mata Decomposition, Melly's Modification of Machado-Mata Decomposition, Oaxaca-Blinder Decomposition using Recentered Influence Function, and other decompositions based on quantiles, with their comparisons. - Mixed effect quantile regression models (fixed/random) for wage or gender wage gap analysis - Any other quantile regression methodology modifications for wage or gender wage gap analysis Specifically, I'm seeking an expert who can comfortably delve into the above themes, focusing on the recent advancements in the field, and summarize the latest works and findings in a succinct yet comprehensive manner. The research gap is to be determined in the context of already done work in India for wage gap analysis. The freelancer must be well-versed in economics, especially labor economics, and have experience in research. Proficiency in quantile regression and strong research and analytical skills are crucial. The work should be in thematic form, using Times New Roman 12, 1.5 line spacing, and APA7 referencing, spanning 20–25 pages, with a Turnitin Similarity Score below 10. Please feel free to message me for further details regarding the work. I will be fully committed to working side-by-side since I have already done a basic level of work in this field.