Econometrics Regression Modelling with Decomposition Analysis

This project seeks a highly qualified professional with extensive experience in econometrics and statistical regression modeling (specifically Quantile Regression-QR), particularly utilizing STATA software. The project involves a comprehensive analysis of employee wage data using quantile regression and decomposition techniques. OBJECTIVE Determine the impact of various worker characteristics on their wage in the Sectors (regular and casual separately) using centred regression for different percentiles. Then, the gender wage gap (between males and females) will be decomposed using Oaxaca-Blinder Decomposition (or Machado-Mata-Melly Decomposition) using this centred regression coefficient for different percentiles for regular and casual sectors separately. Centred Regression Model explanation The attached Research Paper explains an alternative estimation of the marginal effects for Recentered Influence Functions (RIF)-Ordinary Least Square (OLS) for interpreting RIF regressions. In this, post-estimation restricted least squares (RLS) regression analysis is combined with centered continuous variables to provide a regression output that should be easier to understand and interpret. Also, the RIFs are used to analyze unconditional partial effects on unconditional quantiles in the regression analysis framework. WORK EXPLANATION For my work, the regression methodology proposed in the attached research paper is to be followed to determine the impact of various characteristics of workers on their wage in the Sectors (regular and casual separately) using centred regression on different percentiles (10th, 25th, 50th, 75th and 90th can be considered). As a next step, you need to get the Oaxaca-Blinder decomposition technique (or Machado-Mata-Melly decomposition) for each percentile using centered regression models. A somewhat similar methodology, i.e., Oaxaca-Blinder decomposition using RIF (through a user-defined oaxaca_rif function in STATA), is explained in this publicly available paper: [login to view URL] For my work, a similar type of decomposition is to be done, but using the “centered regression model” instead of RIF regression. Dependent Variable: Log_Daily_Wages Predictors/Independent Variables: Sex, Residential Area, Age, Religion, Social Group, Marital Status, General Education, Technical Education, Occupation Type, Industry Type, Employment Type, and Enterprise Type (These can be increased/decreased depending on the model efficiency.) WEIGHTS OR MULTIPLIERS For the analysis, ALWAYS use sample weights to ensure that the results accurately represent the entire population despite being based on a smaller sample. DELIVERABLES STATA code for the above and output with some interpretation. ATTACHMENT Research Paper: The master paper describes the methodology (and some codes in its appendix section) based on which the majority of the work (especially the first part of the objective) is to be done. EXPECTED DEADLINE: Approximately one week. It can be extended based on the work requirements. I eagerly await your bid and potential collaboration to conduct this wage gap analysis successfully.