Sample Selection Adjusted Regression Modelling
Budget: ₹1,500 – ₹12,500 INR
This project seeks a highly qualified professional with extensive experience in econometrics and statistical regression modeling, particularly utilizing STATA software (as an alternate, R/RStudio can also be used). The project involves a comprehensive analysis of employee wage data using quantile regression and decomposition techniques.
OBJECTIVE
Determine the impact of various characteristics of workers on their wage in the Sectors (regular and casual separately) using the BEST sample selection adjustment technique along a conditional quantile regression model, and then decompose the gender wage gap (between males and females) with the same sample selection adjustment technique using Melly’s Machado-Mata Decomposition for regular and casual separately.
WORK EXPLANATION
Sample Selection Adjustment: It addresses a potential bias, i.e., we only observe wages for people who are employed. But factors influencing labor force participation (like childcare responsibilities) might also affect wages. Women might be more likely to be out of the workforce for these reasons, leading to a biased sample of employed women with potentially lower wages. This can underestimate the true gender wage gap, particularly at certain points in the wage distribution.
Methodology for incorporating sample selection adjustment in quantile regression:
A separate model is estimated to predict the probability of being employed, considering factors like education, marital status, and the presence of young children. The estimated probability of employment from the first model is then used to weigh the wage regression in the second model. This adjusts for the underrepresentation of certain groups (like women with young children) who might have lower wages but are not observed in the wage data.
Note: The variable(s) required for the sample selection can be determined from the variable’s description. The concept here is to adjust the sample based on entering the labor force, like marital status affecting the choice of females to enter the labor force.
For my work, the effectiveness of these three sample selection adjustment methodologies in quantile regression modelling framework is to be tested, and the best one is to be further used in “decomposition” analysis.
The three types of sample selection adjustment methodologies are:
• Albrecht et al. (Research Paper): This procedure combines a semiparametric binary model for the participation equation with a linear quantile regression model for the wage equation and follows Buchinsky's (1998) approach. The research paper is based on this approach.
• Arhomme (Stata Package): ssc install arhomme
• Qregsel (Stata Package): ssc install qregsel
WORK PLAN
To determine the impact of various characteristics of workers on their wage in the regular and casual sectors using “sample selection correction for conditional quantile regression”: Run a quantile regression model with sample selection adjustment for the two Sectors (Regular and Casual separately). The code for these is: [Sectors = 1 and 2].
To decompose the gender wage gap (between males and females) with the same sample selection adjustment technique for regular and casual separately: Run the Melly’s Machado-Mata Decomposition (available on the page: https://sites.google.com/site/blaisemelly/home/computer-programs/stata---decomposition-of-differences-in-distribution-using-quantile-regression) incorporating the best-suited sample selection correction adjustment procedure.
Dependent Variable: Log_Wages
Predictors/Independent Variables: Sex, Residential Area, Age, Religion, Social Group, Marital Status, General Education, Technical Education, Occupation Type, Industry, Employment Type, Enterprise
(These can be increased/decreased depending on the model efficiency)
ATTACHMENTS: Complete and detailed workplan to follow for the project, dataset file in STATA format, and a supporting research paper.
DELIVERABLES: STATA code for the above and output with some interpretation.
EXPECTED DEADLINE: Approximate one week (17 March, 2024). It can be extended based on the work requirements.
I eagerly await your bid and potential collaboration to conduct this wage gap analysis successfully.
OBJECTIVE
Determine the impact of various characteristics of workers on their wage in the Sectors (regular and casual separately) using the BEST sample selection adjustment technique along a conditional quantile regression model, and then decompose the gender wage gap (between males and females) with the same sample selection adjustment technique using Melly’s Machado-Mata Decomposition for regular and casual separately.
WORK EXPLANATION
Sample Selection Adjustment: It addresses a potential bias, i.e., we only observe wages for people who are employed. But factors influencing labor force participation (like childcare responsibilities) might also affect wages. Women might be more likely to be out of the workforce for these reasons, leading to a biased sample of employed women with potentially lower wages. This can underestimate the true gender wage gap, particularly at certain points in the wage distribution.
Methodology for incorporating sample selection adjustment in quantile regression:
A separate model is estimated to predict the probability of being employed, considering factors like education, marital status, and the presence of young children. The estimated probability of employment from the first model is then used to weigh the wage regression in the second model. This adjusts for the underrepresentation of certain groups (like women with young children) who might have lower wages but are not observed in the wage data.
Note: The variable(s) required for the sample selection can be determined from the variable’s description. The concept here is to adjust the sample based on entering the labor force, like marital status affecting the choice of females to enter the labor force.
For my work, the effectiveness of these three sample selection adjustment methodologies in quantile regression modelling framework is to be tested, and the best one is to be further used in “decomposition” analysis.
The three types of sample selection adjustment methodologies are:
• Albrecht et al. (Research Paper): This procedure combines a semiparametric binary model for the participation equation with a linear quantile regression model for the wage equation and follows Buchinsky's (1998) approach. The research paper is based on this approach.
• Arhomme (Stata Package): ssc install arhomme
• Qregsel (Stata Package): ssc install qregsel
WORK PLAN
To determine the impact of various characteristics of workers on their wage in the regular and casual sectors using “sample selection correction for conditional quantile regression”: Run a quantile regression model with sample selection adjustment for the two Sectors (Regular and Casual separately). The code for these is: [Sectors = 1 and 2].
To decompose the gender wage gap (between males and females) with the same sample selection adjustment technique for regular and casual separately: Run the Melly’s Machado-Mata Decomposition (available on the page: https://sites.google.com/site/blaisemelly/home/computer-programs/stata---decomposition-of-differences-in-distribution-using-quantile-regression) incorporating the best-suited sample selection correction adjustment procedure.
Dependent Variable: Log_Wages
Predictors/Independent Variables: Sex, Residential Area, Age, Religion, Social Group, Marital Status, General Education, Technical Education, Occupation Type, Industry, Employment Type, Enterprise
(These can be increased/decreased depending on the model efficiency)
ATTACHMENTS: Complete and detailed workplan to follow for the project, dataset file in STATA format, and a supporting research paper.
DELIVERABLES: STATA code for the above and output with some interpretation.
EXPECTED DEADLINE: Approximate one week (17 March, 2024). It can be extended based on the work requirements.
I eagerly await your bid and potential collaboration to conduct this wage gap analysis successfully.