Pooled OLS and Fixed Effect Estimation of Wage Structure and Differential in Handloom Sector: Choosing the Better Method

Abstract

This study analyzed the wage structures and differentials of weavers in the handloom sector taking pooled Ordinary Least Square (OLS) and fixed effect model. Within Assam, India, primary data was collected from Bodoland Territorial Area Districts (BTAD), both rural and urban areas in the years 2018 and 2021. The result estimated by using pooled OLS shows production, productivity, education, experience, training, and distance of weavers from weaving residence to have a positive impact on the wages of weavers. Age, distance of weaving, and location of weaving were found to have a negative impact on wages. However, the result estimated using panel least square (fixed effect) showed age and location of weaving to have a negative impact on wages. The study also chose the better method among pooled OLS and fixed effect model. Since the probability value the Wald test statistic is less than 0.05, the fixed-effect model is preferred to Pooled OLS.

Factors influencing India’s fertility patterns and differential of women

“…The crucial models for modeling such as Pooled Ordinary Least-Squares (OLS), random effect (RE), fixed effect (FE), and feasible generalized least square (FGLS) models are all used to analyze panel data regression (Wooldrige, 2013). However, the independent OLS model, RE model, and FE model are the three most often used panel studies (Basumatary & Devi, 2022). Assuming homogeneity across all sections in panel data research, pooled panel analysis does not treat any section differently.…”