Factors Affecting Informal Worker’s Wage in Thailand
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Abstract
This study aims to (1) analyze factors affecting informal workers' wages in Thailand,(2) analyze the differential impacts of various factors on informal workers' wages at different wage levels using alternative econometric approaches, namely Ordinary Least Squares (OLS) and Quantile Regression, and (3) propose guidelines for improving wages and welfare of informal workers. The analysis uses data drawn from the Labor Force Survey for the third quarter of 2022 by the National Statistical Office, with a sample of 31,338 individuals.
The study found that education level is the factor with the highest impact on informal workers' wages. Those with a bachelor's degree or higher earn 79.80% more than those with less than primary education according to the OLS model. Quantile Regression results reveal that returns to education differ significantly across wage levels. The low-wage group (Q25) shows a 65.20% return to bachelor's degree education, while the high-wage group (Q75) shows returns as high as 91.20%, demonstrating that education is creating increasing education premium inequality. Males earn 16.0% more than females (OLS), and this gap increases to 17.2% in the high-wage group (Q75), reflecting a worsening gender wage gap at higher wage levels. Regional inequality remains a significant problem, with the Northern region earning 33.10% less than Bangkok (OLS), and this gap widening to 37.20% in the high-wage group. Private sector employees earn 31.10% less than government employees, and this difference increases to 34.80% in the high-wage group.
The comparison between OLS and Quantile Regression demonstrates that OLS estimates represent only averages that may obscure important differences between low and high wage groups. Using Quantile Regression helps reveal that the impacts of various factors are not equal across all levels of wage distribution, particularly returns to education and skills that increase with wage levels. This study highlights the necessity of designing policies that account for differences across wage groups, rather than adopting a uniform “one-size-fits-all” approach.
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