Occupational feminization, gender, and wages: evidence from Thailand

Abstract

This study examines the relationship between occupational feminization and wages in Thailand using the Thailand Labor Force Survey data (LFS) (2008-2017). Employing the Augmented Inverse Probability Weighting (AIPW) and the Inverse Probability Weighted Regression Adjustment (IPWRA) double robust matching techniques, this study investigates the effects of occupational feminization and the interaction effect of occupational feminization and gender on wages. The findings indicate that occupational feminization decreased wages in Thailand, and the negative effect weakened over time. While the gender wage gap persisted in feminized occupations over the years, there was a reduction in the gender wage gap in non-feminized occupations. The wage gap between females in feminized occupations and males in non-feminized occupations decreased over time, even though it remained larger than the overall gender wage gap. Male workers continued to take advantage and ride the “glass escalator” in both feminized and non-feminized occupations.

Keywords:

• Occupational feminization
• gender
• wages
• glass escalator
• Thailand

JEL:

• J24
• J31
• J71

Acknowledgment

The authors would like to thank the National Statistical Office of Thailand for access to the data used in this paper.

Authors’ contributions

Both authors made substantial contributions to the conception or design, drafted, and revised this article.

Data availability statement

The data that support the findings of this study are available from the National Statistical Office (NSO) of Thailand.

Disclosure statement

The authors declare that they have no competing interests.

Notes

1 The Duncan dissimilarity index is $0.5*\sum {|f}_j-m_j|,$ where m_i is the share of males in occupation j and f_i is the share of females in occupation j (Duncan and Duncan 1955).

2 The majority of Thailand workers are aged 19-60. The 19 is the year after the legal age, 18, while 60 is the retirement age.

3 Specifically, the high-skill level includes managers, legislators, professionals, and technicians; the middle-skill level includes clerks, service workers, and plant and machine workers; and the low-skill level includes craft workers, agricultural workers, and unskilled workers.

4 Nine industries include agriculture, mining, manufacturing, utilities, construction, commercial, transportation and communication, services, and others.

5 The assumption is that one of the models, either the treatment or outcome, does not omit a confounding variable (Cattaneo 2010; Cattaneo, Drukker, and Holland 2013).

6 Note that we test all covariates for statistical significance in the models to avoid bias from matching on irrelevant variables (we do not report the results here).

7 In 2008 and 2009, the global financial crisis affected economic growth, generating slower progress in wage growth and stalling improvements in inequality.

8 To build the pseudo panel, we define 10 birth year cohorts from 1948 to 1988 and 8 age groups from 19 to 60, and interact them with the time periods.

Additional information

Funding

Lusi Liao was supported by Guangdong Academy of Sciences (GDAS) Special Funding Project (2022GDASZH-2022030601).