The gender wage gap and the presence of foreign firms in Vietnam: evidence from unconditional quantile regression decomposition

PurposeThe objective of this study is to estimate the gender wage gap in Vietnam and its rural and urban areas, especially with the presence of foreign firms.Design/methodology/approachThe authors use cross-sectional data from three rounds of the Vietnam Household Living Standards Survey (VHLSS 2008, 2012, and 2016) to investigate this issue. The unconditional quantile regression and Oaxaca–Blinder (OB) decomposition are used in this article.FindingsThe article finds the gender wage gap favouring men, especially in higher quantiles of the wage distribution. The gap in urban Vietnam was higher than in rural areas. The OB decomposition indicates that gender wage gap is mainly driven by gender discrimination. The differences in return to participation in foreign companies only contributed significantly and positively to such a gap in some models. It suggests that the gap in those models is affected by gender discrimination in employment opportunities in foreign companies. Regarding the endowment effect, some models provide the significantly negative impacts of foreign firms on gender wage inequality.Originality/valueThe study suggests that policies to reduce the gender wage gap should pay more attention to foreign firms, especially at higher wage classes.

Moving Beyond Linear Regression: Implementing and Interpreting Quantile Regression Models with Fixed Effects

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR) often result in divergent findings that are not always well understood. In light of such discrepancies, this paper reviews how to implement and interpret a range of LR, CQR, and UQR models with fixed effects. It also discusses the use of Quantile Treatment Effect (QTE) models as an alternative to overcome some of the limitations of CQR and UQR models. We then review how to interpret results in the presence of fixed effects based on a replication of Budig and Hodges's (2010) work on the motherhood penalty using NLSY79 data.

Better Dietary Knowledge and Socioeconomic Status (SES), Better Body Mass Index? Evidence from China—An Unconditional Quantile Regression Approach

Obesity is a rapidly growing public health threat in China. Improvement of dietary knowledge may potentially reduce the risk of obesity and being overweight. However, existing studies focus on measuring the mean effects of nutrition knowledge on body mass index (BMI). There is a lack of literature on the effect of dietary knowledge on BMI, and the potential heterogeneity of the effect across the whole BMI distribution and across socioeconomic status (SES) groups. This study aims to investigate the heterogeneous nature of the relationship between dietary knowledge, SES, and BMI, using data from the China Health and Nutrition Survey (CHNS) in 2015. We employed unconditional quantile regression (UQR) to assess how the relationship between dietary knowledge, SES, and BMI varies across the whole BMI distribution, and conducted subgroup analyses using different socio-economic subsamples. Results indicate that dietary knowledge had no statistically significant impact on BMI across the BMI distribution. There was a large degree of heterogeneity in the SES effect across the BMI distribution as well as a major gender difference in the SES effect on BMI. Education had a significant and inverse association with BMI across the BMI distribution, greater at higher BMI quantiles. Income growth had a larger effect on the 50th quantile of BMI for males in the middle-income group, but was not significant for females. As income increased, males without college educations had higher BMI while females with college or higher education generally had lower BMI. The findings of this study reveal the heterogeneous nature of the relationship between SES, gender, and obesity across the entire BMI distribution, suggesting that quantile regressions might offer a valuable framework for exploring the complex relationship of dietary knowledge, demographic, and socio-economic factors on obesity.