2022/02/01 by Fernando Rios-Avila, Fernando Ríos‐Avila, Michelle Lee Maroto +1 · 112 citations
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Computer science #Econometrics #Economics #Fixed effects model #Labor market dynamics and wage inequality #Linear regression #Mathematics #Panel data #Quantile #Quantile regression #Range (aeronautics) #Regression #Regression analysis #Replication (statistics) #Retirement, Disability, and Employment #Statistics #Wage
paper · doi:10.1177/00491241211036165
published in Sociological Methods & Research 53(2), 639-682 (SAGE Publishing)
openalex publication_date 2022/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
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 work on the motherhood penalty using NLSY79 data.