arbiter
JournalsAccount

Estimating the impact of NABE member characteristics on compensation using quantile regression

Christopher Swann & Lilianna Ruby

Business Economics2025https://doi.org/10.1057/s11369-025-00418-1article
AJG 1ABDC B
Weight
0.50

Abstract

Since 1964, NABE has provided members with a profile of members’ salary, compensation, and characteristics through its bi-annual salary survey. Since 2006, several econometric estimates of the relationship between member characteristics and compensation have been developed using the salary survey data. This paper presents the results of this year’s model estimation based on the 2024 salary survey published in August 2024. The base model is similar in structure to past models and estimated using ordinary least squares (OLS). However, we extended the analysis by applying quantile regression, which enabled estimating the impact of variables by quartile in the distribution.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1057/s11369-025-00418-1

Or copy a formatted citation

@article{christopher2025,
  title        = {{Estimating the impact of NABE member characteristics on compensation using quantile regression}},
  author       = {Christopher Swann & Lilianna Ruby},
  journal      = {Business Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1057/s11369-025-00418-1},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Estimating the impact of NABE member characteristics on compensation using quantile regression

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.