abstract: 'Wage inequality is a source of many social and economic problems, and is

  the target of mitigating programs both nationally and internationally.

  The primary step toward developing effective programs to reduce or

  eliminate wage inequality is identifying employees at risk of such

  inequalities. This study used 17,889 data points from USDOT workforce

  demographic information and salary data to analyze wage inequality and

  develop a novel framework to identify employees at risk of wage

  inequality. The evaluation framework includes (1) a salary prediction

  model, developed using artificial neural networks (ANNs), to estimate

  employees'' salaries based on demographic information and identify

  underpaid employees; (2) a minority index, which is defined to score the

  underrepresentation of each employee regarding gender, ethnicity, and

  disability, based on the current status of employee diversity in the

  organization; and (3) a decision model, which uses the salary prediction

  model and minority index based on historical data to determine if new

  employees are at risk of wage inequality. The analysis showed that

  although women are underrepresented among USDOT employees, there was no

  significant wage inequality between men and women. Furthermore, the

  lowest minority index was for White men without disability, and the

  highest for American Indian/Alaska Native women with disability. In

  addition, the results of evaluating the proposed framework had an

  accuracy of 98\%, with a harmonic mean (F1) score of 81.8\%. The

  framework developed in this study can enable any engineering

  organization to establish an unbiased wage rate for its employees,

  resulting in reduction or elimination of wage inequality and its

  consequent challenges among its employees. (C) 2020 American Society of

  Civil Engineers.'
affiliation: 'Jafari, A (Corresponding Author), Louisiana State Univ, Bert S Turner
  Dept Construct Management, Baton Rouge, LA 70803 USA.

  Jafari, Amirhosein, Louisiana State Univ, Bert S Turner Dept Construct Management,
  Baton Rouge, LA 70803 USA.

  Rouhanizadeh, Behzad; Kermanshachi, Sharareh, Univ Texas Arlington, Dept Civil Engn,
  Arlington, TX 76019 USA.

  Murrieum, Munahil, Calif State Univ East Bay, Coll Business \& Econ, Hayward, CA
  94542 USA.'
article-number: '04020072'
author: Jafari, Amirhosein and Rouhanizadeh, Behzad and Kermanshachi, Sharareh and
  Murrieum, Munahil
author-email: 'ajafari1@lsu.edu

  behzad.rouhanizadeh@mavs.uta.edu

  sharareh.kermanshachi@uta.edu

  mmurrieum@horizon.csueastbay.edu'
author_list:
- family: Jafari
  given: Amirhosein
- family: Rouhanizadeh
  given: Behzad
- family: Kermanshachi
  given: Sharareh
- family: Murrieum
  given: Munahil
da: '2023-09-28'
doi: 10.1061/(ASCE)ME.1943-5479.0000841
eissn: 1943-5479
files: []
issn: 0742-597X
journal: JOURNAL OF MANAGEMENT IN ENGINEERING
keywords-plus: 'JOB QUALITY; GENDER INEQUALITY; UNITED-STATES; RACE; GAP; IMPACT;
  WOMEN;

  LABOR; DISABILITY; EMPLOYMENT'
language: English
month: NOV 1
number: '6'
number-of-cited-references: '77'
orcid-numbers: 'Jafari, Amirhosein/0000-0002-0356-2282

  Kermanshachi, Ph.D., F.ASCE, F.ICE, P.E., PMP, LEED AP, DBIA, ENV SP, CMIT, Sharareh
  (Sherri)/0000-0003-1952-2557'
papis_id: ce56fe89b41b5e757e9b8e47fb6d0296
ref: Jafari2020predictiveanalytics
researcherid-numbers: 'Jafari, Amirhosein/B-7375-2016

  '
times-cited: '9'
title: Predictive Analytics Approach to Evaluate Wage Inequality in Engineering Organizations
type: Article
unique-id: WOS:000609482800020
usage-count-last-180-days: '0'
usage-count-since-2013: '14'
volume: '36'
web-of-science-categories: Engineering, Industrial; Engineering, Civil
year: '2020'