116 lines
3.7 KiB
YAML
116 lines
3.7 KiB
YAML
abstract: 'Wage inequality is a source of many social and economic problems, and is
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the target of mitigating programs both nationally and internationally.
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The primary step toward developing effective programs to reduce or
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eliminate wage inequality is identifying employees at risk of such
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inequalities. This study used 17,889 data points from USDOT workforce
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demographic information and salary data to analyze wage inequality and
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develop a novel framework to identify employees at risk of wage
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inequality. The evaluation framework includes (1) a salary prediction
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model, developed using artificial neural networks (ANNs), to estimate
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employees'' salaries based on demographic information and identify
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underpaid employees; (2) a minority index, which is defined to score the
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underrepresentation of each employee regarding gender, ethnicity, and
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disability, based on the current status of employee diversity in the
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organization; and (3) a decision model, which uses the salary prediction
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model and minority index based on historical data to determine if new
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employees are at risk of wage inequality. The analysis showed that
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although women are underrepresented among USDOT employees, there was no
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significant wage inequality between men and women. Furthermore, the
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lowest minority index was for White men without disability, and the
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highest for American Indian/Alaska Native women with disability. In
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addition, the results of evaluating the proposed framework had an
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accuracy of 98\%, with a harmonic mean (F1) score of 81.8\%. The
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framework developed in this study can enable any engineering
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organization to establish an unbiased wage rate for its employees,
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resulting in reduction or elimination of wage inequality and its
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consequent challenges among its employees. (C) 2020 American Society of
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Civil Engineers.'
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affiliation: 'Jafari, A (Corresponding Author), Louisiana State Univ, Bert S Turner
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Dept Construct Management, Baton Rouge, LA 70803 USA.
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Jafari, Amirhosein, Louisiana State Univ, Bert S Turner Dept Construct Management,
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Baton Rouge, LA 70803 USA.
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Rouhanizadeh, Behzad; Kermanshachi, Sharareh, Univ Texas Arlington, Dept Civil Engn,
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Arlington, TX 76019 USA.
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Murrieum, Munahil, Calif State Univ East Bay, Coll Business \& Econ, Hayward, CA
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94542 USA.'
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article-number: '04020072'
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author: Jafari, Amirhosein and Rouhanizadeh, Behzad and Kermanshachi, Sharareh and
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Murrieum, Munahil
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author-email: 'ajafari1@lsu.edu
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behzad.rouhanizadeh@mavs.uta.edu
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sharareh.kermanshachi@uta.edu
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mmurrieum@horizon.csueastbay.edu'
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author_list:
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- family: Jafari
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given: Amirhosein
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- family: Rouhanizadeh
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given: Behzad
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- family: Kermanshachi
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given: Sharareh
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- family: Murrieum
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given: Munahil
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da: '2023-09-28'
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doi: 10.1061/(ASCE)ME.1943-5479.0000841
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eissn: 1943-5479
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files: []
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issn: 0742-597X
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journal: JOURNAL OF MANAGEMENT IN ENGINEERING
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keywords-plus: 'JOB QUALITY; GENDER INEQUALITY; UNITED-STATES; RACE; GAP; IMPACT;
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WOMEN;
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LABOR; DISABILITY; EMPLOYMENT'
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language: English
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month: NOV 1
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number: '6'
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number-of-cited-references: '77'
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orcid-numbers: 'Jafari, Amirhosein/0000-0002-0356-2282
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Kermanshachi, Ph.D., F.ASCE, F.ICE, P.E., PMP, LEED AP, DBIA, ENV SP, CMIT, Sharareh
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(Sherri)/0000-0003-1952-2557'
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papis_id: ce56fe89b41b5e757e9b8e47fb6d0296
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ref: Jafari2020predictiveanalytics
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researcherid-numbers: 'Jafari, Amirhosein/B-7375-2016
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'
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times-cited: '9'
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title: Predictive Analytics Approach to Evaluate Wage Inequality in Engineering Organizations
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type: article
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unique-id: WOS:000609482800020
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usage-count-last-180-days: '0'
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usage-count-since-2013: '14'
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volume: '36'
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web-of-science-categories: Engineering, Industrial; Engineering, Civil
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year: '2020'
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