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AMEO 2015: A dataset comprising AMCAT test scores, biodata details and employment outcomes of job seekers

Published: 13 March 2016 Publication History

Abstract

More than a million engineers enter the global workforce every year. A relevant question is what determines the jobs and salaries these engineers are offered right after graduation. Previous studies have shown the influence of various factors such as college reputation, grades, the field one specializes in and market conditions for specific industries. An important input which such analyses do not have is a standardized measures of job skills done at the time of completion of studies. We present here Aspiring Minds' Employability Outcomes 2015 (AMEO 2015), a unique dataset which provides engineering graduates' employment outcomes (salaries, job titles and job locations) together with standardized assessment scores in three fundamental areas - cognitive skills, technical skills and personality. Coupled with biodata information, AMEO 2015 provides an opportunity for a unique and comprehensive study of the entry level labor market. The data could be used to make an accurate salary predictor, but also understand what influences salary and job titles in the labor market. In this paper we describe the details of the dataset and discuss a spectrum of questions around meritocracy in labor markets, biases in labor selection and other prevalent market forces it can help uncover and answer. You can download the dataset at: http://research.aspiringminds.com/resources/

References

[1]
Aspiring minds. http://www.aspiringminds.com.
[2]
ACM IKDD CODS. Data challenge, March 2016. http://ikdd.acm.org/Site/CoDS2016/.
[3]
Aspiring Minds. National employablity report - engineers annual report, 2015. http://www.aspiringminds.com/research-reports.
[4]
Aspiring Minds. Introduction to machine learning, 2016. https://lms.aspiringminds.in/home.
[5]
Government of India, Ministry of Statistics and Programme Implementation. Employment and Unemployment: NSS 61st. round, 2004. {Online; accessed 15-February-2016}.
[6]
ICDM. ASSESS: Data Mining for Educational Assessment and Feedback, 2015.
[7]
OECD. Education indicators in focus. pages 2--3, May 2012.
[8]
United States Department of Labor, Bureau of Labor Statistics (BLS). National compensation survey, 2015. {Online; accessed 15-February-2016}.

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CODS '16: Proceedings of the 3rd IKDD Conference on Data Science, 2016
March 2016
122 pages
ISBN:9781450342179
DOI:10.1145/2888451
  • General Chairs:
  • Madhav Marathe,
  • Mukesh Mohania,
  • Program Chairs:
  • Mausam,
  • Prateek Jain
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 13 March 2016

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Author Tags

  1. data mining
  2. dataset
  3. labour market

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