Employers’ preferences for IT-retrainees: evidence from a discrete choice experiment

Purpose - The purpose of this paper is to present the results of a discrete choice experiment (DCE) on the competencies of potential information technology (IT)-retrainees. The results give insights in the monetary value and relative returns to both soft and hard skills. Design/methodology/approach - The authors apply a DCE in which the authors propose seven pairs of hypothetical candidates to employers based in the municipality of Amsterdam, the Netherlands. These hypothetical candidates differ on six observable skill attributes and have different starting wages. The authors use the inference from the DCE to calculate the marginal rates of substitution (MRS). The MRS gives an indication of the monetary value of each skill attribute. Findings - Employers prefer a candidate who possesses a degree in an exact field over a similar candidate from another discipline. Programming experience from previous jobs is the most highly valued characteristic for an IT-retrainee. Employers would pay a candidate with basic programming experience a 53 percent higher starting wage. The most high-valued soft skill is listening skills, for which employers are willing to pay a 46 percent higher wage. The results of this paper show that both hard and soft skills are important, but not all soft skills are equally important. Originality/value - The results on the returns to skills provide guidelines to tailor IT training and retraining programs to the needs of the business environment. A key strength of this paper is that the authors have information on the preference orderings for different skills and kinds of experience.

[1]  Lawrence F. Katz,et al.  The Value of Postsecondary Credentials in the Labor Market: An Experimental Study , 2014 .

[2]  D. Deming,et al.  The Growing Importance of Social Skills in the Labor Market , 2017 .

[3]  Dan Silverman,et al.  Factors Determining Callbacks to Job Applications by the Unemployed: An Audit Study , 2015, RSF.

[4]  Ryan A. Beasley,et al.  Technical Aspects of Correspondence Studies , 2016 .

[5]  Mary K. Hamman,et al.  College Major, Internship Experience, and Employment Opportunities: Estimates from a Résumé Audit , 2016 .

[6]  Dieter Verhaest,et al.  Do Employers Prefer Overqualified Graduates? A Field Experiment , 2018 .

[7]  Robert T. Jensen,et al.  The (Perceived) Returns to Education and the Demand for Schooling , 2010 .

[8]  Alex Radermacher,et al.  Gaps between industry expectations and the abilities of graduates , 2013, SIGCSE '13.

[9]  S. Eriksson,et al.  Do Employers Use Unemployment as a Sorting Criterion When Hiring? Evidence from a Field Experiment , 2014, SSRN Electronic Journal.

[10]  W. Groot,et al.  Preferences of Bulgarian consumers for quality, access and price attributes of healthcare services—result of a discrete choice experiment , 2017, The International journal of health planning and management.

[11]  Paul J. Kovacs,et al.  DETERMINING CRITICAL SKILLS AND KNOWLEDGE REQUIREMENTS OF IT PROFESSIONALS BY ANALYZING KEYWORDS IN JOB POSTINGS , 2008 .

[12]  J. Giret,et al.  The effect of soft skills on French post-secondary graduates’ earnings , 2018, International Journal of Manpower.

[13]  Rolf van der Velden,et al.  Skills and the graduate recruitment process: Evidence from two discrete choice experiments , 2015 .

[14]  Robert B. Mitchell,et al.  Industry Perceptions of the Competencies Needed by Computer Programmers: Technical, Business, and Soft Skills , 2006, J. Comput. Inf. Syst..

[15]  E. Hanushek,et al.  Coping with Change: International Differences in the Returns to Skills , 2016, SSRN Electronic Journal.

[16]  H. Patrinos,et al.  Returns to investment in education: a further update , 2002 .

[17]  Mark F. Owens,et al.  The Effects of Unemployment and Underemployment on Employment Opportunities , 2017 .

[18]  Dieter Verhaest,et al.  Unemployment or Overeducation: Which is a Worse Signal to Employers? , 2018, De Economist.

[19]  Princely Ifinedo,et al.  Interactions between Organizational Size, Culture, and Structure and Some it Factors in the Context of ERP Success Assessment: An Exploratory Investigation , 2007, J. Comput. Inf. Syst..

[20]  Jill Hewitt,et al.  Case Study: A Retraining Solution to Skills Shortages , 1989 .

[21]  Joanna Coast,et al.  Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. , 2012, Health economics.

[22]  Richard Norman,et al.  USING DISCRETE CHOICE EXPERIMENTS TO VALUE GENERIC PREFERENCE-BASED MEASURES: A SYSTEMATIC REVIEW , 2016 .

[23]  Christian A. Vossler,et al.  Contemporary Guidance for Stated Preference Studies , 2017, Journal of the Association of Environmental and Resource Economists.

[24]  G. G. Merode,et al.  Using conjoint analysis to estimate employers preferences for key competencies of master level Dutch graduates entering the public health field , 2007 .

[25]  Mandy Ryan,et al.  Using discrete choice experiments to value health and health care , 2008 .

[26]  Angela L. Duckworth,et al.  The Economics and Psychology of Personality Traits , 2008, The Journal of Human Resources.

[27]  Tracie M. Dodson,et al.  Curriculum Decisions: Assessing and Updating IS Curriculum , 2008, AMCIS.

[28]  Luisa Helena Pinto,et al.  Perceived employability of business graduates: The effect of academic performance and extracurricular activities , 2017 .

[29]  James M. Bieman,et al.  Competencies of exceptional and nonexceptional software engineers , 1995, J. Syst. Softw..

[30]  S. Watson,et al.  Understanding the consequences of consequentiality: Testing the validity of stated preferences in the field , 2012 .

[31]  Gary A. Davis,et al.  AN EMPIRICAL STUDY OF THE RELATIVE IMPORTANCE OF SPECIFIC TECHNOLOGY SKILLS, GENERAL BUSINESS SKILLS, AND GENERAL TECHNOLOGY SKILLS , 2009 .

[32]  J.A.M. Heijke,et al.  Fitting to the job: the role of generic and vocational competenties in adjustment and performance , 2003 .

[33]  Christian A. Vossler,et al.  Truth in Consequentiality: Theory and Field Evidence on Discrete Choice Experiments , 2010 .

[34]  Jeffrey W. Merhout,et al.  Soft Skills versus Technical Skills: Finding the Right Balance for an IS Curriculum , 2009, AMCIS.

[35]  Angela L. Duckworth,et al.  Personality Psychology and Economics , 2011, SSRN Electronic Journal.

[36]  J. Heckman,et al.  Hard Evidence on Soft Skills , 2012, Labour economics.

[37]  Michael Hout,et al.  Social and Economic Returns to College Education in the United States , 2012 .

[38]  Dieter Verhaest,et al.  Work Hard or Play Hard? Degree Class, Student Leadership and Employment Opportunities , 2021, Oxford Bulletin of Economics and Statistics.

[39]  Bart H. H. Golsteyn,et al.  De arbeidsmarkt naar opleiding en beroep tot 2022 , 2017 .

[40]  Axel Böttcher,et al.  Expectations and deficiencies in soft skills , 2012, Proceedings of the 2012 IEEE Global Engineering Education Conference (EDUCON).

[41]  Mark E. McMurtrey,et al.  Critical Skill Sets of Entry-Level IT Professionals: An Empirical Examination of Perceptions from Field Personnel , 2008, J. Inf. Technol. Educ..

[42]  Helen E. Higson,et al.  Graduate Employability, 'Soft Skills' Versus 'Hard' Business Knowledge: A European Study , 2008 .

[43]  Mark P. Sena,et al.  A Modular Approach to Delivering an Introductory MIS Course , 2010 .

[44]  Wendy Ceccucci,et al.  Integrating Soft Skill Competencies Through Project-based Learning Across the Information Systems Curriculum , 2010 .

[45]  Matthew J. Notowidigdo,et al.  Duration Dependence and Labor Market Conditions: Evidence from a Field Experiment* , 2013 .

[46]  S. Groothuis,et al.  Discrete-choice experiments versus rating scale exercises to evaluate the importance of attributes , 2015, Expert review of pharmacoeconomics & outcomes research.

[47]  Katryna Johnson Non-Technical Skills for IT Professionals in the landscape of Social Media , 2015 .