Martin Humburg, Rolf van der Velden. Skills and the Graduate Recruitment Process: Evidence from two discrete experiments RM/14/003

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1 Martin Humburg, Rolf van der Velden Skills and the Graduate Recruitment Process: Evidence from two discrete experiments RM/14/003

3 2

10 graduate characteristics in these kind of choice settings. The mixed logit model we use in this study is an advanced version of the original conditional logit model. While the mean coefficients estimated by both types of models do not differ qualitatively (not shown here) we apply a mixed logit model as it estimates the standard deviation of the mean coefficients, thereby providing information on the extent to which employers preferences for particular characteristics differ. Like any discrete choice model consistent with random utility theory, the mixed logit model assumes that an individual faces a choice amongst J alternatives in each of T choice situations. The model is well explained in Hensher and Greene (2003) and Train (2009), on which the following description is strongly based. In our specification, coefficients that enter utility are treated as varying over individuals but being constant over choice situations for each individual. The utility that individual n obtains from alternative j in choice situation t can be written as U njt = β n x njt + ε njt, where x njt is the vector of observed variables (in our case attribute levels), coefficient vector β n is unobserved for each n and varies in the population with density f(β n θ * ) where θ * are the true parameters of this distribution, and ε njt is an unobserved random term that is distributed iid extreme value over choice situations, individuals, and alternatives. Conditional on β n, the probability that individual n chooses alternative i in choice situation t is logit: (1) L nit n ' xnit e ( n). n ' xnjt e j The unconditional probability is the integral of the conditional probability over all possible values of β n, which depends on the parameters of the distribution of β n. We specify the density of β n to be normal with mean b and covariance W. This allows the coefficients to have positive or negative signs for different decision makers. The choice probability under this density becomes: (2) Qnit b, W ) ( n ' xnjt j e n ' xnit e ( b, W ) d. n n where ϕ(β n b,w) is the normal density with mean b and covariance W. b and W are the parameters of interest to be estimated. If, as normally the case in stated choice experiments, a sampled individual faces a sequence of choice situations, it is the probability of the sequence of observed choices which matters for maximum likelihood estimation. Let i(n,t) denote the alternative that individual n chose in choice situation t. Conditional on β n, the probability that individual n made this sequence of choices is the product of logit formulas: (3) S T n ' xni( n, t ) t e n) n xnjt. ' t 1 e j n( The unconditional probability for the observed sequence of choices is [see Train 2009]: 9

16 contrast to the first stage of the experiment, the estimated parameters represent employers preferences for actual skills, not for some other dimension of human capital. [Table 4] As expected, employers always prefer higher skill levels over lower skill levels, no matter what skill type they are considering. There is, however, an obvious asymmetry between the penalty for low 10 skill levels and the reward for high skill levels: the positive coefficients of the dummies indicating that graduates have a high skill level are always less than half the size, in absolute terms, of the negative coefficients of the dummies indicating that graduates have a low skill level. With respect to the probability of being hired, the reward for having a high skill level as opposed to an average one is largest for professional expertise and smallest for commercial/entrepreneurial skills. The reward for having a high skill level is second-largest for interpersonal skills, followed by innovative/creative skills and strategic/organizational skills. The reward for a high level of general academic skills lies somewhere in between that of strategic-organizational skills and commercial/entrepreneurial skills. These differences are, however, not always statistically significant. 11 The penalty 12 for having low skill levels as opposed to average skill levels is larger than the premium for having high skill levels. Two findings stand out. First, the negative coefficient of the dummy indicating that graduates have a low level of interpersonal skills is largest. While not statistically significantly different from that of professional skills, this result highlights the importance of interpersonal skills in today s workplaces. Employers attach a high value to having at least an average level of interpersonal skills, as one team member with a low level of interpersonal skills can endanger the functioning and thus productivity of an entire team. Second, while the reward for a high level of commercial/entrepreneurial skills is relatively small, the penalty for a low level is relatively large, comparable to that of having a low level of professional expertise. In line with the findings of Mason (1998, 1990) employers avoid employees with no commercial intuition or sense of entrepreneurship. The reward for having a high skill level is similar for innovative/creative and strategic/organizational skills. However, the penalty for having a low skill level is higher for innovative/creative skills. The penalty for having a low level, as opposed to an average one, is similar for strategic/organizational and general academic skills. The coefficient of the none-option dummy is negative and highly significant, indicating that employers are on average better off hiring a graduate with average levels of all skills and earning the average salary than hiring no one. However, when running a model with the bottom skill level and a starting salary of 25% above average as the reference category (not 10 Note that in accordance with the attribute levels used in the choice experiment, a graduate has a low skill level if he or she belongs to the bottom 25% of his or her reference group, and a high level of skill if he or she belongs to the top 25% of his or her reference group. 11 The coefficient of the dummy indicating a high level of professional expertise is not statistically significantly different from that of interpersonal skills, yet is statistically significantly different from the other coefficients (at the 10% level for innovative/creative skills). The coefficient of a high level of interpersonal skills is statistically significantly different from that of general academic skills and commercial/entrepreneurial skills (10% level and 5% level, respectively) and the reward to a high level of innovative/creative skills as well as strategic/organizational skills is statistically significantly different from that of commercial/entrepreneurial skills (10% level). 12 We deliberately ran the model with average skills as the reference category to emphasise that there is a penalty for having low skill levels, rather than a reward for having average skill levels. The vast majority of employers interviewed in-depth state that they consider an average skill level as the minimum skill level necessary to come into consideration for the job (note that in our definition, the average level contains 50% of individuals around the mean). This implies that graduates with skill deficiencies are seriously disrupting the production process, incurring costs to the organization. 15

19 employability. When envisaging employment with a particular employer, graduates need more detailed information on this employer s preferences when making decisions regarding their skill profile. Merely depending on characteristics such as firm size or the sector is insufficient for making optimal choices. Individuals who aim to maximize their average chances on the labour market without having a particular employer in mind do best by investing in their professional expertise and interpersonal skills. The implications of our results for higher education institutions are similar. Aggregate skill supply should match aggregate skill demand reasonably well, and some graduate skill profiles need to be developed in higher quantity than others. Our findings show that some types of skills are more important for labour market entry while others are more important for individuals future careers. From the employer s perspective types of skills such as commercial/entrepreneurial skills, innovative/creative skills and strategic/organizational skills are more important and better developed after having acquired a few years of work experience. This suggests that initial higher education should focus on the transmission of occupation-specific knowledge, problem-solving skills, general academic skills and interpersonal skills. Unless it is a central characteristic of a study programme aligned to the needs of particular employers, providing individuals with high levels of commercial/entrepreneurial skills, innovative/creative skills and strategic/organizational skills may not be optimal for graduates employability. 18

20 References Aigner, D. J. & Cain, G. G. (1977). Statistical Theories of Discrimination in Labor Markets. Industrial and Labor Relations Review, 1977, 30 (1), Allen, J., & van der Velden, R. (2001). Educational Mismatches versus Skill Mismatches: Effects on Wages, Job Satisfaction, and On-the-Job Search. Oxford Economic Papers, 53(3), Allen, J. (2011). Mobilization of Human Resources. In: J. Allen, & R. van der Velden (eds.), The Flexible Professional in the Knowledge Society: New Challenges for Higher Education. Dordrecht, Heidelberg, London, New York: Springer, Allen, J. & van der Velden, R. (2011). The Flexible Professional in the Knowledge Society: Required Competences and the Role of Higher Education. In: J. Allen, & R. van der Velden (eds.), The Flexible Professional in the Knowledge Society: New Challenges for Higher Education, Higher Education Dynamics, 35. Dordrecht, Heidelberg, London, New York: Springer, Altonji, J.G. & Pierret, C.R. (2001). Employer Learning and Statistical Discrimination. Quarterly Journal of Economics, 116 (1), Arrow, K.J. (1998). What has Economics to Say about Racial Discrimination? Journal of Economic Perspectives, 12 (2), Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70, van Beek, K. W. H., Koopmans, C. C. & van Praag, B. M. S. (1997). Shopping at the labour market: A real tale of fiction. European Economic Review, 41(2), Biesma, R. G., Pavlova, M., van Merode, G. G., & Groot, W. (2007). Using conjoint analysis to estimate employers preferences for key competencies of master level Dutch graduates entering the public health field. Economics of Education Review, 26(3), Bils, M. & Klenow, P. (2000). Does Schooling Cause Growth? American Economic Review, 90(5), Borghans, L. & de Grip, A. (eds) (2000). The Overeducated Worker? The Economics of Skill Utilization. Cheltenham: Edward Elgar. Borghans, L., Romans, M. & Sauermann, J. (2010). What makes a good conference? Analysing the preferences of labour economists. Labour Economics, 17(5), Bowles, S. & Gintis, H. (1976). Schooling in Capitalist America: Educational Reform and the Contradictions of Economic Life. New York: Basic Books. Carroll, J. D., & Green, P. E. (1995). Psychometric methods in marketing research: Part I, conjoint analysis. Journal of Marketing Research, Cattin, P., & Wittink, D. R. (1982). Commercial use of conjoint analysis: A Survey. Journal of Marketing, 46, Dede, C. (2010). Comparing Frameworks for 21st Century Skills. In J. Bellanca, & R. Brandt (eds.), 21st Century Skills. Bloomington, IN: Solution Tree Press, Eriksson, S., Johansson, P. & Langenskiöld, S. (2012). What is the Right Profile for Getting a Job? A Stated Choice Experiment of the Recruitment Process. IZA Discussion Papers 6691, Institute for the Study of Labor (IZA). Felstead, A., Gallie, D., Green, F., & Zhou, Y. (2007). Skills at Work, 1986 to Universities of Oxford and Cardiff, ESRC Centre on Skills, Knowledge and Organisational Performance. Hartog, J. (1992). Capabilities, allocation and earnings. Dordrecht: Kluwer Academic. 19

21 Heckman, J. J., Stixrud, J., & Urzua, S. (2006). The Effects Of Cognitive and Noncognitive Abilities On Labor Market Outcomes and Social Behavior. Journal of Labor Economics, 24 (3), Heijke, H., Meng, C., & Ris, C. (2003). Fitting to the job: the role of generic and vocational competencies in adjustment and performance. Labour Economics, 10, Hensher, D., & Greene, W. (2003). The Mixed Logit model: The state of practice. Transportation, 30(2), Humburg, M., & van der Velden, R. (2013). What is expected of higher education graduates in the 21st century? Research Memorandum 2013/13, Research Centre for Education and the Labour Market (ROA). Forthcoming in: J. Buchanan, D. Finegold, K. Mayhew, & C. Warhurst (eds.), Oxford Handbook of Skills and Training. Oxford: Oxford University Press. McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (ed.), Frontiers in Economics, New York: Academic Press, Knight, J.B. (1979). Job Competition, Occupational Production Functions, and Filtering. Oxford Economic Papers, 31 (2), pp Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated choice methods: analysis and applications. New York: Cambridge University Press. Mason, G., Williams, G., & Cranmer, S. (2009). Employability Skills Initiatives in Higher Education: what effect do they have on graduate labour market outcomes? Education Economics, 17 (1), Mason, G. (1998). Diversity and change: The challenges facing chemistry higher education. London: Royal Society of Chemistry/Council for Industry and Higher Education. Mason, G. (1999). The labour market for engineering, science and IT graduates: Are there mismatches between supply and demand. Research Report No. 112, Department for Education and Employment, London. Nelson, R., & Phelps, E. (1966). Investment in Humans, Technological Diffusion, and Economic Growth. American Economic Review, 56 (1/2), Phelps, E. S. (1972). The Statistical Theory of Racism and Sexism. American Economic Review, 62, Pouliakas, K., & Theodossiou, I. (2010). Measuring the Utility Cost of Temporary Employment Contracts Before Adaptation: A Conjoint Analysis Approach. Economica, 77(308), Revelt, D., & Train, K. (1998). Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level. The Review of Economics and Statistics, 80(4), Rossi, P., McCulloch, R., & Allenby, G. (1996). On the Value of Household Purchase History Information in Target Marketing. Marketing Science, 15, Ryan, M. (2004). Discrete choice experiments in health care. British Medical Journal, 328, Sattinger, M. (1993). Assignment models of the distribution of earnings. Journal of Economic Literature, 31, Sawtooth Software (2000). An Overview and Comparison of Design Strategies for Choice- Based Conjoint Analysis. Research Paper Series, Sawtooth Software. Schultz, T. W. (1975). The Value of the Ability to Deal with Disequilibria. Journal of Economic Literature, 13 (3), Spence, M. (1973). Job Market Signaling. Quarterly Journal of Economics, 87 (3), Train, K. (2009). Discrete Choice Models with Simulation, Cambridge books, Cambridge: Cambridge University Press. van der Velden, R. (2011). De effecten van betrouwbaarheid van onderwijsdiploma s op arbeidsproductiviteit: toepassing van een simulatiemodel. In: J.Dronkers (ed), Goede 20

22 bedoelingen in het onderwijs: Kansen en missers, boekaflevering Mens en Maatschappij,

23 Table 1: Sample descriptives N Per cent Country Czech Republic France Germany Italy Netherlands Poland Spain Sweden United Kingdom Occupational field Electro-technology Engineering Financial services ICT Legal Services Media and Communication Policy Firm size < >

25 Table 2: Mixed logit model of selecting graduates for a job interview Attributes with normally distributed coefficients: Degree: Bachelor s Master s Mean coefficient (0.05) SD of coefficient 0.977*** (0.07) Doctorate Mean coefficient *** (0.06) SD of coefficient 1.195*** (0.08) Match of field of study and job tasks Unrelated Incomplete Mean coefficient 0.709*** (0.06) SD of coefficient 0.762*** (0.10) Complete Mean coefficient 1.137*** (0.07) SD of coefficient 1.228*** (0.07) Relevant work experience No 1 year Mean coefficient 0.556*** (0.05) SD of coefficient 0.775*** (0.07) 2 years Mean coefficient 0.711*** (0.06) SD of coefficient 1.179*** (0.08) Study experience abroad No Partly Mean coefficient 0.123** (0.04) SD of coefficient 0.542*** (0.10) Entirely Mean coefficient (0.05) SD of coefficient 0.795*** (0.07) 24

26 Table 2: Mixed logit model of selecting graduates for a job interview, continued Grades Below average Mean coefficient 0.355*** (0.05) SD of coefficient 0.575*** (0.07) Above average Mean coefficient 0.549*** (0.06) SD of coefficient 0.784*** (0.08) Top 10% Mean coefficient 0.579*** (0.06) SD of coefficient 0.790*** (0.08) Prestige/reputation of university High Mean of coefficient 0.130*** (0.04) SD of coefficient 0.457*** (0.07) Fixed coefficient attributes Salary 25% below average 0.935*** (0.08) 10% below average 0.980*** (0.07) 1.044*** (0.07) 10% above average 0.600*** (0.06) 25% above average None (0.16) Standard errors adjusted for 903 clusters (individuals) in parentheses. * p < 0.05, ** p < 0.01, *** p < Estimation based on 1000 random draws per iteration. Log likelihood , N= The Wald-test of the joint significance of the attributes with 17 degrees of freedom is and has a p-value > The model has no alternative-specific constants, as is common practice with data from so-called unlabeled choice experiments, where randomly generated alternatives (graduates) have no utility beyond the characteristics attributed to them in the experiment. 25

27 Preference Figure 3: Graphical illustration of salary attribute coefficients displayed in Table 2 1,2 1 0,8 0,6 0,4 0, Deviation from average salary for this position in % Table 3: Employers' willingness to pay for CV attribute levels Mean WTP SD Degree: Bachelor s Master s 1.0% 22.0% Doctorate -8.0% 26.9% Match of field of study and job tasks Unrelated Incomplete 16.0% 17.2% Complete 25.6% 27.6% Relevant work experience No 1 year 12.5% 17.5% 2 years 16.0% 26.6% Study experience abroad No Partly 2.8% 12.2% Entirely -0.1% 17.9% Grades Below average 8.0% 12.9% Above average 12.4% 17.7% Top 10% 13.0% 17.8% Prestige/reputation of university High 2.9% 10.3% Willingness to pay is assumed to be distributed normally in the population with mean MeanWTP and standard deviation SD. For calculation, attribute level mean coefficients (and their standard deviations) are multiplied by 10 and divided by the difference between the coefficients of the average salary level and the 10% above average salary level (0.444). 26

28 Table 4: Mixed logit model of selecting graduates for hiring Attributes with normally distributed coefficients: Professional expertise High Mean coefficient *** (0.08) SD of coefficient *** (0.12) Low Mean coefficient *** (0.09) SD of coefficient *** (0.12) General academic skills High Mean coefficient *** (0.05) SD of coefficient *** (0.07) Low Mean coefficient *** (0.08) SD of coefficient *** (0.10) Innovative/creative skills High Mean coefficient *** (0.05) SD of coefficient *** (0.10) Low Mean coefficient *** (0.07) SD of coefficient *** (0.21) Strategic/organizational skills High Mean coefficient *** (0.06) SD of coefficient *** (0.11) Low Mean coefficient *** (0.07) SD of coefficient *** (0.08) Interpersonal skills High Mean coefficient *** (0.06) SD of coefficient *** (0.08) Low Mean coefficient *** (0.09) SD of coefficient *** (0.10) 27

29 Table 4: Mixed logit model of selecting graduates for hiring, continued Commercial/entrepreneurial skills High Mean coefficient *** (0.06) SD of coefficient *** (0.13) Low Mean coefficient *** (0.07) SD of coefficient *** (0.09) Fixed coefficient attributes Salary 25% below average (0.07) 10% below average (0.06) 10% above average *** (0.07) 25% above average *** (0.1) None *** (0.12) Standard errors adjusted for 903 clusters (individuals) in parentheses. * p < 0.05, ** p < 0.01, *** p < Estimation based on 1000 random draws per iteration. Log likelihood , N= The Wald-test of the joint significance of the attributes with 17 degrees of freedom is and has a p-value > The model has no alternative-specific constants, as is common practice with data from so-called unlabelled choice experiments, where randomly generated alternatives (graduates) have no utility beyond the characteristics attributed to them in the experiment. 28

30 Preference Figure 4: Graphical illustration of salary attribute coefficients displayed in Table 4 0, ,2-0,4-0,6-0,8-1 Deviation from average salary for this position in % Table 5: Employers' willingness to pay for skills Mean WTP SD Professional expertise High 14.9% 28.5% Low -35.9% 38.2% General academic skills High 9.0% 19.3% Low -26.5% 33.5% Innovative/creative skills High 11.5% 19.4% Low -30.7% 34.6% Strategic/organizational skills High 11.1% 20.4% Low -25.8% 25.3% Interpersonal skills High 12.4% 24.8% Low -39.1% 39.7% Commercial/entrepreneurial skills High 7.3% 33.4% Low -32.8% 32.5% Willingness to pay is assumed to be distributed normally in the population with mean MeanWTP and standard deviation SD. For calculation, attribute level mean coefficients (and their standard deviations) are multiplied by 10 and divided by the coefficient of the 10% above average salary coefficient multiplied by -1 (0.340). 29

31 Appendix Table A1: Proportional frequencies of attributes in choice set and selected graduate profile Attribute Proportional Frequency in Choice Set Proportional Frequency in Selected Graduate Profile First Stage (CV attributes) Degree: Bachelor s Master s Doctorate Match of field of study and job task Unrelated Incomplete Complete Relevant work experience No year years Study experience abroad No Partly Entirely Grades Below average Above average Top 10% Prestige/reputation of university High Salary 25% below average % below average % above average % above average

32 Table A1: Proportional frequencies of attributes in choice set and selected graduate profile, continued Attribute Proportional Frequency in Choice Set Proportional Frequency in Selected Graduate Profile Second Stage (Hiring) Professional expertise Top 25% Bottom 25% General academic skills Top 25% Bottom 25% Innovative/creative skills Top 25% Bottom 25% Strategic/organizational skills Top 25% Bottom 25% Interpersonal skills Top 25% Bottom 25% Commercial/entrepreneurial skills Top 25% Bottom 25% Salary 25% below average % below average % above average % above average

33 Figure A1: Concave relationship between productivity and skill levels Figure A1 contains scatter plots indicating the concave relationship between productivity (the mixed logit coefficients) and skill levels by type of skill. The fitted values are based on the prediction for the mixed logit coefficient from a regression of the mixed logit coefficient on skill level and skill level squared. 32

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