Abstract
We identify the causal effects of three types of internships (mandatory intra-curricular, voluntary intra-curricular, and voluntary extra-curricular) among university graduates on job interview and hiring chances, and explore the mechanisms underlying these effects. To this end, we perform a vignette experiment among HR professionals in Belgium. Our results indicate that internships improve one’s job interview and hiring chances, with voluntary extra-curricular internships having the strongest effect. With respect to the mechanisms, we find that internships improve employers’ perceptions about the job seekers’ skills acquired during the educational career, their pre-existing abilities and motivations, and their knowledge of the job content and working conditions. The first two types of perceptions are also found to be strong predictors of the hiring outcomes. The effects of internships on hiring chances are not found to be reduced or reinforced by the presence of other work experiences (student work, volunteering).
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Data Availability
The data and replication files can be obtained by sending an email to the corresponding author.
Notes
While also Neyt et al. (2022) provided causal estimates on the impact of internships, their focus was on internships during secondary rather than tertiary education.
The other quasi-experimental studies were based on field experiments with fictitious resumes (Baert et al., 2021; Nunley et al., 2016) or stated choice experiments (Stasio, 2014; Stasio & Werfhorst, 2016). Also Jaeger et al. (2020) investigated the impact of internships in an experimental setting, but looked at access to another (second) internship.
Note that, while human capital models (HCM) and sorting models (SM) are often presented as rivalling models, they are not incompatible with each other (Weiss, 1995). For instance, Spence (2002) developed a model in which education serves both as a producer of human capital and a signal of pre-existing skills. In addition, the distinction between HCM and SM is sometimes framed in terms of a distinction between a context with (HCM) and one without perfect information (SM) about job seekers’ productivity. However, while the existence of asymmetric information is crucial within the sorting framework, perfect information may be considered to be rather a simplifying assumption within HCM (Bills, 2003). Indeed, as formally shown by Graetz (2021), the core implication of HCM that investing in education is likely to be effective both from a private and social point of view is not altered in a context of qualifications being merely proxies (instead of perfect measures) of the skills obtained through education in combination with a sufficiently high speed of employer learning about these skills once employed.
Stiglitz (1975) developed both the idea of on-the-job and educational screening, with the latter being a type of educational sorting that is somewhat different from educational signalling. The main difference between these types of sorting is related to whether the informed (student) or the uninformed (firm) moves first (Stiglitz & Weiss, 1990).
Strictly speaking, this differentiation is also possible based on quasi-experimental designs. However, in practice, there is usually exogenous variation in one, if any, of these internship types only within the same context.
Another approach may be to rely on direct elication questions. However, this direct approach is less suited when the relative importance of various attributes are to be compared (Hanley, Mourato, & Wright, 2001), or when judements may depend on the context (Wallander, 2009). Moreover, this approach is more prone to all sorts of cognitive biases when participating in surveys such as social desirability (Auspurg and Hinz, 2015; Wallander, 2009) or acquiescence bias (Hanley et al., 2001), and to lack of awareness of unconscious biases when making decisions in real life (Wallander, 2009).
To improve the external validity of the vignette experiment, we ensured the vignette descriptions accurately represent reality and include all essential information necessary to evaluate the candidates. This was checked through pilot studies, wherein respondents assessed whether the descriptions were comprehensive and realistic. Additionally, respondents provided feedback on survey length.
We included masters from the sub-disciplines History, Linguistics and Literature, Philosophy, and Art sciences (Humanities), Economics, Law, Political sciences, and Sociology (Social Sciences), Math, Statistics, and Computer sciences (Basic Sciences), Bio-engineering, Biosciences, Industrial sciences, and Engineering (Applied Sciences). Appendix Table A3 includes descriptive statistics on the composition of our sample with regard to these disciplines.
Additional analyses with respect to this potential social desirability bias (available upon request), also suggest this not to be a major issue, as results are largely similar to those for the full sample.
Mandatory intra-curricular internships are compulsory internships embedded in the study programme. Voluntary intra-curricular internships are facultative internships which are an elective in the study programme. Voluntary extra-curricular internships are internships that are not included in the curriculum and are chosen on the student’s own initiative.
2 months is the maximum duration for extra-currular interships to be recognized by the university. Both intra-curricular and (recognized) extra-curricular internships are unpaid.
While this is a standard characteristic of intra-curricular internships, also extra-curricular intersnhips must be related to the study programme in order to be recognized by the university.
We assigned the vignettes to sets using the SAS macro% mktblock. This macro facilitates the allocation of the design to different sets, aiming to achieve maximum orthogonality and level balance within each individual set.
The post-survey correlations between our vignette factors do not exceed 0.053 (Cramer’s V).
Given the aforementioned design features, also the post-survey vignette level balances and correlations within various subsamples are satisfactory. These results are available upon request.
The statements for all mechanisms were presented in a random order within a single question after each vignette. Confirmatory factor analysis confirms that the data fit our hypothesized theoretical framework. First, the standardized root mean squared residual (SRMR) is equal to 0.042, where a value of less than 0.08 is considered a relatively good fit between the hypothesized model and the data (Hu & Bentler, 1999). Second, the coefficient of determination (CD) equals 0.990, which also points towards a good fit (a value of one indicates a perfect fit). Note that, while it is important to account for clustering when performing confirmatory factor analysis (see Huang & Cornell, 2016), this impedes the use of fit measures other than SRMR and CD.
According to Randstad (2019), 54% of the Belgian job seekers rely on the PEA to search for jobs and 28% find their job by means of the PEA. Also among the highly skilled, the PEA is a popular job search channel (about 50% rely on the PEA to find jobs). According to Randstad, the dominance of the PEA is partly due to their early adoption of digital tools and their collaborations with many private actors, agencies and job sites.
For our analyses, we only retained observations from participants who answered all statements for all five vignettes.
To account for the potential correlation of observations within individuals, we cluster standard errors at the respondent level. Further, while the addition of the individuals fixed effects is not required in the case of a perfect experimental design, randomization is unlikely to be perfect in finite samples. Nonetheless, using standard linear and random effects linear regression, we obtain similar results. Finally, additional ordered logistic and ordered probit regression analyses also show our results to be robust to the cardinality assumption implied by the adoption of linear regression. These additional results are all available upon request.
We do not observe substantial differences depending on the recruiters’ experience hiring candidates with a master’s degree for junior profiles.
Note that, unlike based on the benchmark sample, only one of the two types of intra-curricular internships now has a statistically significantly (p < 0.05) lower effect on hiring relative to extra-curricular internships. However, when considering intra-curricular internships as one category (which is justified as the estimated difference in effects between both types of intra-curricular internships is negligible and highly insignificant), their effect on hiring remains statistically different from the effect of extra-curricular internships.
This conclusion is based on the assumption that respondents rate the scales for both mechanisms uniformly and consistently.
This conclusion holds based on both the normal and the standardized values on the two scales.
This conclusion is drawn from additional regression analyses based on the deviance between each pair of scales. These analyses are available upon request.
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Acknowledgements
We thank Rolf van der Velden and Nick Deschacht for their valuable comments on an earlier version of this article, and Sofie Blokken for her research assistance.
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The authors declare that Prof. dr. Stijn Baert is an Editor of De Economist. Apart from the previous declaration, the authors have no further competing interests to declare that are relevant to the content of this article.
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Participants were informed about the aim and the scope of our research (understanding decision-making in recruitment in Flanders) at the start of the survey. Informed consent was obtained from the participants at the time of data collection and all data are anonymised. For our type of research, there was no legal or institutional requirement to apply for an approval from the ethics committee.
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Tobback, I., Verhaest, D. & Baert, S. Internships, Hiring Outcomes and Underlying Mechanisms: A Stated Preferences Experiment. De Economist 172, 25–48 (2024). https://doi.org/10.1007/s10645-023-09432-0
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DOI: https://doi.org/10.1007/s10645-023-09432-0
Keywords
- Internships
- Work experience
- Tertiary education
- Job opportunities
- Employability
- Human capital
- Signalling
- Matching