Labour market information & human capital investments: The effect of message framing on training behaviour

Last registered on October 17, 2023

Pre-Trial

Trial Information

General Information

Title
Labour market information & human capital investments: The effect of message framing on training behaviour
RCT ID
AEARCTR-0012260
Initial registration date
October 15, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
October 17, 2023, 1:51 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Region

Primary Investigator

Affiliation
Maastricht University

Other Primary Investigator(s)

PI Affiliation
Maastricht University
PI Affiliation
Maastricht University

Additional Trial Information

Status
In development
Start date
2023-10-25
End date
2024-11-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The changing skills requirements of the labour market pose a potential threat to the employability of the workforce. A large body of literature shows that training can reduce the risk of unemployment by improving and updating individuals' skills. This is particularly relevant for low-skilled flexible workers, who are often less motivated to participate in training and face relatively lower job security. While previous research has examined the impact of labour market information interventions on individual behaviour and labour market outcomes, such as job search behaviour, there remains a lack of evidence on the impact of labour market information on training behaviour. To fill this research gap, this study presents a field experiment, designed in collaboration with a prominent Dutch employment agency, to investigate the influence of labour market information and it's framing on the (re)training behaviour of low-skilled flexible workers.
External Link(s)

Registration Citation

Citation
Künn-Nelen, Annemarie , Raymond Montizaan and Anna-Lena Wittich. 2023. "Labour market information & human capital investments: The effect of message framing on training behaviour." AEA RCT Registry. October 17. https://doi.org/10.1257/rct.12260-1.0
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Experimental Details

Interventions

Intervention(s)
The information intervention is with flex workers at a large Dutch employment agency investigating the effect of (framed) labour market information on training intentions and actual training behaviour.

Specifically, we provide (framed) information on the potential outcomes of training, such as the opportunity to deepen or update knowledge, the chance to work in a different occupation, the prospects for a future wage increase, the potential for increased job satisfaction, and the opportunity for a new job.
The information we provide participants is discussed during three focus groups consisting of individuals with similar characteristics as our target group. Moreover, we will run a pilot to pre-test. There will be a control group that receives no labour market information, but do receive an email to inform individuals about the training portal, and a link to the portal, and three treatment groups:

• General labour market information (in a neutral way) on the current changes in the labour market
• General labour market information + information highlighting the benefits of engaging in training (gain-framed message)
• General labour market information + information highlighting the costs of not engaging in training (loss-framed message)
Intervention (Hidden)
The challenges of the 21st century, such as technological change, require individuals and organisations to continuously maintain and develop their human capital in order to stay up-to-date. However, several groups of workers are lagging behind in their participation in training (Bassanini et al., 2007). For example, low-skilled flex workers are less motivated to train (Künn et al., 2020). As it is well known that a lack of training participation is often associated with lower employability (Picchio & Van Ours, 2013), it is of great importance to motivate low-skilled individuals to train (Narushima et al., 2018). However, causal evidence providing insights into how to motivate people to participate in training is rare and limited to the use of vouchers (Fleuren et al., 2020). While Schwerdt et al. (2012) and Hidalgo et al. (2014) show that vouchers increase training participation, Fleuren et al. (2020) show that characteristics of the voucher matter for training take-up. Görlitz and Tamm (2017) show that the information provision about a German voucher targeted to employees with low or medium income does not lead to more training take-up, which the authors explain by financial burdens seldom being the main reason for not participating in training. Instead Görlitz amd Tamm (2017) show that the main reason for not participating in training among their sample was a perception of need for training. Our study therefore seeks to investigate whether the provision of labor market information and the consequences training can have for individuals’ careers, can serve as a motivating factor, especially for low-skilled flex workers.

Individuals often have misconceptions about the extent to which labour market dynamics affect them (de Koning, 2022). To address this, our intervention involves educating individuals about relevant labor market developments, including technological changes and the impact of skill adjustments. Building on the existing literature (Frey & Osborne, 2017; Schmidt, 2007; Schmidpeter & Winter-Ebmer, 2021; Veum, 1999), we highlight the relationship between training and labour market outcomes. Specifically, we provide (framed) information on the potential outcomes of training, such as the opportunity to deepen or update knowledge, the chance to work in a different occupation, the prospects for a future wage increase, the potential for increased job satisfaction, and the opportunity for a new job.

The information we provide participants is discussed during three focus groups consisting of individuals with similar characteristics as our target group. Moreover, we will run a pilot to pre-test. There will be a control group that receives no labour market information, but do receive an email to inform individuals about the training portal, and a link to the portal, and three treatment groups:

• General labour market information (in a neutral way) on the current changes in the labour market
• General labour market information + information highlighting the benefits of engaging in training (gain-framed message)
• General labour market information + information highlighting the costs of not engaging in training (loss-framed message)


Ref:

Bassanini, A., Booth, A. L., Brunello, G., De Paola, M., & Leuven, E. (2005). Workplace training in Europe. Available at SSRN 756405.

de Koning, B. K. (2022). Empirical studies on information, beliefs, and choices in education and work. [Doctoral Thesis, Maastricht University]. Maastricht University. https://doi.org/10.26481/dis.20220926bk

Fleuren, B. P., de Grip, A., Kant, I., & Zijlstra, F. R. (2020). Time equals money?: A randomized controlled field experiment on the effects of four types of training vouchers on trainingparticipation. Journal of Vocational Behavior, 118, 103403

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological forecasting and social change, 114, 254-280.

Görlitz, K., & Tamm, M. (2017). Information, financial aid and training participation: Evidence from a randomized field experiment. Labour Economics, 47, 138-148.

Hidalgo, D., Oosterbeek, H., & Webbink, D. (2014). The impact of training vouchers on low-skilled workers. Labour Economics, 31, 117-128.
Künn, A., Poulissen, D., van Eldert, P., Fouarge, D., & de Grip, A. (2018). Leren onder werkendenmet een kwetsbare positie op de arbeidsmarkt. ROA. ROA Reports Nr. 005

Narushima, M., Liu, J., & Diestelkamp, N. (2018). Lifelong learning in active ageing discourse: its conserving effect on wellbeing, health and vulnerability. Ageing & Society, 38(4), 651-675.

Picchio, M., & Van Ours, J. C. (2013). Retaining through training even for older workers. Economics of Education Review, 32, 29-48.

Rothman, A. J., Martino, S. C., Bedell, B. T., Detweiler, J. B., & Salovey, P. (1999). The systematic influence of gain-and loss-framed messages on interest in and use of different types of health behavior. Personality and Social Psychology Bulletin, 25(11), 1355-1369.

Schmidpeter, B., & Winter-Ebmer, R. (2021). Automation, unemployment, and the role of labor market training. European Economic Review, 137, 103808.

Schmidt, S. W. (2007). The relationship between satisfaction with workplace training and overall job satisfaction. Human resource development quarterly, 18(4), 481-498.

Schwerdt, G., Messer, D., Woessmann, L., & Wolter, S. C. (2012). The impact of an adult education voucher program: Evidence from a randomized field experiment. Journal of Public Economics, 96(7-8), 569-583.

Veum, J. R. (1999). Training, wages, and the human capital model. Southern Economic Journal, 65(3), 526-538.

Intervention Start Date
2023-11-05
Intervention End Date
2024-11-30

Primary Outcomes

Primary Outcomes (end points)
We measure individuals training behaviour. More specifically, we will investigate a) individuals’ training intentions and b) actual training behaviour (1, 3 or 6 and 12 months after the information intervention) and c) potential mediating variables. Moreover, we plan to conduct a heterogeneity analyses (d)


a) Training intentions
• Engagement: Clicking behaviour in the information treatment email (on the link to the training portal)
• Training website behavior: how many training-related pages has been visited, time spent on training-related pages, search queries on Randstad website for information about the Randstad training options, the number of applications / registrations for a training
• Self-reported training intentions via an additional survey


b) Training behaviour
• Including registration person (teamleader vs student), registration date, category of course, specification of course, self-chosen (self-planned) vs mandatory (blended, planned), institution providing the courses, costs of the training, subsidy for the training (yes/no)


c) Mediator variables assessed via an additional survey
• Perceived benefits of training
• Perceived need to update skills
• Perceived fear of automation


d) Heterogeneity analysis
• Interaction terms between the treatment dummies and the following personal characteristics will be included: age, gender, employment characteristics.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We seek to understand individuals' job search patterns and outcomes. More specifically, we are interested in whether individuals engage in job search, the frequency with which they view vacancies, the number of times they apply, the number of successful placements and the preferred roles they seek. We aim to pay additional attention to broad job search behavior, job search outside of the current occupation. Earlier studies (Altmann et al., 2018, Belot et al., 2019) have found that labor market information results in a broader job search behavior, however, does not result in a larger probability to receive job offers outside of their own occupation. This could be due to an insufficient set of skills and knowledge needed to be successful in broad job search. While this experiment focuses on the effect of information provision on training behavior, we will also analyse whether the information leads to broad job search and whether the success of this can be explained by training behavior.


Ref:

Altmann, S., Falk, A., Jäger, S., & Zimmermann, F. (2018). Learning about job search: A field experiment with job seekers in Germany. Journal of Public Economics, 164, 33-49.

Belot, M., Kircher, P., & Muller, P. (2019). Providing advice to jobseekers at low cost: An experimental study on online advice. The review of economic studies, 86(4), 1411-1447
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The proposed study will take place within a prominent Dutch employment agency and will target a cohort of 80,000 registered flexible workers. The experiment consists of two parts: 1) an information intervention delivered by mail with a link to the training portal, and 2) an additional survey supplementing the labour market information with an additional short survey. Both segments of the experiment have the same experimental set-up, with one control group and three treatment groups.
More specifically, the three treatment groups will look the following way:

• General labour market information (in a neutral way) on the current changes in the labour market
• General labour market information + more specific labour market information highlighting the benefits of engaging in training (gain-framed message)
• General labour market information + more specific labour market information highlighting the costs of not engaging in training (loss-framed message)
Experimental Design Details
The proposed study will take place within a prominent Dutch employment agency and will target a cohort of 80,000 registered flexible workers. The experiment consists of two parts: 1) an information intervention delivered by mail with a link to the training portal, and 2) an additional survey supplementing the labour market information with an additional short survey. Both segments of the experiment have the same experimental set-up, with one control group and three treatment groups.
More specifically, the three treatment groups will look the following way:

• General labour market information (in a neutral way) on the current changes in the labour market
• General labour market information + more specific labour market information highlighting the benefits of engaging in training (gain-framed message)
• General labour market information + more specific labour market information highlighting the costs of not engaging in training (loss-framed message)

In the initial phase, participants in the experiment will receive an email containing a link to the training portal. For the three treatment groups, these emails will encompass information about the labour market, specifically addressing challenges and delineating training benefits derived from focus group results. Importantly, the experiment goes beyond merely gauging the impact of labour market information; it delves into exploring the influence of framing, grounded in the principles of prospect theory. While framing has found application in various contexts such as retirement planning (Eberhardt et al., 2021) and health behaviour (Rothman et al., 1999), its examination within the domain of lifelong learning remains unexplored. We anticipate a 5% email open rate and plan to conduct a pilot study with 10% of the final population before the full email intervention.
Following this, at a time yet to be determined (but no later than 4 weeks after the first email), a second email will be sent to the same individuals, inviting them to participate in a survey. The survey will begin with a review of the same labour market information as the first email, with individuals asked to read it. Subsequently, a brief survey will be administered, beginning with two questions related to the labour market information's comprehension and perceived importance. For mediation analysis, participants will answer questions regarding their perceptions, specifically focusing on the perceived benefits of training, the perceived need for training, and the perceived fear of automation. This information aims to unveil the underlying mechanisms in the relationship between (framed) labour market information and training behaviour.

Additionally, as an supplementary outcome variable, the survey will encompass self-reported training intentions and information engagement, assessing the likelihood that individuals a) consider looking into training or retraining and b) plan to undergo such training in the next 12 months.

Following this, the long-term impact of the labour market information on individuals' training behaviour, as registered with the employment agency, will be monitored up to one year after the treatment (at 1 month, 3 months, 6 months, and 12 months post-treatment).
All experimental and survey data will be matched with administrative data from the employment agency, including age, sex, and residence, necessary for heterogeneity analyses. Additionally, employment information such as the starting date of the current position and the number of hours worked per week will be obtained from the employment agency records. Moreover, information on individuals’ job search behaviour will be matched.

Ref:

Eberhardt, W., Brüggen, E., Post, T., & Hoet, C. (2021). Engagement behavior and financial well-being: The effect of message framing in online pension communication. International Journal of Research in Marketing, 38(2), 448-471.

Rothman, A. J., Martino, S. C., Bedell, B. T., Detweiler, J. B., & Salovey, P. (1999). The systematic influence of gain-and loss-framed messages on interest in and use of different types of health behavior. Personality and Social Psychology Bulletin, 25(11), 1355-1369.
Randomization Method
Email distribution will follow a rule-based randomisation, using criteria including odd/even days and months of birth, to ensure the robustness of the experimental design. This rule will guide the allocation of participants to different treatment groups, with individuals being randomly assigned to specific groups within the prescribed rule parameters. Each of the four groups, including one control group and three treatment groups, will comprise 25% of the total population. The groups will remain the same for both studies, the information treatment (email 1) and the survey (email 2). As each individual opens the emails independently, and considering that the employees work in different companies, we expect to avoid spillover effects. Nevertheless, the opening and clicking behaviour of the emails will be closely monitored in order to detect any abnormal patterns that might indicate the occurrence of spillover effects.
Randomization Unit
Individual level of randomisation
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Cluster: individual level
The emails will be sent to ca. 80,000 individuals:

Pilot study: 8,000 individuals
Main study: 72,000 individuals
Sample size: planned number of observations
Estimation based on 5% opening of the information treatment emails and/or response to the survey (we only receive data of individuals that opened the email and/or responded to the survey) Pilot study: 400 individuals Main study: 3600 individuals
Sample size (or number of clusters) by treatment arms
Full sample that email will be send to ca. 80,000 individuals.

1/4th: control group
1/4th: treatment group 1
1/4th: treatment group 2
1/4th: treatment group 3

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

Documents

Document Name
Information treatment (Email 1)
Document Type
other
Document Description
Information treatment (in Dutch) including labour market information and link to the training portal.
File
Information treatment (Email 1)

MD5: ba90d55b62a7b26a230dd33e4d400056

SHA1: 3e24a5de246829386f3125a62abf1eb41f0684ce

Uploaded At: October 15, 2023

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IRB

Institutional Review Boards (IRBs)

IRB Name
Ethical Review Committee Inner City faculties (ERCIC) at Maastricht University
IRB Approval Date
2023-08-16
IRB Approval Number
ERCIC_467_22_06_2023

Post-Trial

Post Trial Information

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials