Long-run effects of information provision in high school on labor market outcomes

Last registered on August 05, 2025

Pre-Trial

Trial Information

General Information

Title
Long-run effects of information provision in high school on labor market outcomes
RCT ID
AEARCTR-0016208
Initial registration date
June 11, 2025

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
June 16, 2025, 7:22 AM EDT

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

Last updated
August 05, 2025, 11:25 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

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Primary Investigator

Affiliation
BiB

Other Primary Investigator(s)

PI Affiliation
DZHW
PI Affiliation
TU Dresden

Additional Trial Information

Status
In development
Start date
2025-07-01
End date
2028-12-31
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
The economic literature highlights various non-financial and financial barriers that help explain why some individuals do not invest adequately in education. Among these non-financial barriers are informational and behavioral factors. For example, biased beliefs about the costs and returns of college education are one plausible reason why some suitable high school graduates, especially of low socioeconomic status, refrain from enrolling at college, despite overall increasing enrolment rates in recent years (see Dynarski et al., 2023, for an overview).

This study draws on two information interventions in German high schools (AEARCTR-0004586) and at colleges (AEARCTR-0002446) that assessed the effects on enrolment in college or vocational education and training as well as consecutive postgraduate studies. The open question we want to answer is: Do such information interventions influence not only enrolment rates but also later labour market outcomes? Evaluating the monetary returns for targeted individuals is crucial in assessing the sustainability and effectiveness of such interventions. However, the related literature usually cannot study earnings due to the lack of appropriate panel data available (see, e.g., Lavecchia, Oreopoulos, and Brown, 2020, for a notable exception).

To answer the question of long-term effects, we construct a unique data set that links survey data from the Berliner Studienberechtigten Panel (Best Up) with administrative data of the Institute for Employment Research (IAB) in Germany. The Best Up survey follows students from treatment and control groups (initially 1,574 students from nine treatment and eighteen control schools in Berlin) over seven years, starting in their penultimate year in high school, when the first intervention occurred. It also includes a question that allows the linkage of the survey data with the administrative data on later labour market outcomes. This linkage allows for the joint study of various information on labour market expectations and other topics when the students were still in high school, such as students' educational expectations, their educational trajectories after school, and their performance in the labour market. Hence, such a combination of survey and administrative data allows for a detailed evaluation of the long-term effect of information interventions in high schools.
External Link(s)

Registration Citation

Citation
Leibing, Andreas , Frauke Peter and Katharina Spieß. 2025. "Long-run effects of information provision in high school on labor market outcomes ." AEA RCT Registry. August 05. https://doi.org/10.1257/rct.16208-1.2
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-07-01
Intervention End Date
2028-12-31

Primary Outcomes

Primary Outcomes (end points)
Primary outcomes derived from registry data are:
(i) Earnings
(ii) Occupational choice
(iii) Employment
(iv) Hours worked
(v) Unemployment periods
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes derived are:
(i) Job (mis)match
(ii) Rank in actual earnings distribution relative to implicitly expected rank
(iii) Employer and firm characteristics
(iv) Spatial Mobility
(v) Parental Leave periods
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We built on two interventions implemented as field experiments into the Berliner-Studienberechtigten-Panel. In the first intervention, participants were initially randomized at the school level for an information intervention within schools in spring 2013 as part of the Best Up-Project registered under BestUp (AEARCTR-0004586). The second intervention exploited the rich existing information of students within the Berliner-Studienberechtigten-Panel and at the end of the Best Up-Project in 2016, participants were transferred to the PostGrad-Project registered under PostGrad (AEARCTR-0002446), which comprised a second information intervention randomized at the student-level. While these two projects studied the short- and medium-term effects of each intervention on educational decisions, in particular on enrollment in undergraduate studies, on primary choice, and subsequent enrollment in postgraduate studies, we are interested in the labor market outcomes of these initial high school graduates, who graduated in 2014. More precisely, we are mainly interested in participants' earnings, occupational choices, employment spells, outcomes measuring job (mis)match, comparing current earnings profiles to the expectations measured prior to undergraduate enrollment, and firm-level outcomes.
BestUp (AEARCTR-0004586): The information treatment in the Best Up-Project, the initial project, was randomized at the school-level, where in nine schools out of 27 schools an information workshop took place of about 20 minutes in length. At the end of this workshop, a 3-minute video summarized the presented information. Students participating in the workshops received information about the costs and benefits of studying compared to vocational education and training (VET). The material comprised information about costs and benefits of undergraduate studies compared to VET in terms of (i) income, (ii) occupational positions, and (iii) unemployment risks, which were based on scientific results. In addition, students learned about major-specific earnings and gender earnings differences and about financial opportunities to fund an undergraduate degree (student financial aid, stipends, etc.).

PostGrad (AEARCTR-0002446): In the PostGrad-Project based on the data of the Best-Up-Project, the information intervention was randomized at the student-level, drawing from rich pre-PostGrad-treatment information in the data from the Berliner-Studienberechtigten-Panel. Students in the treatment group were provided with information about the costs and benefits of postgraduate studies, such as information about (i) income, (ii) occupational positions, and (iii) unemployment risks, which were based on our calculations using the German Microcensus. This treatment was administered online and was embedded within the baseline questionnaire. This ensured that participants read the provided information. Questions that followed the treatment comprised only statistical facts, such as gender, birth year, and birth month.

To examine the long-term treatment effects of these information provisions, we included a question in the surveys within the Berliner Studienberechtigten Panel that allows us to link the survey data with the registry data of the Federal Employment Agency (IAB).
For those participants who answered yes, i.e., provided their consent to this question, the Research Data Center (RDC) of the IAB links their survey data with information available in the registry data.
For our new project, we will receive access to a project-specific and password-protected working space of the IAB's RDC at the end of June/beginning of July 2025 (after submitting this registration report). We expect a potential sample size of nearly 400 adults who consented to the above question and were traceable in the registry data. We will estimate the long-term treatment effects for both high school graduates who pursued a bachelor’s or master’s degree and for those who pursued vocational education and training directly after high school graduation.
Regarding the IRB, we refer to the IRB approval for projects AEARCTR-0004586 and AEARCTR-0002446, which include the information interventions we built upon.
Experimental Design Details
Not available
Randomization Method
BestUp (AEARCTR-0004586): Stratification using coarsened exact matching with school type, district, cohort size, share of female students, and share of students with migration background. Matched schools were randomly selected within school types: one for information treatment, one for financial treatment, and one for the control group. Randomization was done with a random draw from the hat.

PostGrad (AEARCTR-0002446): Pairwise matching
Randomization Unit
BestUp (AEARCTR-0004586): School-level randomization

PostGrad (AEARCTR-0002446): student-level randomization
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
BestUp (AEARCTR-0004586): 27 schools, 9 treated
Sample size: planned number of observations
We expect a potential sample size of nearly 400 adults who consented to link their survey data to the Federal Employment Agency (IAB) registry data. The actual sample size will depend on the success of tracing the Best Up participants in the registry. A shrinkage of 2-5% is to be expected.
Sample size (or number of clusters) by treatment arms
We expect nearly 137 adults in the Best Up information treatment arm, 120 adults in the PostGrad information treatment arm, and 247 in the control group. Please note that the 120 adults in the PostGrad treatment arm are not mutually exclusive from the Best Up treatment arm.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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