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PostGrad Enrollment: Can Information Provision Increase Postgraduate Enrollment of Students with Low Socioeconomic Background?
Last registered on October 26, 2017

Pre-Trial

Trial Information
General Information
Title
PostGrad Enrollment: Can Information Provision Increase Postgraduate Enrollment of Students with Low Socioeconomic Background?
RCT ID
AEARCTR-0002446
Initial registration date
September 21, 2017
Last updated
October 26, 2017 8:38 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
DIW Berlin
Other Primary Investigator(s)
PI Affiliation
DIW Berlin
PI Affiliation
DIW Berlin
Additional Trial Information
Status
On going
Start date
2017-08-01
End date
2020-07-31
Secondary IDs
Abstract
The choice to study at university is influenced by the socio-economic status (SES) background of students, holding constant ability and other observable factors. In Germany, there is a gap of 21 percentage points based on SES background of students regarding the decision to study for a Bachelor’s degree. This gap widens by another 11 to 17 percentage points at the transition from undergraduate (Bachelor's) to postgraduate (Master's) studies. The exact size of the gap remains debated and might partly be explained by differences between low- and high-SES background students, which are usually unobserved to the researcher. Nevertheless, increasing the share of low-SES background students at university is an important objective of German higher education policy (and worldwide).
This study will implement and evaluate a randomized controlled trial (RCT) to study how the provision of information about costs and benefits of postgraduate studies affect study-decisions of low-SES background students. The focus on postgraduate studies is warranted for three reasons. First, the existing literature almost exclusively studies the decision to enroll in undergraduate studies. Second, almost every student in the post-Bologna world has to make this decision of whether to pursue a Master's degree. Finally, very recent research on college wage returns that splits the college-group into undergraduate and postgraduate degrees documents that wage returns to postgraduate studies are particularly high. Socio-economic differences in decisions to seek Master's degrees will, therefore, directly affect societal income inequalities. This project will examine if information provision about the costs and future returns of postgraduate studies affects intentions and enrollment, in particular for low-SES background students. The focus of this project is on providing the reduced form effect of information provision on postgraduate study intentions and enrolment of low-SES background students. In addition, we will also estimate results against a different (and much larger) control group that we draw from the National Educational Panel Study (NEPS) through a matching procedure. This allows us to partially address potential weaknesses of RCTs with respect to external validity in our setting, while maintaining the advantages of RCTs with respect to internal validity.
External Link(s)
Registration Citation
Citation
Peter, Frauke, C. Spiess and Felix Weinhardt. 2017. "PostGrad Enrollment: Can Information Provision Increase Postgraduate Enrollment of Students with Low Socioeconomic Background?." AEA RCT Registry. October 26. https://doi.org/10.1257/rct.2446-2.0.
Former Citation
Peter, Frauke et al. 2017. "PostGrad Enrollment: Can Information Provision Increase Postgraduate Enrollment of Students with Low Socioeconomic Background?." AEA RCT Registry. October 26. http://www.socialscienceregistry.org/trials/2446/history/22737.
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Experimental Details
Interventions
Intervention(s)
We will provide students in the treatment group with information about costs and benefits of postgraduate studies, such as information about (i) income; (ii) occupational positions; and (iii) unemployment risks, which will be based on scientific results that we collect and prepare. This treatment will be administered online and will be embedded within the baseline questionnaire. This ensures that participants read the provided information. Of course, questions that will follow the treatment comprise only statistical facts, such as gender, birth year, and birth month.
Intervention Start Date
2017-11-01
Intervention End Date
2017-12-31
Primary Outcomes
Primary Outcomes (end points)
Intention to enroll in postgraduate studies (Master's degree); Application and number of applications to postgraduate studies; Enrollment in postgraduate studies; Knowledge about costs, admission requirements, and labor market outcomes (benefits); Life satisfaction; Collected data will also be used to examine long-run effects of first trial
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We exploit existing knowledge about students from from the Berliner-Studienberechtigten-Panel (Best Up). Best Up contains very detailed information about students of the cohort that graduated from high school either in summer 2014 (Abitur 2014) or one year later. These students come from a relatively homogenous environment and are followed from the last year prior to earning their high-school degree (Abitur) to the first year of university, college, or vocational training. We expect that the majority of the subset of Best Up students who started studying for a Bachelor's degree to start the final year of their Bachelor-studies in Fall 2017. In order to sample from these students, we will first conduct a light-touch status update survey to understand who ended up in the "target group" of final year undergraduates. Many of these students come from non-academic family backgrounds, i.e. from families where neither parent earned a post-secondary degree. The oversampling of low-SES students was a specific aim of the Best Up study. This is useful for us because we expect the effects of information on actual postgraduate costs and returns to be large among this group. Indeed, focusing on these students gives us access to a unique data set of students who are currently pursuing their undergraduate studies.
The linkage with an existing data set and, in particular, the linkage with Best Up has advantages compared to drawing a new sample of students from final year low-SES undergraduate students. The Best Up project on Bachelor's enrollment decisions has already collected a large number of background variables for each participating student. Such data on variables, such as high-school degrees, former aspirations, and detailed parental background information are of central importance for our RCT on postgraduate decisions. These variables can serve as controls and be used for testing randomization. Moreover, measurement errors that are related to recall errors can be avoided as the pretreatment information that is needed was collected when it was fresh. The second advantage is that in addition to a rich set of background variables, the Best Up project asked students about their intentions to study for a Master's degree before starting their first degree. This variable will be important for understanding if there are critical periods when these intentions can be affected. Again, retrospective data collection of such information would be plagued by methodological problems such as recall biases. The third reason for reliance on the Best Up sample is of methodological nature. The availability of rich pre-treatment information allows for more efficient randomization. In a nutshell, we can use this data to match students of similar observable pre-treatment characteristics and then conduct pairwise randomization. Another methodological advantage is that the Best Up students attend many different colleges. This mitigates concerns about spillovers effects from treatment to control groups compared to a setting where final-year undergraduate students are sampled anew from a single university.
Experimental Design Details
Randomization Method
randomization by pairwise matching
Randomization Unit
student-level-randomization
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
-
Sample size: planned number of observations
350 students
Sample size (or number of clusters) by treatment arms
175 students treatment
175 students control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Ethical review committee „Ethics in Experiments“, assigned by the Freie Universität Berlin School of Business and Economics
IRB Approval Date
2017-10-20
IRB Approval Number
Approval letter from commission, 2017-10-20
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is 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