Career goals and investments in education: Experimental evidence from Cambodia
Last registered on February 18, 2020


Trial Information
General Information
Career goals and investments in education: Experimental evidence from Cambodia
Initial registration date
February 16, 2020
Last updated
February 18, 2020 2:16 PM EST

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Primary Investigator
Wageningen University and Research
Other Primary Investigator(s)
PI Affiliation
University of Hannover
PI Affiliation
University of Goettingen
Additional Trial Information
In development
Start date
End date
Secondary IDs
In this project, we analyze whether an interest exploration tool – combined with information about potential careers, paths to higher education and financing options – can provide guidance to students in rural Cambodia, help them to develop long-term career goals and thereby motivate them to continue in school. We target the intervention to adolescents in grade 9, who are about to decide whether to enroll in high school. We use survey data, as well as individual-level administrative data obtained from treatment and control schools to track educational decisions.
External Link(s)
Registration Citation
Gehrke, Esther, Friederike Lenel and Claudia Schupp. 2020. "Career goals and investments in education: Experimental evidence from Cambodia." AEA RCT Registry. February 18.
Experimental Details
In our intervention, students will go through an “interest exploration tool” on a tablet. This tool is designed to help students discover their personality, and to help them understand which jobs their interests could match with. After the students completed the tool, they participate in an information session about educational paths, which provides detailed information on high schools and vocational training options as well as scholarship possibilities. This information session will be conducted in small groups.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Primary outcomes will be (i) attendance, (ii) drop-out, (iii) grades, (iv) exam scores, and (v) transition to high school.
Primary Outcomes (explanation)
Individual-level information on attendance, drop-out, grades and test scores will be collected by the project team from each school three times: before the intervention (baseline), roughly three months after intervention (midline), and roughly six months after intervention (endline). Attendance is tracked in the class book and is defined as the number of days student i appeared for class. Students will be classified as having dropped out if marked as such in the student class book. Grades are given by the teacher on a monthly basis and reflect test performance. We will use grades from the main subjects. Grades are standardized (by subject) across all schools to have mean zero and a standard deviation of one. Exam scores are the scores obtained in the end-of-term exam, which is a standardized exam across all schools in Cambodia. The records of each student’s performance in this exams are kept by the teacher. Again, exam scores will be standardized. Finally, roughly 6 to 9 months after the intervention, the project team will collect information regarding high school transition from the high schools in the area (i.e. did student i enroll in that particular institution).
Secondary Outcomes
Secondary Outcomes (end points)
Secondary outcomes are stated educational aspirations and career goals.
Secondary Outcomes (explanation)
These outcomes will be collected on the day of the intervention (endline), and from treatment schools only. Since we do not conduct surveys in control schools (due to limited budget), we can only estimate if the IET has an effect on educational aspirations and career goals over and above the effect of the information session. In other words, we can only test if these variables are different across treatment arms within treated schools.
Educational aspirations are measured as (i) the highest degree the student wishes to attain (categorical answer), and (ii) how confident the student is that she will achieve this level of education. Career goals are measured (i) by open ended questions about up to three preferred jobs the student wishes to do at age 25, and (ii) how confident the student is that she will be able to get her most preferred job.
Experimental Design
Experimental Design
We target students from grade 9 in Northeast Cambodia. We construct a sample of lower secondary schools from which we randomly select 50% for our intervention. We then randomly select one class of grade 9 to participate in the intervention (treatment schools), or to have only their students' data collected (control schools). Within these ‘treatment’ schools, we then invite all students of the selected class to participate in a workshop carried out at their school. All students that are willing to participate are randomly allocated into one of three arms: a) full-treatment group (40%), b) placebo-treatment group (40%), c) information-only group (20%). Students in the control schools serve as controls.
The expected sample size is a) 540 students in treatment group, b) 540 students in placebo group, c) 270 students in information-only group, d) 1350 students in pure control.
Experimental Design Details
Not available
Randomization Method
The assignment into treatment arms is done randomly in a two-stage process. First all sample schools will be randomly allocated to treatment schools (50%) or control schools (50%). Randomization will be done by computer.
Students within selected classes in treatment schools will then randomly be allocated to one of three treatment arms before the intervention begins (on the same day). Randomization will be done by having students blindly draw numbered badges from a bag.
Randomization Unit
School and individual
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
60 schools
Sample size: planned number of observations
2,700 students
Sample size (or number of clusters) by treatment arms
The expected sample size is a) 540 students in treatment group, b) 540 students in placebo group, c) 270 students in information-only group, d) 1350 students in pure control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We calculate the smallest real effect sizes that we can detect at a 5% significance level with 80% power. We perform the calculation based on 30 treatment and 30 control schools with approximately 18 students per cluster (who participate in the full intervention: IET + Information session). When calculating the minimum detectable effect size (MDE), we take into account that we have baseline information on student age, gender, test scores, attendance and class size. We expect that these baseline characteristics can explain between 20% and 50% of the variation in the outcome variable. We further assume that the intra-cluster correlation coefficient (ICC) ranges between 0.1 and 0.3 depending on the variable considered. Depending on the ICC and explained variance, the MDEs range between 0.20 of a SD and 0.38 of a SD. These are the detectable effect sizes for outcomes with mean zero and a standard deviation of one (such as standardized grades).
IRB Name
Ethics committee of the University of Goettingen
IRB Approval Date
IRB Approval Number
Analysis Plan

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