Parents' preferences for their children's education and career paths

Last registered on May 08, 2024
View Trial History

Pre-Trial

Trial Information

General Information

Title
Parents' preferences for their children's education and career paths
RCT ID
AEARCTR-0011903
Initial registration date
August 14, 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
August 16, 2023, 11:17 AM EDT

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

Last updated
May 08, 2024, 4:00 AM EDT

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

Locations

Country
Switzerland
Region

Primary Investigator

Name
Christian Gschwendt
Affiliation
University of Bern
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Maria Cattaneo
PI Affiliation
Swiss Coordination Centre for Research in Education, University of Lucerne
Contact Investigator
PI Name
Stefan C. Wolter
PI Affiliation
University of Bern, Swiss Coordination Centre for Research in Education
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2023-09-25
End date
2024-04-03
Keywords
Education, Gender, Labor
Additional Keywords
preferences, parents, children, occupation, survey, digitalization, social status, wages, automation
JEL code(s)
J24, O33
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We study the preferences of adults for their hypothetical child’s educational and career paths. For that purpose, we implement a discrete choice experiment among a representative survey of 6000 adults aged between 25 and 60 in Switzerland where we ask them in multiple choice situations which of two “careers” they would prefer for their child. These careers are defined by the highest educational attainment, wage, the hierarchical position in their job, and the risk that their job will be substituted within the next 10 years. We ask half of the survey sample about their preferences for their hypothetical daughter, and the other half about preferences for their hypothetical son. We investigate how career attributes affect the likelihood of a career being chosen, how these preferences for career attributes vary depending on whether respondents are asked about their hypothetical daughter or son, and whether preferences depend on respondent characteristics.
External Link(s)

Registration Citation

Citation
Cattaneo, Maria, Christian Gschwendt and Stefan C. Wolter. 2024. "Parents' preferences for their children's education and career paths." AEA RCT Registry. May 08. https://doi.org/10.1257/rct.11903-2.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
No intervention planned.
Intervention Start Date
2023-09-25
Intervention End Date
2023-10-31

Primary Outcomes

Primary Outcomes (end points)
The primary dependent variable is the choice variable of an alternative (the “child’s career”) being chosen in the discrete choice experiment among two alternatives with four attributes each. The variable takes the value 1 for the chosen alternative and 0 for the alternative that was not chosen.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Our mixed multinominal logit model allows for the estimation of heterogeneous preferences among respondents. Therefore, preference heterogeneity will be investigated depending on respondent characteristics, such as sex, age, education, language region, political orientation, migration background, field of occupation, or whether respondents have children of their own or not.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We examine a discrete choice experiment that is embedded into an online survey of 6000 adults between the age of 25 and 60 in Switzerland. The respondents are asked about their preferences for their hypothetical daughter’s or son’s career.
Experimental Design Details
We base our analysis on a survey experiment that will be implemented in a large-scale representative online survey among the resident population of Switzerland in 2023. The survey will be carried out by a professional survey institute among Swiss residents between the age of 25 and 60.
Prior to the discrete choice experiment, respondents are assigned to two subsamples by stratified random sampling based on respondent sex, age, and language region. In subsample 1, respondents will be asked about their hypothetical daughter, and in subsample 2 about their hypothetical son. Each of the two subgroups is additionally subdivided into three subgroups, again by stratified random sampling based on the same respondent characteristics. This second subdivision is necessary due to the large number of degrees of freedom in our model which require a large number of choice situations to be answered which must be distributed to multiple respondents in order to maintain response efficiency. Respondents are asked to imagine having a 40-year-old daughter or son today (depending on which of the two subsamples they belong to), and in a fixed selection of subsequent choice situations (the selection depending on which of the three subgroups they belong to) which of two alternatives (“child’s careers”) they would prefer for their child.
The alternatives are defined by four attributes:
• Highest educational attainment (qualitative, 3 levels): University degree, University of Applied Sciences degree, apprenticeship degree
• Hierarchical position (qualitative, 2 levels): High (top management), low (no management function)
• Yearly gross wage (quantitative, 4 levels): 75’000 CHF, 100’000 CHF, 115’000 CHF, 130’000 CHF
• Job automation risk (quantitative, 3 levels): 30%, 45%, 60%
Job automation risk is defined as the risk that the job could be completely replaced by technologies such as robots or artificial intelligence within 10 years.
The primary outcomes of interest are the coefficients corresponding to our four attributes and, in the case of educational attainment and hierarchical position, the corresponding levels, as well as all their two-way interactions. These coefficients are estimated using mixed multinominal logit models (MXL) with the likelihood of an alternative being chosen based on given attribute levels as the dependent variable depending on whether individuals are asked about their hypothetical daughter or their hypothetical son.
Randomization Method
Respondents are assigned to the subsamples by stratified random sampling based on respondent sex, age, and language region by computer.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clustering.
Sample size: planned number of observations
6000
Sample size (or number of clusters) by treatment arms
3000 are asked about their hypothetical daughter, 3000 are asked about their hypothetical son
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Bern Ethics Committee
IRB Approval Date
2023-07-25
IRB Approval Number
222023
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Intervention

Is the intervention completed?
Yes
Intervention Completion Date
October 31, 2023, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
October 31, 2023, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
5952 individuals
Final Sample Size (or Number of Clusters) by Treatment Arms
2977 hypothetical daughters, 2975 hypothetical sons
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
No
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials