General Information
Project description
The Ontario Ministry of Labour, Immigration, Training and Skills Development (MLITSD) seeks to remove barriers to apprenticeship enrolment, training, and program completion as part of their mandate to increase the skilled trades workforce in the province. Amongst other strategies, these efforts have led to an expansion of existing grant schemes that aim to offset some of the costs of training and encourage training completion. In collaboration with MLITSD, the Ontario Behavioural Insights Unit (BIU) plans to run a randomized controlled trial to test whether behaviourally informed changes to eligibility notices that the ministry sends to apprentices can increase application rates for financial incentives.
Analysis Plan
Hypothesis
The behaviourally informed eligibility notice will increase application rates for the financial incentive relative to the status quo eligibility notice.
How hypothesis will be tested
Two conditions in a randomized controlled field experiment. Participants will be randomly assigned to receive either the status quo eligibility notice (control condition) or the behaviourally informed eligibility notice (treatment condition) upon meeting the requirements for the financial incentive.
Dependent variables
Submission of application for the financial incentive (yes/no) as created based on administrative data recording the date of application (the presence of an application date will be coded as submission; the absence of an application date will be coded as no submission).
Analyses
The effect of behaviourally informed eligibility notices on application rates will be analyzed by means of a multilevel logistic regression model that assesses apprentices’ probability of applying for the incentive. The model will specify a simple fixed effect for the eligibility notice variant apprentices received (control or treatment). To account for any potential nesting within the data, the model will further specify random effects for sponsor, trade, and/or region as determined by model fit. The random-effects structure (i.e., random intercepts vs. random slopes) will be selected based on the maximal complexity supported by the data.
Sample Size. How many observations will be collected or what will determine sample size?
5,000 apprentices who become eligible for the financial incentive during the trial period (2,500 per condition)
Who is behind the project?
Project status:
Pre-registration
Methods
What is the project about?
Date published:
3 March 2023