Students Mastering Intriguing Testable Hypotheses
Laurier Economics Student Competition 2024-25
Overview
Description
Welcome to the second annual Students Mastering Intriguing Testable Hypoheses (SMITH) competition. We run this event annually to create a fun learning experience for Laurier Economics students outside of the classroom.
The objective of this year’s competition is to predict individual hourly wages using the Canadian Labour Force Survey (LFS). Economists have studied the determinants of wages for decades - in particular the relationship between education and wages - since labour compensation is the main way individuals are compensated for their work and therefore how they support themselves and their families. Knowing more about what determines wages can help answer questions about income inequality and upward mobility.
How to Participate
Evaluation
Each submission is scored on its Root Mean Square Error (RMSE), a measure of the average error of the prediction. The submission with the lowest RMSE wins.
Timeline
The competition will run through most of the Winter 2025 semester. See below for key dates:
- Sign Up Window - January 6, 2025 to January 24, 2025
- Competition Opens - January 27, 2025
- Competition Closes - 11:59pm, March 21, 2025.
Prizes
Prizes are allocated separately by year level of the team members. If a team has a mix of year levels, the prize is allocated to the highest year level of the team members. Year levels are: 1st, 2nd, 3rd, 4th.
- 1st Place at Each Year Level: $300 split between team members.
Your achievement will also be celebrated at a year-end event where we will celebrate the accomplishments of economics students over the past academic year.
Data
The underlying data for the competition is the Canadian Labour Force Survey (LFS), a monthly survey of about 50,000 households on matters related to employment that forms the primary source of information on the Canadian labour market.
You are provided with two datasets: 1) the “training” data, and 2) the “evaluation” data. You will create and estimate your model with the training dataset, which is the March 2024 LFS filtered to include only full-time, full-year workers. Once you have a submission ready you will generate your predictions for wages with the evaluation dataset, which is the LFS from a different month which for now will remain hidden. Both datasets contain the same variables, except the evaluation data does not contain wages, which is the variable you are predicting.
When you submit your predictions based on the evaluation data, we will compute the RMSE using the true wages for those records and report it on the leaderboard at the end of each day.
Download the data in your preferred format below.
| File Type | Training Data | Evaluation Data |
| csv | traindata.csv | evaldata.csv |
| dta | traindata.dta | evaldata.dta |
A website that describes each variable is here: March 2024 Labour Force Survey
Submissions
All submissions must be in .csv format, and contain only two columns: 1) rec_num, the numeric ID number for the person; 2) phrlyearn, the prediction of wages for the individual.
The .csv files must be named with your group name in all lower case letters and no spaces. For instance, if your team name is “The Deadweight Losses” then your submissions must all be named thedeadweightlosses.csv
Submit your .csv files here: link to submission form
Questions
If you have any questions about the competition or require help with your predictions, contact jusmith@wlu.ca
Rules
Students can only belong to one team
A student can only be on a single team
No sharing code privately between teams
Each team must work independently to complete the predictions.
Team limits
Teams of up to 3 students are allowed.
Data
Teams can only use the data provided to create your models. No external data is permitted.
Eligibility
Eligible participants are Laurier undergraduate students whose major is Economics, Economics and Accounting, Economics and Financial Management, and Economics and Data Analytics during the signup period. If a student subsequently switches programs but remains enrolled at the university, they can remain in the competition.
Submitting Code
Students must provide the code that generated their predictions for their final submission only. The code must run and generate the submitted output, otherwise the team is disqualified.