The RoEDU valuation model is Tuition Covered’s estimate of the value of education. A RoEDU incorporates public and user-submitted data into Tuition Covered’s proprietary formula, also taking into account graduation stats, location and market trends. It is not financial advice and it should be used as a starting point to determine trade offs when seeking education opportunities.
This question has become increasingly complex in today's rapidly evolving job market and rising education costs. While data consistently shows that higher education leads to better earnings and lower unemployment rates, the value of a degree, school, or post-graduate job location can significantly change the value of education.
To help you make informed decisions about your educational future, we've developed the Return on Education (RoEDU) metric. This comprehensive metric combines financial indicators like lifetime earnings and employment rates with the cost of college broken out by a variety of higher education traits. By presenting all relevant data in one place, RoEDU allows you to easily compare different educational paths and make tradeoffs based on your unique aspirations and circumstances.
The educational and earnings date we have compiled to generate an RoEDU value of education varies by individual. Some individuals will leverage mean and median data, while others will provide personalized inputs like household income, savings, location, etc. The more data we have, the more accurate the RoEDU.
Public Datasets:
Private/Institutional Datasets:
Our methodology for calculating the RoEDU combines rigorous data analysis with forward-looking projections to provide a comprehensive view of educational value. We start with foundational variables such as 'total cost of college' – encompassing tuition, fees, room and board, and opportunity costs – and 'earnings potential' based on degree type and field of study. These core metrics are then overlaid with dynamic trend analysis, incorporating factors like industry growth forecasts, technological disruption indices, and regional economic indicators. We apply machine learning algorithms to historical data to predict future job market demands and salary trajectories. Our model also accounts for non-monetary benefits such as job satisfaction and career flexibility. By running these inputs through thousands of simulations, we generate a nuanced RoEDU spectrum across various traits including degree programs, institutions, and student demographics. This multifaceted approach allows us to present a robust, forward-looking RoEDU that adapts to the ever-changing landscape of education and employment.
Breakdown of RoEDU ranges, quality and key descriptions: