Modeling Medical School ROI

12 Aug 2018 » James Diao » Boston, MA


I built an app to model net summed earnings over time for different medical specialties and non-medical careers: Using it, we can quantify and validate the common wisdom: don’t go into medicine for the money! It can take a very long time for physician earnings to outpace debt and opportunity costs, and in many cases, they never do.

When does medicine let you break even?

As my friends and I were deciding on schools, one of the biggest considerations that kept coming up was the crazy cost of attendance. Medical school alone can cost more than $350,000 over four years, and lost salary and opportunity costs can independently amount to even more. Most doctors-in-training (myself included) are happy to give up higher financial earnings for their dream job. Still, it’s important that we all have a clear understanding of what we’re getting ourselves into, and what we might have to give up when choosing medicine, or a particular specialty.

My parents and many of my friends have faith that physicians make enough to make this a moot question. But why are they so sure? In an effort to draft a quantitative answer, I compiled data on medical school tuition costs (AAMC), physician salaries (Doximity and Medscape), residency salaries (Medscape), and residency info (UW Medicine), and fed them into a simple financial model. To measure opportunity cost, I also modeled three comparative career paths: academia, consulting, and tech, chosen for their popularity among high-achieving students and roughly standardized income progressions.

The inputs and assumptions of the model can be adjusted in an interactive web app:

Just to give a quick peek, a few representative plots are taken as screenshots from the app:

Specialties Comparison Plot

Net Summed Income by Specialty

The specialties comparison plot reflects conventional knowledge: surgical subspecialties earn more, primary care specialties earn less, and salaries are positively correlated with training duration. Interestingly, we see that even high-earning specialties (like oncology) might not outpace debt and opportunity cost until one’s forties.

Careers Comparison Plot

Net Summed Income by Career

Unsurprisingly, partner-track consultants are basically off the charts, and academia (while generally well-compensated compared to the US median salary) is at the low end of the spread. The lag time of medical training becomes quite clear; neurosurgeons don’t catch up with tech until around 44 years old, and most non-surgical subspecialties never catch up at all.


Long story short: don’t go into medicine for the money! It can take a very long time for physician earnings to outpace debt and opportunity costs, and often, they never do.

Most doctors-in-training care about a lot more than just finances, but physicians definitely don’t earn enough for us to ignore money entirely. I’ve had several friends choose state schools over HMS or other top schools because they wanted to practice in a lower-earning specialty (e.g., Ob/Gyn) and it would not be financially feasible to turn down a full-ride. Based on the data, it seems like a good idea to write out some numbers and think twice before making the big investments.


This model does not take into account several things; among them: “early money” vs. “late money” investments, interest payments on debt, state-by-state cost-of-living, and state-by-state tax rates, among others I probably didn’t think of. Of these, I suspect that only the first will significantly affect the shape of the plots, favoring shorter training times and non-medical careers. Additionally, the income progressions of alternate careers (academia, consulting, and tech) are difficult to estimate because they vary greatly between companies and between individuals. I attempted to control for this by taking average values from the highest earning institution for each path (tabulated at the bottom).

Model Implementation

Every year, the change in summed net income is computed as salary - costs, where costs include federal taxes, state taxes (benchmarked to MA), and a flat cost-of-living. These changes are cumulatively summed into a piecewise linear plot. Plots are, be default, shown for a sample of specialties (chosen for a range of incomes and training lengths). Checkboxes also exist for displaying summed net incomes for non-medical careers, including academia, tech, and consulting. These careers were chosen for their relatively standardized salary brackets and popularity among top undergraduate students.

Potential Improvements

  1. Adding functionality for input of income progression for non-default paths, or modification of default paths
  2. Accounting for the advantage from investment of early money
  3. Accounting for interest from loans

Career Comparisons

Academia (Harvard)

Position    Duration    Salary
PhD Student  5 years  $36,720
Postdoc  2 years  $48,804
Assistant Professor  5 years  $109,800
Associate Professor  5 years  $120,900
Full Professor  Until retirement  $198,400

Consulting (McKinsey)

Position    Duration    Salary
Analyst  2 years  $75,000
Business School  2 years  $0
Associate  2 years  $135,000
Engagement Manager  2 years  $250,000
Associate Principal  3 years  $600,000
Partner  Until retirement  $1,000,000

(Non-partner track is modeled as maintaining EM-equivalent salary until retirement)

Tech (Software Engineer at Facebook; includes vested stock)

Position    Duration    Salary
E3  2 years  $142,000
E4  2 years  $194,000
E5  5 years  $260,000
E6  Until retirement  $339,000

5 (mid-range salary + 50% of stock + 50% of max bonus)

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