How the numbers are built.

For every school in the dataset, we pull the most recent public employment report. Each provides median base salary, median signing bonus, median performance bonus, and placement rate at 3 months post-graduation.

Sources by tier:

• European elite: INSEAD Career Report, IMD Employment Report, LBS Employment Report
• US M7: HBS, Stanford GSB, Wharton, Booth, Columbia, Kellogg, MIT Sloan — each publishes a detailed MBA Employment Report annually
• US Top 15: Yale SOM, Tuck, Ross, Stern, Haas, Darden, Fuqua, Anderson — same structured format
• Asian top: NUS, CEIBS, HKUST, ISB — publish localized reports with regional comp

Where multiple years differ significantly, we use the most recent available (2024 or 2025 where reported). Schools that decline to publish certain fields are noted as directional estimates.

The comp-by-city matrix (8 career tracks × 41 cities) is built from a layered combination of:

• Transparent Career — self-reported post-MBA offers from top-program alumni
• Levels.fyi — tech + product + data-science comp (primary source for PM/Analytics track)
• Wall Street Oasis — IB + PE/VC bonus threads (primary source for IB/PE track)
• Heidrick & Struggles — partner-level consulting comp (used to calibrate promotion multipliers)
• Blind (anonymized tech comp) — cross-reference for Haas/Stern tech placements
• Local partner firm reports (for APAC) — Korn Ferry / Robert Half Singapore/HK/Japan reports

Where sources disagree, we use the median of sources, not the mean — outlier top-quartile posts get discounted. Each comp point has a low/high range (~p25–p75) visible in the tool UI when you hover.

The lifestyle coverage % ("can your salary fund an affluent life here?") is computed from a 22-item Numbeo basket per city:

Rent (1BR and 3BR, center and suburbs), meal costs (inexpensive and mid-range), groceries (milk, bread, eggs, chicken, apples), transit (one-way, monthly pass, taxi, gasoline), utilities, internet, mobile, gym membership, cinema, and local McDonald's combo (a surprisingly good CoL proxy).

Three tiers are modeled per city: middle, upper middle, affluent. Each has its own rent assumption, private-school assumption (where culturally relevant), and domestic-help budget.

We also layer in tax: each city has an effective marginal tax rate applied to the post-MBA comp before computing disposable income. Singapore/Dubai model zero income tax; NYC models federal + state + city combined; European cities model national social security + income tax. US capital gains + dividend treatment differs from APAC — the tool flags US-treaty traps for US citizens living abroad.

Verified against expat finance forums (Blind APAC, Singapore Expats, ShanghaiExpat) for sanity. Numbers update quarterly.

This is the single most important assumption in the tool, and the most-debated one. Promotion multipliers represent a median performer who survives to each milestone — not top-quartile, not someone who flames out year 3.

Top-quartile performers typically clear 1.2–1.4× the modeled trajectory; strugglers plateau one level below. For example:

• MBB Partner (year 9): median total comp ~$900k–$1.1M · top-quartile $1.5M+
• IB Managing Director (year 7): median ~$1M · top performers 2–3× that
• FAANG VP Product (year 10): median $800k–$1.2M · top-quartile $1.5M+

Multipliers are applied against Y0 TOTAL comp (which includes the one-time signing bonus), which means Y2–Y4 numbers look slightly conservative compared to tools that normalize signing bonuses out.

We'd rather under-promise and be accurate than project top-of-funnel numbers someone won't hit.

For India (Mumbai/Bangalore) and China (Shanghai/Beijing/Shenzhen), the comp figures are local-hire in USD terms — not expat packages.

This reflects the realistic post-MBA path for international students returning to Asia: Chinese nationals returning from top programs, Indian ISB/IIM alumni staying in India, or international hires accepting local terms to live in-market.

Full expat packages (trailing-spouse allowance, international school stipend, tax equalization) are 1.5–2× higher but come with tax complexity, relocation constraints, and are rarely offered for post-MBA entry roles. When applicants assume "I'll get an expat package" without an existing senior role, they're usually wrong.

The verdict isn't a single formula — it's a scoring rubric across four dimensions:

• Payback period — years to recover total investment (tuition + opportunity cost + loan interest)
• 10-year wealth delta — net worth at year 10 vs staying in current path
• Lifestyle coverage — % of affluent lifestyle your post-MBA disposable income actually funds
• Risk — loan-to-income ratio at year 1, industry-specific bonus variance, geographic flexibility

A fifth verdict — Purchasing-Power Arbitrage — triggers when an Asian city (Shanghai, Mumbai, Bangkok, etc.) shows ≥120% lifestyle coverage at median comp. This is the Asia-wins-on-life finding that differentiates the tool from Western-centric calculators.

Brutal insights (the scrolling bullets on the right column of the main dashboard) are rules-based — 13 specific patterns ranging from "your loan amortization exceeds likely Y1 signing" to "your track has high washout rate in this geo." Each has a citation visible in the tool.

• Immigration / visa friction. H-1B lottery, Tier-2 UK, EU Blue Card, Singapore EP — all have probabilistic outcomes we don't currently model. Assumes you get the visa. If you don't, the math changes materially.
• Dual-income household dynamics. Partner earning potential, spousal work-visa rights, childcare arbitrage (housekeeper economics in Asia vs US) — modeled lightly.
• Industry-specific shocks. 2022–2023 tech layoffs, 2024 consulting freeze — we use 2024–25 reports which capture partial recovery. A forward-looking shock model doesn't exist.
• Individual offer variance. The tool uses school × track × city medians. Your actual offer may differ — use the comp override field in the UI to recalibrate.
• Non-linear career paths. If you plan to switch industries post-MBA twice (e.g. banking → PE → startup founder), the tool assumes you stay on the initial track. Brutal insights flag when this is suspect.

• Employment reports update each spring — we refresh annually when each school publishes
• Numbeo CoL data is live-pulled; city-specific rent/meal prices refresh quarterly
• Promotion multipliers recalibrate when Heidrick & Struggles or specific firm leaks surface (e.g. the 2024 McKinsey partner comp band leak)
• If you spot a number that looks wrong, email lawrence.kuok@gmail.com with the source. We'd rather fix it than defend it.