How We Calculate Everything

Every number on this site comes from a documented formula with a public data source. No black boxes. If we got something wrong, you can check our work.

Live calculation proof
🇵🇹
Portugal
$75,000/yr gross salary
74
NLV Score
79
Economic
70
Quality
Tax rate: 42.5%PPP: $6,342/mo
sqrt(79 x 70) = 74

Net Life Value (NLV)

NLV answers one question: “Where does your salary buy you the best life?”

It's a single score from 0 to 100 that combines three things: how much money you actually keep after taxes (adjusted for local prices), how good daily life is (safety, healthcare, climate, internet), and how realistic it is to move there (visas, language, community).

NLV is personalized to your salary. The same country can score 70 at $50K and 55 at $200K, because taxes, purchasing power, and the economic cap interact differently at each income level.

NLV Formula & Geometric Mean

NLV = √(Economic Power × Quality of Life)
Two pillars, equal weight (50/50), geometric mean

Two pillar scores (each 0–100) are combined using a geometric mean— the same aggregation principle the UN uses for the Human Development Index. Equal weights: 50/50.

Why geometric mean instead of a weighted average?

Arithmetic mean
(95 + 20) / 2 = 57

Looks okay, but living there would be miserable.

Geometric mean
√(95 × 20) = 44

Punishes imbalance. You can't offset poverty with sunshine.

Why only two pillars?

Previous versions included Accessibility (visa openness, English proficiency, expat community) as a third pillar. We removed it from the score because visa requirements depend on your nationality, not the country's quality. A US passport holder and an Indian passport holder face completely different realities — a single score can't capture both. Accessibility data is still displayed alongside the NLV score as useful context.

Equal weights

50%Economic Power

What your salary is actually worth locally, after taxes and adjusted for prices. The financial reality.

50%Quality of Life

Safety, healthcare, climate, internet, cost of living. What daily life actually feels like. Taxation is excluded from QoL — it's already captured in Economic Power via the tax engine.

Pillar 1: Economic Power (50%)

Economic Power measures what your salary is actually worth locally, after taxes. The pipeline:

💵1
Gross Salary
Your input salary in USD
💱2
Convert to Local Currency
Using the country's exchange rate
🏛️3
Tax Engine
Country-specific progressive brackets, social contributions, personal allowances, and credits. Details below.
📊4
Net Monthly USD
Gross ÷ 12 × (1 − effective rate)
🌍5
Rent-Adjusted PPP
Blend two price signals: 0.65 × priceLevel + 0.35 × rentRatio. Rent is ~35% of an expat's budget.
🎯6
Score
(PPP Net Monthly ÷ $8,000) × 100, capped at 100

The $8,000 cap

Based on Kahneman & Deaton (2010): beyond approximately $8,000/month in purchasing power, additional income has sharply diminishing returns on life satisfaction. This cap prevents ultra-low-tax countries from scoring 100 purely on tax savings at very high salaries, which wouldn't reflect real quality-of-life gains.

Pillar 2: Quality of Life (50%)

Five dimensions, each scored 0–100, combined into a single QoL score using weighted averages. Taxation is intentionally excluded — it's already captured in Economic Power via the real tax engine.

🛒

Cost of Living

25%

Price Level (50%), CPI (30%), PPP (20%)

Source: World Bank

☀️

Climate

15%

Sunshine Hours (40%), Temperature (30%*), Precipitation (30%)

Source: Open-Meteo

🛡️

Safety

25%

Homicide Rate (50%), Global Peace Index (50%)

Source: UNODC, IEP

🏥

Healthcare

25%

Life Expectancy (35%), Health Spending (20%), Physicians (25%), Hospital Beds (20%)

Source: WHO

📡

Internet

10%

Internet Users (40%), Mobile Subs (30%), Broadband (30%)

Source: ITU

* Temperature uses a bell curve, not linear scoring. See below.

Accessibility (contextual, not in score)

Accessibility measures how practical it is to relocate. It is displayed alongside the NLV score but does not affect it. Why? Because visa requirements depend on your passport, not the destination's quality. A universal score can't fairly represent this.

Visa Openness

40%

Henley Passport Index score. Higher means more nationalities can enter without complex visa processes.

Source: Henley & Partners

English Proficiency

30%

EF English Proficiency Index. Practical for daily life, bureaucracy, and social integration.

Source: EF Education First

Expat Community

30%

Foreign-born population as % of total. Capped at 50% = score 100. Larger communities mean better infrastructure for newcomers.

Source: World Bank

Bonus: countries with an official Digital Nomad Visaprogram are flagged (but this doesn't affect the numeric score).

Tax Engine

We model each country's tax system individually. No generic “average tax rate” shortcuts. The engine handles:

  • Progressive income tax brackets with country-specific thresholds
  • Social security contributions (employee-side, with caps where applicable)
  • Personal allowances, standard deductions, tax credits
  • Country-specific logic:
    • FranceQuotient familial (household income splitting)
    • UK — Personal allowance taper above £100K
    • Netherlands — Combined tax + social insurance brackets
    • Japan & Korea — Employment income deduction before brackets
    • NorwayTrinnskatt (step tax) on top of flat bracket tax
    • India — Surcharge + health/education cess on top of income tax
    • Belgium — Municipal surcharge on top of federal tax
    • DenmarkAM-bidrag (labour market contribution) before income tax

30 countries have full tax engine coverage. Sources: IRS, HMRC, DGFiP, Bundesfinanzministerium, CRA, ATO, and 24 other national tax authorities.

QoL Dimension Scoring

Each metric is normalized to 0–100 using min-max normalization:

score = (value − min) ÷ (max − min) × 100

For metrics where lower is better (homicide rate, precipitation, cost), the score is inverted. Values outside the min-max range are clamped. Missing data defaults to a neutral score of 50.

Normalization bounds

MetricMinMaxDirection
Price Level20180Lower is better
CPI50250Lower is better
PPP0.515Lower is better
Temperaturen/an/aBell curve (22°C optimal)
Sunshine Hours1,0003,500 h/yrHigher is better
Precipitation2002,500 mm/yrLower is better
Homicide Rate030 /100KLower is better
Peace Index1.03.5Lower is better
Life Expectancy5585 yearsHigher is better
Health Expenditure2%18% GDPHigher is better
Physicians0.16 /1KHigher is better
Hospital Beds0.513 /1KHigher is better
Internet Users10%100%Higher is better
Mobile Subscriptions30180 /100Higher is better
Fixed Broadband0.550 /100Higher is better
Tax Revenue5%50% GDPLower is better
Corporate Tax0%40%Lower is better
VAT Rate0%27%Lower is better

Temperature Bell Curve

Unlike other metrics, temperature is not scored linearly. Both extreme heat and extreme cold reduce quality of life. We use a Gaussian (bell curve)centered on 22°C — the midpoint of the 20–24°C range widely cited as optimal for human comfort and productivity.

score = 100 × exp(−0.5 × ((temp − 22) ÷ 8)²)
Sigma = 8 controls the spread
Temperature vs Score
025507510022°C optimal0°C10°C20°C30°C40°CCanada24Portugal73Spain94Thailand60UAE54
Canada5°C24
Portugal16°C73
Spain18°C94
Thailand27.5°C60
UAE28.5°C54

This replaces the previous linear scoring (where 30°C = 100 and −5°C = 0), which unfairly rewarded extreme heat. The bell curve better reflects the reality that 28°C year-round is not more comfortable than 20°C year-round.

Real Price Index

30 everyday products priced in USD across 36 countries. We don't rely on composite indices — we track specific, identifiable products you can price-check yourself.

Price Index = average( local price ÷ US price × 100 )
US = 100 baseline. A country scoring 72 is 28% cheaper than the US.

Product categories

🛒
Food & Groceries
Big Mac, eggs, rice, chicken, bread, milk, bananas
Cafe & Dining
Latte, beer, pizza, water
📱
Tech & Digital
iPhone 16, Netflix, Spotify, PS5, mobile data
🚗
Transport
Gasoline, transit ticket, taxi, Toyota Corolla
👟
Fashion & Lifestyle
Nike AF1, Levi's 501, haircut, gym, cinema
🏠
Housing & Utilities
Internet, electricity, mobile plan, Airbnb

Data Sources

All data comes from official, publicly verifiable sources. No proprietary datasets.

Tax Authorities

International Organizations

City Data — 15 Government Sources

Other Sources

ClimateOpen-Meteo
English ProficiencyEF EPI
Rent BaselineAirbnb/AirDNA nightly rates
Product PricesThe Economist, Apple, Numbeo, Cable.co.uk
Median SalariesNational statistics offices, ILO
Exchange RatesECB, Federal Reserve

Limitations

Employment income only

The tax engine models salary income. Self-employment, capital gains, rental income, and dividends are not included. Special tax regimes (Portugal NHR, Netherlands 30% ruling, UAE free zones) are not modeled.

City coverage is partial

38 cities across 17 countries have city-specific cost and rent multipliers from official government statistics. Other cities inherit country-level averages. City data comes from 15 national statistics offices with Numbeo as fallback — each city page shows its exact data source.

Opinionated weights

The 50/35/15 NLV split and the QoL dimension weights reflect our editorial judgment. Different people value different things. We publish the formula so you can mentally adjust.

Point-in-time data

Exchange rates, prices, and tax brackets are snapshots, not live feeds. Data is refreshed periodically. Always verify current rates before making financial decisions.

Climate is averaged

Avg temperature and sunshine hours are annual averages. A country with mild winters and hot summers might average 18°C but feel very different from one that's 18°C year-round.

Accessibility is simplified

Visa openness is modeled at the country level, not by nationality. A US citizen and an Indian citizen face very different visa requirements — our score doesn't capture this yet.

Questions about the methodology? Found an error? The entire codebase, including all scoring functions, is deterministic and can be traced from the JSON data files through the calculation functions to the rendered output. No machine learning, no opaque models — just arithmetic.