How WhereNext Scores Countries: Our Methodology

Most “best countries” lists on the internet are editorial opinion dressed up as data. A writer visits three cities, surveys a handful of expat forums, and publishes a ranking that looks authoritative but is impossible to reproduce. We built WhereNext to be the opposite of that. Every score on this platform is derived from institutional data sources, normalized to a common scale, and weighted by your personal priorities — not ours.

This article explains exactly how the system works: what we measure, where the data comes from, how we normalize it, and how your preferences shape the final ranking. If a number on WhereNext surprises you, this page will tell you why.

The Seven Dimensions

We evaluate every country across seven dimensions. Each one captures a distinct aspect of what it means to live somewhere, and each one is scored independently before being combined into an overall ranking.

1. Cost of Living

What it measures: the real, day-to-day expense of living in a country relative to a global baseline. This includes rent, groceries, transport, dining, and utilities — not just a single “cost index” number. We pull from multiple price databases and cross-reference with purchasing power parity data from the World Bank to capture not just how cheap a country is, but how far your money actually goes.

2. Safety

What it measures: personal security and political stability. We combine the Global Peace Index, homicide rates from the UN Office on Drugs and Crime, and political stability indicators from the World Bank Governance Indicators. Countries with low violent crime, stable institutions, and minimal conflict risk score highest.

3. Healthcare

What it measures: system quality, accessibility, and affordability for residents. Data sources include WHO health system performance rankings, the Healthcare Access and Quality Index from the Global Burden of Disease Study, physician density, hospital bed availability, and out-of-pocket cost share. A country with world-class hospitals that are inaccessible to expats will score lower than one with a good universal system that enrolls foreign residents.

4. Climate

What it measures: weather comfort, air quality, and natural disaster exposure. We use historical weather data from Open-Meteo to calculate average temperatures, humidity, sunshine hours, and precipitation patterns. Air quality data comes from the WHO Global Air Quality Database. Natural disaster risk is drawn from the World Risk Index. The result is a composite that reflects how livable a country's environment actually is — not just whether it has nice beaches.

5. Education

What it measures: the quality and accessibility of education systems, from primary school to university. We draw on the UNDP Human Development Index education component, PISA scores from the OECD, tertiary enrollment rates, and international school availability for expat families. This dimension matters whether you have children, are considering further education yourself, or simply want to live in a society that invests in human capital.

6. Career & Economy

What it measures: economic opportunity, employment conditions, and business environment. Sources include World Bank GDP per capita and ease-of-doing-business rankings, OECD labor market statistics, unemployment rates, and internet infrastructure quality for remote workers. Countries with strong economies, reasonable tax environments, and reliable digital infrastructure score highest.

7. Infrastructure

What it measures: the physical and digital systems that define daily quality of life. This covers transport networks, internet speed and reliability, electricity stability, water quality, and mobile coverage. Data comes from the World Bank logistics performance index, ITU broadband and connectivity statistics, and the World Economic Forum infrastructure competitiveness rankings. A country can be cheap and safe but deeply frustrating to live in if the power cuts out daily and the internet crawls.

Where the Data Comes From

Credibility starts with sources. We deliberately restrict our data pipeline to institutional and peer-reviewed sources that publish transparent methodologies of their own. Here is the core stack:

• World Bank Open Data — GDP, purchasing power parity, governance indicators, infrastructure metrics, ease of doing business
• World Health Organization (WHO) — health system performance, air quality, physician and hospital density
• United Nations Development Programme (UNDP) — Human Development Index, education attainment, life expectancy
• OECD — PISA education scores, labor market data, healthcare cost analysis, broadband statistics
• Open-Meteo — historical and real-time weather data: temperature, humidity, precipitation, sunshine hours
• Global Peace Index (IEP) — composite peace and safety scoring for 163 countries
• UN Office on Drugs and Crime — homicide rates and crime statistics

We refresh data on a rolling basis. Most institutional sources publish annually; Open-Meteo weather data updates continuously. When a source publishes a new edition, we ingest it within two weeks and recompute all affected scores.

Normalization: Making Apples Equal to Oranges

Raw data is useless for comparison. A homicide rate of 1.2 per 100,000 and a GDP of $45,000 are both numbers, but they measure fundamentally different things on different scales. To make them comparable, every metric is normalized to a 0–100 scale using min-max normalization across all countries in our dataset.

The formula is straightforward: for a metric where higher is better (like life expectancy), a country's score equals (value - min) / (max - min) × 100. For metrics where lower is better (like homicide rate or cost of living), the scale is inverted. The country with the best value in our dataset scores 100. The worst scores 0. Everyone else falls proportionally in between.

Within each of the seven dimensions, multiple normalized metrics are combined using fixed internal weights. For example, the Safety dimension gives 40% weight to the Global Peace Index, 35% to homicide rate, and 25% to political stability. These internal weights are fixed based on how predictive each metric is of real-world lived experience, and they are not adjustable by users — they reflect our analytical judgment about what each dimension actually measures.

Confidence Scoring: Acknowledging What We Do Not Know

Not all data is equally reliable. A World Bank GDP figure for Germany is measured with far greater precision than a cost-of-living estimate for a country with limited economic reporting. Rather than pretending all data is perfect, WhereNext assigns a confidence score to every dimension for every country.

Confidence is determined by three factors: data recency (how old is the most recent source), source coverage (how many independent sources contribute to the dimension), and measurement precision (whether the metric comes from census-level data or modeled estimates). A country with recent, multi-source, high-precision data earns high confidence. A country where we are relying on a single five-year-old estimate earns low confidence — and we show that on the country profile so you can interpret the score accordingly.

This is a feature, not a limitation. When you see a confidence indicator flagged as moderate or low, it means we are being honest about uncertainty rather than hiding it behind a precise-looking number.

How Weights Work: Your Priorities, Your Ranking

Here is where WhereNext diverges fundamentally from static ranking lists. When you take the personalization quiz, you tell us what matters most to you. A retiree prioritizing healthcare and cost will see a very different top-10 than a remote worker who cares about internet speed and safety. Same data, different weights, different results.

Each of the seven dimensions receives a weight between 0 and 1, and all weights sum to 1. The overall country score is a weighted average: each dimension score is multiplied by its weight, and the results are summed. By default — if you apply no personalization — all seven dimensions receive equal weight. This produces what we call a “balanced” ranking, which tends to favor well-rounded countries that perform consistently across every metric rather than excelling in one area while failing in another.

Change the weights and the ranking shifts. Prioritize cost of living, and countries like Portugal, Thailand, and Mexico rise dramatically. Prioritize safety and healthcare, and Scandinavian and Western European countries dominate. This is by design: the data stays objective, but the lens through which you view it is yours.

What Makes This Different From a Listicle

There is no shortage of “top 10 countries to live in” articles. Most of them share three problems that WhereNext is specifically built to avoid.

First, reproducibility. If you cannot see the data, the formula, and the sources, you cannot evaluate whether a ranking is credible. Every WhereNext score is traceable back to its source data. We do not ask you to trust us; we show the work.

Second, personalization. A single ranked list assumes everyone has the same priorities. They do not. A family with two school-age children evaluating countries has fundamentally different needs than a solo digital nomad. Static lists force you to mentally re-weight on the fly. WhereNext does that computation for you, with precision.

Third, coverage. Most listicles feature 10 to 20 countries, usually the same ones. WhereNext scores and ranks over 100 countries across all seven dimensions. The country that is perfect for you might not be one that shows up in a BuzzFeed-style roundup, and our system will still surface it.

The result is not a replacement for on-the-ground research — no data model can tell you whether you will love the food in Lisbon or hate the humidity in Bangkok. But it is a rigorous, transparent starting point that narrows the world from 195 countries down to the handful that genuinely fit your life.

Limitations and Ongoing Improvements

We believe transparency includes being upfront about what we do not yet do well. A few known limitations:

• Sub-national variation — country-level scores cannot capture differences between, say, Mexico City and rural Oaxaca. We are working on city-level data integration for major metropolitan areas.
• Visa and immigration complexity — eligibility requirements vary enormously by nationality and personal circumstances. Our visa data covers common pathways but cannot replace consultation with an immigration specialist.
• Cultural and social fit — no dataset can quantify whether you will feel at home somewhere. We provide the structural data; the human judgment is yours.

We update the model continuously. As better data sources emerge, as users provide feedback, and as we add new dimensions, the system gets sharper. The methodology described here is a living document — when we change something material, we update this page and note what changed.

See It in Action

The best way to understand the scoring system is to use it. Take the two-minute quiz, set your priorities, and watch how the rankings shift in real time as you adjust what matters to you. Every score links back to the underlying data. Every dimension is explorable. The methodology is the product.