Compare how a move could change purchasing power, work, life evaluation, healthy years, healthcare, bureaucracy and safety across 54 countries with an interactive relocation calculator.
In this article
- Why a move is not a country swap
- When the financial answer reverses
- What changes with the destination
- Does happiness move with you?
- Why health follows a different rule
- How to use the calculator
- What we know, and what we still do not
A Finn who moves to Spain does not become a statistical Spaniard at passport control. The person brings the same biography across the border, but not the same economic or institutional environment.
That distinction sounds obvious. It becomes surprisingly awkward once you try to turn it into numbers.
Choose two countries and your income scenario to start.
Compare a move
See what changes when you move: money, work, happiness, healthy years, healthcare, bureaucracy and safety. Country-level estimates, not a personal forecast.
Why this result?
Why this result?
Why this result?
Why this result?
Why this result?
Why this result?
Why this result?
Key takeaways
- The same move can reverse financially. Keeping the same income and taking a local job are different economic experiments, even when the origin and destination are identical.
- Some outcomes seem to move only part of the way. Migrant life satisfaction is often substantially destination-like, but observational data do not identify a simple causal percentage.
- Population health cannot be transferred to a migrant by arithmetic. A destination with higher healthy life expectancy does not automatically add those years to a newcomer’s life.
Why a move is not a country swap
The first version of this project followed the natural logic of country rankings. Compare income, prices, working hours, happiness, health, safety and institutions; then see which country comes out ahead.
The weakness is hidden in the unit of comparison. National averages describe populations. Migration changes the environment around a person without replacing the person with an average resident of the destination.
Some things may travel almost intact. A remote employee may keep the same salary and working schedule. Other things change as soon as the border is crossed: local prices, tax rules, administrative systems and the surrounding labour market. A third group, including health and subjective wellbeing, reflects both past and present environments.
| Type of variable | Examples | What a move changes |
|---|---|---|
| Portable | Remote income, existing job | May remain broadly unchanged unless the move alters the arrangement |
| Destination exposure | Prices, local labour market, administration, everyday safety | The new environment becomes relevant quickly, although national averages remain coarse |
| Adaptive or accumulated | Life evaluation, health | Origin history remains important; some outcomes may converge partly, while others have no defensible universal transfer rate |
This is also why the calculator does not produce a single score. Combining these dimensions would require us to decide how much one point of safety is worth in euros, or how many working hours equal one year of population healthy life expectancy. Those are value judgements, not statistical conversions.
Practical takeaway
Before comparing countries, ask what actually travels with you. A national gap means something different for portable income than for accumulated health or life evaluation.
When the financial answer reverses
Finland to Spain is a useful test because the financial conclusion changes before anything else in the model changes.
Using the calculator’s frozen 2024 World Bank price-level snapshot, someone who preserves the same spendable income would have roughly 33.8% more purchasing power in Spain. This calculation uses relative price levels, not a prediction of actual household expenditure. [1]
Now change one assumption: instead of keeping the same income, the migrant enters the destination economy.
The calculator then uses GNI per capita at purchasing-power parity as a deliberately broad benchmark. In the stored 2024 data, Spain is about 11.0% below Finland on that measure. [1]
The same move has changed financial sign.
That does not mean that a Finnish worker should expect an 11% pay cut in Spain. GNI per capita is not a wage measure, still less an occupation-specific wage prediction. It is being used because it provides globally comparable economic context across the calculator’s country set.
| Finland → Spain | Calculated change | What the number means |
|---|---|---|
| Same income & work | +33.8% | Estimated purchasing-power change if spendable income remains approximately portable |
| Local income & work | −11.0% | Broad GNI-per-capita PPP comparison, not expected personal salary |
| Local weekly hours | +2.85 h | Difference in ILO modelled average weekly hours actually worked, 2025 |
| Life evaluation | about −0.80 / 10 | Calculator heuristic for migrant-group convergence, not a personal prediction |
| Healthy life expectancy | +1.18 years | Difference between destination populations, not years added to the migrant |
| Out-of-pocket health spending | +13.5% | Difference in observed PPP-adjusted per-capita spending, 2023 |
| Feeling safe walking alone at night | −7 points | Difference between national Gallup percentages, 2024 |
The work-hours figure needs its own caution. ILOSTAT’s measure is average weekly hours actually worked per employed person. It reflects part-time work, labour-force composition and other structural differences, so it should not be read as the normal schedule for a comparable full-time job. [2]
A more personalised economic model is possible. OECD Taxing Wages 2026, for example, calculates taxes, social contributions and cash benefits for eight household types at different earnings levels. That is much closer to disposable household income than GNI per capita. But its rich modelling does not give us equivalent coverage for the full international set used here. [3]
Adding detailed household questions to every country while using them only where the data happen to support them would make the interface look more personal without making the underlying comparison consistently more personal. For this version, broad coverage won.
Key finding
Finland → Spain changes financial sign when the income assumption changes. Keeping current income gives about +33.8% purchasing power; the broad local-income benchmark is about −11.0%.
What changes with the destination
Some cards describe conditions that plausibly become relevant soon after moving, but even these are not personal forecasts.
Safety is based on Gallup’s question about whether people feel safe walking alone at night in the city or area where they live. In the 2024 data, 88% answered yes in Finland and 81% in Spain. The calculator therefore displays a difference of seven percentage points. [4] Gallup’s samples are nationally representative, but the measure is subjective and country-level; neighbourhood, sex, age and other factors can matter greatly.
The bureaucracy card is even more explicitly a proxy. It uses the World Bank’s Government Effectiveness indicator, which combines perceptions of public services, civil-service quality, policy implementation and government credibility. It is not a direct measure of how many forms an immigrant will have to complete. The World Bank itself describes WGI scores as estimates rather than definitive ratings and reports uncertainty around them. [5]
Healthcare also resists being folded neatly into general living costs. World Bank data sourced from the WHO Global Health Expenditure Database put 2023 out-of-pocket spending at about $907 PPP per person in Finland and $1,029 in Spain, a difference of roughly 13.5%. [6] That is observed spending, not a price quote. Utilisation, insurance design, public coverage and population health all affect the number.
The point of keeping these cards separate is not that each one is individually precise. It is that they fail in different ways. A single composite score would conceal those differences.
Does happiness move with you?
Subjective wellbeing is harder because neither extreme is plausible. Migrants do not arrive as blank slates, but neither does life evaluation appear fixed at the level of the origin country.
The World Happiness Report’s main ranking uses the Cantril Ladder, a 0–10 evaluation of life as a whole. In the 2026 report, Finland averaged 7.764 and Spain 6.540 for 2023–2025. These are evaluations by resident populations, not measurements of daily pleasure and not forecasts for future migrants. [7]
This measurement distinction mattered in our earlier Mike’s Balance investigation, Can a Country Make You Happier? We found that country ordering can shift when the question changes from life evaluation to life satisfaction, general happiness or recent emotional experience. That earlier article is useful here because the calculator specifically needs a measure that can be interpreted consistently; the underlying scientific claims still rest on the survey datasets and migration literature rather than on the Mike’s Balance article itself. [8]
Migration research suggests substantial destination alignment. The World Happiness Report 2018 found immigrant-happiness rankings to be strikingly similar to the rankings of locally born populations. Its modelling still detected a source-country “footprint”, estimated at roughly 10–25%, so convergence was strong but incomplete. [9]
Helliwell, Shiplett and Bonikowska reached a related result using immigrants to Canada and, in the published version, the United Kingdom. Immigrant life-satisfaction distributions were far more similar to those of people in the destination than to distributions in their countries of origin, including after adjustments intended to address selection. The design remained observational. [10]
We then treated this established pattern as something to challenge, rather than something to rediscover.
In an exploratory Null Institute reanalysis of European Social Survey rounds 1–11, a stricter adult post-1991 specification contained 20 origin-destination routes and 2,361 migrants. The destination-transfer slope for life satisfaction was 0.662, with a route-bootstrap 95% interval of 0.564–0.844. ESS global happiness produced a very similar point estimate. [11]
A slope near zero would mean migrant-group life satisfaction remained close to the origin-country level. A slope near one would mean it matched the destination-country gap almost completely. The estimate therefore describes substantial destination alignment.
It does not identify a causal effect of moving.
Migrants select themselves into migration, select destinations, differ from people who stay behind and may selectively return home. The ESS analysis also found that numerical response style itself became more destination-like after migration. Adjusting life-satisfaction responses using other 0–10 survey items did not remove the destination pattern, but that robustness test cannot eliminate the broader selection problem. [11]
A rare migration lottery shows why the distinction matters. Stillman and colleagues compared successful and unsuccessful applicants in a lottery giving Tongans the opportunity to move to New Zealand. Migration substantially improved several objective outcomes and mental health, while a direct happiness measure fell. [12] The context was highly specific, so it should not be universalised, but its causal design demonstrates something observational averages can hide: different components of wellbeing can move in opposite directions.
The calculator therefore uses 0.65 as a modelling heuristic for destination convergence in life evaluation. It is anchored to our ESS estimate, not presented as a meta-analytic constant. The external literature supports the broader claim of substantial incomplete convergence, but the studies do not estimate exactly the same quantity.
There is one further mismatch worth keeping visible. Our 0.662 estimate comes from an ESS life-satisfaction item, whereas the calculator applies the heuristic to World Happiness Report Cantril Ladder scores. The constructs overlap, but they are not identical.
A result such as Finland → Spain’s −0.80 points should therefore be read as a rough group-level benchmark, not “how much less happy you will become”.
Why health follows a different rule
Once we had a convergence estimate for life satisfaction, an attractive next step was to do the same for health.
The evidence stopped us.
WHO healthy life expectancy at birth, or HALE, combines mortality and morbidity into the expected number of years lived in full health under a population’s current health conditions. In 2021, WHO estimates were approximately 69.9 years for Finland and 71.1 for Spain. [13]
The 1.2-year difference is a legitimate population comparison. It is not a migration effect.
A Finnish adult has already accumulated decades of exposures before arriving in Spain. Early-life conditions, previous disease, occupation, behaviour and healthcare history do not reset when residence changes. Destination conditions may affect future health, but there is no obvious reason to expect every component of the HALE gap to transfer at the same speed or in the same direction.
The migrant-health literature reinforces that caution. A systematic review by Vang and colleagues identified 78 eligible Canadian studies and found that the healthy-immigrant advantage varied across life stages and outcomes. It was more robust for mortality than morbidity and tended to be stronger among recent immigrants. The authors found no uniform foreign-born health advantage across health outcomes and the life course. [14]
That is not proof that migration has no health effect. It is evidence against pretending that one universal coefficient can convert a country HALE difference into healthy years gained by an individual migrant.
So the calculator leaves HALE unadjusted and labels it as population context.
The asymmetry is deliberate. Happiness gets a rough transfer heuristic because several independent datasets show substantial destination alignment. Health does not because the evidence is too heterogeneous to justify the same move.
Important nuance
Population health is context, not a migrant forecast. Spain’s +1.18-year HALE gap does not mean a Finnish adult gains 1.18 healthy years by moving there; the evidence does not support a universal transfer coefficient.
Where the destination suggestions come from
Once an origin country is selected, the calculator also shows five major destinations for migrants from that origin.
These come from the UN DESA International Migrant Stock 2024 bilateral data, which cover 233 countries and areas. The dataset describes migrant stocks, meaning where people from an origin are living, not where they moved during 2024. Historical migration, border changes and long-established diasporas can therefore shape the rankings. [15]
UN DESA fully reassessed trends for 60 countries and areas in the 2024 edition; estimates for many of the remainder extrapolate from the 2020 edition. [15] The Top 5 should therefore be read as context, not as a recommendation algorithm.
If a genuine top-five destination falls outside the calculator’s 54-country coverage, it remains visible but disabled. Replacing it with a supported destination would make the interface tidier and the underlying ranking wrong.
Why the calculator stops where it does
A personalised relocation model could ask about occupation, household structure, age, education, city, visa status, tax residence, medical needs, housing, climate preferences and dozens of other variables.
Most of those variables matter.
That is the reason not to add them casually.
A useful input needs three things: a plausible mechanism, enough effect to change the answer, and comparable data across the destinations where the calculator claims to work. Detailed OECD household-tax models illustrate the trade-off. They can substantially improve an economic comparison inside their coverage, but cannot simply be projected onto countries for which equivalent modelling is unavailable. [3]
More questions do not automatically produce more information. They can produce a longer form attached to the same weak national proxy.
The calculator therefore asks one unusually important personal question indirectly through its economic scenarios: does your income move with you, or does it become local?
That single distinction can reverse the result.
How to use the calculator
The calculator works best as a contradiction detector rather than a decision machine.
Start by choosing the economic scenario that most closely resembles the move. Then look for cards that disagree.
If purchasing power improves but the local-income benchmark falls, the move depends heavily on income portability. If population HALE is higher but perceived safety is lower, neither statistic cancels the other. If the destination has a lower national life evaluation, the migrant-adjusted estimate may still matter, but it remains much less personal than a known job offer or a known housing cost.
The labels underneath the numbers are therefore part of the result. They tell you whether you are looking at a scenario calculation, a national exposure, a migration heuristic or population context.
The model becomes misleading if those categories are forgotten.
Frequently asked questions
Does the calculator predict my salary after moving?
No. The local-income scenario uses GNI per capita PPP as a broad national benchmark because it is available across the country set. Actual pay depends on occupation, experience, employer, tax system, working hours and many other factors.
If Spain has 1.2 more healthy years than Finland, would I gain 1.2 healthy years by moving?
No. The number is the difference between WHO population HALE estimates. No defensible universal coefficient currently converts that difference into healthy years gained by a migrant.
Why is there no overall relocation score?
Because combining money, work, life evaluation, health, safety and institutions requires subjective weights. The calculator shows the trade-offs and leaves those weights to the user.
Why does the calculator adjust happiness but not health?
Because different evidence exists for the two outcomes. Multiple migration datasets show substantial, though incomplete, destination alignment in life evaluation. Migrant-health effects are much more heterogeneous, so applying one health-transfer coefficient would create unsupported precision.
Are the “popular destinations” current migration flows?
No. They are based on bilateral migrant stocks from UN DESA 2024, which describe where migrants from each origin are living. A stock can reflect decades of migration rather than current-year choices. [15]
What seems reasonably well established?
Moving countries changes several distinct systems at once, and they should not be treated as one effect.
Prices and other local conditions genuinely differ between destinations. Whether those differences improve a migrant’s finances depends critically on what happens to income. National labour, safety, healthcare and governance indicators can provide useful context, but they remain population-level proxies.
For subjective wellbeing, several independent observational datasets show that migrant life evaluation tends to resemble the destination more strongly than the origin while retaining some origin-country influence. Our ESS reanalysis reproduces that broad pattern and shows that measured response-style adaptation does not fully explain it. [9–11]
The evidence is much weaker for converting population health differences into individual migration effects. HALE can describe the destination population without telling a migrant how many healthy years the move will add or subtract.
What remains uncertain?
The largest uncertainty is individual heterogeneity.
The people who migrate are not randomly drawn from origin populations. They choose destinations for reasons related to employment, family, language, wealth, health and personality. Some later return. These processes can make migrant groups look more destination-like, or less destination-like, without revealing the causal effect that a particular person would experience.
National averages also conceal variation within countries. A specific city, employer, neighbourhood or household tax position may dominate the country-level difference.
The happiness coefficient deserves particular restraint. The calculator’s 0.65 parameter is a useful working convention supported by our ESS estimate, not a universal biological or social constant. Different routes, populations and time horizons may produce different degrees of convergence.
What is the most interesting question left?
The useful scientific object may not be “the best country” at all.
It may be transferability.
For each outcome, we would like to know how much of an origin-destination difference is actually experienced by migrants, how quickly it appears, what part persists from the origin, and which mechanisms produce the change.
Income shows that transferability can depend on a single practical condition: whether earnings remain portable.
Life evaluation suggests substantial but incomplete adaptation.
Health shows why the same rule cannot simply be copied from one outcome to another.
A mature relocation model would eventually estimate these transfer functions separately and condition them on the few personal variables that genuinely change them.
Until then, the most useful calculator is not the one that tells you where to move.
It is the one that makes clear which parts of its answer you should believe, and which parts remain only informed context.
References
- World Bank. Price level ratio of PPP conversion factor to market exchange rate; GNI per capita, PPP (current international $). Price-level data; GNI per capita PPP data.
- International Labour Organization. ILOSTAT, Average weekly hours actually worked per employed person, modelled estimates. ILOSTAT data catalogue.
- OECD. Taxing Wages 2026: The Progressivity of Labour Taxation in OECD Countries. OECD Publishing, 2026. Source.
- Gallup. Global Safety Report 2025, using 2024 Gallup World Poll data. Source.
- World Bank. Worldwide Governance Indicators, Government Effectiveness. Source.
- World Health Organization / World Bank. Out-of-pocket expenditure per capita, PPP (current international $). Source.
- Helliwell JF, Layard R, Sachs JD, De Neve J-E, Aknin LB, Wang S, eds. World Happiness Report 2026. Report.
- Mike’s Balance. Can a Country Make You Happier? Happiness Rankings Explained. 2026. Article.
- Helliwell JF et al. World Happiness Report 2018. Chapters on international migration and subjective wellbeing. Report.
- Helliwell JF, Shiplett H, Bonikowska A. “Migration as a Test of the Happiness Set-Point Hypothesis: Evidence from Immigration to Canada and the United Kingdom.” Canadian Journal of Economics. 2020;53(4):1618–1641. Working-paper version.
- Nikitin I. How Much of Migrant Life-Satisfaction Convergence Is Measurement? The Null Institute, 2026. Exploratory observational reanalysis; not peer reviewed. Research report.
- Stillman S, Gibson J, McKenzie D, Rohorua H. “Miserable Migrants? Natural Experiment Evidence on International Migration and Objective and Subjective Well-Being.” World Development. 2015;65:79–93. DOI.
- World Health Organization. Healthy Life Expectancy (HALE). Global Health Estimates, 2021. WHO data and definition.
- Vang ZM, Sigouin J, Flenon A, Gagnon A. “Are immigrants healthier than native-born Canadians? A systematic review of the healthy immigrant effect in Canada.” Ethnicity & Health. 2017;22(3):209–241. PubMed.
- United Nations DESA Population Division. International Migrant Stock 2024. Dataset and documentation.
Medical information
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