About the data

Where you were born

The country is chosen in proportion to where babies are actually born: population multiplied by birth rate, not population alone. The city is chosen in proportion to its population, so a large city comes up far more often than a small town.

City data covers settlements above about 5,000 people. Below that a place is not in the dataset at all, so the city you see is the notable population centre you were born in or near, rather than an exact birthplace.

The map below the city line marks that same settlement, from the same source, with the same limits: the marker covers an area rather than a point, and zooming in stops well short of individual buildings. It is the town or city, never an address. Map tiles come from OpenStreetMap and are not requested until the map is actually reached on the page.

Whether you live in a city or the countryside

Drawn separately from the country and city above, in proportion to the real share of that country’s population living in urban areas. Real data exists for every country here, so this is never left out the way some other facts sometimes are.

City data only covers settlements above about 5,000 people, so nobody in the countryside can be assigned one as their actual home. When you’re placed in the countryside, the city named alongside you is the nearest notable population centre you were born near. It is the same figure shown for everyone, just described honestly for where you actually live.

Your name

A few countries publish real counts of how many people hold each name, and there the name is weighted by those counts. Everywhere else, names come from a list of common names for that country with no frequencies attached, so each is equally likely.

Even where counts exist they cover a country’s most common names rather than everyone. Brazil is the clearest case: the 88 names available account for about 38% of women, so the names that are listed come up about two and a half times more often than they do in life. Common names are therefore over-represented: Maria appears for roughly 29% of Brazilian girls here, against a real share of about 11%.

What a name describes also varies by country, because sources differ: for most it is the names given to babies recently, for others the names of everyone alive today. Forcing a single basis would have meant dropping names for dozens of countries, so each one keeps whichever its source publishes.

For most of the world, though, nobody publishes name data at all. A fifth of the world’s births used to land in a country with no names on file, almost all of it in sub-Saharan Africa, and those profiles carried no name. Every country now has one, but for about ninety of them the names were written for this project rather than taken from a published source. They are meant to be names people in that country actually have, chosen to span its languages and religions, but no ranking says how common any of them is.

Where that is the case there are no real frequencies behind the draw, so an uncommon name turns up as often as a common one. Generating several lives in the same country will also repeat names, because these lists run to a few dozen rather than the thousands a statistics office publishes.

Your ethnicity and religion

Both are drawn from the published composition of the country you were born in. Those figures rarely total exactly 100%. The range across countries runs from about 89% to 141%, because categories sometimes overlap and sometimes leave people out, so they are used as relative proportions among the groups a source actually names.

Entries like “other” and “unspecified” name no group, so nobody is ever assigned one. The share they carried is spread across the named groups instead, which makes those slightly more likely than their published figure. Having no religion is a real recorded outcome and is kept as one.

Names, ethnicity and religion are drawn separately

These three are each drawn from their own country statistic, independently of the others. No dataset records how names, ethnicity and religion actually occur together, so combinations turn up that are each accurate for the country but uncommon in the same person.

Inventing that relationship would mean deciding which names “belong” to which religion, which is not a claim this project is willing to make. So the traits are shown as separate facts and never composed into one description.

Sexual orientation

No credible per-country breakdown of sexual orientation exists. Surveys that ask people to self-identify undercount hardest in exactly the places where the legal consequences of answering honestly are most severe, so drawing from per-country figures would show the fewest gay and bisexual people where the law matters most.

Instead, every profile draws from a single worldwide rate: 3% gay or lesbian and 4% bisexual, from Ipsos’ LGBT+ Pride 2023 global survey (30 countries, self-identified). That figure is a floor, not a settled measurement: the same concealment effect that rules out per-country figures still applies within those 30 countries, most of which are wealthier and more permissive than average, so the real worldwide share is probably higher than this.

Legal status where you live

Whether same-sex relationships are legal where you live, the maximum penalty if not, and five related protections and recognitions, are facts about the country you were assigned to, not about you. They are shown only when your own drawn orientation makes them personally relevant.

Your IQ score

Your score is drawn from the standard normal distribution IQ tests are built around, average 100 with a spread (standard deviation) of 15, not from any measurement of a country or group. Lynn & Vanhanen’s national-IQ studies are the only dataset that would let this be sourced per country, and are excluded: they are scientifically contested, and their real-world use has been to rank countries and ethnicities by intelligence, the exact comparison this project won’t make. The qualitative label (“average,” “high average,” and so on) follows the classic Wechsler classification scale.

Your personality type

MBTI has no established scientific validity: psychologists broadly regard it as unable to reliably predict behavior, so there is no real distribution to draw from, per country or worldwide. 16personalities, whose published data was considered as a closer-to-real-world alternative to a flat guess, turned out to publish no single global percentage per type anywhere on its own site. Your type is drawn from an even 1-in-16 split instead, honest about not being measured rather than dressed up as more precise than it is.

Whether you smoke

Drawn from the share of adults aged 15 and over who smoke in the country you were born in, for your own sex where that is published and the country-wide figure otherwise. Your own age is not part of this profile, so the band the rate is measured over is stated here rather than implied. You are not being described as any particular age.

Your height, your weight, and whether you are overweight

Your weight is not a separate figure. It follows from your height and your body-mass index, which is what body-mass index means, so the three numbers can never contradict each other. Whether you are shown as overweight follows from the same index rather than being decided on its own.

Your index is drawn from the full distribution your country publishes for your own sex, seven bands from underweight through severely obese, so the spread of real bodies is kept rather than flattened into an average. Two things in it are assumptions and not measurements: the lowest and highest bands are published open-ended, so how far they reach is a judgement, and where inside a band your exact number falls is a choice, because no source says. The five bands with an end at each side are filled evenly. The two open ones thin out towards the extreme, so most people in them sit near the published edge, which is what measured populations look like. Neither changes which band you are in.

Your height is drawn around your country’s average for your sex. The source publishes that average but not how much people vary around it, so the spread used is a general figure from the literature rather than your country’s own. The averages are measured at age 18 for people born in 1996, the most recent group the source covers. You are not being described as any particular age.

Countries with no published figures show no height, weight or index, rather than a borrowed one.

Whether you were overweight as a child

A separate figure from the one above, drawn independently: the share of children under five who are overweight, for your own sex where published. As with smoking, the age band describes the measurement, not you.

It is a country-wide prevalence rate. It carries no information about any individual’s choices, and is not shown as if it did.

What your monthly income figure is

It is what households in your country actually spend, per person, per month, divided across the country’s income distribution and read at the position you were placed in. It is not a wage: it describes what you have to live on, not what work pays.

This used to be built from national income instead, which counts business profits and government revenue no household ever sees, and so ran roughly two to two and a half times higher than what household surveys record. Household spending is the closer figure.

Your position is drawn from a curve fitted to your country’s published inequality, rather than from one of five bracket averages. An average has no tail, and the tail is real: in the poorest countries a small number of people earn many times the average of even the richest fifth, and the older method could never produce them.

A few countries publish no such national total, and lives drawn in them used to show no income at all. For those the figure is the average of the household survey itself, the same survey your country’s inequality figure comes from, and it is used only where that survey is recent enough to describe someone alive now. Nigeria is the largest of them. The two are not the same measurement: a survey asks a sample of households what they spend, while the figure above divides a national total by everyone alive, and the national total comes out higher, usually around three times higher. An income built from a survey therefore sits lower than one built from a national total, for a reason of measurement rather than of circumstance.

Your two social classes

You are shown two, because neither alone is true. The first is where you sit among people in your own country. The second is where the same income would place you inside the world’s high-income countries.

They often disagree sharply, and that disagreement is the point: someone in the richest fifth of a poor country is genuinely at the top there, and still far below where a high-income country’s scale begins. Showing only the first would hide how much was decided by where you were born.

The comparison is made in purchasing-power terms rather than raw dollars, because the same dollar buys very different amounts in different countries, and comparing the raw figures would overstate the gap it is meant to measure. That is why it is a position rather than an amount, and why the income shown above, which is in ordinary dollars, is not the number being compared.

The figure shown beside it

Next to your income, you are shown roughly what a typical person in your country has to live on. That figure is not a published median, because no source publishes one on the basis this project uses, so the middle fifth of the country’s population stands in for it, on the same basis as your own figure so the two are honestly comparable.

How far someone's schooling went

Schooling is shown as a ladder: primary, secondary, university, and a level beyond university, each one implying every level below it. The country’s recorded rates decide how far up the ladder a person goes, including all the way to no completed schooling.

Most countries’ data reaches every rung of the ladder, but not all. Where a country’s data stops short of the top rung, nobody generated there is ever shown reaching further than the data actually goes.

Electricity, water, sanitation, handwashing and internet access

Each is drawn from a real recorded share of the population, used directly, never invented. For electricity, water, sanitation and handwashing, that share is specific to whether you live in a city or the countryside where a country publishes the two separately; where it doesn’t, your draw uses the country’s overall figure instead. Internet access always uses the country’s overall figure, because no country publishes a separate city/countryside split for it.

Where a country’s handwashing figure isn’t published, which is about 97 of the world’s countries including several high-income ones, it is inferred from that same country’s water and sanitation figures for your own residence, but only when both are effectively universal (98% or higher). That threshold is what tells the two cases apart: a country like this one wasn’t surveyed because handwashing access there is assumed universal, not because the figure is unknown.

As measured against the current data, about 51 countries get a handwashing figure this way rather than a published one, and it is never shown as though it were measured. Around 46 countries have neither a published figure nor water and sanitation figures complete enough to infer from. For them, the fact is simply left out.

Whether there is a toilet at home

The share of people with basic sanitation and the share who have no toilet at all are two rungs of one ladder, published by the same survey, which counts every household once and places it on the rung of the facility it usually uses. So the two are never drawn separately here: someone shown as having basic sanitation is not also shown without a toilet, and the second figure is only ever drawn for the people the first one leaves out. Your country’s own published rate for each is unchanged by this.

One limit is worth naming. A household that owns a latrine and still uses the field is counted by what it usually uses, so the survey does not see that case and neither does this page.

Whether you married, and how many children you have

Marital status is drawn from the recorded shares for people aged 30 to 34 in your country, so the person described is in their early thirties.

The number of children follows from that age. It is centred on how many children women in your country have had by the end of age 34, added up from the rate of births at each age below that, and not on the number they have over a whole lifetime. The lifetime figure is about a quarter higher, and using it gave people in their early thirties the families of people at the end of their childbearing years.

People who never married are given fewer children, half as many on average. The direction is well evidenced and no source publishes the size worldwide, so the half is this project's own round assumption rather than a measured figure. Where a country has no figure for births by age, the number of children is left out rather than filled in from the lifetime rate.

Why your schooling, your work and your services tend to go together

How far your schooling went, whether you have electricity, water, sanitation, handwashing and internet at home, whether you work, and three facts about the day you were born are not drawn one by one. They are drawn so that they follow where you sit in your country’s income distribution: someone near the top is more likely to have gone further at school and to have all five services, someone near the bottom less likely. Drawn separately, every number was right on its own while the person was not, and combinations almost nobody lives came up as often as chance allowed. Your country’s own rates are not changed by this, and they still decide how common each outcome is there. How strongly each fact follows your position is this project’s own stated assumption rather than a published figure: the direction is well evidenced, the exact strength is not, so the values used are deliberately round. The IQ score follows how far your schooling went rather than where you sit in the income distribution. How strongly it follows is this project’s own assumption too, and the overall spread of scores is unchanged by it.

The facts do not all follow your position equally. Work follows it only weakly, because the link pulls both ways: the poorest often cannot afford not to work, and the richest can afford not to. Whether a trained attendant was there at your birth, and whether your birth was registered, follow it more strongly, because both mean reaching a service that costs time and often money. Whether you were born underweight is the one fact here that runs the other way, and only slightly: a better-off mother is somewhat less likely to have a small baby.

Where you live is not linked to your income, because several of the facts above use a rate your country publishes separately for cities and the countryside, and those figures must stay as published.

Two of these facts follow another fact as well as your position. Whether you use the internet follows whether you have electricity at home, and whether you have somewhere to wash your hands follows whether you have water. Neither link is a rule, and neither changes a published share: a phone charged somewhere other than home is a real life, and your country’s internet figure counts that person. Where a country’s two figures are far apart the link can only do so much. If 1 person in 100 there has electricity and 20 in 100 use the internet, at least 19 of those 20 have no electricity at home however the draw is made.

Surviving infancy and early childhood

Whether a generated person survived their first year, and their first five years, is drawn from the two mortality rates recorded for their country. Both are published as deaths per 1,000 live births rather than as percentages, so a rate of 60 means 6%, and the two are cumulative: the under-five figure counts the infant deaths inside it, so someone who did not reach their first birthday is never also counted as reaching their fifth.

The figures come from the UN Inter-agency Group for Child Mortality Estimation, published through the World Bank, and cover 196 of the 217 countries here. The 21 without them are small territories and special administrative regions the group does not model separately, together about 0.06% of the world’s annual births. For those, nothing is shown: no outcome is drawn at all, rather than assuming the person lived.

A teacher can switch survival off for a class link, and that one setting changes the draw rather than the page: every life the class is handed is taken from the children who reached five. Those lives are as real as any other here, and the sample the room reads is not the world’s.

Whether a trained attendant was there, how much you weighed, and whether your birth was registered

These three facts describe the birth itself, drawn from the country’s own recorded rates just like every other fact here. They apply exactly the same whether or not the generated person survived infancy or early childhood: they are settled before either question arises.

Skilled birth attendance is recorded for 168 of the 217 countries here, and low birthweight for 157, the sparsest of the three, missing for roughly a quarter of countries. Neither is ever shown split by city or countryside: no source publishes that split for either figure.

Birth registration is recorded nationally for 144 countries. A split by city or countryside exists for only 74 of those, sparser still, so most people’s draw uses the national rate rather than a split specific to where they live.

These are the oldest figures this project carries: most of the data here is from 2024 or 2025, while these three mostly date to 2019 or 2020.

Your country's development score

The Human Development Index is one number combining how long people in your country live, how much schooling they get, and what they have to live on. It describes the country, not you: two people in the same country have the same one, whatever their own lives look like.

The band it falls in is the publisher’s own, not ours: the thresholds between low, medium, high and very high human development are set by the UN Development Programme, so where the line sits is not a judgement this project made. Countries the report does not cover show no score rather than an estimated one.

Statistics about a country are not facts about you

Your name, sex, city, ethnicity, religion, schooling, work and income are yours. Every percentage or comparison figure shown beside them describes the country, not your life. Where a country has no usable data for one of these, it is left out rather than guessed.