How is world population calculated? And how accurate is the number? Is it possible that the number can be entirely wrong?
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As others have said, a census counts the number of residents in a country. However, there are a few issues:
1) Who counts as a resident?
New Zealand counts everyone in the country on Census Day, regardless of how long they are staying for. The UK counts everyone who has stayed, or intends to stay, for at least 6 months.
2) What about non-response?
Usually there are big advertising campaigns so that everyone is aware that there is a census, and why it’s important. There are people called enumerators who visit non-responding households to encourage them to return their form.
The number of responses is the *counted population*. However, some people don’t respond. They may refuse, they may forget, they may be on holiday. So a second small-scale survey is done, a few months later. This is part of the “capture-recapture” method mentioned by u/marisbluesky which is also used for animal population estimates. Identifying information (name, DoB, sex, address) is used to work out who responded in one survey and not the other, who responded in both, and hence estimate how many were missed in the census. This is used to produce the *population estimate* for the whole country.
If the census is done well, the counted population will be at least 95% of the estimated population.
A census, as mentioned by others, is an attempt at counting everyone resident in a country. Different countries use different definitions – in New Zealand, for example, they count everyone who is physically in the country in Census Day, whereas in the UK they’re only interested in people staying, or intending to stay, in the UK for 6 months or more.
Some people are counted more than once, such as children with parents who live apart. Others are not counted at all, for example people who refuse to respond, or who didn’t get out ask for a form, or who were on holiday at the time.
People called enumerators are hired to chase up non-responding households and encourage them to respond. Advertising plays a huge part. In many countries, it’s a legal requirement to fill in and return your census form.
To work out how many didn’t respond, a second, small-scale survey is done about a couple of months after Census Day. This is part of a method called “capture-recapture”, mentioned by u/marisbluesky – you match the results by name, address, sex, DOB (this is one of the main reasons for asking your name) and work out who you counted in one survey but not the other, who was counted in both, and from that work out who was missed in both. Then you extrapolate to the entire country’s population, which gives you your population estimate. Done well, the counted population in a census is about 95% of the estimated population.
During the First Margrave War, a near complete count of Nuremberg was made, and the result was a population of 20,000. This is about 4 times higher than 50 years earlier. 300 years later, a census showed there were only 30,000 people.
Go back to 1857 in Minnesota. They were doing a census prior to joining the union. Lawmakers made up fake towns and filled out fake census forms, in order to ensure their party got more seats. They were rigging elections.
Ancient population estimates just make no sense. Even if you take the lowest population estimates, and factor in a fraction of a percent growth each year, you end up with a population that is larger than today. In the past couple hundred years, when we’ve had the deadliest wars known to man, huge population losses under folks like Mao and Stalin, the one child policy in the largest country on earth, and yet we’ve had record population growth.
We’ve long been told that population growth in the past was low due to disease, child mortality rates, and low life expectancy. Yet during this period of rapid population growth, we are seeing lower and lower birth rates in the first world, where low child mortality and high life expectancy are the norm. The world population growth has literally been fueled by the countries with high disease, high child mortality, and low life expectancy. And those are also the war torn countries.
North Korea, with its crazy famines, high disease, malnourished, and under sanctions, has had a faster growth rate than South Korea.
Some have estimated that there were more than 100 million people living in the pre-Columbia Americas. If you just look at population estimates and growth rates for that era, it would mean that *at least* 25% of the world migrated to the Americas 15,500 years ago. The lower estimates are around 8 milllion, which would be about 2% of the world’s population migrating to the Americas 15,500 years ago.
My understanding is there’s a roll-call every four years where everyone says their name and a guy with a clipboard writes it down. If a child is born, the parents call out “girl” or “boy” and if someone dies a relative calls out “dead one!”
Population estimates are “guesses” with error factored in. Let’s say you had to estimate how many hairs were on your body. This is a seemingly impossible task…if you would like to be exact. Hairs are being added and removed by the second so to approach this problem we guess really a range of numbers.
To calculate that range you might pick a fixed unit (e.g. hairs per square inch of your body). You wake up one morning and you count the hairs on randomly selected square inches of your body. You do this several times under different conditions (e.g. time of day or area if your body).
You may find that some of the factors greatly impact the density of hair. These factors need to be collected too (e.g. proximity to your head). The factors can be used to weight you’re overall calculation and predict counts that are missing or you’re less sure about.
You can calculate how much your guesses vary under similar conditions. Let’s say one day you count 75 hairs/square-inch then another day 120 hairs/square-inch. You assume this is due to randomnesses. Now you can calculate an overall average and a range around that average (based on how much your sample estimates varied).
Now given you have an average estimate for your unit of analysis (hairs/square-inch), you meticulously calculate the number of square inches on your body (also an estimate with error). These are aggregated to determine the total number of hairs. However, this isn’t a single number, it’s a range based on how much your numbers varied when you collected data. We tend to communicate it as a single number because it’s easier to interpret.
Initial data collection is really important because if you aren’t identifying the factors that affect your estimates (let’s say you didn’t take different parts of the body into account) or aren’t appropriately sampling in a way that is representative of the population (if you only took measurements for your head as opposed to the different areas of the body)- your overall estimate will be flawed.
I’ve worked with census data (ACS data) and the measurement accuracy is questionable. The amount of personnel, expertise, and money needed is substantial, yet it’s clear the government doesn’t invest as they should.
This is extremely important because: 1) informs political polls and predictions (which have been subpar in terms of prediction); 2) Prediction of crime or domestic terrorism; 3) pretty much any prediction made about people on a macro-level.
I challenge all of the young-bloodz about to head of to college to look into measurement and statistics. We need more passionate minds in the field.
One thing not mentioned is that a lot of developing countries have a huge incentive to over-report their populations because the foreign aid they receive from more wealthy countries is linked to population. And, frankly, I’m not sure I trust the UN to do more than repeat what their member states say. So it depends on the source for the number. If it comes from the UN, you have this problem. If it comes from somewhere using formulas like the logistic differential equation mentioned here, it’s based on a guess of fertility rates that we don’t know is right. They all mostly agree, but their only source of ground truth is each other, so they could be agreeing with each other but not agreeing with reality. The estimates are probably mostly right, but probably off by more than their reported margins of error would have you believe.
Normally on these things what you are after is a trend. A certain number is not that relevant, just to have a ballpark will give enough information so humans can take decisions based on it. I hardly doub0t it is completely wrong, but trying to be more exact won’t provide much more relevant information for humans to make better decisions.
Regarding accuracy, population numbers are often at least as accurate as they need to be. For a family of 5 getting a non-exact value is a significant error, however, for a population of 1 million then getting an estimate wrong by 100 will most likely not change the decisions made based on said population estimate.
Well we have a formula/regression to calculate world population and every country has a set of statistics (the data for which is collected via national census) regarding population, mortality rate, birth rate, child mortality rate etc. These statistics are compiled and plugged into said formula and we get a world population number including the number of deaths and births per year. Although this number is fairly accurate in representing the statistical significance, when it comes to running a regression, there is room for error which is captured via an error term, this allows for a certain degree of variance shift. It’s a fairly accurate estimate. But, there are probably more people who slip out of the census then are a part of it so, there are probably more people then we estimate there are, not less.
The truth is, we actually don’t know how accurate the estimate is because we’d have to know the near exact number in a sufficiently large representative area to compare it with the estimate. My guess is it can be several percentage points off, though certain undercounts (mentioned by others) can potentially be compensated by the folks with multiple citizenships that presumably are reported by each home country.
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