Create your own future demographic scenarios — in seconds.

The starting point, and the only topic where you change just two things: the birth rate per thousand women aged 15–49, and net migration as a percentage of total population, positive or negative. Everything else here — women of childbearing age, total births, deaths, population, household size and total households — is calculated from those. The absolute number of migrants appears near the foot of the table, so you can see what a percentage actually means in people.

Nothing to change here; this is a read-out. The model works in single years of age, allowing for births in the year, the age profile of migrants and death rates by age and gender, then sums the result into the groups that matter: 0–14, 15–24, 25–39, 40–64 and 65+, plus school-aged, younger and older working-aged, and the 75+ population. It is usually the fastest way to see what a scenario has actually done.

Working-age numbers arrive from the age structure; you cannot type over them. What you can change is the propensity to be employed, separately for males and females and separately for 15–64 and 65–74. The range is 0–100%, though real countries sit between about 70% and 90% for the prime-age groups. The 65–74 propensities default to zero, so raising them is how you model later retirement.

Output per worker is driven by accumulated fixed capital investment per worker and the education index. Change either and GDP per worker, total GDP and GDP per capita all follow — multiplied, of course, by however many workers the Labour Force table has produced. This is where a demographic story becomes an economic one.

Household income is a function of two things the model already knows: the share of GDP per worker actually paid out in wages — the wage ratio — and the number of workers per household from the Labour Force table. From there come average wage, average household income, income per capita and, through the propensity to spend, total consumer expenditure and PCE as a share of GDP.

An average hides more than it reveals, so the model also distributes households across five income segments — you set the four breakpoints, or let the program place them around average household income. Change the median as a percentage of average and you are changing income inequality directly. A companion topic then breaks expenditure into twelve categories for each of those five segments. Both are single-country topics.