Methodology- Country Database

Forecasts

The source for the database is primarily the demographic and socio-economic data published by the World Bank and the United Nations.  These are used as they are probably the most reliable data source for historical information as these organisations have sufficient influence to ensure the validity of data reported to them.

The database is harmonised as much as possible to facilitate modelling and, hence, consistency of the forecasting process.

The database is updated at least twice a year as new data is made available about different countries.

The variables included in the database are listed together with definitions at

HOUSEHOLD INCOME

Measuring household income is complicated and unreliable. The usual method is to do what is generally called a ‘Household Income and Expenditure Survey’. This is done by most countries with a viable government but with widely varying degrees of reliability. It suffers from the usual issues of social research – that is, sample validity and size, representation, respondent error, and analysis error. This is not to say these studies are totally unreliable – that is not the case at all – but rather they are at best a good indicator only.

There is also the issue of what constitutes ‘income’. Is it earned income only? Does it include social payments, capital gains (including mortgaging the increased value of a household), and credit? A credit card increases a household’s spending power by the amount of the credit limit. Yes, it must be paid back, but it is an ongoing, continuous cash float.

Because of this uncertainty around this measure, Global Demographics Limited has taken a different approach. A good measure of average household expenditure is provided by dividing the Private Consumption Expenditure component of total GDP by the number of households. It pays to adjust this slightly to allow for spending by charitable institutions. Typically, we lower the Private Consumption Expenditure by seven per cent to allow for charitable institutions. It is also assumed that the expenditure of tourists is offset by the overseas expenditure of residents. Again, this is an assumption, but there is no easy way to define the amounts so spent.

As Private Consumption Expenditure (PCE) is a component of total GDP, it is subject to some rigour in method and data collection. In addition, as the total number of households is an accessible variable to measure, it is also relatively reliable. So, total PCE divided by total households gives a relatively reliable measure of the average expenditure of households in the country, which by definition is also the medium-term definition of minimum household income

The next step is to determine the likely maximum funds available to households before tax, savings, and expenditure. Here, we resort to the Household Income and Expenditure Survey, which is available. Some give not only expenditure but also gross income. Global Demographics divides that by the average number of workers in the household to get the average income (wage) per employed person, which is compared with the overall GDP per worker. Using the distribution of this variable across 50 countries where this data is available, there is a 95% probability that the average wage per worker will be less than 70% of GDP per worker. So, 70% of GDP per worker provides a likely maximum for the average worker wage per household – and that multiplied by the number of workers gives the likely maximum accessible funds for a household in a year.

The range between the minimum (Private Consumption Expenditure per household) and the maximum (70% of GDP per worker times the number of workers in the household) is quite small. Furthermore, given that most households pay tax and/or save, the available funds must be greater than the minimum. Similarly, few countries pay over 80% of GDP, meaning the likely maximum is lower, and the potential range is less. So Global Demographics Ltd use the average of the two numbers, meaning the possible error in available income is plus or minus 5%.

Yes, this process can be debated, but it does nonetheless mean that there is some consistency between estimated available income and total Private Consumption Expenditures as well as wages. Using Household Income and expenditure surveys alone underperforms on this criterion.

Our Strength

Our unique strength is our econometric models of each country’s historical demographic and socio-economic profile, which we use to forecast their demographic and socio-economic landscape. The forecasts, covering a wide range of variables, including age by gender, households, labour force, education, and household income and expenditure, are a useful single source for strategic planning purposes.

Additionally, our algorithms are accessible through this website’s recently released ‘Interactive Demographics’ Model.  As such, you can make assumptions about the future trend of the ‘influenceable’ variables (e.g. birth rates) and see the impact out to 2065 using the established algorithms, which have logic built into them due to historical relationships.

Our Database

The key drivers of the overall model are education and birth rates. The education profile of the adult population is forecast by using the projected time series trend in the enrolment profile of persons aged 5 to 17. This, in turn, gives good estimates of the profile of those exiting the education system each year, which are then added to the education profile of the adult population of that year, as well as deducting the estimated education profile of those who die each year.

An Education Index is then used to drive the projected trends in urbanisation, occupation profile, productivity per worker (together with Fixed capital per worker) and household size (together with age profile). Time series trends drives birth rates, death rates and education enrollments.

The user is expected to use these forecasts as a base point – if the past relationships and trends continue, then this will happen. That means they are a defensible baseline – all changes can be related to a past trend or relationship (and then ultimately source data) rather than the opinion of an individual.

In working form, the key projections are run to 2065 to see the long-term impact of the projected trends. However, we only publish 20 years beyond the latest actual, with confidence obviously being higher for 10-year forecasts.

Our Models

1) Are proprietary to Global Demographics Ltd.
2) Are based on our comprehensive database.
3) Use recognised statistical methods and processes – mainly econometric in style.
4) Can be explained to users (not a ‘black box’).
5) Are ‘constrained’ – meaning that they continuously check that different data items fit together.
6) Are well tested and continuously improved as more data becomes available.