The reliability of the data, and the use made of it

Overview

Age heaping, main uses made of population data in an MEDC and population data mainly used for in an LEDC.

What you need to know

Answer each one before you reveal it.

Questions from this subtopic

Each one with the model answer underneath.

1. 1

   Demonstrate knowledge and understanding of why population data is generally more reliable in an MEDC.

   Model answer

   Coverage is close to complete, because a full address register plus legal compulsion plus follow-up of non-responders leaves few households unrecorded. Respondents are literate and complete their own forms, so answers are less likely to be mistranscribed. Near-complete vital registration keeps the figures updated between censuses. And an independent statistical office publishes its methods, estimates the size of the undercount and states margins of error, so the remaining errors are not only small but known and correctable.

2. 2

   What is age heaping, and why does it matter?

   Model answer

   Age heaping is the tendency for reported ages to cluster on numbers ending in 0 or 5 — 30, 35, 40 — because people who have no birth certificate do not know their exact age and estimate it. It is common wherever birth registration is incomplete. It distorts the age-sex pyramid into a stepped shape and corrupts every age-specific rate calculated from it, so statisticians have to smooth the data before it can be used, which introduces assumptions of its own.

3. 3

   Which groups are most likely to be missed by a census in an MEDC?

   Model answer

   Young adult men, students, recent international migrants, people in shared and multi-occupancy housing, people with no fixed address, and anyone who fears that answering will bring them to the attention of the immigration or benefits authorities. The undercount is therefore not spread evenly: it concentrates in inner-city districts, which is precisely where small-area figures are used to allocate funding, so the areas of greatest need can be the ones most understated.

4. 4

   Why can under-registration of births and deaths be worse for girls in some societies?

   Model answer

   Where sons are more valued than daughters, a family may see less reason to make the journey and pay the fee to register a daughter's birth, and a female infant's death may go unrecorded too. Where this happens the recorded sex ratio is wrong, the number of girls is understated, and female infant mortality appears lower than it really is. The effect is a feature of particular societies rather than of LEDCs in general, and it has to be stated as such.

5. 5

   Why might population figures be deliberately misreported?

   Model answer

   Because money and power follow the numbers. Where central government funding, development aid, parliamentary seats or ethnic and religious power-sharing depend on how many people a region or group has, there is a direct incentive for local officials and communities to inflate the count, and sometimes for a government to adjust a politically awkward total. Censuses in several countries have been disputed, delayed or had their results withheld for exactly this reason.

6. 6

   If LEDC data is often incomplete, how are world population figures produced at all?

   Model answer

   They are estimates rather than counts. Agencies such as the United Nations take the most recent census, adjust it for the known undercount, and combine it with sample surveys of households, model life tables and standard projection techniques to fill the gaps and roll the figures forward. The result is a best estimate with a margin of error attached, and it should be quoted as such rather than treated as a measured fact.

Other ways to work this

The same content, four other ways.

What is inside Levo

Six ways to work the same material.