Age (information about this variable and its quality)

Priority level

Priority level 1

We assign a priority level to all census variables: Priority 1, 2, or 3 (with 1 being highest and 3 being the lowest priority).

Age is a priority 1 variable. Priority 1 variables are core census variables that have the highest priority in terms of quality, time, and resources across all phases of a census.

The census priority rating for this variable remains the same as 2013.

Quality Management Strategy and the Information by variable for age (2013) have more information on the priority rating.

Overall quality rating for 2018 Census

Very high quality

Data quality processes section below has more detail on the rating for this variable.

The External Data Quality Panel has provided an independent assessment of the quality of this variable and has rated it as very high quality. Initial Report of the 2018 Census External Data Quality Panel has more information

Subject population

Census night population

This question applies to all people in New Zealand on census night. However, data on age is usually output for the census usually resident subject population.

‘Subject population’ means the people, families, households, or dwellings to whom the variable applies.

How this data is classified

Age – New Zealand Standard Classification V1.0.0

Age is a flat classification with single-year categories from 0 years to 119 years inclusive, plus a category for 120 years and over.

000 Less than one year

001 One year

to

119 119 years

120 120 years and over

No provision is made for residual categories (for example ‘not stated’). It is Stats NZ policy to account for the age of every person in the census, which is in line with international practice.

Age is often output in 5 and 10 year groups, for example:

01 0-4 years

02 5-9 years

to

20 95-99 years

21 100 years and over

Age is also grouped to meet different needs (for example under-18 years, 18-years and older).

While the classification for birth year is updated every year to cover the last 120 years, the classification of age in the 2018 Census is consistent with that of the 2006 and 2013 Censuses.

The Standards and Classifications page provides background information on classifications and standards.

Question format

Age is derived from date of birth on the individual form (question 2 on the paper form).

If the individual form does not have enough information to derive a person’s age from the date of birth question, age information from the household set-up form or dwelling form may be used. The person filling out the form on behalf of the household was asked to provide the ages of all persons listed (question 17 on the paper form).

Stats NZ Store House has samples for both the individual and dwelling paper forms.

There were differences in question wording, layout and the way a person could respond between the modes of collection (online and paper forms):

Online individual form:

• guidance was provided for the individual form with the format dd/mm/yyyy
• date of birth had to be answered with a valid date for the respondent to submit the individual form (ie 1 to 31 for the day, 1 to 12 for the month, and 1898 to 2018 for the year).

Online household set-up form:

• the respondent could only provide a valid response from 0 to 120 years.

Paper individual and dwelling forms:

• an example was provided for the correct response format on the individual form for example day (28), month (2), year (1984)
• non-response and responses outside the valid range were possible for both the individual date of birth question and the age question on the dwelling form.

Data from the online forms may therefore be of higher overall quality than data from paper forms. However, processing checks and edits were in place to improve quality of the paper forms.

The data quality processes section has more information on the effect of survey mode on data quality for this variable.

How this data is used

Age is a demographic variable at the core of the census. Its various uses include:

Outside Stats NZ

• Central government agencies, and regional and local authorities use age data for policy, town planning, allocating funding and analysing and understanding the population across New Zealand.
• Universities and educational institutions use age data for population research and to predict roll numbers.

Within Stats NZ

• Age defines the subject population for other census variables (for example income for those aged 15 years and over).
• Date of birth (age) is a core variable for data integration in the Integrated Data Infrastructure (IDI).
• To produce population estimates and projections.
• To provide the base population for many derived series including labour force, fertility, mortality, morbidity, suicide, accident, and crime statistics.

2018 data sources

We used alternative data sources for missing census responses and responses that could not be classified or did not provide the type of information asked for. Where possible, we used responses from the 2013 Census, administrative data from the Integrated Data Infrastructure (IDI), or imputation.

The table below shows the breakdown of the various data sources used for this variable.

The ‘no information’ percentage is where we were not able to source age data for a person in the subject population.

In 2018, this was zero because if a respondent did not complete the date of birth question on the individual form, and there was no age information available on the household set-up form or on the dwelling form, we took the best estimate from a range of sources within the IDI. If this was not possible, or the individual was an overseas visitor and therefore not in the IDI, a response was imputed.

Please note that when examining data across different population groups for age, the percentage of administrative data and imputation may differ from the proportions above for the census night population.

Addition of administrative records to the New Zealand 2018 Census Dataset: An overview of statistical methods provides information on the linking of census responses to the IDI, including the use of the date of birth question.

Missing and residual responses

Age does not have a non-response (‘not stated’) or any other residual category. Responses that could not be classified or did not provide the type of information asked for were replaced by data derived from admin sources or by statistical imputation.

Age also did not have a non-response (‘not stated’) or any other residual category in recent previous censuses due to the use of imputation. This included imputation for substitute records.

2013 Census data user guide provides more information about non-response and imputation in the 2013 Census.

Data quality processes

Overall quality rating: Very high quality

Data was evaluated to assess whether it meets quality standards and is suitable for use.

Three quality metrics contributed to the overall quality rating:

• data sources and coverage
• consistency and coherence
• data quality.

The lowest rated metric determines the overall quality rating.

Data quality assurance for 2018 Census provides more information on the quality rating scale.

Data sources and coverage: Very high quality

We have assessed the quality of all the data sources that contribute to the output for the variable. To calculate a data sources and coverage quality score for a variable, each data source is rated and multiplied by the proportion it contributes to the total output.

The rating for a valid census response is defined as 1.00. Ratings for other sources are the best estimates available of their quality relative to a census response. Each source that contributes to the output for that variable is then multiplied by the proportion it contributes to the total output. The total score then determines the metric rating according to the following range:

• 98–100 = very high
• 95–<98 = high
• 90–<95 = moderate
• 75–<90 = poor
• <75 = very poor.

Admin data was highly comparable to census responses while data sourced through statistical imputation was moderately comparable to census responses. The high proportion of data from census responses and admin sources in comparison to the low proportion sourced from statistical imputation contributed to the score of 1.00, determining the very high quality rating.

Data sources, editing, and imputation in the 2018 Census has more information on the Canadian census edit and imputation system (CANCEIS) that was used to derive donor responses.

Consistency and coherence: Very high quality

Age data was assessed for consistency with expectations and time series for the usually resident subject population. Data is highly consistent with expectations across all consistency checks at the SA2 level of geography.

Population changes, primarily births, deaths and migration drive the majority of change for this variable across the time series. For example, there is a high level of migration for the age group between 25–29 years into New Zealand in the period between the 2018 and 2013 Censuses.

However, the process of admin enumeration and the use of admin data has brought the age data even closer in line with expectations:

• age data was available for people who may have previously been missed from the census, for example males between 20–30 years of age
• in 2018, missing responses from paper forms, responses outside of scope and other residuals have largely been replaced with admin data, with statistical imputation accounting for the small remainder. In 2013, imputation accounted for all missing and residual responses for the age variable.

Data quality: Very high quality

The data quality checks for the age variable included edits for consistency within the dataset and cross-tabulations to the SA2 level of geography, for the overall census night subject population.

Age data has no data quality issues that have an observable effect on the data. The quality of coding is very high. Other data sources used do not create any quality impacts for this variable. Any issues with the variable appear in a very low number of cases (typically less than a hundred).

As for all variables with a write-in value, responses received from paper forms will be of slightly lower quality due to potential scanning issues, writing differences and human error. As a priority 1 variable, we made every effort to check and ensure accuracy of responses where possible.

Recommendations for use and further information

We recommend that the use of the data can be similar to its use in 2013.

However, when using this data you should be aware that:

• there was a higher than expected count of individuals listed as 100 years or over. Those that were found to be incorrect were fixed but there is a chance that a small number of incorrect ages in this range remain in the data.
• age data has been assessed to be consistent at the SA2 level of geography.

Comparisons with other data sources

Although there are surveys and sources other than the census that collect age data, data users are advised to familiarise themselves with the strengths and limitations of the sources before use.

• census aims to be a national count of all individuals in a population by age while other surveys (such as the Household Labour Force Survey and the General Social Survey) measuring this variable are only based on a sample of the population.
• differences in wording/question format – for example, some surveys ask for date of birth while others may ask for age.

Contact our Information Centre for further information about using this variable.