Quality Statement

Label
Occupation - 2023 Census: Information by concept en-NZ
Definition

An occupation is a set of jobs that require the performance of similar or identical sets of tasks by employed people aged 15 years and over in their main job.

en-NZ
Overall quality rating

Moderate quality
Data quality processes section below has more detail on the rating.

en-NZ
Priority level

Priority level 2
A priority level is assigned to all census concepts: priority 1, 2, or 3 (with 1 being highest and 3 being the lowest priority).
Occupation is a priority 2 concept. Priority 2 concepts cover key subject populations that are important for policy development, evaluation, or monitoring. These concepts are given second priority in terms of quality, time, and resources across all phases of a census.
The census priority level for occupation has increased from priority level 3 in 2018.
The 2023 Census: Final content report has more information on priority ratings for census concepts.

en-NZ
Subject population

Employed census usually resident population count aged 15 years and over
‘Subject population’ means the people, families, households, or dwellings that the variable applies to.

en-NZ
How this data is classified

Occupation is classified into the following categories:

Australian and New Zealand standard classification of occupations V1.3.0 – level 1 of 5

Code Category
1 Managers
2 Professionals
3 Technicians and Trades Workers
4 Community and Personal Service Workers
5 Clerical and Administrative Workers
6 Sales Workers
7 Machinery Operators and Drivers
8 Labourers
9 Residual Categories (Operational Codes only)

The Australian and New Zealand Standard Classification of Occupations (ANZSCO) is a skill-based classification used to classify all occupations and jobs in the Australian and New Zealand labour markets. The classification is a 5-level hierarchical classification with level 1 presented in the table above. Follow the link above the table to examine the classification.

The 2023 Census classification for occupation is consistent with that used in 2018 Census. The version of the classification has changed between the 2018 and 2023 Censuses, from V1.2.0 to V1.3.0. The latest release of the classification was limited to a targeted update of occupations relating to construction-related trades occupations and some emerging occupations. The level 1 residual category ‘Residual Categories (Operational Codes only)’ contains the residual categories ‘Response Unidentifiable’, ‘Response Outside Scope’, and ‘Not Stated’.

Follow the link above the table to examine the classification in more detail.

Standards and classifications has information on what classifications are, how they are reviewed, where they are stored, and how to provide feedback on them.

en-NZ
Question format

Occupation data was collected on the individual form (question 43 paper form).

There were differences in the way a person could respond between the modes of collection (online and paper forms).

On the online form:

  • built-in routing functionality directed individuals who were usually resident and employed on census night to the occupation question
  • there was an ‘as-you-type’ list, which is a drop-down menu of occupations selected from a list of probable survey responses and their classification categories
  • this list reduced vague text responses online, which improved accuracy of automatic coding and reduced the need for manual coding.

On the paper form:

  • it was possible for respondents who were not part of the usually resident population or un-employed to respond to the occupation question
  • respondents wrote in their occupation, which can lead to vague responses. For example, manager, nurse, teacher.

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 the quality of paper forms.

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

en-NZ
Examples of how this data is used

Data-use outside Stats NZ:

  • to monitor employment in different sectors and to understand skill shortages in those sectors
  • to analyse structural changes in the labour market over time and plan for changes in labour demand by occupation
  • for the study of occupational accidents, mortality, and morbidity rates
  • to analyse and classify socioeconomic status in studies of social disadvantage, poverty, and inequity
  • by researchers to examine pay gaps and occupation inequality, for example based on gender, ethnicity, or disability.

Data-use by Stats NZ:

  • to analyse and monitor structural changes in the labour market
  • alongside industry, sector of ownership, and status in employment to reweight the Labour Cost Index. This index provides a measure of wage inflation and is used in wage negotiations, contract escalation clauses, economic research and policy-making.
en-NZ
Data sources

Alternative data sources were used for missing census responses and responses that could not be classified or did not provide the type of information asked for. The table below shows the distribution of data sources for occupation data.

Data sources for occupation data, as a percentage of employed census usually resident population count aged 15 years and over, 2023 Census
Source of occupation data Percent
2023 Census response 83.7
Historical census 0.0
Admin data 0.0
Deterministic derivation 0.0
Statistical imputation 16.3
 CANCEIS(1) donor's response sourced from 2023 Census form 16.3
No information 0.0
Total 100.0
1. CANCEIS = imputation based on CANadian Census Edit and Imputation System

Note: Due to rounding, individual figures may not always sum to the stated total(s) or score contributions.


CANCEIS imputation was used to replace missing or residual responses.

Editing, data sources, and imputation in the 2023 Census describes how data quality is improved by editing, and how missing and residual responses are filled with alternative data sources (admin data and historical census responses) or statistical imputation. The paper also describes the use of CANCEIS (the CANadian Census Editing and Imputation System), which is used to perform imputation.

en-NZ
Missing and residual responses

Missing and residual responses represent data gaps where respondents either did not provide answers (missing responses) or provided answers that were not valid (residual responses).

Where possible, alternative data sources have been used to fill missing and residual responses in the 2023 and 2018 Censuses.

Percentage of ‘Not stated’ for the employed census usually resident employed population count aged 15 years and over:

  • 2023: 0.0 percent
  • 2018: 0.0 percent
  • 2013: 2.8 percent

For output purposes, the residual category responses are grouped with ‘not stated’ and are classified as ‘Residual Categories’.

Percentage of ‘Residual Categories (Operational Codes only)’ for the employed census usually resident employed population count aged 15 years and over:

  • 2023: 0.0 percent
  • 2018: 0.0 percent
  • 2013: 5.0 percent
en-NZ
Data quality processes

Overall quality rating: Moderate
Data has been evaluated to assess whether it meets quality standards and is suitable for use.

Three quality metrics contribute to the overall quality rating:

  • data sources and coverage
  • consistency and coherence
  • accuracy of responses.

The lowest rated metric determines the overall quality rating.

Data quality assurance in the 2023 Census provides more information on the quality rating scale.

Data sources and coverage: Moderate quality
The quality of all the data sources that contribute to the output for the variable were assessed. To calculate the 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:

  • 0.98–1.00 = very high
  • 0.95–<0.98 = high
  • 0.90–<0.95 = moderate
  • 0.75–<0.90 = poor
  • <0.75 = very poor.

The high proportion of occupation data received from 2023 Census forms, along with the proportion of occupation data sourced from statistical imputation, resulted in a score of 0.90, leading to a quality rating of moderate.

CANCEIS ratings have been reviewed for 2023 Census and differ from the 2018 Census rating for this variable. This is not reflective of a difference in the quality of imputation, but refinement in our assessment methodology.

Data sources and coverage rating calculation for occupation data, employed census usually resident population count aged 15 years and over, 2023 Census
Source of occupation data Rating Percent Score contribution
2023 Census response 1.00 83.67 0.84
CANCEIS(1) nearest neighbour imputation 0.40 16.33 0.07
No information 0.00 0.00 0.00
Total 100.00 0.90
1. CANCEIS = imputation based on CANadian Census Edit and Imputation System
Note: Due to rounding, individual figures may not always sum to stated total(s) or score contributions.

Consistency and coherence: High quality
Occupation data is consistent with expectations across nearly all consistency checks, with some minor variation from expectations or benchmarks that makes sense due to real-world change, incorporation of other sources of data, or a change in how the variable has been collected.

There are minor timeseries comparability issues for certain occupations resulting from changes in coding methodology, which impact level 5 of the ANZSCO classification and to a lesser extent, level 4 of the classification. This means that some ‘Not further defined’ categories at level 5 have increased from previous censuses, with higher thresholds to code less precise responses to specific categories. These changes to coding methodology do not have an impact on data quality.

Accuracy of responses: Moderate quality
Occupation data has various data quality issues involving several categories or aspects of the data, or an entire level of a hierarchical classification. The data quality issues could include problems with the classification or coding of data, such as vague responses resulting in coding issues, or responses that cannot be coded to a specific (non-residual) category, thereby reducing the amount of useful, meaningful data available for analysis.

Data quality issues impacting accuracy of responses for occupation result from coding of free-text responses, as well as responses that are not to the level of detail of level 5 of the classification. The quality issues are minor and impact the data similarly to the 2018 data. However, there was increased scrutiny around the accuracy of coding in the 2023 Census, which led to a lower quality rating for this metric compared to 2018.

There is a minor level of respondent error in the data impacting accuracy of responses, particularly for age groups 65 years and over, caused by people who were unemployed or not in the labour force answering work questions about employment. While these types of responses were resolved through edits, in some cases, there is less certainty of accuracy.

en-NZ
Recommendations for use and further information

Occupation data can be used in a comparable manner to the 2013 and 2018 Censuses.

When using this data, users should be aware of the following:

  • It is not recommended that counts are compared across census years at level 5 of the ANZSCO classification as there were changes to the coding methodology in the 2023 Census, leading to differences in the way occupations were classified at level 5.
  • The lower text coding accuracy for paper forms is expected to disproportionately impact occupation categories. There is a higher likelihood of undercounting occupations that have high paper form use such as ‘Labourers’, ‘Machinery operators and drivers’, ‘Community and personal service workers’, and ‘Technicians and trades workers’.

Care should be taken when using data at the statistical area 2 geographical level and below due to small counts and high levels of imputation in some areas. This means some small geographies will have a poorer quality rating than the overall quality rating.

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

Key considerations when comparing occupation information from the 2023 Census to other sources include:

  • census is a key source of information on occupation for small areas and small populations. Many other sources do not provide detail at this level.
  • census aims to be a national count of all individuals in a population while other surveys (such as the Household and Labour Force Survey and the General Social Survey) measuring this variable are only based on a sample of the population.
en-NZ
Information by variables from previous censuses

To assess how this concept aligns with the variables from the previous census, use the links:

Contact our Information centre for further information about using this concept.

en-NZ

Information

History

View Full History
Revision Date Responsibility Rationale
23 26/09/2024 10:00:57 AM