Quality Statement
The 2023 Census implemented the Data standard for gender, sex, and variations of sex characteristics. The gender concept is the default demographic variable for census outputs, in alignment with this standard.
It is recommended viewing this gender information by concept alongside the information by concepts for cisgender and transgender status, sex at birth, and variations of sex characteristics for important context about how these concepts interrelate and the recommended use of data.
Gender refers to a person’s social and personal identity as male, female, or another gender or genders that may be non-binary. Gender may include gender identity and/or gender expression. A person’s current gender may differ from the sex recorded at their birth, and may differ from what is indicated on their current legal documents. A person’s gender may change over time. Some people may not identify with any gender.
High quality
Data quality processes section below has more detail on the rating.
Priority level 1
A priority level is assigned to all census concepts: priority 1, 2, or 3 (with 1 being highest and 3 being the lowest priority).
Gender is a priority 1 concept. Priority 1 concepts are core census concepts that have the highest priority in terms of quality, time, and resources across all phases of a census.
Gender is a new concept in the 2023 Census.
The 2023 Census: Final content report has more information on priority ratings for census concepts.
Census usually resident population count
This question applies to all people in New Zealand on census night. However, gender data is usually output for the census usually resident subject population.
‘Subject population’ means the people, families, households, or dwellings that the variable applies to.
Gender data is classified into the following categories:
Census classification of gender V1.0.0 – level 1 of 1
Code | Category |
---|---|
1 | Male / Tāne |
2 | Female / Wahine |
3 | Another gender / He ira kē anō |
Gender uses a 1-level flat classification with level 1 categories presented in the table above.
Gender was collected in the census for the first time in 2023.
Follow the link above the table to examine the classification.
Standards and classifications has more information on what classifications are, how they are reviewed, where they are stored, and how to provide feedback on them.
Gender data is collected on the individual form (question 3 paper form).
The question asked, ‘What is your gender?’ and included response options of ‘male’, ‘female’, and ‘another gender’.
If gender is not answered on the individual form, gender information from the following questions may be used:
- for people present in the dwelling on census night, the gender question on the online household summary set-up form or paper dwelling form (question 18)
- for absentees, the gender question on the online household set-up form or paper dwelling form (question 21).
The 2018 Census did not include a question on gender. The question on the forms read ‘Are you?’, with ‘male’ and ‘female’ as the response options. While data collected from this question was output as ‘sex’, the question was not labelled as such on forms and was open to respondent interpretation as to whether sex or gender was being asked.
There were differences in the way a person could respond between the modes of collection (online and paper forms).
On the online form:
- Gender could only be answered with a single response.
- If ‘another gender’ was selected on the individual form, then a text box was presented, saying ‘please state’ that included as-you-type functionality to help respondents provide valid responses.
- If ‘another gender’ was selected, and then either the ‘male’ or ‘female’ response option was selected, the ‘another gender’ response and any free text provided would be cleared, preventing inconsistent responses. A response to either ‘Gender’ or ‘Sex at birth’ was required to complete filling in and submit the individual form.
On the paper form:
- non-response and multiple responses were possible.
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 the paper forms.
Stats NZ Store House has samples for both the individual and dwelling paper forms.
Data-use outside Stats NZ:
- by central and local government for planning, service provision, policy development, and to understand the demographics of regions
- in combination with other census concepts to understand differences in outcomes between genders, such as in education, employment, and income.
Data-use by Stats NZ:
- used to produce population estimates and projections
- used in combination with sex at birth to derive cisgender and transgender status.
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 sources for gender data.
Data sources for gender data, as a percentage of census usually resident population count, 2023 Census | ||
---|---|---|
Source of gender data | Percent | |
2023 Census response | 89.7 | |
2023 Census form | 86.3 | |
2023 Census individual variable sourced from dwelling/household set-up form | 3.4 | |
Historical census | 0.0 | |
Admin data | 2.6 | |
Deterministic derivation | 0.0 | |
Statistical imputation | 7.6 | |
CANCEIS(1) donor's response sourced from 2023 Census form | 7.6 | |
CANCEIS donor's response sourced from 2023 Census dwelling/household set-up form | <0.1 | |
CANCEIS donor's response sourced from admin data | <0.1 | |
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. |
Where appropriate, statistical imputation was used to replace missing responses. When this was not possible, admin data pulled from the Ministry of Social Development was used.
Methodologies for filling gaps in gender and sex and birth concepts for the 2023 Census provides more detailed explanations about the methodology.
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. This webpage also contains a spreadsheet that provides additional detail on the admin data sources.
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 responses.
Gender does not have a non-response or any other residual category.
Overall quality rating: High
Data has been 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
- 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: Very high 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 data received from 2023 Census forms, alongside the quality of admin data sources, resulted in a score of 0.99, leading to a quality rating of very high.
Data sources and coverage rating calculation for gender data, census usually resident population count, 2023 Census | |||
---|---|---|---|
Source of gender data | Rating | Percent | Score contribution |
2023 Census response | 1.00 | 89.72 | 0.90 |
Admin data | 0.99 | 2.64 | 0.03 |
CANCEIS(1) nearest neighbour imputation | 0.90 | 7.64 | 0.07 |
No information | 0.00 | 0.00 | 0.00 |
Total | 100.00 | 0.99 | |
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
Gender data for the ‘male’ and ‘female’ categories is highly consistent with expectations and trends with historical census sex data. It is in line with the impact of the introduction of a change in concept and introduction of the ‘another gender’ category, which has had a minor impact on both male and female proportional counts.
As this is the first census collection of the ‘Gender’ concept, there are limited sources available to compare data for the ‘another gender’ category. However, ‘another gender’ count is slightly higher than expected when compared with data from household surveys.
As another gender is a small population, some quality issues with a small number of responses may affect data. Inconsistencies may be apparent when cross-tabulating with other small populations.
Accuracy of responses: Very high quality
The accuracy of responses collected from both paper and online forms as well as bilingual and English paper forms is high. The rating reflects the overwhelmingly high level of data quality for coded responses to gender. There were some smaller issues related to the quality of received and coded responses relating to text responses of the ‘another gender’ category.
The quality of gender data is high overall, however, there are some small issues with the ‘another gender’ data. Gender data for ‘male’ and ‘female’ categories are appropriate for use at low levels of geography and cross-tabulation with other census concepts.
Data for ‘another gender’ is appropriate for use at a national, regional council and territorial authority local board level, and cross-tabulated by other census data at a high level. It is not recommended to break down data of ‘another gender’ at SA2 level or smaller, or to cross tabulate with other census variables at detailed levels of the classification.
Any time series comparison between gender and sex should be done with caution and awareness of the conceptual differences between the two variables.
Comparisons to other data sources
Although there are surveys and sources other than the census that collects gender data, data users are advised to familiarise themselves with the strengths and limitations of the sources before use. Differences between census data and other collections may help explain differences in the overall data.
Data about gender was not collected in previous censuses. As the gender and sex at birth concepts are replacing sex, to assess how these concepts align with sex from the previous censuses, use the links:
Contact our Information centre for further information about using this concept.
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