Sector of ownership (information about this variable and its quality)

The sector of ownership variable has changed from moderate quality to high quality

The data quality rating for sector of ownership has been changed from moderate to high quality. This has resulted in an overall quality rating increase from moderate to high quality for the sector of ownership variable. The Data quality processes section (data quality subsection) has more information.

Priority level

Priority level 3

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

Sector of ownership is a priority 3 variable. Priority 3 variables do not fit in directly with the main purpose of a census but are still important to certain groups. These variables are given third priority in in terms of quality, time, and resources across all phases of a census.

The census priority level for the sector of ownership variable remains the same as 2013.

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

Overall quality rating for 2018 Census

High quality

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

Subject population

Employed census usually resident population aged 15 years and over

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

How this data is classified

Census Sector of Ownership Classification V1.0.0

Sector of ownership is a flat classification with the following categories:

1 Central Government

2 Local Government

3 Private

9 Not Stated

The classification of sector of ownership in the 2018 Census is consistent with the classification used in the 2013 and 2006 Censuses.

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

Question format

Sector of ownership data is derived from ‘name of business/employer’, ‘main activity of the business/employer’ and ‘address of the place where worked’ on the individual form (questions 41–43 on the paper form).

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

The information provided in these questions allowed us to identify the details of the respondent’s employer and therefore find the sector of ownership in the Stats NZ Business Register.

There were no differences between the wording or question format in the online and paper versions of this question. However, there were differences in the way a person could respond between the modes of collection (online and paper forms):

On the online form:

• respondents were provided with an as-you-type list when responding to the main activity of business question
• built-in routing functionality directed individuals who were usually resident and employed on census night to the sector of ownership input questions.

On the paper form:

• it was possible for unemployed people or those not in the labour force to respond to the sector of ownership input questions.

Data from the online forms may therefore be of higher overall quality than data from 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

Outside Stats NZ

• State Services Commission uses the sector of ownership variable to look at public sector trends.
• It is also used to compare trends between the public and private sector.

Within Stats NZ

• Sector of ownership is used alongside industry and occupation to reweight the Labour Cost Index. This index provides a measure of wage inflation and is used outside of Stats NZ in wage negotiations, contract escalation clauses, economic research and policy-making.

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 ‘response from 2018 Census’ percentage includes only those responses from received forms which we were able to match to the Business Register. The ‘no information’ percentage is where we were not able to source sector of ownership data for a person in the subject population. In 2018 the percentage of ‘no information’ was zero as when we were unable to match a form response to the Business Register or when we were missing a response, we used administrative data or statistical imputation.

Administrative data sources

Using the Individual Tax Return (IR3) and the Employer Monthly Schedule (EMS) data from Inland Revenue, we found the employer and linked the record to the Stats NZ Business Register to find the sector of ownership.

Please note that when examining sector of ownership data for specific population groups within the subject population, the percentage that is from administrative data and statistical imputation may differ from that for the overall subject population.

Missing and residual responses

‘No information’ in the data sources table is the percentage of the subject population coded to ‘not stated’. In the 2013 and 2006 censuses, non-response was the percentage of the subject population coded to ‘not stated’. In 2018, the percentage of ‘not stated’ is zero due to the use of the additional data sources described above.

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

• 2018: 0.0 percent
• 2013: 4.0 percent
• 2006: 5.7 percent.

In 2018, there are no other residual categories in the sector of ownership data, as with the 2013 and 2006 Censuses.

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

Data quality processes

Overall quality rating: 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: 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 forms and data sourced through statistical imputation was mostly comparable to census forms. Despite the low proportion of responses from received forms which we were able to match to the business register, the high level of comparability between the alternative sources of data and the form responses contributed to the score of 0.95, determining the 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: High quality

Sector of ownership data is consistent with expectations across nearly all consistency checks at national and regional council levels of geography, with some minor variation from expectations or benchmarks that makes sense due to real-world change and the incorporation of other sources of data.

Data quality: High quality

Sector of ownership 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 or incorporation of other sources of data.

• As with all variables with write-in values, responses derived from paper forms will be of slightly lower quality due to potential scanning issues, handwriting differences and human error. Effort has been made to check and ensure accuracy of responses where possible, including cross-variable checks with work and income variables for consistency within the dataset.

Recommendations for use and further information

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

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

• data has been assessed to be consistent at the regional council level of geography. Some variation is possible at geographies below this level.

Comparisons with other data sources

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

Key considerations when comparing sector of ownership information from the 2018 Census with other sources include:

• census is a key source of information on sector of ownership data 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 aged 15 years and over by sector, while other sources measuring this variable may only be based upon a subset of the population
• census captures information only on the respondent's main job and may therefore undercount employment in sectors where individuals may work additional jobs in another sector.

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