Household income

Methods - how we monitor

• Where and how we collect the data

  Monitoring sites

  The data are sourced from the annual income survey (NZIS) administered by Statistics New Zealand. NZIS is a supplement to the June quarter Household Labour Force Survey (HLFS) and provides a snapshot of income levels for people and households. The NZIS gives average weekly income for the June quarter from most sources including government transfers, investments, self-employment and wages and salaries. For those receiving income from wages and salaries, statistics on average and median hourly earnings are also available. The NZIS includes “income for everyone over the age of 15 years and over, including those not in paid employment” (Statistics NZ, 2008a, p2).

  This means that, unlike the Quarterly Employment Survey (QES), the Labour Cost Index (LCI) and the Linked Employer-Employee Dataset (LEED) job-level datasets, the NZIS includes people who are not engaged in paid employment (for example beneficiaries, superannuitants, stay at home parents and students) as well as those receiving both wages and salaries and other forms of income such as government transfers. 

  Monitoring frequency

  The income survey is administered annually in the June quarter and published soon afterwards.

  Monitoring history

  Income survey data are available for the period 1998 onwards. There are no temporal breaks in the data.

  Measurement technique

  The NZIS dataset is collected from households.In terms of wages and salaries, surveys collected from households show earnings at the person-level rather than the job-level. Person-level measures of wages and salaries encompass earnings from multiple job holding and for those whose engagement in paid employment is discontinuous. The NZIS and LEED person-level datasets are both useful for looking at the earnings of those in paid employment at the individual level. (Statistics NZ, 2008a, p6)

  Surveys collected from households typically provide richer demographic data such as ethnicity, qualifications, and household type. This means that household surveys enable us to look at how well particular demographic groups are doing in comparison with others. At the same time, household information, such as the number of dependent children, offers a greater context for understanding people's economic situation. For example, while an annual income of $60,000 may be considered a reasonably high income for a single person, it would not be so for a household with six dependent children.
• How this indicator is compiled

  The median and average income data is sourced from Table Builder on the Statistics New Zealand website. The income distribution data was purchased as a custom data set.
• Limitations

  The Income Survey is limited because it is sample based. The NZIS sample contains about 15,000 private households and about 30,000 individuals each year.

  Two types of error are possible in estimates based on a sample survey: sampling error and non-sampling error. Sampling errors can be measured and quantifies the variability that occurs by chance because a sample rather than an entire population is surveyed. A non-sampling error is very difficult to measure, and if present can lead to biased estimates.

  Statistics New Zealand endeavours to minimise the impact of these errors through the application of best survey practices and monitoring of known indicators (eg non-response). In general, the sampling errors associated with subnational estimates (eg breakdowns by regional council area or ethnic group) are larger than those associated with national estimates. 

  The NZIS reports on weekly income and relates specifically to an average week during the June quarter; that is a snapshot in time. Conversion of this weekly income into an annual equivalent is not recommended as an individual's circumstances can change significantly during a year (that is change of job, a period out of work, etc).
• Quality control procedures

  Statistics New Zealand does not check the quality of the data supplied by individuals. Checks are made on the quality of data entry and on the analysis.