Labour market statistics provide a comprehensive picture of the New Zealand labour market.

The Labour Market Statistics quarterly information release provides New Zealand's official employment and unemployment statistics and wage and salary information. They contain information previously published in the Household Labour Force Survey, Quarterly Employment Survey, and Labour Cost Index (Salary and Wage Rates) releases.

The Household Labour Force Survey (HLFS) provides a picture of New Zealand's labour force – these statistics relate to employment, unemployment, and people not in the labour force.

The Quarterly Employment Survey (QES) estimates the demand for labour by New Zealand businesses – the levels and changes in jobs, total weekly gross earnings, total weekly paid hours, average hourly and average weekly earnings, and average weekly paid hours in the industries we survey.

The Labour Cost Index (LCI) measures changes in salary and wage rates for a fixed quantity and quality of labour input. It is a measure of wage inflation, reflecting changes in the rates that employers pay to have the same job done to the same standard.

The Labour Market Statistics (Income) annual release produces a comprehensive range of income statistics.

The Income module of the HLFS is conducted every June quarter. Income statistics allows analysis of the links between labour force status, educational achievement and the income of individuals and households, both at an aggregate level and for sub-populations of interest.

The purpose of Labour Market Statistics is to provide New Zealand's official statistics on:

• the number of people employed, unemployed, and not in the labour force
• actual and usual hours worked
• the numbers of people in occupations and industries
• the number of filled jobs and full-time equivalent jobs
• total average weekly and average hourly earnings
• total and average weekly paid hours
• movements in base salary and ordinary time wage rates, and overtime wage rates
• mean and median income from self-employment, wages and salaries, and government transfers

Labour Market Statistics (Income) provides annual information about individual and household income, including wages and salaries, self-employment, and government transfers income. We analyse data by age, sex, ethnicity, region, highest qualification, industry, and occupation.

The LMS (Income) release reports on information collected from the Household Labour Force Survey (HLFS) during the June quarter. As this income content is integrated into the HLFS questionnaire, many of the same design, collection and processing aspects apply as for the HLFS.

Design

Data source

The target population is the entire group from which you would ideally like to get information. The target population for the HLFS/Income is the working-age population of New Zealand. We define this as "the non-institutionalised population 15 years and over, who usually live in New Zealand." Specifically the target population excludes:

• people who have been living in New Zealand for less than 12 months, and who do not propose to stay in New Zealand for a total of 12 months or more
• long-term residents of homes for older people, hospitals, and psychiatric institutions (long-term is defined as six weeks or more)
• people in prison

The survey population consists of the group members (from the target population) who have a chance of being selected as part of the sample (ie they can be identified through the sampling frame). For the HLFS/Income, we apply further exclusions to the target population to create the survey population (often due to cost and practical reasons), from which we then select the HLFS/Income sample. These exclusions are a small percentage of the population and the bias introduced is minimal. The survey population is the target population with these exclusions. People:

• residing in non-private dwellings (eg people in hotels, motels, hostels, military camp)
• residing in non-permanent dwellings (eg people in tents or caravans not permanently sited)
• residing at a wharf or landing place (eg people in ships, boats)
• residing on islands other than the North, South, and Waiheke islands (eg people on Great Barrier, Kawau, Chatham, and Stewart islands)

Sample design

The HLFS/Income sample has a stratified design with two stages of clustering. Firstly we select a random sample of primary sampling units (PSUs) from each stratum (first stage of clustering), then we select a systematic sample of households from each PSU (second stage of clustering). Every person in a selected household aged 15 years and over is eligible for the survey. PSUs are aggregations of one or more meshblocks, where meshblocks are the smallest geographical area unit in New Zealand. PSUs constructed from the 2013 Census have an average of 70 occupied and under construction dwellings.

Stratification is the process of dividing the population (or survey frame) into homogeneous subgroups before sampling. Stratification is used to 1) reduce sampling errors for survey estimates and ensure that sample sizes for strata are of their expected size and 2) target subgroups by disproportionate sampling (or over-sampling) certain strata.

Stratification for the new HLFS/Income sample design includes five dimensions. PSUs are stratified by region, urban/rural status, a high-NILF (not in the labour force) status, groups based on New Zealand Deprivation Index values, and territorial authority (in that order). The first four dimensions are explicit, or primary, strata (ie the sample is split by these groups and a random sample selected from each group), while the final dimension is implicit (PSUs are sorted by territorial authority within the primary strata and selected from the ordered list).

Sample size

The HLFS/Income aims to achieve interviews with 15,000 households, which equates to roughly 30,000 individuals.

Collection

Interviews

The period of surveying/interviewing for the June quarter is the 13 weeks between April and June (inclusive). Income information obtained relates to the respondent's most-recent pay period, except for questions on annual income, and self-employment income which cover the 12 months before the interview. HLFS information obtained relates to the week before the interview (referred to as the ‘survey reference week’). Respondents in the HLFS are first interviewed face-to-face at their home. Subsequent interviews are by telephone wherever possible (including for income content in June quarters).

Proxies

The HLFS/Income allows interviewers to take responses from proxies if a respondent is unavailable or unable to answer the questions themselves. Although the evidence regarding the quality of proxy responses is mixed, we expect proxies may not be as accurate as self-responses. Therefore, the HLFS/Income monitors the rate of proxy responses – to gauge the quality of responses. The proxy rate is calculated as the percentage of respondents who had someone else respond on their behalf divided by the total number of respondents.

Response rate and achieved sample rate

The achieved sample size measure is the number of eligible households and individuals that responded to the HLFS/Income in the quarter. The achieved sample size typically increases over time as the population grows and more dwellings are added to the survey sample.

We calculate the response rate by determining the number of eligible households that responded to the survey as a proportion of the estimated number of total eligible households in the sample.

Processing

Weighting

To enable us to infer from the sample to the target population we must weight the sample data. This entails assigning each responding or imputed individual a weight, which can be thought of as the number of people in the population that each individual represents.

Selection weight

The first stage of the weighting is the selection weight (also called a design weight). The overall selection weight for a household is made up of the PSU selection weight and the household selection weight. We calculate the selection weight for each PSU as the inverse of the probability of selection, so PSUs with a lower probability of selection receive a higher selection weight. Within strata, PSUs are selected with probability proportional to size. This means that larger PSUs have a higher probability of being selected.

We next multiply the PSU selection weight by a household selection weight to give the overall selection weight. The household selection weight accounts for the sampling of households within PSUs – we calculate it as the inverse of the selection probability, where the selection probability is the number of selected addresses in the PSU divided by the total number of addresses in the PSU.

Calibration

The final stage of weighting for the HLFS/Income is the calibration to benchmarks (auxiliary information), which are the expected counts of people in the total target population. This adjusts for undercoverage of the target population and undercounting of some groups in the population due to differential response rates. We set the calibration weights to sum to a set of benchmarks. The benchmarks we use for Income are five-year age groups by sex, the number of Māori adults by sex by two age groups (age 15–29, 30+), and 12 regions. Integrated weighting is used in the calibration to assign a weight to each individual in the sample. Each individual an a sampled household is given the same weight, which is also the same as the household weight. This allows the production of household estimates which are consistent with person estimates.

Imputation

Imputation is the process where missing values are substituted with an estimate of what the respondent might have provided for a particular variable. This process aims to minimise the loss of data and improve the accuracy of estimates.

Imputation is first applied to core variables in the HLFS where individuals who belong to eligible responding dwellings and have missing values for sex, age, ethnicity, looking for full-time employment, and usual and actual hours. Then imputation is applied to individuals who have missing values for income from jobs and income from government transfers.

Imputation is the process where missing values are substituted with an estimate of what the respondent might have provided for a particular variable. This process aims to minimise the loss of data and improve the accuracy of estimates. Imputation is first applied to core variables in the HLFS where individuals who belong to eligible responding dwellings and have missing values for sex, age, ethnicity, looking for full-time employment, and hours. Then imputation is applied to individuals who have missing values for income from jobs and income from government transfers.

All variables are imputed using donor imputation, where the donor is a respondent with similar characteristics (nearest-neighbour imputation).

Usual hours are imputed where respondents have provided their actual number of hours worked, and not their number of usual hours.

For those respondents who have not provided either total usual or total actual hours, donor imputation is applied (imputation of usual hours based on the usual hours from respondents with similar characteristics).

Hours used in Income are derived from the HLFS usual or actual hours depending on the route taken to provide income about their job.

Income from jobs (wages and salaries, self-employment, and business owners) and income from government transfers are also imputed from a donor using ‘nearest-neighbour imputation’. If a respondent has been imputed hours for the HLFS, the same donor is used to impute their income from jobs. If a respondent has been imputed income from jobs, their hours used in Income are also imputed, regardless of whether they answered the HLFS hours questions or not. This is to ensure we don’t end up with outliers for hourly income.

Sampling errors

Sampling errors quantify the variability that occurs by chance because a sample rather than an entire population is surveyed.

We calculate the sampling errors of means (averages) using the jackknife method while the sampling errors of medians are calculated using the bootstrap method. These are based on the variation between estimates of different subsamples taken from the whole sample. This is an attempt to see how estimates would vary if we were to repeat the survey with new samples of individuals.

As the size of the sampled group decrease, the relative sampling errors will generally increase. For example, the estimated number of Pacific peoples employed would have a larger relative sampling error than the estimated total number of people employed. Likewise, the estimated number of people unemployed would have a larger relative sampling error than the estimated number of people employed.

A change in an estimate, either from one adjacent quarter to the next, or between quarters a year apart, is said to be statistically significant if it is larger than the associated sampling error.

Non-sampling error

A non-sampling error is very difficult to measure, and if present can lead to biased estimates. We aim to minimise the effect of these errors by applying best survey practices and monitoring known indicators.

Classifications

The labour market statistics release includes specific statistics about industry, occupation, study, ethnicity, and region. This section lists the classifications we use for these statistics.

• Industry statistics (NZSIOC, based on ANZSIC06): see Industrial classification for more information
• Occupation statistics (ANZSCO): see occupation for more information
• Region: see regional council for more information
• Total response ethnicity: see Statistical Standard for Ethnicity – 2005 for more information

Email info@stats.govt.nz for further information about the classifications we use.

Household statistics

The household categories incorporate the concept of dependent children rather than just children. A child is a person of any age who usually resides with at least one parent (natural, step, adopted, or foster), and who does not usually reside with a partner or child(ren) of his or her own. Statistics NZ defines a 'dependent child' as a child aged under 18 years and not in full-time employment.

The household income statistics table in this release excludes households where all members are outside the ages of 18 to 64 years. This exclusion primarily affects 'couple only' and 'one person' households. These households typically contain two distinct groups of the population: couples and single people who are likely to be in the labour force, and couples and single people who are primarily retired. Because these groups can have very different income characteristics, the household income table excludes older households where all members are aged 65 years and over (65+).

Analysis

Rounding

We round figures presented in this release. Figures are rounded to the nearest hundred for people counts, nearest dollar for means and medians, and nearest cent for hourly figures.

Suppression of data

Cells that represent fewer than 1,000 people are suppressed and appear with the symbol 'S' in the tables. These estimates are subject to sampling errors that are too great for most practical purposes. We may remove records for quality and confidentially purposes in some publications.

Interpreting the data

Compositional effects

Movements in average and median income statistics are influenced by many factors. As well as changes in levels of income, movements are also influenced by the population's composition from survey to survey. These changes occur between males and females, different ethnic groups, different labour force statuses, numbers of full-time and part-time workers, between or within industries, and between or within occupations.

Income averaged across all people from all sources includes those who have zero income for some income sources. Income averaged across those receiving income from a particular source only includes those who received income from that source.

Period effects

LMS (Income) reports on 'weekly income' that relates to a week during the June quarter – it 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 (eg change of job or a period out of work).

Dissemination

Confidentiality

Only people authorised by the Data and Statistics Act 2022 are allowed to see your individual information, and they must use it only for statistical purposes. Your information is combined with similar information from other people or households to prepare summary statistics.

Timing

Our information releases are delivered electronically by third parties. Delivery may be delayed by circumstances outside our control. Statistics NZ accept responsibility for any such delay.

Liability

While all care and diligence has been used in processing, analysing, and extracting data and information in this publication, Statistics NZ gives no warranty it is error-free and will not be liable for any loss or damage suffered by the use directly, or indirectly, of the information in this publication.

More information

Statistics in this release have been produced in accordance with the Official Statistics System principles and protocols for producers of Tier 1 statistics for quality. They conform to the Statistics NZ Methodological Standard for Reporting of Data Quality.

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