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Household Expenditure Statistics: Year ended June 2016
Embargoed until 10:45am  –  02 December 2016
Data quality

Information on New Zealand households’ expenditure, income and material well-being is based on data collected as part of the Household Economic Survey: 2015/16 (HES).  

Period-specific information
This section contains data information that has changed since the last release.

General information
This section contains information that does not change between releases.

Period-specific information

Recall period

The full HES was carried out continuously from 1 July 2015 to 30 June 2016, therefore different households have different recall periods. Households interviewed on 1 July 2015 would have recall periods earlier than the date interviewed, so from 1 July 2014 to 30 June 2016.

Expenditure data was collected by the following methods:

  • 3-month recall for large or irregular expenditure types, such as health and travel
  • 12-month recall for housing-related costs and recreation and culture
  • latest payment (for regular commitments such as electricity, telephone, rates, rent, insurance, and superannuation)
  • 14-day diary keeping for smaller, more regular expenditure types.

Income has several recall periods, including 12-month recall and latest payment.

External influences

Changes in income and expenditure may be influenced by one-off real-world events. Events that could have influenced the HES 2015/16 data are the:

  • increase in the adult minimum wage from $14.75 to $15.25 (effective from 1 April 2016)
  • increase in government transfer maximum rates for people with dependent children of $25 for main benefits and student allowances (effective from 1 April 2016)
  • increase in New Zealand Superannuation rate of 2.73 percent (effective from 1 April 2016).

Response rate to HES 2015/16

The sample size for HES is approximately 5,000 households.

The target response rate for full HES is 70 percent of eligible households. We achieved a 78.1 percent response rate for the year ended 30 June 2016 (post-imputation).

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

Even though the response rate has been declining over time, and minimal bias is present, our non-response adjustment and the calibration to population benchmarks remove the impact of this bias.

Changes to New Zealand Household Expenditure Classification

The New Zealand Household Expenditure Classification (NZHEC) was reviewed between 2012/13 and 2015/16. We made minor changes at the item level to reflect real-world changes and any redevelopment changes. An example of a change is the removal of the package deal item codes for travel.

Sampling errors

The table below shows the sampling errors for the level of expenditure by expenditure type in 2012/13 and 2015/16.

We advise care when interpreting expenditure estimates with sampling errors greater than 20 percent. They are less statistically reliable than estimates with sampling errors less than or equal to 20 percent.

Sampling errors for average weekly household expenditure, by expenditure type
Year ended June 2013 and 2016

 Expenditure type  Level sampling error (%)
 2012/ 13(1)  2015/ 16
 Food 3.3  3.1
 Alcoholic beverages  8.3 10.6
 Clothing and footwear  11.6 10.1
 Housing and household utilities  5.9 8.7
 Household contents and services  7.2 6.5


 9.1 11.6
 Transport  6.7 7.9
 Communication  3.8 4.0
 Recreation and culture  4.5 6.6
 Education  14.7 18.7
 Miscellaneous goods and services  5.0 5.1
 Other expenditure  8.9 11.1
 Sales, trade-ins, and refunds  22.4  32.8

 1. Data for this year is revised
Source: Statistics NZ


Income level sampling errors can be provided on request. There will be a delay in supplying this information due to the November 2016 earthquake. Contact for more detailed sampling errors.

General information

Scope of the survey

The target population for HES is the usually resident population of New Zealand living in private dwellings, aged 15 years and over. This population does not include:

  • overseas visitors who expect to be resident in New Zealand for less than 12 months
  • people living in non-private dwellings such as hotels, motels, boarding houses, hostels, and homes for the elderly
  • patients in hospitals, or residents of psychiatric or penal institutions
  • members of the permanent armed forces in group living facilities; for example, barracks
  • people living on offshore islands (excluding Waiheke Island)
  • members of the non-New Zealand armed forces
  • non-New Zealand diplomats and their families.

Children at boarding schools are also not surveyed, but expenditure on behalf of those children is included in the record-keeping of the parent or guardian. The survey population is therefore marginally different from the target population.

For survey purposes, a ‘household’ comprises a group of people who share a private dwelling and normally spend four or more nights a week in the household. They must share consumption of food or contribute some portion of income towards the provision of essentials for living as a group.

HES components

The HES has five survey components:

  • a household questionnaire
  • an expenditure questionnaire
  • an income questionnaire for each household member 15 years and over
  • expenditure diaries for each household member 15 years and over
  • a material well-being questionnaire for one member per household who is aged 18 years or over (chosen randomly).

We use computer assisted interviewing for HES – first introduced in the 2006/07 interview period.

Sample design information

We select the sample for the HES using a two-stage stratified cluster design. Households are sampled on a statistically representative random basis, from rural and urban areas throughout the North and South islands.

The HES sample has approximately 5,000 private households. Information is obtained for each member of sampled households that fall within the scope of the survey and meet survey coverage rules.

Reliability of survey estimates

Two types of error are possible in estimates based on a sample survey – sampling error and non-sampling error.

Sampling error: is a measure of the variability that occurs by chance because a sample rather than an entire population is surveyed.

We calculate sampling errors using the jackknife method. It is based on the variation between estimates of different subsamples taken from the whole sample.

Given a certain sample size, the level of sampling error for any given estimate depends on the number of sampled households/individuals in the category of interest and the variability of the estimate due to the random nature of the sample selection.

As the size of the sampled group decreases, the relative sampling errors (RSEs – sample error as a percentage of the estimate) will generally increase. For example, the estimated average annual household income from self-employment would have a larger relative sampling error than the estimated average annual household income for households receiving income from wages and salaries.

In the tables provided with this release, only income or expenditure estimates with RSEs less than or equal to 20 percent are considered sufficiently reliable for most purposes. However, estimates with RSEs between 21 percent and less than 50 percent, are included and are preceded by an asterisk to show they are subject to high sampling error and should be used with caution. Estimates with RSEs between 50 and 100 percent are considered unreliable for most uses, and are flagged with double asterisks. Estimates with RSEs over 100 are also provided and are flagged with triple asterisks. They are deemed to not be very useful.

Non-sampling errors: arise from biases in the patterns of response and non-response, questionnaire design, inaccuracies in reporting by respondents, and errors in recording and coding data. We endeavour to minimise the impact of these errors by applying best-practice survey methods and monitoring known indicators (eg non-response).


A proxy may provide information in ‘family type’ households where:

  • the whole household is informed about the survey. All agree to participate, but are not able to be present when the questionnaires are administered
  • children are away at boarding school
  • people don't work and have no source of income
  • people are elderly, sick, or mentally incapacitated.

In all proxy interviews, the interviewer must be convinced the proxy is totally familiar with the other respondent’s information.


Imputation replaces missing values with actual values from similar respondents. As a result of imputing records, the response rate for the year ending 30 June 2016 improved from 75.7 percent to 78.1 percent.

Two imputation methods are used in HES – nearest neighbour donor imputation and mean imputation (latter for expenditure only).

The nearest neighbour donor imputation method replaces missing values by data values from another record called a donor. A donor is selected by finding a respondent with matching characteristics to the recipient. Mean imputation uses the mean of the acceptable values to replace a missing value.

We introduced donor imputation into HES in 2009/10, and use it in all subsequent HES (Expenditure) and HES (Income) releases. We also applied imputation to every previous HES cycle and revised the data accordingly.

The donor imputation is applied to a household where the household does not supply all the required income or expenditure information, but supplies sufficient information to be retained in the sample.

For households where at least one significant person in the household has completed at least two modules out of the three core ones (Job, Government transfers, and Investment) of the income questionnaire, we impute income questionnaires for other household member(s) who have not fully completed their income questionnaire(s). In HES (Expenditure) years, we apply the same process when expenditure diaries are not supplied by all eligible members of the household. In addition, we impute age for respondents who do not provide an age.

We also impute local and regional council rates for respondents who have not provided enough information for us to calculate their rates. A form of manual imputation is used to impute interest rates.

Before imputation was introduced, we discarded households with one or more questionnaire(s) missing. With imputation, we recover some of these households.

Population rebase

The HES is a sample survey that uses statistical weights to calculate income, expenditure and material well-being estimates for the total New Zealand population. We revise the weights following each census, based on the latest population counts (called a population rebase). For the current HES, we used the weights based on the Census 2013 population.

The rebase was first applied in the 2014/15 year. The revised data applies to the income and material well-being data from 2006/07 to 2015/16, and to the expenditure data for the years 2006/07, 2009/10, 2012/13 and 2015/16.

See Household Economic Survey population rebase: year ended June 2007–15 for more information about the revisions.

Population weighting adjustments

The population weighting process takes account of undercoverage in the survey for specific population groups, such as young males and Māori.

Weighting plays a vital role in estimation. Each unit in the sample is given a weight that indicates the number of people it represents in the final population estimate. Weighting ensures that estimates reflect the sample design, adjusts for non-response, and aligns estimates with the current population estimates. For household surveys, deriving the weight is a multi-phase process.

The first stage of weighting involves calculating a unit’s initial weight. The initial weight depends on the sample design and equals the inverse of the selection probability.

The second stage involves adjusting the initial weights to account for unit non-response. This refers to a household without information, or where the amount of information provided (and/or quality of) is insufficient to be a response. The initial weight of a non-responding unit is reduced to zero, while initial weights of responding units are scaled up – by combining factors within the estimation group (eg region, ethnic densities, urban/rural, and interview quarter).

The final stage in the weighting process is integrated weighting. This process ensures we give all eligible responding individuals within a household the same weight so we can produce household statistics. Integrated weighting also aligns estimates with externally sourced population individual and household benchmarks, and adjusts for under-count of specific sub-population groups (eg young males and Māori).

The population used for the integrated weighting was benchmarked to estimates based on the 2013 Census.

HES benchmarks

The person benchmarks used for HES are: regional population estimates; children sub-population estimates by three age groups; adult sub-population estimates by sex and 13 age groups (including 75 years and over); and adult Māori sub-population estimates by two age groups (including 30 years and over).

The household benchmarks are two categories of household composition (two-adult households and non-two-adult households), and these categories split further by regions.

Population estimates are based on the 2013 Census.

Under-reporting expenditure

For some types of expenditure, the estimated amount for all private households is less than expenditure reported from other data sources. 

There are three main reasons for this difference.

  •  Expenditure by residents of non-private households, or by those ineligible for the survey (eg overseas visitors), is excluded from this survey.
  • Respondents to the survey forget or omit some types of purchases because they are unable to recall expenditure, or cannot refer to records at the time of the interview. Some of this has been reduced due to changes in the recall period.
  • A bias associated with non-response affects some statistics.

We don't adjust the data to compensate for any under-reporting.

Consistency with other periods

HES has a relatively small sample size (approximately 5,000 households). Although we adjust survey results for various demographic variables (age, sex, and region), there can be variability in survey estimates from one survey collection period to the next. This variability is because a different group of households is selected for each survey. For example, in 2014/15 the sample was boosted to 8,000 households because it was a HES (savings) collection. Although weighting should adjust for sample size differences, there could be some variability between 2014/15 and 2015/16.

Interpreting the data

Factors influencing a household's expenditure or income include household size, household composition, geographic location, and employment-related factors.

All income figures refer to gross (before tax) income, and housing-cost expenditure includes GST, where it applies.

The five broad regions we report on are based on the regional council areas of Wellington and Canterbury, the Auckland Council area, and the combined regions of ‘Rest of the North Island’, and ‘Rest of the South Island’. This level of geographical breakdown is the lowest available for the HES surveys due to the sample design.

Full HES estimates in the HES (Income) release: data exclusions

To make the HES (Income) and full HES as comparable as possible, we exclude some expenditure that is not collected in HES (Income) from our separate housing costs tables – such as expenditure not related directly to the building (so excludes expenditure on utilities).

A small amount of insurance expenditure that is only collected in the full HES years is assigned to an expenditure code used by the HES (Income). To further increase comparability between full HES and HES (Income), starting with the 2010/11 release, this expenditure is now also excluded when comparing housing costs between years.

We do not adjust for other differences between the surveys, including questionnaire structure. There is evidence that these structural differences (eg level of detail and length of questionnaire) are affecting the comparability of housing-costs data between HES (Income) and HES (Expenditure) years. These differences particularly affect the mortgages and loans expenditure data, which are a significant component of total housing costs. We have moved the order of the mortgages and loans questions to earlier in the questionnaire, but we can’t measure the impact of doing this.

Using material well-being data

The material well-being questionnaire asks about ownership of particular items, or doing certain activities, and the extent that people economise. We also ask respondents how they rate their life satisfaction and whether income meets every day needs.

From the material well-being questionnaire we publish selected results for satisfaction levels, and adequacy of income to meet every day needs. Statistics NZ does not produce an index measurement of material well-being from this data. Other agencies can use such index data in conjunction with other measures (eg income, expenditure on housing costs, or household demographics), to give an indication of the material standard of living of New Zealanders.

Suppressed estimates

Data in the information release are suppressed if based on fewer than five people or households. Data is no longer suppressed if a relative sample error is 51 percent or higher (21 percent for cross-tabulated data). 

See the Reliability of survey estimates section above.

Data validation and editing

As part of the quality check process, expenditure, income and material well-being data goes through a validation process at the 6-month, 9-month and 12-month stages of the survey cycle. We look for any unexplained outliers and compare data against previous years of data for any movements we can't explain by real-world changes.

During validation, we noted there were times when respondents did not interpret some questions as we intended. Such responses were edited after we considered the respondent’s response to other questions or demographic information.
Using computer-assisted interviewing allows numerous range and consistency edits in the questionnaire, enabling interviewers to check improbable values and consistency of the responses at the point of contact. This reduces errors within the data. 

Customised data

The tables in this information release do not contain all possible analyses of full HES data. Data requests can be customised to customers' specifications. There may be a delay in providing customised requests due to the November 2016 earthquake. Contact for more information.

More information

See HES and HES Income for more information about HES.


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.


Timed statistical releases are delivered using postal and electronic services provided by third parties. Delivery of these releases may be delayed by circumstances outside the control of Statistics NZ. Statistics NZ accepts no responsibility for any such delays.

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