Household net worth data collection 2023/24

Response rate for HES net worth 2023/24 The achieved sample size for HES income 2023/24 was approximately 19155 households. The achieved sample rate was 68.4 percent. The target for the net worth subsample was 5760 households. The final sample included approximately 5357 households, resulting in an achieved sample rate of 67.0 percent. Achieved sample rate compared with the response rate The achieved sample rate is calculated as the number of eligible households that responded divided by the total number of dwellings sampled. Essentially, it tells you what percentage of the sample responded to the survey. Expressing the achieved sample as a rate controls for population growth. Eligible responding Achieved Sample Rate = ____________________ Ineligible + eligible responding + eligible non-responding The response rate is calculated as the number of eligible households that responded to the survey as a proportion of the estimated number of total eligible households in the sample. Eligible responding Response rate = ___________________ Eligible responding + eligible non-responding The achieved sample rate differs from the response rate because it includes the ineligible dwellings in the denominator. This difference means that the response rate is particularly sensitive to the classification of household eligibility. As a result, the achieved sample rate is more stable over time than the response rate. While response rates have been declining over time, the impact of any bias arising from this is minimised by non-response adjustment and the calibration to population benchmarks. Imputation for HES Net Worth 2023/24 Imputation replaces missing values with actual values from similar respondents. Imputation was carried out both to replace missing values for certain HES income variables, and to impute values for assets and liabilities. There are three situations where we imputed for assets and liabilities: For non-trust and non-business asset and liability records where a value is not provided, we replace only that value with a value from a selected donor. For trust and business records where a value is not provided for any asset or liability of the trust or business, we replace all the asset and liability records for that trust or business with those from a selected donor. We impute income questionnaires for household members of eligible responding households that do not fully complete their income questionnaire. The asset and liability records for these people are replaced with the records of a donor. Imputation is done at the individual level, which may cause inconsistencies at the household level. For example, ownership of a property may appear to add up to more than 100 percent. Sampling errors We calculate sampling errors for means and totals using the jackknife method. It is based on the variation between estimates of different subsamples taken from the whole sample. The bootstrap method was used for median estimation. Sampling errors by asset and liability type for HES net worth 2014/15, HES net worth 2017/18, HES net worth 2020/21 and HES net worth 2023/24 are shown in the tables accompanying the main release. Customers should take care when interpreting estimates with sampling errors greater than 20 percent – they are statistically less reliable than estimates with sampling errors less than or equal to 20 percent. Age Standardisation To mitigate the effects of the Māori and Pacific population having a much younger age structure than the total New Zealand population, we have adjusted for age in the demographics tables (8.01 and 8.02) through age standardisation. Without age standardisation, median and mean figures for the variable of interest (e.g. net worth) by age-group, can potentially be distorted. Age standardisation is a commonly applied technique to control such distortions; it allows more meaningful comparisons between the sub-populations. We standardise age by re-scaling the underlying weights of the unit record data for each ethnic group – to reflect a 'standard' age distribution. We use the age distribution for the overall population of the net worth sample. Tables 10.01 and 10.02 are alternatives for demographics data, as they are separated by age groups rather than age-standardised. Important notes Property There are occasions where respondents mentioned they have other property, but it is for business purposes. In this case the property is scoped out of this part of the questionnaire. However, sometimes the respondent then did not mention it in the property, business, or trust sections (where we expected they would). This may have resulted in under-reporting of the value of property assets. Bank accounts In HES net worth 2014/15 many respondents did not see bank accounts as investments. As a result, the number and value of assets held in bank accounts were undervalued. To improve this, from HES net worth 2017/18 onwards bank accounts were asked about separately from other investments and details of many more bank accounts were collected. Superannuation In 2017/18, we found some respondents said they received superannuation contributions from their employer but did not then give details of a superannuation scheme. This may have led to under-reporting of superannuation schemes. To improve this, a prompt was added to improve respondent understanding. Life insurance In HES net worth 2023/24 data on life insurance was not collected. The life insurance question was not collecting data of good enough quality. It was decided to remove the question to reduce respondent burden. Businesses We suspect some respondents did not report all the businesses they owned – whether they had sole ownership or were in partnership with others. For example, there are occasions where if one person in the household mentioned a business their partner may have felt they did not need to mention it. This has potentially led to under-reporting of business assets. Some partners living together in a household responded that they both owned all of a business. Where the above was identified and could be verified, it was corrected. Family trusts We only asked questions on the assets and liabilities of trusts, of settlors or quasi settlors (a person in the household who reported being both a trustee and beneficiary of the trust). We did this because those who were only a beneficiary, or only a trustee, were less likely to know about the contents of the trust. Some respondents were unsure of their relationship to the trust, which may have led to fewer respondents identifying as a settlor or quasi settlor, and therefore an under-reporting of trust wealth. Minimum for certain collected values To reduce respondent burden, respondents were not asked to provide the value of smaller-value items. This may have resulted in some under-reporting. All assets and liability values we collected had no minimum value unless mentioned in the table below. Items that needed a minimum value, by type of asset or liability Year ended June 2024 Type of asset Lower limit collected ($) Valuables 5,000 New Zealand and foreign currency and vouchers etc 1,000 Loans to the respondent 1,000 Other assets' (e.g. sporting equipment, cameras, boats, and musical instruments) 5,000 Type of liability Loans from family members or friends 500 ‘Other debt’ (anything that hasn’t been covered elsewhere) 500 Sampling In previous iterations of the HES Expenditure and HES net worth, the Primary Sampling Units (PSUs) and households were selected as random subsamples from the HES Income sample. But for HES net worth 2023/24, we refined the number of PSUs in the extreme ends of the NZDep decile scale (i.e., deciles 1 and 10) to enhance the representativeness of these subgroups within the broader population. While PSUs continued to be randomly selected, households within each PSU were chosen using a systematic sampling method. Interpreting the data Customers need to consider the following when interpreting data from this survey. The five broad regions reported are based on the regional council areas of Wellington and Canterbury, and the Auckland Council area. Regions also include the combined ‘Rest of the North Island’, and ‘Rest of the South Island’. This level of geographical breakdown is the lowest available for HES net worth due to the sample design. Where a trust exists that owns assets (or owes liabilities), the entirety of the trust's share of the assets and liabilities were allocated to settlors and quasi-settlors of the trust in the household. Each individual received an equal share of the trust assets/liabilities. Where a household (or individual) has equity (assets minus liabilities) held in a trust, the net value of all these (the value of all assets less the value of all liabilities) is recorded as a single entry – as a financial asset in the 'Shares and other equity' component. Note: this net equity value can be negative (where the value of the trust liabilities exceeds the value of the trust assets). Some tables in this release use median and mean values calculated for the entire population, rather than only individuals or households with the specific asset or liability. For example, the median value of owner-occupied dwellings is the median value for everyone (whether they have an owner-occupied dwelling or not), not the median value for only those who have an owner-occupied dwelling. Tables 2.01, 2.02, 3.01, 3.02, 7.01, 7.02, 8.01, 8.02, 9.01, 9.02, 10.01, and 10.02 are calculated in this way. Tables 1.03 and 1.04 in the Excel tables of the release show differences between the means for those with the specific asset/liability and the means for the total population. Methodology The target population for HES is the usually resident population of New Zealand living in private dwellings, aged 15 years and over (15+). 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 (e.g. 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 (e.g. 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 housing costs on behalf of those children are 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 As in HES income, HES net worth has five survey components: a household questionnaire a housing expenditure questionnaire an income questionnaire for each household member aged 15+ a self-completed demographic questionnaire for each household member aged 15+. a material well-being questionnaire for one member per household who is aged 18+ (chosen randomly) Topics covered in the survey to collect data on wealth include: Household net worth = (what you own) LESS (what you owe) Assets (what you own) Liabilities (what you owe) Real estate Real estate loans Owner-occupied residences Owner-occupied residence loans Other residential and non-residential property Other residential and non-residential property loans Other physical assets Other liabilities Consumer durables Consumer durables loans Valuables Other debt (e.g. credit cards) Education loans Financial assets Currency and deposits Investments (e.g. shares, mutual funds) Net equity in unincorporated businesses Net equity in trusts Pension funds (superannuation funds) Total Assets Total Liabilities We ask questions on assets and liabilities either within existing HES modules in the income and expenditure questionnaire or collect the information as separate sets of questions (modules) at the end of the income questionnaire. Topics related to net worth covered within existing HES Income modules include: principal residence [housing costs] other non-investment properties [other property] mortgages for principal residence and non-investment properties [mortgages and loans] superannuation schemes [private superannuation]. Topics covered in separate modules include: Inheritance and gifts equity in businesses motor vehicles, collectibles, and cash assets household durables trusts non-property debt New Zealand financial assets [investments] New Zealand investment property assets and liabilities [investments] overseas property and financial assets [overseas income]. The HES net worth survey, besides collecting information from sampled New Zealand households on the above topics. The table below sets out what has changed specifically in the questionnaires since the HES net worth 2020/21 by module. Household net worth = (what you own) LESS (what you owe) Inheritances and gifts A brand-new module has been included to gather data on inheritances and gifts. Inheritances of any amount are collected. Gifts $5000 and over and in the last 10 years are collected. Irregular Income (IRI) Added more exclusions into showcard P81 From: Don't count: payments from overseas winnings from betting and gambling, including lotto money from sale of possessions withdrawals from savings loans tax refunds To: Don't count: payments from overseas winnings from betting / gambling, including lotto money from sale of possessions / property withdrawals from savings loans tax refunds / Working for Families tax credits redundancy payments Furthermore: Updated response option 12 from ‘matrimonial settlements’ to ‘matrimonial/relationship settlements. This change also extends to the ING module pg. 1 and 2 of the flowchart where showcard 81 is also used. Overseas Income (OSI) Exclusions added into showcard P91 Don’t Count: Money from sale of overseas possessions / property Payment from within New Zealand. Whole of Life Insurance (WLI) Removed whole of life insurance module. Money Owing (MOW) We have reduced threshold to $500 for consistency with the individual loan (ODT) module. We have increased options to family member; other private person or household; trust; business run by household member; other business or organisation; someone’s else; Don’t know and Refused. Trusts (TRU) We have deleted variables qFullValGifted, qGiftRemVal, and qDecEstate This also affects routing for the first decision on page 3 (it now ends the series) and if qTrustAssetTypes = Don’t know or Refused (it now ends the series) Value of investments (VIN) Changes to showcard P141 have been made. ‘Not bonus bonds’ has been removed from option 12 description. Bonus bonds scheme ended on the 26 February 2024, which meant we no longer had to clarify not including bonus bonds as an option. A new option for cryptocurrencies e.g. Bitcoin has been added. Bank accounts (BAC) and Charge Cards and Hire Purchase (CHP) The order in which the modules have been completed has changed. CHP now immediately follows BAC. (CHP) References to “Buy now pay later” such as AfterPay have been introduced to qJointHP, qSepHP, qIndivHP questions. AfterPay has also been added into inserts iJointly (pg3), ilntro (pg4) and iNoMoreCards(pg5) Household Durables (HHD) Showcard P193 has been replaced with showcard P172 which will bring it in line with the same showcard shown in early module Non-cash Assets (NCA). This ensures the same ranges are used in both modules. The upper ranges of the showcard now go higher than the previous survey. This means that estimates of individual values and means/totals in output tables may be higher than before. Self-Complete Demographics (SCD) and Interviewer Administered Demographics (IND) The 18 years and over age check before the qSexID and qSexIdOth questions have been removed and replaced with a ‘respondent 15 year and over’ check. This brings the standard in line with the Census. Housing Costs (HOU) Table 1 ‘Any other council charges’ now includes a showcard with inclusions/exclusions examples. As a new showcard has been added, references to the original E8 showcard have been updated to E9. Use following showcard for qOthChargesAny Showcard E8 Don't include: dog registrations refuse collection fees (rubbish bags/stickers/bins, skip hire) Examples of other council charges late rates payment penalties resident parking permits inspection fees (eg swimming pools, septic tanks) building or resource consent fees LIM or other property information reports sub-division fees repayment of insulation loans Farming Business (FAB) and Non-Farming Business (BUS) Improved routing to ensure more businesses are collected Property (PRO) “other” has been removed as a stand alone response but added as a "please state” in “other residential property” Reliability of survey estimates Two types of errors are possible in estimates based on a sample survey – sampling error and non-sampling error. 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 primarily calculated sampling errors using the jackknife method. It is based on the variation between estimates of different subsamples taken from the whole sample. In the case of median values, the bootstrapping method was used. 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 RSE than the estimated average annual household income for households receiving income from wages and salaries. In the tables accompanying the Household net worth statistics, only income or expenditure estimates with RSEs less than or equal to 20 percent are considered sufficiently reliable for most purposes. Although estimates with RSEs over 21 percent are also included, these should be used with caution. Estimates with RSEs over 100 percent are also provided, however these are not deemed very useful. Non-sampling errors: 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 (e.g. non-response). Proxy To ensure accurate information is collected, we aim to interview selected respondents directly. In some circumstances, however, we cannot reach all household members, and in exceptional cases we ask another person in the household to respond on behalf of the selected respondent. This is known as a proxy response. Proxy responses are used in ‘family-type’ households for: those unable to be present during the interview, but who were in households where all had agreed to participate children who are away at boarding school people who are elderly, sick, or mentally incapacitated. In all proxy interviews, the interviewer must be convinced that the proxy respondent is totally familiar with the selected respondent’s information and the person being proxied for must agree to the proxy. Such interviews occur only in rare instances – most of the time it is preferable to accept a non-response from unreachable household members Population weighting adjustments Weighting plays a vital role in estimation. We give each unit in the sample 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 (and/or quality) of information provided 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 (e.g. 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. The population used for the integrated weighting was benchmarked to estimates based on the 2018 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); adult Māori sub-population estimates by two age groups (including 30 years and over); income distribution of individuals aged 15+; and the number of people receiving any type of benefit. The household benchmarks are two categories of household composition (two-adult households and non-two-adult households), household income (total gross income from the 2018 Census), and the New Zealand Deprivation Index. These categories are split further by regions. Suppressed estimates We suppress estimates in this release if based on fewer than six people or households for total or mean values, or fewer than 10 people or households for median values. Publishing would be a risk to respondents’ confidentiality. Use of the IDI Administrative data from the Integrated Data Infrastructure (IDI) is used to collect sources of income for eligible individuals. Use of administrative data improves income data accuracy. en-NZ