This section is for information that changes between periods.
This section has information about data that does not change between releases.
Sampling errors for the December 2011 quarter
From the December 2011 quarter onwards the way we calculate sampling errors has changed – from a model-based approach to a jackknife method. The jackknife method is based on the variation between estimates, based on 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.
From the December 2011 quarter, the Household Labour Force Survey (HLFS) publishes ethnicity data using the total response ethnicity output. Using this method, people who reported that they belonged to more than one ethnic group are counted once in each group reported. This means that the total number of responses for all ethnic groups can be greater than the total number of people who stated their ethnicities.
An alternate method of classifying ethnicity is using the single/combination output method. Using the single/combination ethnicity output, people are counted just once according to the ethnic group or combination of ethnic groups they have reported. This means that the total number of responses equals the total number of people who stated an ethnicity. The table below shows single/combination data for the working-age population for the September 2011 and December 2011 quarters of the HLFS.
Single/combination HLFS ethnicity data for working-age population
||September 2011 quarter
||December 2011 quarter
|Pacific peoples only
|Other ethnicity only
|Two or more groups not elsewhere included
|Total all ethnic groups
|1. MELAA = Middle Eastern/Latin American/African.
See the 2005 New Zealand standard classification of ethnicity for more information.
School holidays (secondary school)
The term times for the 2011 school year were altered to accommodate the Rugby World Cup tournament. Keep this in mind when making annual comparisons.
| Term 1
|| 2 February – 1 April
|| 31 January – 15 April
| Term 2
|| 19 April – 2 July
|| 2 May – 15 July
| Term 3
|| 19 July – 24 September
|| 1 August – 7 October
| Term 4
|| 11 October – 14 December
|| 25 October – 13 December
The target population for the HLFS is the civilian, usually resident, non-institutionalised population aged 15 years and over.
The statistics in this release do not cover:
- long-term residents of homes for older people, hospitals, and psychiatric institutions
- those living in non-private dwellings (eg hotels, motels, hostels)
- inmates of penal institutions
- members of the permanent armed forces
- members of the non-New Zealand armed forces
- overseas diplomats
- overseas visitors who expect to be resident in New Zealand for less than 12 months
- those aged under 15 years
- people living on offshore islands (except Waiheke Island).
Accuracy of the data
The HLFS sample contains about 15,000 private households and about 30,000 individuals each quarter. We sample households on a statistically representative basis from areas throughout New Zealand, and obtain information for each member of the household. The sample is stratified by geographic region, urban and rural areas, ethnic density, and socio-economic characteristics.
Households stay in the survey for two years. Each quarter, one-eighth of the households in the sample are rotated out and replaced by a new set of households. Therefore, up to seven-eighths of the same people are surveyed in adjacent quarters. This overlap improves the reliability of quarterly change estimates.
The period of surveying/interviewing is13 weeks. The information obtained relates to the week before the interview (referred to as the ‘survey reference week’). We first interview respondents face-to-face at their home. Subsequent interviews are by telephone wherever possible. Respondents also have the option to file self-completed questionnaires.
Where practicable, we obtain information directly from each household member. Otherwise a proxy interview is conducted, in which details are obtained from another adult in the household.
Sampling errors can be measured. They quantify the variability that occurs by chance because a sample rather than an entire population is surveyed.
We calculate sampling errors for each cell in the published tables and for estimates of change between adjacent quarters. For example, the estimated total number of people employed in the December 2011 quarter is 2,237,000 before seasonal adjustment. This estimate is subject to a sampling error of plus or minus 24,100, or 1.1 percent (measured at the 95 percent confidence level). This means that there is a 95 percent chance that the true number of employed people lies between 2,212,900 and 2,261,100.
Smaller estimates, such as the number of people who are unemployed, are subject to larger relative sampling errors than larger estimates. For example, the estimated total number of people unemployed in the December 2011 quarter is 150,700 before seasonal adjustment. This estimate is subject to a sampling error of plus or minus 11,600 or 7.7 percent (measured at the 95 percent confidence level). This means that there is a 95 percent chance that the true number of unemployed people lies between 139,100 and 162,300.
Estimates of change are also subject to sampling error. For example, the survey estimate of change in total employment from the December 2010 quarter to the December 2011 quarter is an increase of 34,100. This estimate is subject to a sampling error of plus or minus 24,600 (at the 95 percent confidence level). Therefore, the true value of the change in surveyed employment from the December 2010 quarter to the December 2011 quarter has a 95 percent chance of lying between 9,500 and 58,700.
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. Therefore, the example quoted above represents a significant movement.
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.
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 by applying best survey practices and monitoring known indicators (eg non-response).
Suppression of data
Cells with estimates of less than 1,000 are suppressed and appear as ‘S’ in the tables. These estimates are subject to sampling errors too great for most practical purposes.
The target response rate for the HLFS is 90 percent. The response rate is calculated 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. The following table shows the HLFS response rates for the last five quarters.
|HLFS response rates
||National response rate
Seasonal adjustment and trend series
In the labour market, cyclical events that affect labour supply and demand occur around the same time each year. For example, in summertime a large pool of student labour is both available for, and actively seeking, work. Demand for labour in the retail sector and in many primary production industries also increases.
For any series, the estimates can be broken down into three components: trend, seasonal, and irregular. Seasonally adjusted series have had the seasonal component removed. Trend series have had both the seasonal and irregular components removed, and reveal the underlying direction of movement in a series.
The series for each labour market statistic is adjusted separately. For this reason, the sum of the seasonally adjusted estimates for employment, unemployment, and people not in the labour force will usually not add up to the working-age population estimates.
See Seasonal adjustment in Statistics New Zealand for more information about how we seasonally adjust our statistics. Seasonal adjustment makes data for adjacent quarters more comparable by smoothing out the effect on the times series of any regular seasonal events. This ensures that the underlying movements in the time series are more visible. All seasonally adjusted and trend series are produced using the X-12-ARIMA Version 0.2.10 package developed by the U.S. Census Bureau.
Quality of seasonal adjustment
We monitor our data to make sure that our seasonal adjustment is robust.
The X-12-ARIMA programme is highly customisable and can produce a wide variety of possible adjustments for any particular input series. Consequently, X-12-ARIMA produces a number of diagnostics which are useful in assessing the quality of the chosen adjustment.
The following table provides a selection of diagnostics. The reference value provides an indication of the desired value for each. Most are acceptable, though there is evidence of a changing seasonal pattern for the number of males who are unemployed and females who are not in the labour force. More detail about seasonal adjustment in the HLFS is available upon request.
|Seasonal adjustment diagnostics
||Male not in labour force
||Female not in labour force
|Test for seasonality
|Test for moving seasonality
|Periods until trend dominates
|Trend contribution to change
|Seasonal contribution to change
|Irregular contribution to change
During the seasonal adjustment process, X-12-ARIMA can give less weight to the irregular component. Specifically, if the estimated irregular component at a point in time is sufficiently large compared with the standard deviation of the irregular component as a whole, then the irregular component at that point can be downweighted or removed completely and re-estimated. Such observations are referred to as partial and zero-outliers, respectively. In practice, the downweighting of outliers will do little to seasonally adjusted data, but the impact of the outliers on the trend series will generally be reduced. However, if an outlier ceases to be an outlier as more data becomes available, then significant revisions to the trend series become possible. There are no outliers present over the last four quarters of data.
Each quarter, the seasonal adjustment process is applied to the latest quarter and all previous quarters. This means that seasonally adjusted estimates for any of the previously published quarters may change slightly. The following table lists the change in estimates between the current and previous publication for the seasonally adjusted data. For example, the seasonally adjusted number of males employed for the September 2011 quarter was 1,184,000. In the December 2011 quarter release, that same estimate has been revised to 1,183,000. These numbers are rounded to the nearest 1,000, but the relative change derived from the unrounded estimates is a downward revision of 0.08 percent.
|Percent revision from last published, seasonally adjusted
||Male not in labour force
||Female not in labour force
The following table presents information on how the trend estimates have been revised. Trend revisions are generally larger than those of the seasonally adjusted data.
|Percent revision from last published, trend
||Male not in labour force
||Female not in labour force
Every estimate is subject to revision each quarter as new data is added, though in practice estimates more than two years from the end-point will change little. For example, the trend estimate of male employment for the December 2010 quarter was 1,167,000 when first published. In the December 2011 quarter, one year later, the trend estimate of male employment for the December 2010 quarter is 1,170,000, an increase of 3,000 (or an increase 0.27 percent using the unrounded estimates). This is an example of a '4-step ahead' revision. The table below shows the average of all such absolute revisions expressed relatively and gives some indication of how much the current estimates might be revised when the March 2012 data becomes available.
|Mean absolute percent revisions
|Male not in labour force
|Female not in labour force
Figures presented in this release are rounded. Figures are rounded to the nearest hundred or to the nearest thousand for seasonally adjusted and trend estimates. This may result in a total disagreeing slightly with the sum of the individual items as shown in the table. Where figures are rounded the unit is shown as (000) for thousands.
How labour force statistics are classified
The HLFS release includes specific statistics about industry, occupation, study, ethnicity, and region. This section defines what we measure for each of these statistics.
Since the September 2009 quarter, the industry statistics are based on the Australian and New Zealand Standard Industrial Classification 2006 (ANZSIC06), the latest edition of the classification. When ANZSIC06 was introduced, Statistics NZ developed the New Zealand Standard Industrial Output Categories (NZSIOC). Classifying industries using NZSIOC helps to standardise outputs. Industry outputs defined using ANZSIC06 are not comparable with those based on ANZSIC96, the version used before the September 2009 quarter.
See Implementing ANZSIC 2006 in the Household Labour Force Survey for more information.
Since the September 2009 quarter, we have used the Australian and New Zealand Standard Classification of Occupations (ANZSCO) to classify occupation data in the HLFS. ANZSCO is a harmonised classification developed by Statistics NZ, the Australian Bureau of Statistics, and the Australian Department of Employment and Workplace Relations, for use in both Australia and New Zealand. Occupation data was previously based on the New Zealand Standard Classification of Occupations 1999 (NZSCO99). The occupation data is available on Infoshare.
See Implementing ANZSCO in the Household Labour Force Survey for more information.
Before April 2009, we did not benchmark the Māori working-age population to population estimates. This, along with other sample design restrictions, caused a high degree of volatility in Māori statistics in the HLFS. Movements in the working-age population estimates of certain ethnic groups, such as Māori, may reflect this volatility rather than a real change in the estimated ethnic demographic. Including Māori benchmarks in the working-age population mitigates the known undercount of Māori in the HLFS and also results in smoother time series for Māori statistics in the HLFS. However, introducing the Māori population benchmarks does not necessarily translate to improved estimates for non-Māori ethnic groups.
A household's labour force status is derived by looking at the labour force status of household members aged 18–64 years. For example, if a couple is living by themselves and one is aged 64 years and the other is aged 65 years, this couple will be assigned to the 'All employed' or 'None employed' category, depending on the labour force status of the 64-year-old.
Households that have no members aged 18–64 years are excluded from this analysis. 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 less than 18 years and not in full-time employment.
Updated regional classification
In November 2010, the new Auckland territorial authority replaced the existing Rodney district, North Shore city, Auckland city, Waitakere city, Manukau city, Papakura district, and part of Franklin district councils. This resulted in a minor change in the boundary between the Auckland and Waikato regions.
From the June 2011 quarter, the statistics in the HLFS release were produced using the new boundaries and backcast for the March 2011 quarter. The new boundaries do not significantly affect measures from the HLFS.
Comparability with other datasets
See Comparing our labour market statistics for more information on how the HLFS compares with the other labour market statistics that we produce. This page explains which measures of employment are included in each of our employment releases, and the timings and coverage of each release.
See A Guide to Unemployment Statistics for more information on comparing the HLFS with other datasets on unemployment. This page explains which measures of unemployment are included in the HLFS, the unemployment benefit, and the job-seekers register. It also includes information on the timings, coverage, and different purposes of each of these measures.
International comparability of the labour force participation rate and the employment rate
Several alternative definitions of labour force participation rate and employment rate are used by other organisations and countries; they differ in the age of the working-age population and the inclusion of military personnel. A common definition is to restrict the labour force and working-age population to the 15–64-year age group, particularly in countries with a compulsory retirement age. Generally, this definition leads to a higher labour force participation rate and employment rate. Using this definition for the New Zealand HLFS in the December 2011 quarter gives a surveyed figure of 78.2 percent (labour force participation rate) and 73.1 percent (employment rate).
Interpreting the data
Information releases contain seasonally adjusted, trend, and survey statistics for the latest quarter. These statistics are averages for the three-month period and do not apply to any specific point in time. Data sourced from the seasonally adjusted series and trend series are identified as such in the table or section headings. All other data, in the commentary or in tables, are sourced from the original survey series and are unadjusted.
Timing of published data
The HLFS is published within six weeks after the end of the quarter's reference period.
Only people authorised by the Statistics Act 1975 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.
For more technical information, see Information about the Household Labour Force Survey.
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.
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