Data Collection
Labour Market Statistics: March 2016 quarter
#Period-specific information
##Response Rates
Survey | Reference period | Response rate | Achieved Sample rate |
---|---|---|---|
HLFS | Each week during the quarter (3 January 2016 – 2 April 2016) | Target: 90 percent Achieved: 86.6 percent |
Target: 76 percent Achieved: 74.9 percent |
QES | The pay week ending on, or before,19 February 2016 | Target: 89 percent Achieved: 87.7 percent |
N/A |
LCI | Pay rates at 15 February 2016 | Target: 94 percent Achieved: 95.4 percent |
N/A |
###HLFS See New quality measures for the Household Labour Force Survey for more information on the sample rate and response rates.
###QES In the March 2016 quarter, there were 16,509 businesses in the final sample for the Quarterly Employment Survey. Due to a system issue, 2.6 percent of these businesses were originally excluded from the sample and were not sent survey requests. The result of these businesses not receiving surveys was a slightly lower response rate than usual, and a slightly higher number of units being imputed for. The response rate by weighted FTEs achieved for the remaining sample was 90.2 percent, above the targeted response rate of 89 percent. We applied the standard imputation method to the units that did not receive survey requests, to impute for their missing data.
##Rounding ###LCI |Percentage changes calculated from rounded and unrounded index numbers, March 2016 quarter||||| |:-----------------------|:-------------------------|:---------------|:--------------------|:---------------| ||Quarter percentage change||Annual percentage change|| |All salary and wage rates|rounded| unrounded |rounded |unrounded| |All sectors| 0.4| 0.4| 1.6| 1.7| |Public sector |0.4 |0.3 |1.4| 1.3| |Central government |0.4 |0.3 |1.4 |1.3| |Ordinary time wage rates ||||| |Private sector |0.4 |0.4 |1.8 |1.7 | |Public sector |0.4 |0.3 |1.4 |1.3 | |Central government |0.4 |0.3 |1.3 |1.3 |
##Outliers ###HLFS
During the seasonal adjustment process, X-13-ARIMA-SEATS 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. We refer to such observations as partial- and zero-outliers, respectively. In practice, the downweighting of outliers does 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.
The table below shows any partial (P) and zero (Z) outliers for the last year of each time series
Outliers | ||||||
---|---|---|---|---|---|---|
Quarters | Male employed | Female employed | Male unemployed | Female unemployed | Male not in the labour force |
Female not in the labour force |
Jun 2015 | ||||||
Sep 2015 | Z | |||||
Dec 2015 | P | |||||
Mar 2016 |
##Weights ###HLFS pre- and post-calibration weight
The following figure shows that while the distribution of the pre- and post-calibration weights differs within a quarter, the difference between the weights typically does not change from quarter to quarter.
The undercoverage rate indicates how representative the pre-calibrated sample is. The higher the undercoverage rate, the less representative the pre-calibrated sample. Usually the undercoverage rate in the HLFS is around 20 percent. The overall undercoverage rate for the HLFS in the March 2016 quarter was 19.2 percent. This compares with 19.8 percent in the December 2015 quarter and 16.9 percent in the March 2015 quarter.
##General information and methodology
For general information and methodology on the specific surveys within the labour market statistics release, please see the following Datainfo+ pages:
en-NZ