Labour Market Statistics: June 2017 quarter

#Period-specific information
##Response Rates

See New quality measures for the Household Labour Force Survey for more information on the sample rate and response rates.

##LCI

The impact of the minimum wage change

The adult minimum wage increased from $15.25 an hour to $15.75 an hour (3.3 percent increase) on 1 April 2017. For the June 2017 quarter, 16 percent of all surveyed salary and ordinary time wage rates increased – 4 percent of rates increased due to the minimum wage increase. If the wages that increase to the new minimum wage had not changed, the LCI including overtime would have increased by 1.6 percent. The effects can also be seen within some industry and skill-level breakdowns.

In the year to the June 2017 quarter, retail trade and accommodation (industry group GH) increased 1.6 percent. If we had processed the increases due to the minimum wage increase in the LCI as no change, then in the June 2017 quarter, retail trade and accommodation (industry group GH) would have increased 1.4 percent.

In the year to the June 2017 quarter, skill level 5 increased 1.7 percent. This level includes occupations that require a New Zealand Register level 1 qualification, no qualification, or a short period of on-the-job training (eg clerical and administrative workers, labourers, sales workers). If the increases due to the minimum wage increase were treated as unchanged, wage rates for skill level 5 occupations would have risen 1.5 percent for the year.

##HLFS

Additional underutilisation series available

The June 2017 quarter Labour Market release includes the release of three new series of underutilisation measurements from the redeveloped Household Labour Force Survey, each explained in more detail below.

• Underutilisation by Sex: Seasonally Adjusted
• Underutilisation by Sex by Regional Council
• Underutilisation by Ethnicity by Sex

Underutilisation measures, together with labour market indicators, provide a picture of labour underutilisation in New Zealand, existing and potential labour resources, and the extent to which they are underutilised. (See the paper Introducing underutilisation in the labour market for more information.) The three new series are available on Infoshare.

Underutilisation by sex: seasonally adjusted

The new seasonally adjusted underutilisation series has removed seasonal movements from the underutilisation time series to increase the ability to analyse movements between quarters. Analysis and diagnostics were carried out to identify which components of the underutilisation measures displayed seasonality.

The table below presents the results of this analysis by each component of underutilisation. Included is how each component is calculated and if the new seasonally adjusted estimates published are; directly adjusted (some previously published), indirectly adjusted as the composite of at least one directly adjusted series, or the actual unadjusted series due to the absence of seasonality. Mainly, the ‘unavailable jobseekers’ component displayed seasonality and is therefore now directly seasonally adjusted, feeding into the calculation of other underutilisation measures. (See the ‘Time Series Estimate’ section of Household Labour Force Survey sources and methods: 2016 for more information on our seasonal adjustment methods.

The new seasonally adjusted underutilisation series are now published in Table 12 ‘Underutilisation by sex, seasonally adjusted series’ of the ‘Household Labour Force Survey tables,’ replacing the previously published Table 12 ‘Underutilisation by sex’ which were unadjusted estimates.

Underutilisation by sex by regional council

Previously we published underutilisation by sex by regional council for the three key regions; Auckland, Wellington and Canterbury. We are now publishing underutilisation measures for the remaining regional councils, as well as the north and south islands. Both quarterly and annual series are available.

Underutilisation by ethnicity by sex

The underutilisation measures are now published by ethnicity, on a total response basis. If a respondent self-identifies as multiple ethnicities they will be included in all ethnicity breakdowns they identify with. Quarterly series are available.

###Outliers 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

###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 June 2017 quarter was 19.3 percent. This compares with 17.8 percent in the March 2017 quarter and 18.2 percent in the June 2016 quarter.

Investigation into the change in employment over the June 2016 quarter

This note provides information on our investigation into the changes that were potentially introduced into our employment indicators, as a result of the redeveloped Household Labour Force Survey (HLFS).

In the June 2016 quarter, the seasonally adjusted number of employed people increased by 2.4 percent, by far the largest quarterly increase ever recorded in the HLFS. While other labour market indicators supported the direction of employment growth, the magnitude of this increase needed to be explored in terms of real world changes versus changes to the survey.

In the project to redevelop the HLFS, options were explored around how the new survey would be introduced into the field, including whether a dual run could be done for a specific period of time. The logistics and cost involved meant that there were no feasible options for doing so. We agreed that our approach would be to do an assessment after several quarters of new HLFS data to determine if there had been any structural changes in key time series as a result of the redevelopment, and to estimate this effect to the best of our ability before investigating options for adjusting the time series (if possible). Unfortunately we had no information prior to the collection of the June 2016 quarter that suggested we might see such a large change in employment.

Some of the increase in employment over the March to June 2016 quarter could be attributed to changes in the HLFS collection. The inclusion of armed forces personnel in the survey population contributed to this increase. Specifically, people living in households and employed in the armed forces were no longer considered ineligible to participate in the survey. The other component of the increase that carries a much higher degree of uncertainty, is the observation that the new HLFS appears to be more likely to identify people as employed, whereas in the old survey they may have been identified as ‘not in the labour force’. Our analysis to date suggests this was particularly apparent with self-employed people. As part of our analysis, we were able to confront the new ‘job tenure’ information in the HLFS to confirm that some people previously identified as not working had in fact been running their own business for a number of years.

However, finding a statistically sound method of disentangling survey changes from real world changes is a challenge. Particularly when taking into account the level of uncertainty that already exists in our employment series – the absolute sampling error is typically around 22,000 people. In order to ‘correct’ for the possible effects of the new survey, there are essentially three things that need to be done:

1. producing an estimate of the size of the ‘level shift’ that occurs between the March and June 2016 quarters,
2. determining a method for estimating the size of this shift at various points along the HLFS time series
3. determining how estimates of the level shift would be implemented; whether this would be done at the unit record level or only to selected published series.

This summary focuses specifically on the first component of this, and what we concluded from our investigation.

Estimating the magnitude of the level shift

For this analysis we prioritised the following seasonally adjusted series:

1. Male employed
2. Female employed
3. Male Not in the labour force (NILF)
4. Female NILF
5. Total actual hours worked
6. Total usual hours worked
7. Employed full-time
8. Employed part-time

Here we will focus specifically on male and female employed but similar results are expected for the other six series.The male, female and total employed series were studied by fitting a time series model (i.e. the underlying data generating process) with a variable to estimate the possible change in the level of the series. Two different methods were used, which presented similar results. The analyses were done independently and the results presented in Table 1. The two models used are described below:

1. An ARIMA model. This is how we would allow for the effect if we wished to add it to our seasonal adjustment system.
2. An Unobserved Components Model (UCM). This is similar to the model we use when seasonally adjusting.

Table 1. Table presenting the level shift estimates for each time series with 95% Confidence Intervals (values in thousands of employed).

Our unadjusted published estimate for total employed in the June 2016 quarter was 2,454,000 people, suggesting that there had been an increase of 45,000 people when compared to March 2016. The UCM method estimated that the shift in employment due to the 2016 redevelopment was between 29,000 and 77,000. Whilst the ARIMA method estimates this effect as being between 7,000 and 92,000 people. The results highlight the large degree of uncertainty in estimating the level shift, and in turn, the uncertainty in whether applying an adjustment would definitively improve the accuracy and comparability of the time series.

The investigation into the impact of the redevelopment on the employed series revealed the following:

1. There is evidence of a level shift in the employed series between March and June 2016.
2. The trend and seasonally adjusted series are unlikely to be impacted by the level shift. The use of moving averages to estimate the trend and seasonal components by the seasonal adjustment program, X-13-ARIMA-SEATS, effectively mitigates any impact the level shift would have on seasonally adjusted outputs.
3. The uncertainty associated with the level shift estimates presents a challenge in making inferences about how much of the changes between the March and June 2016 quarters can be attributed to the re-development
4. Applying any adjustments to the time series prior to June 2016 would require further investigation into how the changes from the re-development may have impacted the labour market series at different time points. The time and resources required for this work, along with the risk appetite for implications on the accuracy and comparability of the time series should be weighed against the potential value added by this intervention.

##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:

• Household Labour Force Survey
• Labour Cost Index
• Quarterly Employment Survey