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Quarterly Employment Survey (QES) resource

This page is a learning resource for the Quarterly Employment Survey (QES). It outlines the purpose of the survey and its main uses, and provides a few practical exercises relating to the survey.

What is the Quarterly Employment Survey?

The Quarterly Employment Survey (QES) is a sample survey of businesses that employ more than two people. This survey is carried out every three months. It is designed to produce estimates of average hourly and weekly earnings, average weekly paid hours, and the number of filled jobs and full-time equivalents, by industry. Estimates can also be broken down by sex, ordinary and over-time paid hours, and can be produced for most regions.


How is the data collected?

Each quarter, 3,500 businesses in 18,000 different locations are surveyed. These businesses include a range of industries. Each of the 18,000 locations is asked to provide details of the number of its male and female, and full and part-time employees, and the hours and earnings of those employees. The reference period is the payweek immediately before the 20th of the middle month of the quarter.


How is the QES used?

The QES is the most timely source of information on earnings, hours, and filled jobs by industry. It is the only source of information on paid hours by industry, which can be broken down by ordinary time and over-time. The QES also provides information on the number of part-time and full-time jobs and full-time equivalents each quarter. Information from the QES is an economic indicator of the volume of labour used in each quarter, and measures from the QES, such as hours and earnings, are used in the production of quarterly gross domestic product (GDP) and in productivity statistics.

Movements in the QES measures ‘average hourly earnings’ and ‘average weekly earnings’, which are used in legislation to benchmark figures such as the level of National Superannuation, Accident Compensation Corporation (ACC) levies and payments, paid parental leave, and the calculation of Child Support.

Read our Labour Market Statistics information release for quarterly data from the QES.


What are the common confusions?

The QES measure ‘average hourly earnings’ can be confused with the labour cost index (LCI). The LCI measures changes in pay rates between quarters for a fixed quantity and quality of work (that is, the LCI excludes pay increases gained due to the acquisition of qualifications or a promotion). The QES measure ‘average hourly earnings’ is calculated by adding the total wage payout for all businesses and dividing this sum by the total number of hours worked across all businesses. Changes in average hourly earnings in the QES therefore, can mean a pay rise OR an increase or decrease in the number of hours worked in a higher or lower paying industry.

The number of filled jobs from the QES is different from ‘employment’ in the Household Labour Force Survey (HLFS). The HLFS is a survey of 15,000 households, where each person aged over 15 years in each household is asked whether they are in the labour force, and if they are employed (among other things). Employment in the HLFS is thus, a count of people rather than jobs. Hence, when unemployment increases it is wrong to say that people have lost their jobs. We cannot know this for sure as the increase in unemployment might be due to people coming into the labour force and not being able to find a job (for example, a married woman re-entering the workforce or school leavers). For more on the HLFS, see the Household Labour Force Survey learning resource.

In the same way, when filled jobs increase it is not correct to say that more people are working – it may be that people have taken on extra jobs, or the self-employed may have taken on paid employment as well.

There is a difference between filled jobs and full-time equivalents. A filled job in the QES is the total of all the employees at each business location, plus the number of working proprietors (see Statistical calculations – definitions below) counted by the survey. If an individual is employed at more than one location, then that person will be counted at each location. Filled jobs are a count of jobs not people. This measure excludes positions held by those in ‘working proprietor’ only businesses.

Full-time equivalents (FTEs) are calculated by adding the total number of full-time employees in the survey and half the number of part-time employees.


Statistical calculations


Total weekly gross earnings – The total of all wages paid (both ordinary and overtime) in the reference payweek.

Total weekly paid hours – The total of all the paid hours (both ordinary and overtime) in the reference payweek.

Full-time employee – An employee who regularly works more than 30 hours per week.

Part-time employee – An employee who regularly works less than 30 hours per week.

Working proprietor – Someone who is self employed in an economically significant business.

Full-time equivalents – The sum of all full-time employees plus half the number of part-time employees.

Filled jobs – All full-time and part-time employees plus those working proprietors who also employ others.

Average hourly earnings – Total ordinary and overtime pay for the reference week divided by the total paid hours recorded for the week.

Average weekly earnings – Total ordinary and overtime pay for the reference week divided by the number of FTEs.

Weighted averages

Weighted averages are used to adjust numbers according to the different degrees of importance of the items these numbers represent. Examples of weighted averages are the average hourly earnings and average weekly earnings in the QES.

The total estimates for average hourly earnings and average weekly earnings are basically the sum of the weighted contributions from each industry group. The weighted contribution of each industry group is measured by multiplying the industry's average earnings by its share of paid hours (for average hourly earnings) or by paid employees (for average weekly earnings). This way, the total estimate includes the effects of both the average hourly earnings in an industry, and the industry's share of total hours. Using the retail trade industry as an example, the average hourly earnings are lower than the average for all industry groups combined. If this industry had a significant increase in total ordinary time paid hours, and all other industries showed no change, then the weighted contribution of all the other industries would decrease relative to the contribution of the retail trade industry. The contribution of the retail trade industry would increase, and therefore, more weight would be given to its ordinary time hourly earnings. This could cause the average for all industries combined to decrease, even though the contribution from the retail trade industry was positive. This average can decrease because there has been a relative decrease in the contribution from higher paid industries.

This is why it is useful to look at the drivers behind the changes in the average hourly earnings and average weekly earnings in the QES. These drivers are mentioned in the media releases that accompany the publication of survey results.



The effect of weighted contributions can be demonstrated in an example with two industries – A and B.

Table 1

Effect of weighted contributions
Period Industry Average hourly earnings
Total hours paid Hours contribution Weighted contribution to average hourly earnings
1 Industry A 10.00 120 0.43 4.29
Industry B 50.00 160 0.57 28.57
Total 32.86 280 1.00 32.86
2 Industry A 11.67 120 0.33 3.89
Industry B 50.00 240 0.67 33.33
Total 37.22 360 1.00 37.22

The hours contribution is calculated by dividing the total hours paid for a specific industry by the total hours paid for all industries.

 Hours contribution =  Total hours paid for a specific industry
 Total hours paid for all industries

For example, hours contribution for industry A at period 1 = 120 ÷ 280 = 0.43.

The weighted contribution to average hourly earnings is calculated by multiplying the hours contribution by the average hourly earnings for a specific industry.

Weighted contribution to = Hours contribution x average hourly earnings
average hourly earnings

For example, weighted contribution to average hourly earnings for industry B at period 2 = 50.00 x 0.67 = $33.33. Note that $33.33 comes from using the unrounded hours contribution.

Important things to note:

  • Total average hourly earnings have risen by 13.3 percent, from $32.86 to $37.22. The increases at the industry level are 16.7 percent and 0 percent for industries A and B respectively. However, the weighted contribution of industry A to average hourly earnings actually decreased in period 2.
  • Even though industry A had an increase in average earnings in period 2 and industry B did not, industry A made a negative contribution to the total overall movement, while industry B made a positive contribution. This happened because industry B has higher average hourly earnings than industry A, and industry B's already high share of total paid hours increased in period 2.
  • This example shows that rather than changes in wages, other changes, such as those in weighted contributions can affect average earnings at the national level.


1) Calculate the missing hours contribution and weighted contribution to average hourly earnings.

2) Looking at the completed table, what are the important things to note here?

Table 2

Effect of Weighted Contributions
Quarter Industry Average hourly earnings
Total hours paid Hours Contribution Weighted contribution to average hourly earnings
March 2008 Forestry & mining 25.27 435,600 0.8 0.2
Manufacturing 22.41 8,946,000 16.8 3.8
Electricity, gas & water 34.02 284,900 0.5 0.2
Construction 21.87 4,472,100 8.4 1.8
Wholesale trade 24.45 4,059,800 7.6 1.9
Retail trade 15.94 6,053,100 a 1.8
Accommodation, cafes & restaurants 15.21 2,404,000 4.5 0.7
Transport, storage & communication 23.19 3,858,100 7.3 1.7
Finance & insurance 30.98 1,779,300 3.3 1.0
Property & business services 26.65 6,450,500 12.1 b
Government admin. & defence 30.93 2,146,400 4.0 1.3
Education 30.31 3,712,600 7.0 2.1
Health & community services 25.25 5,127,500 9.7 2.4
Cultural & recreational services 24.19 1,379,500 c 0.6
Personal & other services 23.40 2,011,800 3.8 0.9
Total all industries 23.66 53,121,200 100.0 23.7
March 2009 Forestry & mining 28.08 396,400 0.8 0.2
Manufacturing 22.95 8,023,100 15.4 d
Electricity, gas & water 36.02 281,200 0.5 0.2
Construction 23.19 3,988,000 7.7 1.8
Wholesale trade 25.60 3,923,000 7.5 1.9
Retail trade 16.84 6,121,900 11.7 2.0
Accommodation, cafes & restaurants 15.97 2,274,600 4.4 0.7
Transport, storage & communication 24.20 3,864,600 7.4 1.8
Finance & insurance 33.76 1,730,800 e 1.1
Property & business services 28.60 6,537,000 12.5 3.6
Government admin. & defence 32.05 2,193,500 4.2 1.4
Education 31.11 4,032,700 7.7 2.4
Health & community services 26.53 5,401,700 10.4 f
Cultural & recreational services 25.36 1,346,700 2.6 0.7
Personal & other services 24.21 1,989,800 3.8 0.9
Total all industries 24.91 52,105,100 100.0 24.9


Further reading

Statistics New Zealand (ongoing). Labour Market Statistics – information releases. Wellington: Author.

Statistics New Zealand. Quarterly employment survey – information releases. Wellington: Author.

Statistics New Zealand (2008). User guide for Statistics New Zealand's wage and income measures. Wellington: Author.


1a) 11.4

1b) 3.2

1c) 2.6

1d) 3.5

1e) 3.3

1f) 2.8

2. Important things to note (could include any of the following):

  • From March 2008 to March 2009, total average hourly earnings have increased 5 percent from $23.70 to $24.90.
  • Average hourly earnings have increased for most industries.
  • The increases in average hourly earnings for the industries with the larger weighted contributions were quite small (that is, less than $2).
  • The larger increases in average hourly earnings were made by the industries with small weighted contributions (that is, less than 1 percent)
  • There were increases in the weighted contribution of a few industries, due to an increase in the number of total paid hours (for example, the education and health and community services industries).
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