Average weekly earnings for employees in the construction industry in New Zealand
By sex, ordinary time plus overtime, 2004 Q2–2024 Q2, NZD
Quarter | Sex | NZD |
---|---|---|
2004 Q2 | Female | 543.49 |
2004 Q2 | Male | 754.05 |
2004 Q3 | Female | 550.76 |
2004 Q3 | Male | 756.66 |
2004 Q4 | Female | 566.91 |
2004 Q4 | Male | 777.67 |
2005 Q1 | Female | 570.31 |
2005 Q1 | Male | 780.57 |
2005 Q2 | Female | 555.76 |
2005 Q2 | Male | 769.42 |
2005 Q3 | Female | 621.82 |
2005 Q3 | Male | 786.79 |
2005 Q4 | Female | 598.43 |
2005 Q4 | Male | 796.72 |
2006 Q1 | Female | 611.92 |
2006 Q1 | Male | 799.73 |
2006 Q2 | Female | 590.55 |
2006 Q2 | Male | 797.86 |
2006 Q3 | Female | 584.7 |
2006 Q3 | Male | 799.05 |
2006 Q4 | Female | 607.38 |
2006 Q4 | Male | 836.46 |
2007 Q1 | Female | 622.86 |
2007 Q1 | Male | 848.45 |
2007 Q2 | Female | 625.55 |
2007 Q2 | Male | 851.00 |
2007 Q3 | Female | 641.45 |
2007 Q3 | Male | 858.95 |
2007 Q4 | Female | 658.18 |
2007 Q4 | Male | 898.71 |
2008 Q1 | Female | 675.69 |
2008 Q1 | Male | 909.02 |
2008 Q2 | Female | 666.91 |
2008 Q2 | Male | 922.22 |
2008 Q3 | Female | 708.8 |
2008 Q3 | Male | 896.57 |
2008 Q4 | Female | 683.75 |
2008 Q4 | Male | 919.06 |
2009 Q1 | Female | 704.8 |
2009 Q1 | Male | 953.5 |
2009 Q2 | Female | 684.02 |
2009 Q2 | Male | 901.91 |
2009 Q3 | Female | 668.73 |
2009 Q3 | Male | 932.43 |
2009 Q4 | Female | 595.16 |
2009 Q4 | Male | 930.95 |
2010 Q1 | Female | 631.91 |
2010 Q1 | Male | 942.48 |
2010 Q2 | Female | 614.87 |
2010 Q2 | Male | 936.22 |
2010 Q3 | Female | 665.87 |
2010 Q3 | Male | 943.94 |
2010 Q4 | Female | 658.75 |
2010 Q4 | Male | 973.99 |
2011 Q1 | Female | 676.49 |
2011 Q1 | Male | 978.12 |
2011 Q2 | Female | 704.00 |
2011 Q2 | Male | 978.74 |
2011 Q3 | Female | 699.8 |
2011 Q3 | Male | 1,007.05 |
2011 Q4 | Female | 708.09 |
2011 Q4 | Male | 1,011.59 |
2012 Q1 | Female | 695.56 |
2012 Q1 | Male | 1,020.35 |
2012 Q2 | Female | 725.79 |
2012 Q2 | Male | 1,023.01 |
2012 Q3 | Female | 736.57 |
2012 Q3 | Male | 1,033.46 |
2012 Q4 | Female | 724.21 |
2012 Q4 | Male | 1,052.88 |
2013 Q1 | Female | 719.8 |
2013 Q1 | Male | 1,070.67 |
2013 Q2 | Female | 744.64 |
2013 Q2 | Male | 1,072.3 |
2013 Q3 | Female | 777.07 |
2013 Q3 | Male | 1,047.95 |
2013 Q4 | Female | 761.76 |
2013 Q4 | Male | 1,045.38 |
2014 Q1 | Female | 801.92 |
2014 Q1 | Male | 1,094.51 |
2014 Q2 | Female | 760.44 |
2014 Q2 | Male | 1,069.13 |
2014 Q3 | Female | 843.58 |
2014 Q3 | Male | 1,086.22 |
2014 Q4 | Female | 804.83 |
2014 Q4 | Male | 1,093.9 |
2015 Q1 | Female | 838.27 |
2015 Q1 | Male | 1,114.48 |
2015 Q2 | Female | 792.42 |
2015 Q2 | Male | 1,130.56 |
2015 Q3 | Female | 821.65 |
2015 Q3 | Male | 1,141.59 |
2015 Q4 | Female | 790.84 |
2015 Q4 | Male | 1,151.14 |
2016 Q1 | Female | 803.09 |
2016 Q1 | Male | 1,163.36 |
2016 Q2 | Female | 848.36 |
2016 Q2 | Male | 1,192.22 |
2016 Q3 | Female | 855.76 |
2016 Q3 | Male | 1,177.46 |
2016 Q4 | Female | 763.79 |
2016 Q4 | Male | 1,169.28 |
2017 Q1 | Female | 807.13 |
2017 Q1 | Male | 1,191.8 |
2017 Q2 | Female | 784.86 |
2017 Q2 | Male | 1,180.91 |
2017 Q3 | Female | 805.58 |
2017 Q3 | Male | 1,165.43 |
2017 Q4 | Female | 832.16 |
2017 Q4 | Male | 1,220.33 |
2018 Q1 | Female | 847.87 |
2018 Q1 | Male | 1,216.29 |
2018 Q2 | Female | 900.22 |
2018 Q2 | Male | 1,218.51 |
2018 Q3 | Female | 900.26 |
2018 Q3 | Male | 1,221.21 |
2018 Q4 | Female | 877.22 |
2018 Q4 | Male | 1,258.75 |
2019 Q1 | Female | 883.51 |
2019 Q1 | Male | 1,259.08 |
2019 Q2 | Female | 912.51 |
2019 Q2 | Male | 1,271.11 |
2019 Q3 | Female | 920.51 |
2019 Q3 | Male | 1,266.71 |
2019 Q4 | Female | 971.33 |
2019 Q4 | Male | 1,291.29 |
2020 Q1 | Female | 981.46 |
2020 Q1 | Male | 1,295.48 |
2020 Q2 | Female | 924.26 |
2020 Q2 | Male | 1,215.97 |
2020 Q3 | Female | 947.88 |
2020 Q3 | Male | 1,243.2 |
2020 Q4 | Female | 987.95 |
2020 Q4 | Male | 1,300.69 |
2021 Q1 | Female | 1,118.03 |
2021 Q1 | Male | 1,326.71 |
2021 Q2 | Female | 1,013.21 |
2021 Q2 | Male | 1,336.09 |
2021 Q3 | Female | 1,020.88 |
2021 Q3 | Male | 1,330.58 |
2021 Q4 | Female | 1,030.47 |
2021 Q4 | Male | 1,405.84 |
2022 Q1 | Female | 1,062.36 |
2022 Q1 | Male | 1,356.43 |
2022 Q2 | Female | 1,060.32 |
2022 Q2 | Male | 1,385.66 |
2022 Q3 | Female | 1,094.33 |
2022 Q3 | Male | 1,452.57 |
2022 Q4 | Female | 1,091.26 |
2022 Q4 | Male | 1,487.7 |
2023 Q1 | Female | 1,064.06 |
2023 Q1 | Male | 1,482.93 |
2023 Q2 | Female | 1,124.32 |
2023 Q2 | Male | 1,472.49 |
2023 Q3 | Female | 1,170.31 |
2023 Q3 | Male | 1,579.18 |
2023 Q4 | Female | 1,178.34 |
2023 Q4 | Male | 1,633.54 |
2024 Q1 | Female | 1,219.91 |
2024 Q1 | Male | 1,574.7 |
2024 Q2 | Female | 1,263.11 |
2024 Q2 | Male | 1,601.73 |
Definitions
Average total weekly earnings (Employees): Total earnings (ordinary time plus overtime payout) divided by full-time plus part-time employees . Total earnings include ordinary time earnings ( with ACC earner premiums, bonuses, paid leave and commission) plus overtime pay.
For more information
Limitations of the data
Compositional effects between industries can affect the Quarterly Employment Survey when industries with higher or lower earnings than the average total hourly earnings for all industries change in relative importance (eg make up a bigger share of the total hours).
Compositional changes within industries can affect the Quarterly Employment Survey, as the composition of the paid workforce is reflected (eg the occupations that firms hire).
Inclusions
Because this survey collects data from employers, jobs filled by overseas workers resident in New Zealand for less than 12 months are included. This is different from the Household Labour Force Survey.
Exclusions
The QES does not include data from the agriculture, fisheries, and several smaller industries, or earnings from self-employment.
Changes to data collection/processing
This survey has been redesigned in 2020. Under the new sample design, the level of data collection has changed from business locations (GEOs) to kind-of-activity units (KAUs).
Data provided by
Dataset name
Quarterly Employment Survey: Average Weekly Earnings (Employees) by Industry (ANZSIC06) and Sex (Quarterly) June 2024
Webpage:
https://infoshare.stats.govt.nz/
How to find the data
At URL provided, select 'Work income and spending > Quarterly Employment Survey (QEM) > Average Weekly Earnings (Employees) by Industry (ANZSIC06) and Sex (Qrtly-Mar/Jun/Sep/Dec)'. All variables were selected to create this dataset.
Import & extraction details
File as imported: Quarterly Employment Survey: Average Weekly Earnings (Employees) by Industry (ANZSIC06) and Sex (Quarterly) June 2024
From the dataset Quarterly Employment Survey: Average Weekly Earnings (Employees) by Industry (ANZSIC06) and Sex (Quarterly) June 2024, this data was extracted:
- Rows: 5-146
- Columns: 2-154
- Provided: 21,726 data points
This data forms the table Employment - Average weekly earnings for employees by industry and sex 1989 Q1–2024 Q2.
Dataset originally released on:
August 07, 2024
About this dataset
The Quarterly Employment Survey data provides information about employment, earnings and hours paid at industry and national levels. Data is obtained from economically significant businesses for the reference period of the pay week ending on, or before the 20th of the middle month of the quarter. Use this dataset when wanting to measure the number of filled jobs from a business’s perspective, or when wanting to measure the number of hours businesses pay for.
Purpose of collection
The purpose of the Quarterly Employment Survey (QES) is to provide a short-term indicator of employment and earnings. In addition, the data is used for compiling the business services industry component of the quarterly national accounts (on the production side). Results from the survey provide a valuable guide to the labour market and general economic conditions within New Zealand.
Method of collection/Data provider
The Quarterly Employment Survey (QES) is a sample of approximately 18,000 business locations selected from the population of economically significant enterprises in surveyed industries.