Worker turnover rate in the agriculture, forestry and fishing industry in New Zealand
By sex, 2000 Q4–2022 Q4, % of jobs
| Quarter | Sex | % of total jobs |
|---|---|---|
| 2022 Q4 | Male | 24.9% |
| 2022 Q4 | Female | 27.7% |
| 2022 Q3 | Male | 24.8% |
| 2022 Q3 | Female | 28.3% |
| 2022 Q2 | Male | 22.4% |
| 2022 Q2 | Female | 27.8% |
| 2022 Q1 | Male | 20.9% |
| 2022 Q1 | Female | 29% |
| 2021 Q4 | Male | 22.5% |
| 2021 Q4 | Female | 28.9% |
| 2021 Q3 | Male | 24.9% |
| 2021 Q3 | Female | 30% |
| 2021 Q2 | Male | 24% |
| 2021 Q2 | Female | 31.4% |
| 2021 Q1 | Male | 22.2% |
| 2021 Q1 | Female | 30.1% |
| 2020 Q4 | Male | 23.4% |
| 2020 Q4 | Female | 30.6% |
| 2020 Q3 | Male | 24.2% |
| 2020 Q3 | Female | 29.7% |
| 2020 Q2 | Male | 22.9% |
| 2020 Q2 | Female | 29.6% |
| 2020 Q1 | Male | 25.6% |
| 2020 Q1 | Female | 32.7% |
| 2019 Q4 | Male | 27.5% |
| 2019 Q4 | Female | 32.1% |
| 2019 Q3 | Male | 27.9% |
| 2019 Q3 | Female | 32.3% |
| 2019 Q2 | Male | 26.6% |
| 2019 Q2 | Female | 32.2% |
| 2019 Q1 | Male | 25.4% |
| 2019 Q1 | Female | 33% |
| 2018 Q4 | Male | 26.4% |
| 2018 Q4 | Female | 31.8% |
| 2018 Q3 | Male | 28% |
| 2018 Q3 | Female | 32.7% |
| 2018 Q2 | Male | 27.2% |
| 2018 Q2 | Female | 33.2% |
| 2018 Q1 | Male | 26.6% |
| 2018 Q1 | Female | 34.8% |
| 2017 Q4 | Male | 26.7% |
| 2017 Q4 | Female | 32.1% |
| 2017 Q3 | Male | 29.1% |
| 2017 Q3 | Female | 32.9% |
| 2017 Q2 | Male | 26.5% |
| 2017 Q2 | Female | 31.7% |
| 2017 Q1 | Male | 25.1% |
| 2017 Q1 | Female | 32.2% |
| 2016 Q4 | Male | 27.3% |
| 2016 Q4 | Female | 31.4% |
| 2016 Q3 | Male | 29% |
| 2016 Q3 | Female | 32.5% |
| 2016 Q2 | Male | 27.4% |
| 2016 Q2 | Female | 32.6% |
| 2016 Q1 | Male | 26.1% |
| 2016 Q1 | Female | 32.6% |
| 2015 Q4 | Male | 26.7% |
| 2015 Q4 | Female | 31.4% |
| 2015 Q3 | Male | 29.5% |
| 2015 Q3 | Female | 32.2% |
| 2015 Q2 | Male | 26.8% |
| 2015 Q2 | Female | 31.8% |
| 2015 Q1 | Male | 25.2% |
| 2015 Q1 | Female | 31.8% |
| 2014 Q4 | Male | 26.5% |
| 2014 Q4 | Female | 31.1% |
| 2014 Q3 | Male | 30% |
| 2014 Q3 | Female | 32.9% |
| 2014 Q2 | Male | 27% |
| 2014 Q2 | Female | 32% |
| 2014 Q1 | Male | 26.9% |
| 2014 Q1 | Female | 32.8% |
| 2013 Q4 | Male | 27% |
| 2013 Q4 | Female | 31.6% |
| 2013 Q3 | Male | 29.6% |
| 2013 Q3 | Female | 32.5% |
| 2013 Q2 | Male | 26.3% |
| 2013 Q2 | Female | 31.6% |
| 2013 Q1 | Male | 25.5% |
| 2013 Q1 | Female | 31.7% |
| 2012 Q4 | Male | 26.5% |
| 2012 Q4 | Female | 31.1% |
| 2012 Q3 | Male | 29.9% |
| 2012 Q3 | Female | 32.5% |
| 2012 Q2 | Male | 27.5% |
| 2012 Q2 | Female | 31.4% |
| 2012 Q1 | Male | 28.2% |
| 2012 Q1 | Female | 33.6% |
| 2011 Q4 | Male | 27.4% |
| 2011 Q4 | Female | 30.8% |
| 2011 Q3 | Male | 31.4% |
| 2011 Q3 | Female | 32.4% |
| 2011 Q2 | Male | 28.5% |
| 2011 Q2 | Female | 32.4% |
| 2011 Q1 | Male | 27.6% |
| 2011 Q1 | Female | 32.2% |
| 2010 Q4 | Male | 27.7% |
| 2010 Q4 | Female | 31.7% |
| 2010 Q3 | Male | 30.4% |
| 2010 Q3 | Female | 31.8% |
| 2010 Q2 | Male | 29.8% |
| 2010 Q2 | Female | 33.1% |
| 2010 Q1 | Male | 27.2% |
| 2010 Q1 | Female | 31.7% |
| 2009 Q4 | Male | 26.9% |
| 2009 Q4 | Female | 30.3% |
| 2009 Q3 | Male | 30.4% |
| 2009 Q3 | Female | 31.3% |
| 2009 Q2 | Male | 29% |
| 2009 Q2 | Female | 32.3% |
| 2009 Q1 | Male | 30.2% |
| 2009 Q1 | Female | 33.8% |
| 2008 Q4 | Male | 29% |
| 2008 Q4 | Female | 32.2% |
| 2008 Q3 | Male | 32.9% |
| 2008 Q3 | Female | 34.4% |
| 2008 Q2 | Male | 30.6% |
| 2008 Q2 | Female | 34.4% |
| 2008 Q1 | Male | 30.6% |
| 2008 Q1 | Female | 35.2% |
| 2007 Q4 | Male | 29.9% |
| 2007 Q4 | Female | 32.6% |
| 2007 Q3 | Male | 33.1% |
| 2007 Q3 | Female | 34.7% |
| 2007 Q2 | Male | 31.2% |
| 2007 Q2 | Female | 34.6% |
| 2007 Q1 | Male | 30.8% |
| 2007 Q1 | Female | 34% |
| 2006 Q4 | Male | 30% |
| 2006 Q4 | Female | 33.1% |
| 2006 Q3 | Male | 32.6% |
| 2006 Q3 | Female | 33.6% |
| 2006 Q2 | Male | 30.3% |
| 2006 Q2 | Female | 33.7% |
| 2006 Q1 | Male | 29.6% |
| 2006 Q1 | Female | 33.7% |
| 2005 Q4 | Male | 28.8% |
| 2005 Q4 | Female | 32% |
| 2005 Q3 | Male | 33.1% |
| 2005 Q3 | Female | 34.8% |
| 2005 Q2 | Male | 32.5% |
| 2005 Q2 | Female | 36.6% |
| 2005 Q1 | Male | 30.6% |
| 2005 Q1 | Female | 34.5% |
| 2004 Q4 | Male | 29.2% |
| 2004 Q4 | Female | 33% |
| 2004 Q3 | Male | 32.4% |
| 2004 Q3 | Female | 34.8% |
| 2004 Q2 | Male | 30.5% |
| 2004 Q2 | Female | 35.3% |
| 2004 Q1 | Male | 30.5% |
| 2004 Q1 | Female | 34.7% |
| 2003 Q4 | Male | 29.9% |
| 2003 Q4 | Female | 33.3% |
| 2003 Q3 | Male | 32.8% |
| 2003 Q3 | Female | 34.8% |
| 2003 Q2 | Male | 30.1% |
| 2003 Q2 | Female | 34.5% |
| 2003 Q1 | Male | 29.7% |
| 2003 Q1 | Female | 33.4% |
| 2002 Q4 | Male | 29.5% |
| 2002 Q4 | Female | 32.3% |
| 2002 Q3 | Male | 34.7% |
| 2002 Q3 | Female | 36% |
| 2002 Q2 | Male | 34.6% |
| 2002 Q2 | Female | 38% |
| 2002 Q1 | Male | 32.7% |
| 2002 Q1 | Female | 36.8% |
| 2001 Q4 | Male | 31.4% |
| 2001 Q4 | Female | 34.4% |
| 2001 Q3 | Male | 35.3% |
| 2001 Q3 | Female | 36.4% |
| 2001 Q2 | Male | 33.7% |
| 2001 Q2 | Female | 37.3% |
| 2001 Q1 | Male | 33.9% |
| 2001 Q1 | Female | 37.5% |
| 2000 Q4 | Male | 32.1% |
| 2000 Q4 | Female | 34.8% |
Definitions
Job: a unique employer-employee pair present on an EMS in the reference quarter.
Full-quarter jobs: jobs that exist continuously over the reference quarter.
Total filled jobs: The number of jobs (defined as an employer-employee match) on the 15th of the middle month of the reference quarter. Does not distinguish between part-time and full-time jobs.
Accessions: The number of employees who have joined employers since the previous reference date.
Separations: The number of employees who have left employers since the previous reference date.
Worker turnover rate: The ratio of the average of the total accessions and separations to the average of the total jobs in the reference quarter (t) and the previous quarter (t-1), as represented in the formula:
[ (accessions + separations)/2 ] / [ (jobs(t) + jobs(t-1))/2 ].
Job creation: The number of jobs created, since the previous reference date, when businesses expand or start up. For example, a business employing 100 workers with 10 accessions and five separations has job creation of five.
Job destruction: The number of jobs lost, since the previous reference date, when businesses contract or shut down. For example, a business employing 100 workers with five accessions and 15 separations has job destruction of 10.
Job turnover rate: The ratio of the average of the total creations and destructions to the average of the total jobs in the reference quarter (t) and the previous quarter (t-1), as represented in the formula:
[ (creation + destruction)/2 ] / [ (jobs(t) + jobs(t-1))/2 ].
Mean/median earnings: Mean (average) or median earnings of all full-quarter jobs.
Mean/median earnings for continuing jobs: Mean (average) or median earnings for jobs that were full-quarter in the reference quarter and previous quarters.
Mean/median earnings for new hires: Mean (average) or median earnings for jobs that were full-quarter in the reference quarter and began sometime in the previous quarter, but were not present in the four previous quarters.
Mean/median earnings ratio: The ratio of the mean or median earnings for new hires to the mean or median earnings for continuing jobs.
Total earnings: The sum of all earnings paid in the reference quarter, including employees with invalid IRD identifiers and individuals under 15 years of age.
For more information
http://www.stats.govt.nz/browse_for_stats/income-and-work/employment_and_unemployment/LEED-quarterly-tech-notes.aspx
http://nzdotstat.stats.govt.nz/OECDStat_Metadata/ShowMetadata.ashx?Dataset=TABLECODE7013&Lang=en
Inclusions
LEED covers all individuals (‘employees’) who receive income from which tax is deducted at source. These payments are made by organisations that are registered with Inland Revenue. Note that the data from LEED includes social assistance payments, such as paid parental leave, student allowances, benefits, pensions, and Accident Compensation Corporation payments, although these are excluded from the quarterly measures.
The fundamental basis of the LEED quarterly measures is ‘jobs’.
For inclusion in the LEED quarterly statistics, the job must:
- relate to a person 15 years of age and over
- have PAYE tax deducted at source
- be in relation to ‘paid employment’ rather than a social assistance payment
- have a valid IRD identifier.
An exception is the total earnings measure, which includes all jobs with PAYE tax deducted at source (irrespective of age and IRD identifier) apart from those relating to social assistance payments.
All the earnings measures represent quarterly earnings inclusive of tax. They include payments reported as lump sums to Inland Revenue.
Data provided by
Dataset name
Linked Employer-Employee Dataset: LEED measures by industry (based on ANZSIC06) and sex December 2022 quarter
Webpage:
http://nzdotstat.stats.govt.nz/WBOS/Index.aspx?DataSetCode=TABLECODE7013
How to find the data
Data is displayed at URL provided. All variables were selected to create this dataset. Industries were selected only at the lowest node to prevent NZ.Stat from generating duplicate records.
Import & extraction details
File as imported: Linked Employer-Employee Dataset: LEED measures by industry (based on ANZSIC06) and sex December 2022 quarter
From the dataset Linked Employer-Employee Dataset: LEED measures by industry (based on ANZSIC06) and sex December 2022 quarter, this data was extracted:
- Rows: 2-63,556
- Column: 5
- Provided: 63,555 data points
This data forms the table Jobs - All LEED measures by industry and sex 1999 Q2–2022 Q4.
Dataset originally released on:
February 26, 2024
About this dataset
The LEED dataset is created by linking a longitudinal employer series from the Stats NZ Business Register to a longitudinal series of EMS payroll data from Inland Revenue.
Purpose of collection
Official quarterly statistics produced from LEED measure labour market dynamics at various levels – including industry, region, territorial authority, firm size, sector, sex, and age – providing an insight into the operation of New Zealand's labour market. Stats NZ releases other official labour market statistics that show changes in employment at an aggregate level. Statistics from LEED, such as job and worker flows, help to explain what causes these aggregate movements and are therefore useful for explaining changes in the labour market.