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Underemployment rates in New Zealand
Key findings

Underemployment data can demonstrate what industries and occupations are not providing workers with the amount of work they are aiming for, or the industries and occupations that are oversaturated with workers. This can provide insight into industries that have the capacity for more workers and those that have enough for the interim. It was found that hospitality, retail and recreation industries were among those with the highest underemployment rates. Individuals that were sales workers, labourers or worked in community service reported to be dissatisfied with their number of working hours. Industries such as mining, financial and insurance services, infrastructure, construction and manufacturing have workers that are mostly satisfied with their hours. These industries likely have room or even a need for more workers. The charts below can be used to explore specific sectors of interest.

Introduction

This study breaks down the underemployment rate by industry and occupation to provide insight into the makeup of New Zealand’s underemployed. An individual classed as underemployed is someone that is employed and would prefer to work more hours. This information can point out industries that are oversaturated with workers, or industries that may need more workers. This may also be useful for training organisations to understand the current demand for workers in certain industries.

Methodology

Underemployment data is collected from the Household Labour Force Survey (HLFS). An individual will be classed as underemployed if they wish for more hours of work. It does not measure skill-related underemployment, e.g. someone with a master’s degree in engineering that is employed at a fast food restaurant.

Note that in the dashboards some industries and occupations are marked with a 0% underemployment rate. This is because fewer than 6 survey respondents with that occupation or industry reported being underemployed and should be treated as unknown/suppressed rather than 0. Margin of errors are available in the data download.

Underemployment by industry

The chart below breaks down the underemployment by industry. Industry level represents how broad the industry category is, level 1 being the broadest. The hospitality, retail and recreation industries have high rates of underemployment. Individuals working in mining, infrastructure, financial and insurance services, construction and manufacturing industries as mostly satisfied with their work hours.

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Methodology

The Household Labour Force Survey (HLFS) asks employed respondents if they would prefer to work more hours. We summarize these results over years industries have been recorded (2009 to 2018) and calculate underemployment rates.

Sectors with 0% values have fewer than 6 persons responding to the HLFS as underemployed. Results from these sectors should be treated as unknown but are left in the chart to show where data is incomplete.

Industries are defined by ANZSIC06 codes.

Underemployment by occupation

This chart breaks down the underemployment rate by occupation. Occupation level corresponds to how broad the categories are, level 1 being the broadest. Community service, sales workers and labourers have high rates of underemployment. On the other hand, there are few managers, trades workers, professionals and machinery operators are dissatisfied with their hours of work.

Download data

Methodology

The Household Labour Force Survey (HLFS) asks employed respondents if they would prefer to work more hours. We summarize these results over years occuaptions have been recorded (2009 to 2018) and calculate underemployment rates.

Sectors with 0% values have fewer than 6 persons responding to the HLFS as underemployed. Results from these sectors should be treated as unknown but are left in the chart to show where data is incomplete.

Occupations are defined by ANZSCO06 codes.

Disclaimer

Access to the data used in this study was provided by Stats NZ under conditions designed to give effect to the security and confidentiality provisions of the Data and Statistics Act 2022. The results presented in this study are the work of the author, not Stats NZ or individual data suppliers.

These results are not official statistics. They have been created for research purposes from the Integrated Data Infrastructure (IDI) which is carefully managed by Stats NZ. For more information about the IDI please visit https://www.stats.govt.nz/integrated-data/.

The results are based in part on tax data supplied by Inland Revenue to Stats NZ under the Tax Administration Act 1994 for statistical purposes. Any discussion of data limitations or weaknesses is in the context of using the IDI for statistical purposes, and is not related to the data's ability to support Inland Revenue's core operational requirements.

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