Authors: Joey Crowley-Shaw, Fred Litchfield & Tom Jackson
There is significant demand for high quality agricultural workforce data in Australia. This data is used by governments, industry bodies, research organisations and the farming community to measure and understand trends in labour markets and the impact of shocks such as the COVID-19 pandemic, and to support policy making and industry planning.
Australian agricultural workforce data is currently supplied in different forms by government agencies, industry bodies, researchers and other agricultural stakeholders to varying degrees of quality and usefulness. Data quality relates to not only the trustworthiness of the data, but also its relevance to particular topics and other characteristics such as spatial detail and timeliness.
This stocktake identifies sources of publicly available agricultural labour data in Australia and current information gaps. A dashboard presents the main sources of Australian agricultural labour data that are currently available, shows which questions each data source can answer, and provides information about the quality of each data source and its usefulness to policy makers and industry.
It is important to note that this stocktake may not be fully exhaustive and is current as at 30 June 2021. The dashboard will be updated intermittently if/when new data sources are made available.
How to use this dashboard
- Select the topic of interest, then select the question of interest. Note the traffic light score detailing how well the available data addresses this question.
- Then down the page, click on a data source from the list available.
- Click on the selected data source at the bottom of the dashboard, then read through the different attributes and data quality of that data source.
The PowerBI dashboard may not meet accessibility requirements. For information about the content of this dashboard contact ABARES.
In this stocktake, data quality is measured using the ABS Data Quality Framework (ABS 2009), which consists of seven dimensions of data quality:
- Relevance (provides useful information for the agricultural sector)
- Timeliness (reporting has a minimal lag time from when the data was collected)
- Accuracy (the data collection process is statistically sound and analysis is reliable)
- Coherence (the data is consistent over time and can be compared with other sources)
- Interpretability (the data is available, including appropriate metadata and definitions)
- Accessibility (data can be easily obtained in suitable formats)
- and Institutional environment (the credibility and objectivity of the data provider is sound).
Each data source is assessed against the criteria of providing quality information for the purpose of answering key questions about the Australian agricultural workforce. The comment on data quality specific to agricultural labour should not be interpreted as an overall judgement of the quality of the data source, as many data sources were not custom built to provide information on agriculture or agricultural labour markets.
Labour Data Gaps
Most of the demand for agricultural labour data relates to understanding the dynamics of the labour market – specifically the demand and supply of farm workers. Demand for this data has increased recently because of the COVID-19 pandemic, which has reduced the availability of farm workers from overseas and placed restrictions on the movement of Australian farm workers.
This stocktake indicates that data currently exists at varying levels of quality, and that some data gaps prevent a complete understanding of the agricultural labour market and emerging issues. There are also topics for which information is available, but the data lacks timeliness, reliability, or relevance. This is particularly the case for datasets which are not built for the purpose of understanding the agricultural labour market.
Where there are gaps in data availability, this can cause adverse outcomes such as being unable to adequately predict current and emerging overall workforce demand, and the demand for specific skill sets. It also means there can be challenges in providing sufficient services to support the agricultural workforce, due to the inability to sufficiently identify some parts of the workforce in current datasets.
The following demands for data are currently unsatisfied or only partially met.
There is no data source that covers labour productivity of the entire agricultural workforce in Australia. There are select studies about differences in labour productivity between Seasonal Worker Programme participants and Working Holiday Maker (backpacker) workers, however these do not extend to the whole workforce and only cover specific industries. It is possible to impute some information about labour productivity through current available data sources and through linking of data, however this is complicated as there are no custom-built datasets.
Employment data across the agricultural value chain
The vast majority of data available on employment by industry and occupation utilises ANZSIC and ANZSCO classifications. Current ANZSCO classifications make it difficult to count the number of workers across the agricultural supply chain as workers are classified into other industries such as transport.
There is some data on skill shortages in the agricultural workforce, but not a detailed or complete list. Furthermore, the information available tends to focus on common occupations with relatively high skill levels. There is some information on business perceptions of how large a problem skill shortages are generally, but these do not cover specific occupations.
There is currently very limited data on the issues of workforce retention or workforce training utilisation.
There is no data currently available that covers this topic across the whole agricultural workforce. Most data currently available focuses on the experiences of overseas workers and temporary residents, with the data provided on this group anecdotal and not statistically representative.
At present there are very few data sources available which provide agricultural employment projections. Some information exists on estimates of the number of long-term jobs in agriculture, but with no industry or regional disaggregation.
Australian Bureau of Statistics (ABS) 2009, ABS Data Quality Framework, cat 1520.0, Canberra, May.