RDP 2020-07: How Many Jobs Did JobKeeper Keep? 4. Data

The challenge in estimating the effect of JobKeeper on employment is that it is hard to disentangle the effect of JobKeeper from the effects of everything else that is happening in the labour market. Approaches that focus on aggregate time series data are not up to the task – there are simply too many confounding factors, especially during a period characterised by a global pandemic and the largest peacetime contraction in the Australian economy in nearly a hundred years.

Another challenge is that JobKeeper is a demand-driven program: firms more adversely affected by COVID-19 were more likely to qualify for the program than those less affected. Receiving JobKeeper helped businesses retain employees but also signalled that the firm expected or had already experienced a material decline in turnover; this leads to a reverse causality bias that needs to be accounted for when estimating the effects of JobKeeper on employment. Controlling for this reverse causation would be a difficult task using time series approaches.

Micro data allows researchers to more credibly isolate the contribution of JobKeeper to employment outcomes, holding constant all the ‘third factors’ that would otherwise bias their estimates. In this paper we use the person-level data from the LFS (known as the Longitudinal LFS, or LLFS). These data have all of the ingredients we need to identify the causal effect of JobKeeper on employment, such as:

  • A panel dimension: the survey follows people over time (every month for up to eight months). This means that we can track workers who were employed before JobKeeper was announced in late March, to see how they fared over the April to July period – that is, the first four months of the scheme.
  • Worker-eligibility criteria: the LLFS has information on the key elements used to determine if the worker passed the worker-eligibility test for JobKeeper in their main job, such as whether they were employed on a casual basis and their job tenure.[13]
  • Labour market outcomes: workers are classified according to the official measures of employment and unemployment in Australia, which means our results more easily map to the official statistics. Unlike most administrative sources (e.g. the Single Touch Payroll data), the LLFS also measures hours worked.
  • A rich set of controls: the LLFS collects data on the industry, occupation and other characteristics of workers that allow us to hone in on the target population of interest and also to control for other economic shocks.
  • Timely: the ABS updates the LLFS micro data around a fortnight after the associated LFS release.

Although the LLFS does not identify JobKeeper recipients directly, we can still use the data to estimate the causal effect of JobKeeper on employment. We can do this because the LLFS provides the main criteria used to determine if an individual passed the worker-eligibility test in their main job. When combined with external data on the fraction of worker-eligible individuals who actually received JobKeeper (namely, those employed at firms that passed the firm-eligibility test and enrolled in the program), this information can be used to estimate the causal effect of JobKeeper on employment. We explain our strategy for constructing this estimate in detail below.

It is worth noting that information on JobKeeper worker eligibility is only available for a person's ‘main job’. In the LFS, a person's main job is the job in which they usually work the most hours. For this reason, any subsequent references to ‘jobs’ refer to ‘main jobs’ unless indicated otherwise.[14]


Casual employment status is inferred based on the reported absence of paid leave entitlements (either paid holiday leave or paid sick leave). According to the ABS (2015), this is ‘the most objective and commonly used measure of casual employment’. [13]

In Appendix D.6, we show that our results are unlikely to be materially affected by our focus on main jobs rather than both main and second jobs. [14]