RDP 2016-06: Jobs or Hours? Cyclical Labour Market Adjustment in Australia Appendix C: Classifying Job Stayers and Movers

While the LLFS can identify whether an individual was employed in consecutive mid-quarter surveys, it does not contain a variable that indicates whether the individual was in the same job. To infer whether an individual changed jobs, we use information on the individual's tenure in their current job or business. This tenure variable (TENUREC) records whether an individual has been employed in their current job or business for less than one year, or at least one year. By comparing this variable to its value in the previous mid-quarter survey – for the matched sample of people employed in both surveys – we can infer whether the individual was in the same job (job stayer) or not in the same job (job mover).

We assume that an individual is a job stayer (i.e. working in the same job as in the previous mid-quarter survey) if they reported to have been in their current job for at least one year. Conversely, an individual is classified as a job mover if his or her tenure changed from at least one year in the previous mid-quarter survey to less than one year in the current survey. This classification scheme is summarised in Table C1.

Table C1: Classifying Job Movers and Stayers Using Job Tenure
Individuals employed in both mid-quarter surveys
  Survey t
<1 year in job ≥1 year in job
Survey t1 <1 year in job Indeterminate Job stayer
≥1 year in job Job mover Job stayer

Note: Indeterminate means that job mover/stayer status cannot be determined using job tenure alone

The mover/stayer status of some workers cannot be determined using the tenure variable alone; specifically, those whose tenure was less than one year in both the current and previous mid-quarter survey. These workers either started a new job prior to the previous survey (but less than one year ago) and were still in that job, or held a different job at both survey dates, both of which were for a period of less than one year. On average, such workers represent around 13 per cent of the total matched sample of people employed in consecutive mid-quarter surveys. In order to categorise these workers as either job stayers or job movers we use information on a number of other factors that may be predictive of changing jobs, namely:

  • the individual changed their industry or occupation of employment
  • the individual moved from part-time to full-time work (or vice versa), or changed from being an employer to an employee or own account worker (or vice versa)
  • the individual experienced a spell of unemployment or NILF in any of the months in between the two mid-quarter surveys
  • in the previous mid-quarter survey, the individual reported that he or she did not anticipate to be working in their current job or business in one year's time
  • the individual reported a ‘change in job’ during the reference week of any of the previous three monthly LFS surveys (including the current survey).

To estimate the extent to which the above factors are associated with job changing, we estimate a probit model for the probability of being a job mover using the above factors as the explanatory variables. The model is estimated on the sample of individuals whose job status is able to be identified using job tenure alone. We then use this model to obtain the predicted probability of being a job stayer for all people employed in consecutive mid-quarter surveys, including those for whom job status was not able to be determined using job tenure. We then assign all people in the latter group to the ‘job mover’ category if their predicted probability of being a job mover exceeds some threshold level, which we set at 0.15.[20] We assign the remaining individuals (those whose predicted probability of being a job mover is less than 0.15) to the ‘job stayer’ category. Our choice of threshold was based on an analysis of what proportion of job movers the probit model correctly classified (the sensitivity), and what proportion of job stayers were correctly classified (the specificity) at every possible threshold. A 0.15 cut-off resulted in job stayers being correctly classified 97.5 per cent of the time, and job movers 33.5 per cent of the time. The ‘default’ threshold of 0.5 would have biased correct predictions towards the more common outcome at the expense of the less common one.[21]


The implicit assumption is that the unclassifiable population is a random subset of the general population, meaning that the results for the classifiable population can be extrapolated. [20]

With a threshold set at 0.5, job stayers and job movers are correctly classified 99.8 per cent and 8.0 per cent of the time, respectively. Fluss, Faraggi and Reiser (2005) recommend that cut-off probabilities be chosen to maximise the sum of sensitivity and specificity. Our threshold of 0.15 is close to this value. [21]