RDP 2016-06: Jobs or Hours? Cyclical Labour Market Adjustment in Australia 4. A Closer Look at the 2008–09 Downturn

Greater insights into the within-job shift to shorter working hours during downturns can be gleaned by looking at the individual-level data underlying the LFS.^[13] The main benefit of these data is that they are longitudinal in nature, meaning that individual workers can be tracked over the period of time during which they remain in the sample to see if they had their hours reduced. The main disadvantage of these data is that they are only available for the period 2008 to 2010, meaning that analysis is restricted to the most recent economic downturn.

The individual-level data let us distinguish changes in aggregate average hours worked due to:

• Labour hoarding: a reduction in hours for workers remaining in the same job. This could reflect, for instance, firms temporarily reducing the hours of their staff, such as by asking them to work shorter hours or to take leave.
• Labour market churn: this includes the churn of workers into new jobs within the same industry (and occupation) or into new jobs in different industries. It also includes the impact on aggregate average hours due to changes in the average hours of people entering or exiting employment. For example, it may be that manufacturing jobs lost during a downturn are replaced with new manufacturing jobs that offer shorter hours. This is broader in definition than the ‘composition effect’, because in addition to capturing net movements of workers into different categories, it also captures changes due to the churn of workers within a given category.

We are not the first to attempt to distinguish between these factors using Australian data. Using data on a panel of workers in the Household, Income and Labour Dynamics in Australia (HILDA) Survey, Wooden (2012) found an increase in the share of employees who remained in the same job and had their hours reduced during the 2008–09 downturn. However, he found that most of the fall in average hours worked was due to the destruction of jobs with relatively long hours and the creation of jobs with relatively short hours. van Wanrooy et al (2009) used longitudinal data on workers from the Australia at Work survey, finding that both labour hoarding and labour market churn were important contributors to the overall decline. In terms of labour hoarding, the main drivers were changes in patterns of leave taking and an increase in the share of full-time workers being asked by their employer to work shorter hours, while in terms of churn, the most important contributors were increases in both the job destruction rate and the share of labour market entrants undertaking part-time work.

However, there are two limitations to these previous studies relating to the data. First, both the HILDA and Australia at Work surveys provide data on usual hours of work, rather than actual hours. It is not clear how survey respondents interpret the word ‘usual’, and whether it picks up short-term changes in work hours. Second, these surveys are administered relatively infrequently – every 12 months – and are thus more likely to miss the high-frequency dynamics in hours worked through a downturn.

Unlike previous studies for Australia, we use the individual-level data underlying the LFS. These data were made available to researchers in 2012 for the first time in the survey's 50-year history. In addition to providing a larger sample size (around 27,000 dwellings per month), these data also allow us to follow cohorts of workers every month. These data are uniquely suited to our research question, since they are the only nationally-representative, high-frequency, longitudinal data on hours worked. The survey also provides information on both actual and usual hours, which aggregate up to the official figures published in the monthly labour force release.^[14]

4.1 Matched Sample

We use the LFS data to construct short panels of individual workers. Individuals are surveyed every month for eight months, before being retired from the sample. Thus, there are up to eight monthly surveys for each individual. While the LFS collects information on hours worked every month, some additional information (e.g. industry, occupation and job tenure) is only collected in the February, May, August and November surveys, which we refer to as the ‘mid-quarter’ surveys. We focus on mid-quarter surveys, since the additional data items are important for our analysis.

We define the period t matched sample as all individuals at time t (e.g. May 2008) who also responded to the mid-quarter survey at t − 1 (e.g. February 2008). Since workers are surveyed for eight consecutive months in the LFS, this suggests that five-eighths of the overall LFS sample at t would be in the period t matched sample, and the remaining three-eighths would be in the unmatched sample. In practice, however, the period t matched sample makes up less than five-eighths of the full sample at t due to attrition from the survey (it is closer to 55 per cent).^[15]

A potential concern with any analysis based on a matched sample is that estimates could be biased if attrition from the sample does not occur at random. For example, young people are systematically less likely to remain in the matched sample than older people, as are individuals that are less stably employed (ABS 2013). The population weights used to produce the monthly labour force statistics (provided in the unit-record data) do not adjust for non-random attrition. Thus, for our analysis, we construct and use a set of ‘longitudinal weights’ that account for differential rates of attrition amongst population sub-groups (at least along the observable dimensions that we can adjust for using the information contained in the dataset).^[16]

4.2 Which Groups Contributed to the Decline in Average Hours?

We classify each worker at time t into one of four categories:

1. Job stayers are workers employed in the same job at t − 1 and t
2. Job movers are workers who move to a different job between t − 1 and t
3. Job finders are workers who move from unemployment or not in the labour force (NILF) at t − 1 into employment at t
4. Job leavers are workers who move from employment at t − 1 to unemployment or NILF in t.

Job finders and job leavers can be directly identified using the LFS individual-level data by simply comparing the labour force status of each individual from survey to survey. We can also directly identify whether individuals were employed in consecutive surveys. But in order to distinguish job stayers from job movers, we need to draw on other information in the dataset (see Appendix C for details).

We estimate that job stayers account for 90 per cent of total employment, on average. This large share means that they are likely to drive most of the movements in overall average hours worked. However, during periods of labour market upheaval, such as during downturns and recessions, those exiting from (or entering into) employment could also influence movements in overall average hours worked, even though they comprise only a small share of employment.^[17]

Figure 8 shows average hours worked for each of the four categories of employment over the period 2008 to 2010 (the period for which individual-level data are available). For job leavers, we plot their average hours before their exit from employment. An increase in the average hours of job leavers will contribute to a fall in overall average hours, all else equal. In contrast, an increase in the average hours of job finders will contribute to a rise in overall average hours. The figure shows that the average hours worked by job leavers rose during the 2008–09 downturn, contributing to the fall in average hours. This is consistent with full-time workers being laid off more extensively than part-time workers, relative to more ‘normal’ times. Average hours worked by job finders also rose during the downturn, but at a more gradual pace, partly offsetting the negative contribution due to job leavers.

Average hours worked by job stayers fell during the 2008–09 downturn – which also contributed to the overall decline in average hours worked – before recovering to their pre-crisis levels (Figure 8). Average hours worked by job movers were volatile, but were little changed overall during the 2008–09 downturn.

The contribution of job stayers, job movers and job finders to the overall decline in average hours worked is estimated in Figure 9. These contributions depend on the change in average hours worked for each category in Figure 8, along with the change in the relative employment share of each category. The combined effect of changes in employment shares is included as a separate term – labelled as the ‘between effect’. An important drawback of the decomposition is that it does not incorporate the contribution of job leavers to the overall decline in average hours. We are unable to include job leavers due to sample rotation in the LFS, which complicates the calculation of the exact contribution for each employment category (see Appendix D for a discussion of this issue and for further details about the decomposition).

We estimate that the decline in average hours worked by job stayers accounted for the majority of the decline in average hours worked during the 2008–09 downturn (Figure 9). This is unsurprising, given that job stayers account for 90 per cent of employment. In contrast, within-category changes in the average number of hours worked by job finders and movers made a negligible contribution to the overall decline.

Changes in the employment shares of each category – the ‘between effect’ – had a positive, but small, impact on overall average hours worked during the downturn. This reflected an increase in the employment share of job stayers – who tend to work longer hours on average than workers in the other categories – and a decrease in the employment share of job movers and job finders, who tend to work shorter hours. These patterns are consistent with a fall in the job-finding rate and a decline in the rate of job-to-job mobility typically observed during economic downturns.

After considering the evidence presented in both Sections 3.4 and 4.2, we conclude that reductions to the hours of job stayers accounted for at least one-half of the overall fall in average hours worked during the 2008–09 downturn, and probably more. The fact that the ‘labour hoarding’ effect in Figure 9 is estimated to be larger than the ‘within effect’ in Figure 7 may indicate that the latter is capturing changes in average hours worked due to churn of workers within given categories (and in particular, within the part-time/full-time employment category).

4.3 Skills Shortages

We can also use the individual-level data to gain a clearer picture about the role that industry skills shortages played in the labour market adjustment. For each industry, Figure 10 shows the adjustment to average hours worked by job stayers during the 2008–09 downturn (y-axis) against the proportion of firms who reported skills shortages during the economic boom leading up to the downturn (x-axis).^[18]

Reductions in hours worked for those staying in the same job were largest for workers in industries that had experienced skills shortages prior to the downturn (Figure 10). Firms in these industries may have been ‘overutilising’ staff prior to the downturn, and started to reduce hours to more normal levels as demand eased. As discussed earlier, firms may also have been reluctant to let go of skilled workers because labour had been so difficult to source just prior to the downturn. We cannot draw strong conclusions from this analysis, however, as we have not controlled for other factors, such as the size of the industry demand shock.

Footnotes

The dataset is called the Longitudinal LFS (LLFS). It currently includes the full set of individual unit records for each monthly LFS conducted between January 2008 and December 2010. [13]

Actual hours worked include paid or unpaid overtime hours, but exclude hours paid for but not worked, such as paid annual leave, public holidays or paid sick leave. The LFS questionnaire does not give a definition of ‘usual hours’, so it is not immediately clear how respondents interpret the term. [14]

We use the term ‘attrition’ loosely to refer to the situation where an individual is not matched from one mid-quarter survey to the next. This will include workers that drop out of the sample (e.g. those that could not be contacted for a subsequent interview), but also those that are not matched for other reasons, such as being rotated out of the survey panel, being surveyed in a non-private dwelling or becoming ‘out of scope’ of the LFS (e.g. by joining the defence force). [15]

Details on the construction of the longitudinal weights are available from the authors on request. The intuition behind this procedure is that it gives more weight to individuals that have similar initial observed characteristics to those that subsequently drop out of the sample than to individuals with characteristics that make them more likely to remain in the sample. For example, if young people are relatively more likely to drop out of the sample, then we adjust for this fact by giving the young people that do remain in the sample a larger weight, relative to the older people in the sample. Nevertheless, we cannot rule out the possibility that some individuals ‘select out’ of the matched sample based on unobservable factors that we are unable to account for. [16]

Job movers account for 4½ per cent of employment, on average over our sample period, while job finders and job leavers account for 5 per cent of employment in periods t and t − 1, respectively. [17]

The hours adjustment plotted on the y-axis of Figure 10 is calculated as the difference between the average change in hours worked during the downturn (2008:M11 to 2009:M8) and the average change in hours worked during the post-downturn period (2009:M11 to 2010:M8). Normalising relative to the post-downturn period helps to control for trend changes in hours in each industry. Taking averages across four quarterly average changes accounts for seasonality. [18]