In the Labour Force Survey (LFS) respondents are interviewed for 5 consecutive quarters over a 12-month period, with 20% of the sample being replaced at each quarter. This allows for a longitudinal dataset to be created over a limited time interval, where respondents’ characteristics can be tracked over their time in the survey.
We publish population-weighted longitudinal datasets for each calendar quarter. These are available for each quarter since 1997 and can be used to analyse changes in labour market characteristics over 2 or 5 quarters. The datasets include "flow" variables, which estimate the size of the movements between the three main labour market statuses of employment, unemployment and economic inactivity.
Monitoring changes in the labour market status of respondents to the LFS aids the understanding of the quarterly changes in the levels of employment, unemployment and economic inactivity. These indicators are published as stocks for a given period, with changes expressed as the difference between successive quarters. These quarterly comparisons represent the net changes between the 3 labour market statuses. The underlying gross flows are usually considerably larger and may not correspond with those implied by the net changes. Estimates of the gross flows between the statuses can be derived from the LFS Longitudinal Datasets and are summarised in this note.Back to table of contents
There are 2 types of Labour Force Survey (LFS) longitudinal datasets: 2-quarter and 5-quarter. These are weighted using the same population estimates as those used in the main quarterly LFS datasets, although the weighting methodology differs (see technical note). Consequently the estimates are broadly consistent with the published aggregates, but not entirely. Also, the datasets are limited to people aged 16 to 64.
Both types of dataset contain a flow variable with 11 categories, with all combinations of employment, unemployment and economic inactivity accounted for, plus 2 categories for those entering and leaving the 16 to 64 population over the quarter. For the purpose of this analysis, those entering or leaving this population are excluded from the measured sample. The stock of the employed, unemployed and inactive at each quarter can therefore be estimated by summing the corresponding flow categories.
For this analysis, the 2-quarter datasets have been used in order to gain some insight into the quarterly changes in the headline published aggregates.Back to table of contents
The charts in this article show the estimated gross flows, that is the total inflow or outflow for 16 to 64 employment, unemployment and inactivity from one calendar quarter to the next. They are seasonally adjusted. Analysis of the net flows, that is the difference between the total inflow and outflow, are also included and these are compared with the quarterly changes in the published aggregates, partly to give an indication of the robustness of the flows analysis.Back to table of contents
The total outflow from unemployment has continued to exceed the total inflow, reflecting a further decrease in the stock of unemployed people.
In the latest quarter, the total flow into employment has eased slightly while the total outflow has picked up a little, reflecting slower quarterly growth in employment overall.
The proportion of people in employment who moved from one job to another in the latest quarter was, at 2.7%, the highest since 2008.
In contrast to employment, the total flow into inactivity has picked up slightly while the total outflow has decreased.Back to table of contents
The diagram below shows the gross flow between each economic status between April to June 2016 and July to September 2016. The stocks for each status represent the latter period and are the seasonally adjusted aggregates for people aged 16 to 64
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The flow from employment to unemployment has increased on the quarter whereas the flow from inactivity has continued to decrease. The gross flow to unemployment, therefore, has shown little change (Figure 1).
The gross outflow from unemployment continued on a gradually decreasing trend, falling to its lowest since Quarter 3 (July to Sept) 2008. This partly reflects the decreasing stock of unemployed people (Figure 2).
Figure 3 shows that the net quarterly flow and change in stock have increased and are closely aligned.
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The total inflow to employment (Figure 4) has decreased slightly in the latest quarter, as reflected in the small decreases from unemployment and inactivity.
The flow from employment to unemployment has increased slightly on the quarter which has caused a slight increase in the total outflow from employment as the flow to inactivity has shown little change (Figure 5).
Figure 6 shows that the job-to-job flow has continued to increase and is at its highest rate since Quarter 1 (Jan to Mar) 2008.
Figure 7 shows decreases for both net flow and change in stock although both remain positive.
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The flow from unemployment into inactivity has increased in the latest quarter by its biggest margin since Quarter 4 (Oct to Dec) 2013 which has caused the gross flow into inactivity to increase.
Figure 9 shows the gross flow from inactivity has continued to decrease, mainly caused by a decrease in the flow to unemployment which is at its lowest point since Quarter 3 (July to Sept) 2006.
Figure 10 shows the quarterly change in stock and net flow have both increased with the change in stock moving above zero for the first time since Quarter 2 (Apr to June) 2015.
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There are differences between the data used for the published Labour Force Survey (LFS) aggregate estimates and the longitudinal data used to estimate the gross flows:
Flows are currently adjusted for non-response bias through special calibration weights in the longitudinal datasets. These aim to account for the propensity of certain types of people to drop out of the LFS between one quarter and the next. For example, housing tenure features in the weighting of the longitudinal data because, historically, households in rented accommodation have been more likely to drop out of the survey than owner-occupiers.
There is some evidence that the longitudinal datasets are affected slightly by response error which causes a slight upward bias in the estimates of the gross flows. For example, if it was erroneously reported that someone had moved from unemployment to employment then, in addition to the outflow from unemployment being overestimated, so would the inflow to employment. In the main quarterly LFS dataset, any such misreporting errors tend to cancel each other out.
The differences in the net flows for inactivity shown in Figure 10 are mainly the result of excluding the entrants to, and leavers from, the population in the flows estimates contained in this piece of analysis. This effect is normally one that increases the number of people who enter inactivity. This is because the increase in inactivity from those people turning 16 is greater than those leaving inactivity due to becoming 65.
The stocks derived from the longitudinal datasets differ from those obtained from the quarterly LFS datasets due to their being based on a subset of the main LFS sample. The restriction to measuring only those who are commonly aged 16 to 64 across successive quarters discounts those entering or leaving the population and also those over 64. All such people are accounted for in the headline LFS aggregates.
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