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 three 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 two 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 two 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 aged 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
- employment net flow increased for the third consecutive quarter despite decreases in the gross outflow and inflow on the quarter
- unemployment net flow remains negative
- inactivity net flow remains negative
The diagram shows the gross flow between each economic status between Quarter 1 (Jan to Mar) 2017 and Quarter 2 (Apr to Jun) 2017. 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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Figure 1 shows the flows from both inactivity and employment into unemployment have decreased, causing an overall decrease in the gross flow to unemployment, consistent with the broad trend over the last five years.
The flow from unemployment decreased after an increase in the previous quarter. This was, driven primarily by a decrease in the flow to employment, which is at its lowest since Quarter 3 (Jul to Sept) 2008 (Figure 2).
Figure 3 shows that both the net quarterly flow and change in stock have decreased for the second consecutive quarter.
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The flow from inactivity to employment has remained broadly stable over the last year whereas the flow from unemployment has decreased over the quarter (Quarter 2 (Apr to June) 2017), driving a decrease in the gross flow to employment (Figure 4).
Quarterly decreases in the flows from employment to inactivity and unemployment have driven the gross flow to decrease to its lowest since Quarter 1 (Jan to Mar) 2006 (Figure 5).
Figure 6 shows that the job-to-job flow rate has decreased slightly after an increase in the previous quarter.
Figure 7 shows both net flow of employment and the change in stock for the quarter have been increasing for the last 3 quarters.
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The gross flow to inactivity has decreased on the quarter, driven mainly by a decrease in the flows from employment to inactivity (Figure 8).
Figure 9 shows the gross flow from inactivity has decreased on the quarter although the trend over the previous four years is broadly stable.
Figure 10 shows the net flow has increased whereas the quarterly change in stock has decreased.
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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 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.
There have been revisions in the flow data going back to Quarter 3 (Jul to Sept) 2012 due to re-weighting, moreover there have been some revisions in the job-to-job tables going further back due to quality improvementsBack to table of contents
Jenkins J and Chandler M (2010) Labour market gross flows data from the Labour Force Survey Economic and Labour Market Review, February 2010.Back to table of contents
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