In the Labour Force Survey (LFS) respondents are interviewed for five 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 two or five 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 article.Back to table of contents
There are two types of Labour Force Survey (LFS) longitudinal datasets: two-quarter and five-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 years.
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 age 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 two-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
the flow from unemployment to employment is at a record low
the unemployment net quarterly flow and the unemployment quarterly change in stock have become positive for the first time since Quarter 2 (Apr to June) 2015
the flow from inactivity to employment is at its highest since Quarter 4 (Oct to Dec) 2015
the gross inflow to inactivity is at its lowest since Quarter 1 (Jan to Mar) 2006
the inactivity net flow and the inactivity quarterly change in stock have reached their lowest since Quarter 2 (Apr to June) 2012
The diagram shows the gross flow between each economic status between Quarter 3 (July to Sept) 2017 and Quarter 4 (Oct to Dec) 2017. The stocks for each status represent the latter period and are the seasonally adjusted aggregates for people aged 16 to 64 years.
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Figure 2 shows the gross flow to unemployment has increased for a second consecutive quarter. This is driven by an increase in the flow from inactivity to unemployment.
The flow from unemployment to employment is at a record low. This decrease, together with a decrease in the flow from unemployment to inactivity has resulted in a third consecutive decrease in the unemployment gross outflow (Figure 3).
Figure 4 shows that the unemployment net quarterly flow and the unemployment quarterly change in stock have increased for a second consecutive quarter. In Quarter 4 (Oct to Dec) 2017, both series became positive for a first time since Quarter 2 (Apr to June) 2015.
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The gross inflow in employment increased in Quarter 4 (Oct to Dec) 2017 as a result of an increase in the flow from inactivity to employment, which more than offset the fall seen in the flow from unemployment to employment (Figure 5).
Figure 6 shows that the flows from employment to both unemployment and inactivity decreased in Quarter 4 (Oct to Dec) 2017, resulting in a decrease in the gross outflow from employment.
Figure 7 shows that the job-to-job flow has increased this quarter, after decreasing in the two previous quarters.
Figure 8 shows that both the employment net flow and the employment quarterly change in stock increased in Quarter 4 (Oct to Dec) 2017.
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Figure 9 shows that both the flows from unemployment and employment to inactivity decreased in Quarter 4 (Oct to Dec) 2017. This has caused a decrease in the gross inflow to inactivity, which is at its lowest since Quarter 1 (Jan to Mar) 2006.
Figure 10 shows that the gross outflow from inactivity increased in Quarter 4 (Oct to Dec) 2017 as a result of increases in the flows from inactivity to both employment and unemployment. The flow from inactivity to employment is at its highest since Quarter 4 (Oct to Dec) 2015.
The inactivity net flow and the inactivity quarterly change in stock have decreased, reaching their lowest since Quarter 2 (Apr to June) 2012 (Figure 11).
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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 11 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 years old is greater than those leaving inactivity due to becoming 65 years old.
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 years across successive quarters discounts those entering or leaving the population and also those over 64 years old. All such people are accounted for in the headline LFS aggregates.Back to table of contents
There was a methodological improvement to the calculation of the job-to-job rate, which resulted in some historical revisions.Back to table of contents
Jenkins J and Chandler M (2010) Labour market gross flows data from the Labour Force Survey (PDF, 145KB) Economic and Labour Market Review, February 2010.Back to table of contents
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