1. Background

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 3 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.

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2. Method

There are 2 types of 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 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 2-quarter datasets have been used in order to gain some insight into the quarterly changes in the headline published aggregates.

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3. The charts provided

The charts in this article show the estimated gross flows, that is, the total inflow or outflow for age 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.

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4. Main points for Quarter 2 (Apr to June) 2016

The LFS longitudinal datasets have been revised from Quarter 3 (July to Sep) 2012 onwards to take account of the 2014 mid-year estimates of population and the 2014-based national population projections. This brings the estimation of the longitudinal data into line with that of the quarterly cross-sectional datasets.

The total inflow to unemployment is at its lowest since January to March 2006.

Employment inflows remain well above employment outflows.

The total inflow to inactivity has been gradually decreasing over the last year mainly reflecting a reduced flow from unemployment.

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5. Quarterly gross flows

The quarterly population flows diagram shows the gross flow between each economic status between January to March 2016 and April to June 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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6. Unemployment

The inflow to unemployment (Figure 1) has decreased on the quarter to the lowest since January to March 2006. This reflects decreases from both employment and inactivity.

A decrease in the unemployment outflow mainly reflects a fall in the flow from unemployment to inactivity (Figure 2).

Figure 3 shows that the net quarterly flow and change in stock decreased on the quarter after 2 previous increases.

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7. Employment

The total inflow to employment (Figure 4) has shown little change on the quarter. The lack of change is reflected in the flows from unemployment and inactivity.

The gross outflow from employment (Figure 5) has decreased slightly on the quarter. This was caused by a decrease in the flow to unemployment, which was offset slightly by a small increase in the flow to inactivity.

Figure 6 shows that the Job to Job flow rate increased slightly on the previous quarter.

Figure 7 shows increases for both the net flow and the change in stock, with both being strongly positive.

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8. Inactivity

The flows from employment and unemployment into inactivity have diverged in the latest quarter as the gross flow to inactivity decreased slightly (Figure 8).

Figure 9 shows the gross flow from inactivity has decreased marginally over the previous 3 quarters, mainly reflecting a reduced flow to unemployment.

Figure 10 shows the inactivity net flow has increased slightly whereas the change in stock decreased, although both remained negative.

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9. Technical note

There are differences between the data used for the published LFS aggregate estimates and the longitudinal data used to estimate the gross flows.

  1. 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.

  2. 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.

  3. 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.

  4. 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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10 .References

Jenkins J and Chandler M (2010) ‘Labour market gross flows data from the Labour Force Survey’ Economic and Labour Market Review, February 2010.

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Contact details for this Article

Mark Chandler
Telephone: +44 (0)1633 455400