These estimates of labour market flows are experimental statistics which have been produced as an aid to understanding the movements in the published Labour Force Survey aggregate estimates. They do not have National Statistics status and are not suitable as labour market indicators in their own right. The headline LFS estimates are published in the monthly Labour Market Statistical Bulletin.
In the Labour Force Survey (LFS) respondents are interviewed for five consecutive quarters over a 12 month period, with 20 per cent 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.
The ONS publishes 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.
There are two types of 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 16-64.
Both types of dataset contain a flow variable with eleven categories, with all combinations of employment, unemployment and economic inactivity accounted for, plus two categories for those entering and leaving the 16-64 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.
Estimates of flows between labour market statuses are available at data table X02 (77.5 Kb Excel sheet) . The charts in this article show the estimated gross flows, that is the total inflow or outflow for 16-64 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.
The diagram below shows the gross flow between each economic status between October – December 2013 and January - March 2014. The stocks for each status represent the latter period and are the seasonally adjusted aggregates for people aged 16-64.
The gross inflow to unemployment (Chart 1) has decreased to its lowest since 2008. This was primarily driven by the continued decrease in the flow from employment.
The total number of people leaving unemployment (Chart 2) fell slightly to just below 1 million. The latest unemployment outflows are similar to the average flows over the last 2 years.
Chart 3 shows that, the net flow remained strongly negative, consistent with the quarterly change in stock.
The gross inflow to employment (Chart 4) decreased slightly from the joint record high seen in the previous quarter. This reflects small decreases in the flows from both unemployment and inactivity.
The gross outflow from employment (Chart 5) decreased to a record low of just under 800,000. This reflects decreases in the flows to both inactivity and unemployment.
Chart 6 shows that the net flow and change in stock were at their highest for 5 years.
The gross inflow to inactivity (Chart 7) decreased on the quarter although the underlying trend appears to be flat.
The gross outflow from inactivity (Chart 8) decreased slightly, reflecting small decreases to both employment and unemployment.
Chart 9 indicates that the net flow and the change in stock were both negative in the latest quarter.
There are differences between the data used for the published 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 Chart 9 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 16-64 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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