This article provides experimental statistics of two measures of labour productivity, GVA per hour worked and GVA per filled job, for the NUTS 2 and NUTS 3 subregions of the UK. The data covers the period from 2002 to 2011. The subregional data has been produced to be consistent with the regional productivity data published in the quarterly labour productivity release. Productivity measures the amount of output produced by a unit of input and is a key indicator of economic performance.
Subregions with the highest levels of productivity in the UK are typically found in either London, the South East region or in Scotland.
In 2011, Inner London had the highest productivity level, with a GVA per hour worked 54% above the UK average in Inner London West and 30% above the UK average in Inner London East.
Outside London, the NUTS 3 subregions with the highest productivity levels were Berkshire in the South East of England and the Scottish capital city of Edinburgh, both with a GVA per hour worked more than 20% above the UK average.
The NUTS 3 subregions showing the lowest productivity levels were often, but not always, rural areas of the UK. Powys and Gwynedd, both in Wales, had the lowest productivity at more than 30% below the UK average.
Only 29 out of 134 NUTS 3 subregions in Great Britain had GVA per hour worked above the UK average; 16 of which are located in the Greater South East and 6 in Scotland.
The median ranked NUTS 3 subregion had a GVA per hour worked 91% of the UK average (mean). When compared with this median productivity level, most subregions within the Greater South East had productivity above this median level, while in Yorkshire and The Humber, West Midlands and Wales 70% or more of NUTS 3 subregions were below this median level.
The data in this report measure labour productivity. Labour productivity measures the amount of output produced by unit of labour input. A higher level of productivity means that a higher level of output is being produced per unit of labour input.
Productivity matters because increasing productivity is vital to improving standards of living. This follows from the fact that economic output can only be increased by either increasing the amount of inputs or by raising productivity.
As such, increasing productivity is an aim of economic policy both nationally and locally. As shown in this report, there is currently a wide spatial divergence in levels of productivity between different subregions.
This section presents the main results of the labour productivity estimates for the 37 NUTS 2 subregions of the UK, focusing on estimates of nominal GVA per hour worked. The data have been smoothed based on a five year weighted average (see background notes for more information).
Figure 1 shows the 10 UK NUTS 2 subregions with the highest GVA per hour worked in 2011. It shows that the six NUTS 2 subregions with the highest levels of productivity in the UK were either in London, the South East region or in Scotland.
In the London region, Inner London showed the highest productivity level, with a GVA per hour worked 44% above the UK average. Although Outer London had a much lower GVA per hour worked (8% above the UK average), it is still among the 10 NUTS 2 subregions with the highest levels of productivity .
In the South East region, the NUTS 2 subregion of Berkshire, Buckinghamshire and Oxfordshire had the highest productivity level, with a GVA per hour worked 17% above the UK average. Surrey, East and West Sussex (with a GVA per hour worked 7% above the UK average) and Hampshire and Isle of Wight (with a GVA per hour worked 6% above the UK average) were also among the 10 NUTS 2 subregions with the highest productivity levels. With a GVA per hour worked 4% below the UK average, Kent was the only NUTS 2 subregion in the South East underperforming the UK average.
In Scotland, two out of four NUTS 2 subregions were also among the 10 subregions with the highest economic performance in the UK. These were North Eastern Scotland, with a GVA per hour worked 16% above the UK average, and Eastern Scotland, with a GVA per hour worked 3% above the UK average.
Cheshire, in the North West of England, and Gloucestershire, Wiltshire and Bristol/Bath area in the South West also had productivity levels above the UK average.
Figure 2 shows the 10 UK NUTS 2 subregions with the lowest GVA per hour worked in 2011.
The 10 NUTS 2 subregions showing the lowest GVA per hour worked had productivity levels at least 16% below the UK average. These NUTS 2 subregions are located across different countries and regions of the UK, but not in London, the South East or in the East of England. Cornwall and Isles of Scilly; Lincolnshire; and West Wales and The Valleys showed the lowest productivity in 2011, with a GVA per hour worked 20% or more below the UK average.
This section presents the results of the labour productivity estimates for the 134 NUTS 3 subregions of Great Britain1 focusing on the data for nominal GVA per hour worked, which is the preferred subregional labour productivity measure2. The data have been smoothed based on a five year weighted average (see background notes for more information).
Figure 3 shows the 15 GB NUTS 3 subregions with the highest GVA per hour worked, in 2011.
Inner London NUTS 3 subregions had the highest productivity levels, with GVA per hour worked 54% above the UK average in Inner London West and 30% above the UK average in Inner London East. Although Outer London NUTS 3 subregions had lower productivity levels compared with the Inner London ones, they were still among the 15 NUTS 3 subregions with the highest GVA per hour worked in 2011.
Outside London, GVA per hour worked was highest in Berkshire, in the South East of England, and in the Scottish capital city of Edinburgh, both subregions with productivity levels more than 20% above the UK average. Buckinghamshire and Surrey, both in the South East of England, and Aberdeen City and Aberdeenshire in Scotland also showed high economic performance, with productivity levels more than 15% above the UK average.
It should be noted that the very high productivity level in Inner London leads to a skewed distribution of productivity levels across the UK, such that relatively few subregions have productivity levels above the UK (mean) average. In fact, in 2011, just 29 out of 134 NUTS 3 subregions across England, Scotland and Wales had a GVA per hour worked above the UK average. Of these, 6 subregions are in Scotland and the remaining 23 are in England. Of those subregions in England, 16 are in the Greater South East of England, 3 in the South West, 2 in the Midlands and 2 in the North West.
Given the skewed nature of the distribution, it is worth considering how the productivity level of the mid-ranking (median) subregion compares with the UK average. In 2011, the subregion occupying the middle position in the productivity ranking of the NUTS 3 subregions was Warwickshire with a GVA per hour worked of 91% the UK average; that is, a productivity level that was 9% below the UK mean average. In other words, half of the NUTS 3 subregions had a higher productivity level than Warwickshire, while the other half had a lower productivity level, that is, lower than 9% below the UK mean average.
While the Greater South East (London, the South East and the East of England) had 83% of its NUTS 3 subregions in the top half of the distribution, Yorkshire and The Humber, the West Midlands and Wales had 70% or more of their NUTS 3 subregions in the bottom half of the distribution. For more detail on the data split by region and country, please see the next section 'Results - NUTS 3 by region/country'.
Figure 4 shows the 15 GB NUTS 3 subregions with the lowest GVA per hour worked in 2011.
The lowest productivity levels were typically found in the more rural areas of the UK or towards the geographical periphery of the UK. The lowest productivity levels were in Powys and in Gwynedd, both in Wales, with GVA per hour worked of more than 30% below the UK average. A number of more urban subregions, namely Blackpool, Wolverhampton and City of Kingston upon Hull, are also to be found in Figure 4 illustrating that low productivity is not confined to a particular type of area. It is also notable that the NUTS 3 subregions with the lowest productivity are very much spread around Great Britain, although not in the Greater South East region.
The preceding section provided the data for GVA per hour worked for all NUTS 3 subregions of Great Britain. In this section, the same data are shown but organised on a region/country basis. Additionally, GVA per filled job data are shown for Northern Ireland.
Figure 5 shows GVA per hour worked data for 2011 for all of the NUTS 3 subregions within the North West region of England. It shows a wide variation in productivity across the subregions. Similar charts for the remaining English regions and also for Scotland and for Wales can be found by opening the excel spreadsheet beneath figure 5.
As mentioned previously, GVA per hour worked data are not available for the NUTS 3 subregions of Northern Ireland. However, GVA per filled job data is available. Figure 6 shows this GVA per filled job for the NUTS 3 subregions of Northern Ireland, in 20101.
In 2010, Belfast was the subregion of Northern Ireland with the highest productivity, with a GVA per filled job 10% below the UK average. GVA per filled job was lowest in the subregions covering the North, the West and South of Northern Ireland, where the productivity levels were more than 23% below the UK average.
When comparing the performance of the Northern Ireland NUTS 3 subregions with the rest of the UK, Belfast was just in the middle of the productivity ranking; that is, half of the UK NUTS 3 subregions had higher productivity levels than Belfast, while the other half had lower productivity levels. Therefore, all the other NUTS 3 subregions of Northern Ireland were ranked below the median average.
GVA per head has historically been, and often still is, used as a catch-all indicator of a subregions economic performance. However, there are some significant drawbacks to using GVA per head in this manner which are discussed below. Therefore, it is considered better to use a suite of different indicators, including the productivity measures published in this article, when assessing the economic performance of regions and subregions.
In the ONS Productivity Handbook, published in 2007, it is stated that 'GVA per head does not provide a good measure of economic productivity of a region or the wellbeing of those living in the region.' This point was further reinforced by the National Statistician in an
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published in January 2009. The article aimed to inform the discussion about the limitations of GVA per head in measuring productivity of a region and the income of its residents, and to promote the use of GVA per hour worked and GVA per filled job as regional productivity measures.
GVA per head is calculated as the simple ratio of economic activity in a region divided by the number of people living in that region. At first sight, GVA per head appears to be an appropriate indicator of productivity as it compares the output of a region (GVA) with an input (population).
However, there are two main limitations in this measure that makes GVA per head unsuitable as a regional productivity measure. Firstly, by including all the residential population and not just those who are in employment, the denominator includes residents who are not directly contributing to GVA. GVA per head is therefore understated in areas with high percentages of young people and pensioners. Secondly, the GVA per head is dividing a workplace-based numerator (GVA) by a residence-based denominator (residential population). This means that this measure does not account for people commuting into and out of a region.
For these reasons, GVA per hour worked and GVA per filled job are the most appropriate measure of regional and subregional productivity. These measures only count the input of those who are directly employed in the production process (rather than the whole population) and additionally, they provide a workplace-based labour input denominator to match the workplace-based GVA numerator, thus fully accounting for the impacts of commuting.
The differences in the results of the subregional labour productivity and GVA per head are not negligible. To illustrate this, Figure 7 shows GVA per head and both measures of labour productivity, GVA per hour worked and GVA per filled job, for two selected NUTS 3 subregions.
As shown in Figure 7, East and North East Outer London and Milton Keynes had very similar labour productivity levels. Both subregions had a GVA per hour that was 7% to 8% above the UK average and a GVA per job that was also above the UK average. As such, both subregions achieved a similar level of output per labour input being utilised within their subregions.
GVA per head, however, was very different in these two subregions. If an analysis of economic performance had only been carried out using the GVA per head measure, then it would appear that the economic performance of Milton Keynes was much greater than that of Outer London – East and North East. Indeed, if GVA per head was used as a measure of economic performance, Milton Keynes would be outperforming the rest of the UK (with a GVA per head 43% above the UK average), while the economic performance of East and North East Outer London would be well below (32%) the UK average. However, such a conclusion would be misleading.
The difference between these two subregions is due to commuting flows rather than differences in labour productivity. High levels of GVA per head compared with GVA per hour worked and GVA per filled job result mainly from high levels of in commuting flows. This is true for Milton Keynes and for many other urban centres such as Inner London, Edinburgh, Glasgow, Bristol and Nottingham.
The opposite is also true. Low levels of GVA per head compared with GVA per hour worked and GVA per filled job result mainly from high levels of out commuting flows. This was the case of Outer London – East and North East. This pattern is typical in many rural and suburban regions such as East Lothian and Midlothian, South Nottinghamshire, Sefton, Medway and Wirral.
Labour productivity measures provide therefore a more comparable indicator of productivity as they measure both the numerator and the denominator on a workplace basis and only account for those that directly contribute to the GVA.
GVA per hour worked and GVA per filled job can both be used as measures of labour productivity, but these two measures are different. GVA per hour worked apportions GVA to the total hours worked by the workforce; GVA per filled job apportions GVA to the number of jobs in the subregion.
As shown in the example depicted in Figure 7, although Milton Keynes and Outer London – East and North East had a very similar GVA per hour worked, there was a slight difference in the GVA per filled job. When using GVA per filled job as a measure of productivity, Milton Keynes has a slightly higher productivity than Outer London – East and North East.
This happens because the average of hours worked per job varies from subregion to subregion as a result of differences in labour market structure and working patterns. For example, a subregion with high levels of part-time employment will tend to have lower average hours worked.
GVA per filled job does not take into consideration regional labour market structures or different working patterns, such as the possible of mix of part-time and full-time workers, home workers and job share availability. For this reason, GVA per hour worked is a more comprehensive indicator of labour productivity and the preferred measure at sub national level.
Note that GVA per hour worked data are currently available for the period 2004-2011 and GVA per filled job data are available for the period 2002-2010
The subregional productivity data in this article have been compiled to be consistent with the regional productivity data published in the quarterly ONS Labour Productivity release.
Both regional and subregional productivity measures are produced by ONS on a nominal basis only. In other words, there is no separation of volume and price in the final output. As such, different levels of nominal productivity across different subregions will be impacted by any difference in prices between these subregions, in addition to differences in production volumes per input.
The methodology used in this article has changed slightly from that used in the previous subregional productivity article published in March 2012. The full revised methodology is outlined below.
Data accompanying this article are based on the NUTS geographical classification that came into use on 1 January 2012. More information on this is available in the background notes section.
Regional productivity data are published by ONS in the ‘Productivity Measures by Region’ table, which is included in the quarterly Labour Productivity release. This regional table includes two productivity measures; GVA per filled job and GVA per hour worked. The subregional productivity data have been compiled to be consistent with the data in this regional table.
This requires ensuring that the subregional measures of GVA, jobs and hours are all consistent with the regional totals. The methodology is therefore concerned with how best to apportion the regional totals to the subregional areas. The approach taken is as follows:
Regional GVA data are published by ONS as either smoothed (Headline) GVA or unsmoothed GVA. Regional (NUTS 1) productivity calculations use the unsmoothed workplace based GVA series, to be consistent with the labour input series used, which are both unsmoothed and workplace based. The aim in the subregional productivity calculations is to apportion out, to NUTS 2 and NUTS 3 subregions, the NUTS 1 GVA series used in the regional productivity estimates, that is, the unsmoothed workplace based GVA at current basic prices series (Table 1.9 in the regional GVA document included in the useful links).
The GVA data being used in these subregional productivity calculations differ slightly from the GVA data series currently published by ONS for NUTS 2 and NUTS 3 subregions. This is because, while the published subregional GVA data are constrained to the Headline NUTS 1 GVA series, data being used in these subregional productivity calculations have been constrained to the unsmoothed NUTS 1 GVA series.
At the regional level, GVA per filled job is calculated using a ‘Productivity Jobs’ series as the denominator. This is compiled from four components; employee jobs, self employed jobs, government supported trainees (GST) and members of Her Majesty’s Forces. For consistency purposes, the regional ‘Productivity Jobs’ series is benchmarked to the national ‘Productivity Jobs’ series, on a quarterly basis. To produce annual totals for regional Productivity Jobs, an average of the four quarters in the year are taken.
For subregional geographies, the ‘Total Jobs’ data series is used to apportion regional productivity jobs to NUTS 2 and NUTS 3 subregions. This Total Jobs measure is a workplace based measure of jobs that ONS produces principally for use in calculating job densities at regional and subregional level. Total jobs data comprise employees (from the Business Register Employment Survey), self-employment jobs (from the Annual Population Survey), government-supported trainees (from Department for Education and Department for Work and Pensions) and HM Forces (from Ministry of Defence).
The total jobs series is used to calculate the proportions of regional jobs within each subregion for each year. These results are then used to apportion the regional ‘productivity jobs’ data series to the subregional level.
At the national and regional level, GVA per hour worked data are calculated using a ‘Productivity Hours’ series as the denominator. These data are calculated quarterly, based mostly using Labour Force Survey, and an annual total is constructed as the average of the four quarters in the calendar year.
At subregional level, only annual productivity data are being produced. Therefore, the Annual Population Survey (APS) is used rather than the Labour Force Survey as it has a larger sample size1. The process involves calculating total hours for each subregion as the sum of employee hours, self employment hours, hours worked in government training schemes and hours worked by HM Forces.
Employee hours are calculated by using the APS to estimate, for each subregion, the average hours worked per employee job by industry. These industry average hours are then multiplied by the number of employee jobs for each industry in each subregion. For the period from 2008 onwards, the number of employee jobs by industry is derived from the Business Register and Employment Survey (BRES). Prior to that, employee jobs by industry were derived from the Annual Business Inquiry (ABI)2. Self employment hours are calculated from the APS. For government training schemes and HM Forces, the regional totals are allocated to subregions based on each subregion’s share of regional employee plus self employment hours, as calculated in the previous stage.
Adding together the sum of employee hours, self employment hours, hours worked in government training schemes and hours worked by HM Forces provides a total hours estimate for each subregion. Once calculated, this NUTS 2 and NUTS 3 subregional data is then constrained regionally to the NUTS 1 ‘Productivity Hours’ data to ensure consistency with regional productivity data.
Data in this article have been revised in comparison to the article published in October 2011. The revisions are due mostly to:
Revisions in the regional and subregional GVA estimates that form the numerator of productivity calculations3
Revisions in the regional productivity estimates, as a result of the former revisions and methodology revisions4
Changes to NUTS 3 areas. This publication uses the most recent 2010 NUTS Classification that came into force on the 1 January 2012.
Changes in the methodology used to derive subregional productivity estimates.
The changes to the methodology used to derive subregional productivity estimates consists in improvements to the method used to estimate the hours worked by employees at NUTS 3 level. In the previous method, the APS was used to derive the overall average employee hours worked per job for the subregion. This was then multiplied by the number of employee jobs in the subregion derived from either BRES or ABI.
The new method introduces an industry split into the method. Therefore, employees average hours worked are now derived from the APS for each industry section in each subregion, which is then multiplied by the number of employee jobs for each industry section in each subregion derived from BRES and ABI. BRES and ABI are both business surveys, providing therefore more reliable estimates of the number of employees by industry, and it is considered preferable to use this industry split than the one assumed within the APS survey.
A project is underway to develop estimates of real regional and subregional GVA growth using a production approach. The future development of such estimates would lead to an improvement in the quality of the regional and subregional productivity data that ONS is able to produce. This is because it would allow for a separation of volume and price in the final outputs. This is currently not possible when calculating productivity using regional and subregional GVA calculated using the income approach.
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of progress to date on development of the production approach to regional and subregional GVA was published in March 2012.
Subregional productivity data are produced in this article for the NUTS 2 and NUTS 3 geographies. Nomenclature of Units for Territorial Statistics (NUTS) is a geography developed by the European Union to allow comparison of regional and subregional data across the EU-25 member states. A number of ONS regional and subregional outputs are produced based on the NUTS geography. These include regional and subregional GVA. These GVA data are an input in the calculation of subregional productivity. Each NUTS 3 subregion covers the same area as either a single local authority or a combination of two or more adjacent local authorities.
The data accompanying this article are based on the NUTS geographical classification that came into use on 1 January 2012.
Compared with the NUTS classification used in previous articles, the new classification involves changes to the following NUTS 3 subregions, Bedfordshire CC, Cheshire CC, Northamptonshire, Dudley and Sandwell, Walsall and Wolverhampton, Halton and Warrington, East Merseyside, and Calderdale, Kirklees and Wakefield as well as the NUTS 2 subregions of Cheshire and Merseyside.
Unsmoothed time series data at small geographies such as NUTS 2 and NUTS 3 tend to show volatility, created by sampling and non-sampling errors. Therefore, a five year weighted average has been used to remove this volatility and produce a smoothed time-series. The results presented in this article are based on the smoothed subregional productivity data series. It should be noted that when calculating the subregional productivity data, unsmoothed data has been used at all times. The smoothing process has only been applied to the final results. For any users who would like to make use of the unsmoothed results, these are included in the data reference table of this publication.
The latest GVA per hour worked data available are for 2011 and the latest GVA per job data available is for 2010. The timeliness of the data is determined by the release calendar of the subregional GVA data, the total jobs data and the Annual Population Survey from which the hours worked are extracted. Subregional GVA data for 2012 will be available in December 2013 and an update to this article will follow shortly afterwards.
Time Series Analysis
The article has focused on a cross-sectional analysis of the subregional productivity data. However, data presented in this publication cover a period of up to nine years and, with due care, it is also possible to look at the economic performance of the NUTS 2 and NUTS 3 subregions over this period of time.
Caution is needed when carrying out a change over time analysis of the subregional productivity data. Particularly for NUTS 3 data, there is volatility in the data that arises from the smaller survey samples inherent within estimates for smaller geographic areas. It is for this reason that smoothed data is presented in this article. The smoothed data reduce the volatility by using weighted data from up to five years in producing the estimate for each year.
When using this smoothed data for time-series analysis, examining a particular year-on-year change does not really make sense, because each year’s data are already a weighted average of a number of different years. Therefore, to examine a year-on-year change, for example from 2010 to 2011, the only suitable method would be to use the unsmoothed data that are available in the accompanying reference tables. However, because of the volatility of the data, this year-on-year change may well be due to the volatility arising from the sample errors, as opposed to a ‘true’ change in the data. Furthermore, in the absence of confidence intervals for the subregional productivity data, it is very difficult to determine which actually the case is.
In view of this, time series analysis of the subregional productivity data is better done over a longer period of time. Trends over a longer period of time are less likely to be the result of the volatility around any single year estimate and more likely to be showing a change in the economic performance of the subregion. Such a trend should show up in the smoothed data, as well as the unsmoothed data, so using the smoothed data would be appropriate when considering the trend over the full data time series.
When looking at changes over time, it is also important to keep in mind that the productivity data in this publication are presented as indices. The productivity index shows how well a subregion has performed compared with the rest of the UK, that is, the UK average (100). Therefore, a decrease in the productivity index number of a subregion does not necessarily mean that the subregion’s productivity has decreased in actual terms; it rather means that the subregion has performed relatively worse than the rest of the UK over the period. In other words, its actual productivity level may have improved, but at a slower rate than the UK overall, thus worsening its performance in terms of the UK=100 index. Similarly, an increase in the productivity index number means that a subregion's productivity has performed better than the rest of the UK.
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