As London looks ahead to a skills devolution deal, the capital has ambitions to create an adult skills system that is more responsive to the needs of the local economy. This work reflects on the area based review which will shape the future of the Further Education sector in London. Analysis by GLA Economics sets out what drives London’s economy, and what this means for future skills needs. In this series of papers we analyse the demand for jobs and skills to inform the Government’s area reviews of post-16 education and training, covering four London sub-regions (working papers 76-79). Thanks to London’s excellent transport links, the job opportunities available to learners are wider than a particular sub-region. The 2011 Census shows that less than half of all workers in London (48%) live in the same sub-regional area as their place of work. This calls for a broader, pan-London view (working paper 75). https://www.london.gov.uk/business-and-economy-publications/skills-londons-economy

The indicators and analysis presented in this bulletin are based on responses from the new voluntary fortnightly business survey, which captures businesses responses on how their turnover, workforce prices, trade and business resilience have been affected in the two week reference period. These data relate to the period 6 April 2020 to 19 April 2020.

The Council Plan for 2011-2015 has been succeeded by the new Council Plan for 2015-2019 and the Priority Board framework has been replaced. Please refer to the Executive Member Portfolio and York Monitor sections of the Open Data site for the council’s latest performance information.

This dataset is about book subjects. It has 1 row and is filtered where the books is British economic growth,1688-1959 : trends and structure. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset contains estimates of green jobs using an industry approach

The Business Structure Database is managed by the Secure Data Service (SDS) and can only be accessed through secure conditions. The ‘domestic use’ input-output matrix, contains domestic trade flows describing intermediate demand between Standard-Industrial-Classification (SIC) coded sectors. This was obtained from the ONS.

GRIT (‘Geospatial Restructuring of Industrial Trade’) is an ESRC-funded project in the School of Geography at the University of Leeds. An energy revolution must take place if the worst effects of climate change are to be avoided. Even without the impact this may have (eg through carbon pricing), fuel costs have a very uncertain future. GRIT has two aims:

create a fine-grained picture of the current spatial structure of the UK economy

consider how changing fuel prices could alter that structure over the long term. GRIT examines the web of connections between businesses in the UK to identify sectors and locations facing the greatest changes.

GRIT will work with a unique dataset: the Business Structure Database contains information for nearly every UK business, including location and sector classification. This will be linked to sectoral trade flow data. These two sources offer an opportunity to map the current spatial distribution of economic activity in the UK and to think about how that distribution may change in the future. GRIT combines this data-driven approach with a plan to engage with organisations directly affected. GRIT will work closely with a small number of organisations and engage others through the project website.

This dataset is about book series. It has 2 rows and is filtered where the books is Austerity politics and UK economic policy. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Welcome to the City Data Strategy for London. This document is built around the following vision: We want London to have the most dynamic and productive City Data Market in the world. In our City Data Market, the capabilities, talents and capacity of all our city data partners will impact on our huge social, economic and service-based challenges. To make this happen, friction in the sharing and value-driven exploitation of city data will be reduced to a minimum. City data will be recognised as part of the capital’s infrastructure. We will use it to save money, incubate innovation and drive economic growth. And London will achieve global renown for data impact. We will of course use this strategy in support of City Hall initiatives like the Smart London Plan, but we acknowledge at the outset that the Greater London Authority simply cannot deliver this strategy on its own. Indeed, data knows no boundaries and is hard to contain, so we should not try. Our simple aim is to make sure that London, its economy and its communities are able to derive maximum benefit from the undoubted potential London’s Data Market can deliver. It therefore signals the start of a plan which will actively integrate and mobilise all the ‘working parts’ of the city data economy. We want the various audiences that the Strategy is aimed at to engage with it. We will listen and evolve the strategy as this rapidly developing part of the cityscape develops.

Annual estimates of balanced UK regional gross domestic product (GDP). Current price estimates and chained volume measures for local authority districts, London boroughs, unitary authorities and Scottish Council areas.

Monthly dataset showing change in sales and jobs recorded by Xero, an online accounting software platform. This dataset is updated on a quarterly basis. These are official statistics in development. Source: Xero.

This dataset is about book subjects. It has 3 rows and is filtered where the books is A general view of the rural economy of England, 1538-1840. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Camden’s Local Economic Assessment (LEA) describes the position of Camden in the London economy and discusses the economic challenges and opportunities for Camden. This should provide the Council and its partners with a sound understanding of the borough’s economic position. The LEA will be used to inform local decisions on economic development and social policy in the context of significantly reduced public funding. Local authorities are required to prepare an LEA under the Local Democracy, Economic Development and Construction Act 2009. See also the Central London Local Economic Assessment at http://www.centrallondonforward.gov.uk/work-programme/economy/lea

United Kingdom UK: Part Time Employment: % of Total Employment data was reported at 33.690 % in 2017. This records a decrease from the previous number of 34.070 % for 2016. United Kingdom UK: Part Time Employment: % of Total Employment data is updated yearly, averaging 33.310 % from Dec 1983 (Median) to 2017, with 35 observations. The data reached an all-time high of 36.160 % in 2010 and a record low of 27.190 % in 1983. United Kingdom UK: Part Time Employment: % of Total Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Employment and Unemployment. Part time employment refers to regular employment in which working time is substantially less than normal. Definitions of part time employment differ by country.; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Relevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsEconomic activityThis dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by economic activity status. The estimates are as at Census Day, 21 March 2021.Definition: People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were:in employment (an employee or self-employed)unemployed, but looking for work and could start within two weeksunemployed, but waiting to start a job that had been offered and acceptedIt is a measure of whether or not a person was an active participant in the labour market during this period. Economically inactive are those aged 16 years and over who did not have a job between 15 March to 21 March 2021 and had not looked for work between 22 February to 21 March 2021 or could not start work within two weeks.The census definition differs from International Labour Organization definition used on the Labour Force Survey, so estimates are not directly comparable.This dataset contains details for Leicester city and England overall.

As London looks ahead to a skills devolution deal, the capital has ambitions to create an adult skills system that is more responsive to the needs of the local economy. This work reflects on the area based review which will shape the future of the Further Education sector in London. Analysis by GLA Economics sets out what drives London’s economy, and what this means for future skills needs. In this series of papers we analyse the demand for jobs and skills to inform the Government’s area reviews of post-16 education and training, covering four London sub-regions (working papers 76-79). Thanks to London’s excellent transport links, the job opportunities available to learners are wider than a particular sub-region. The 2011 Census shows that less than half of all workers in London (48%) live in the same sub-regional area as their place of work. This calls for a broader, pan-London view (working paper 75). https://www.london.gov.uk/business-and-economy-publications/skills-londons-economy

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Household datasets
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.

Change to coding of missing values for household series
From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

End User Licence and Secure Access QLFS Household datasets
Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Changes to variables in QLFS Household EUL datasets
In order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

A London series by industry and a borough series (no industry) 1984 to 2011. These data have been used by GLA Economics to forecast long-term employment projections and within GLA Economics' models for exports, tourism and life sciences. The two series are: 1. London Employment Jobs: a London level series which includes Employee Jobs and Self-Employment Jobs from 1984 to 2010 with industries by GLA Economics sectors on a SIC 2007 basis 2. A borough level Employee Jobs series 1984 to 2010 (no industry breakdown) Methods and assumptions behind the data are explained in the GLA Economics Working Paper 52 Data Download Notes Borough Data Source: ONS Employee Jobs, ONS Business Surveys, GLA Economics assumptions Data are for employees and do not include self-employment jobs Data are adjusted for series discontinuities only - no additional corrections have been made 1998 data for Richmond and 2000 data for Greenwich where employee increases are high have not been adjusted Sector Data Source: ONS Employee Jobs, LFS, GLA Economics assumptions 2010 data are provisional Data include employees and self-employment jobs