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 dataset gathers historical series on the funding and enrolment in the UK public education system from 1833 to 2019. Funding and enrolment are distributed by level of education, funders and economic categories. It is based on the method of quantitative history which follows the principles of national accounting and provides a stable frame to integrate financial and other data, and allow comparisons across time and space

This dataset is about books. It has 1 row and is filtered where the book is The economic impacts of UK labour productivity : enhancing industrial policies and their spillover effects on the energy system. It features 7 columns including author, publication date, language, and book publisher.

Abstract copyright UK Data Service and data collection copyright owner. This study is comprised by the data collected for a wider project exploring the historical relationship between higher education and the UK economy. The project sought to provide a long-term explanation of the relationships between funding, widening access and socio-economic aspects of higher education. Three main areas were considered: -The provision of an in-depth historical account and analysis of the numbers and extent of students and staff for the purposes of evaluating the main characteristics of UK higher education development back the 1920s. -The provision of an in-depth historical account and evaluation of levels and structures of income and expenditure in higher education -The interpretation of these data with reference to major socio-economic indicators. Main Topics: This study is a collation and analysis of statistics on UK higher education which refers to pre-1992 universities and includes all institutions delivering degrees afterwards. The dataset, which gathers historical series on funding and development of universities from the early 1920s, is the result of research into primary and secondary governmental and institutional sources. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. No sampling (total universe) Compilation or synthesis of existing material

Abstract copyright UK Data Service and data collection copyright owner.The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online. This study assembles historical data from the National Insurance system, plus some data from trade union welfare systems gathered and published by the Board of Trade Labour Department. The data were computerised by the Great Britain Historical GIS Project. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales. Most of the data here was originally published by the Ministry of Labour, either in the Labour Gazette, later the Employment Gazette, or in the specialised Local Unemployment Index (LUI), published between 1927 and 1939. The largest dataset here is a complete transcription of the LUI data for each January, April, July and October from January 1927 to July 1939 inclusive, the most detailed information that exists on the geography of the inter-war depression, other than the 1931 census. Unlike census data, these data concern a wide range of regions, "divisions", "districts", towns and sometimes areas within towns, seldom defined (the LUI data do list counties). The study therefore also includes two specially constructed gazetteers which attempt to provide towns and areas within towns with point coordinates. Another limitation is that these data generally provide counts of the unemployed, but not counts of the insured, or numbers in work, so calculation of rates often requires data from other sources such as the census. The study also includes two transcriptions from unpublished tabulations in the National Archives, relating to unemployment in 1928 and 1933. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.For the second edition (February 2024), the data was updated; data running up to 1974 has been added and the former study 3711 has been incorporated.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Nick Fewings on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2021, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2021, and, therefore, combine data collected in three waves (Waves 11, 12 and 13). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2021 dataset is the second of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2022 are included), data structure and additional supporting information can be found in the document '9193_calendar_year_dataset_2021_user_guide'.

As multi-topic studies, the purpose of Understanding Society is to understand the short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move, they are followed within the UK, and anyone joining their households is also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7) and includes a telephone mop-up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended, and the survey has been conducted by web and telephone only but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020, an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset.

Further information may be found on the Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2021 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see the document '9194_eul_vs_sl_variable_differences' for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2021 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,900 variables.

Money Supply M3 in the United Kingdom increased to 3676359 GBP Million in July from 3645165 GBP Million in June of 2025. This dataset provides - United Kingdom Money Supply M3 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The 20,000+ registered companies with a registered address in Glasgow. The information is extracted from Companies House. It includes the company name, number, category (private limited, partnership), registered address, postcode, industry (SIC code), status (ex: active or liquidation), incorporation date... It is likely that some companies may just lie off Glasgow City Council's boundary. If you find a problem in the data, you can check the source either in the full UK list or by looking up a company or let us know. The data dictionary supplied by Companies House can be viewed here. There is also a data dictionary with field names and meanings contained in the resources. This dataset does not imply: - a partnership with Companies House - an endorsement by Companies House - a product approval by Companies House Licence: None glasgow-post-codes-py.txt - https://dataservices.open.glasgow.gov.uk/Download/Organisation/cc57ac4b-12d5-43b1-ad25-434638eec18c/Dataset/3093e34f-6dcb-4980-840b-965421c1b091/File/c2634107-bd43-4537-adb8-9046aeed844e/Version/c8fde78e-5396-4293-ac35-6f6c96a5d642

Daily data showing SAP of gas, and rolling seven-day average, traded in Great Britain over the On-the-Day Commodity Market (OCM). These are official statistics in development. Source: National Gas Transmission.

This dataset contains the code, input sheets, set-up guide and documentation for the EVOLVE research project (https://evolveenergy.eu/) economic dispatch model of Great Britain. Within this research project, a novel modelling framework has been developed to quantify the potential benefit of including higher proportions of ocean energy within large-scale electricity systems. Economic dispatch modelling is utilised to model hourly supply-demand matching for a range of sensitivity runs, adjusting the proportion of ocean energy within the generation mix. The framework is applied to a 2030 case study of the power system of Great Britain, testing installed wave or tidal stream capacities ranging from 100 MW to 10 GW. This dataset contains all of the data, code and documentation required to run this economic dispatch model. The project results found that for all sensitivity runs, ocean energy increases renewable dispatch, reduces dispatch costs, reduces generation required from fossil fuels, reduces system carbon emissions, reduces price volatility, and captures higher market prices. The development of this model, and analysis of the model results, is described in detail in a journal paper (currently in press). A preprint of this paper is included within the folder. It can be referenced as: S. Pennock, D.R. Noble, Y. Verdanyan, T. Delahaye and H. Jeffrey (2023). 'A modelling framework to quantify the power system benefits from ocean energy deployments'. Applied Energy, Volume 347, 1 October 2023, 121413 ( https://doi.org/10.1016/j.apenergy.2023.121413 ).

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2021, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2021, and, therefore, combine data collected in three waves (Waves 11, 12 and 13). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2021 dataset is the second of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2022 are included), data structure and additional supporting information can be found in the document '9194_calendar_year_dataset_2020_user_guide'.

As multi-topic studies, the purpose of Understanding Society is to understand the short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move, they are followed within the UK, and anyone joining their households is also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7) and includes a telephone mop-up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended, and the survey has been conducted by web and telephone only but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020, an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset.

Further information may be found on the Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2021 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see xxxx_eul_vs_sl_variable_differences for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2021 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,900 variables.

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

QUEST Fish was led by Dr Manuel Barange (PML) with 18 co-investigators from POL, PML, CEFAS, University of Plymouth, University of Portsmouth, CSIC (Spain), UEA, WorldFish Centre, IPSL, ICES (Denmark), Met Office, IRD (Paris) and University of North Carolina, as part of QUEST (Quantifying and Understanding the Earth System) This dataset collection contains global fish biomass estimates from the Global Coastal-Ocean Modelling System. QUEST-Fish has delivered a near-global assessment of consequences of climate change for fisheries, demonstrating excellent and innovative bridging of marine biogeochemistry models and socio-economics. QUEST-Fish specifically focused on the added impacts that climate change is likely to cause on global fish production, and on the subsequent additional risks and vulnerabilities to human societies. The team have demonstrated the broad capability of an integrated regional coastal/shelf seas model system. The physical-ecological POLCOMS-ERSEM model that underpinned the research was developed for Europe’s regional seas. Its application to 20 Large Marine Ecosystems (coastal bioregions) worldwide, covering two-thirds of the world’s fish catch, has been critically evaluated and found adequate for most regions (the physical and biogeochemical differences of the upwelling region off Peru presents challenges, with the climate impact likely to be over-expressed in the fisheries projection output).

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

The first census report to tabulate social class was 1951, but this collection also includes a table from the Registrar-General's 1931 Decennial Supplement which drew on census occupational data to tabulate social class by region. In 1961 and 1971 the census used a more detailed classification of Socio-Economic Groups, from which the five Social Classes are a simplification.

This is a new edition. Data from the Census of Scotland have been added for 1951, 1961 and 1971. Wherever possible, ID numbers have been added for counties and districts which match those used in the digital boundary data created by the GBH GIS, greatly simplifying mapping.

Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.

The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The data are divided into 'enterprises' and 'local units'. An enterprise is the overall business organisation. A local unit is a 'plant', such as a factory, shop, branch, etc. In some cases, an enterprise will only have one local unit, and in other cases (such as a bank or supermarket), an enterprise will own many local units.

For each company, data are available on employment, turnover, foreign ownership, and industrial activity based on Standard Industrial Classification (SIC)92, SIC 2003 or SIC 2007. Year of 'birth' (company start-up date) and 'death' (termination date) are also included, as well as postcodes for both enterprises and their local units. Previously only pseudo-anonymised postcodes were available but now all postcodes are real.

The ONS is continually developing the BSD, and so researchers are strongly recommended to read all documentation pertaining to this dataset before using the data.

Linking to Other Business Studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest Edition Information
For the sixteenth edition (March 2024), data files and a variable catalogue document for 2023 have been added.

Understanding Society (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates the British Household Panel Survey (BHPS), which began in 1991.

For full details of the main Understanding Society study, see SN 6614.

The Understanding Society: Waves 1-14, 2009-2023 and Harmonised BHPS: Waves 1-18, 1991-2009: Secure Access dataset contains British National Grid postcode grid references (at 1m resolution) for the unit postcode of each household surveyed, derived from the ONS National Statistics Postcode Directory (ONSPD). Grid references are presented in terms of Eastings and Northings, which are distances in metres (east and north, respectively) from the origin (0,0), which lies to the west of the Scilly Isles. Each grid reference is given a positional quality indicator to denote the accuracy of the grid reference. In the majority of cases, the assigned grid reference relates to the building of the matched address closest to the postcode mean. The grid references provided for Northern Ireland postcodes use the Irish National Grid system that covers all of Ireland and is independent of the British National Grid. No grid references are provided for postcodes in the Channel Islands and the Isle of Man.

The Secure Access version includes all files in the Special Licence version (see SN 6931 for full details), plus a file for each wave that contains four variables relating to the National Grid Reference for each household: easting, northing, positional quality indicator (w_osgrdind), and a variable identifying whether it relates to the British or Irish grid system. The Secure Access version also contains a data file with full dates of birth for Understanding Society and BHPS respondents, which includes the day of birth variable, which is only available in this study.

Related UK Data Archive studies:

The Secure Access version of the dataset has more restrictive access conditions than standard End User Licence or Special Licence access datasets (see 'Access' section). Further details and links to the less restrictive versions can be found on the Understanding Society series Key data webpage.

International Data Access Network (IDAN)

These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and the IDAN website.

Latest edition information

For the 17th edition (November 2024), Wave 14 data has been added. Other minor changes and corrections have also been made to Waves 1-13. Please refer to the revisions document for full details.

m_hhresp and n_hhresp files updated, December 2024

In the previous release (17th edition, November 2024), there was an issue with household income estimates in m_hhresp and n_hhresp where a household resides in a new local authority (approx. 300 households in wave 14). The issue has been corrected and imputation models re-estimated and imputed values updated for the full sample. Imputed values will therefore change compared to the versions in the original release. The variables affected are w_ficountax_dv, w_fihhmnnet3_dv, n_fihhmnnet4_dv and n_ctband_dv.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain over 2,047 variables.

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

United Kingdom UK: Tariff Rate: Most Favored Nation: Weighted Mean: Manufactured Products data was reported at 3.220 % in 2016. This records a decrease from the previous number of 3.310 % for 2015. United Kingdom UK: Tariff Rate: Most Favored Nation: Weighted Mean: Manufactured Products data is updated yearly, averaging 3.340 % from Dec 1988 (Median) to 2016, with 29 observations. The data reached an all-time high of 7.590 % in 1990 and a record low of 3.100 % in 2000. United Kingdom UK: Tariff Rate: Most Favored Nation: Weighted Mean: Manufactured Products 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: Trade Tariffs. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;