The US government is a major producer of economic and financial data, statistics, analysis, and forecasts that are gathered, compiled, and published as public goods for use by citizens, government agencies, researchers, nonprofits, and the business community. There is no market transaction in the publication and dissemination of these government data and therefore no market-determined value. The purpose of this paper is to outline and augment our understanding of the value of government data for business decision-making. We provide an overview of the topic, including results from government reports and a private sector survey. We then provide concrete examples of how these government data are used to make business decisions focusing on three sectors: automotive, energy, and financial services. Examples of new initiatives by the federal government to open access to more data, exploiting technology advances associated with the internet, cloud storage, and software applications, are discussed. With the significant growth in the digital economy, we also include discussion and insights around how digital platform companies utilize government data in conjunction with their privately generated data (or "big data") to foster more informed business decisions.

The statistical data generated through the administration of the Federal milk order program is recognized widely as one of the benefits of this program. These data provide comprehensive and accurate information on milk supplies, utilization, and sales, as well as class prices established under the orders and prices paid to dairy farmers (producers). The sources of this data are monthly reports of receipts and utilization, producer payroll reports, and reports of nonpool handlers filed by milk processors (handlers) subject to the provisions of the various milk orders. The local market administrator (MA) uses these reports to determine pool obligations under the order and to verify proper payments to producers. Auditors employed by the MA review handler records to assure the accuracy of reported information. Reporting errors are corrected; if necessary, pool obligations are revised. After the pool obligations have been determined the local market administrator summarizes the individual handler reports and submits a series of order summary reports to the Market Information Branch (MIB) in Dairy Programs. The MIB summarizes the individual order data and disseminates this information via monthly, bimonthly, and annual releases or publications. Since milk marketing order statistics are based on reports filed by the population of possible reporting firms and not a sample, these statistics are comprehensive. Also, since these individual firm reports are subject to audit and verification, these statistics are accurate. The Federal milk order statistics database contains historical information, beginning in January 2000, generated by the administration of the Federal milk order program. Most of the information in the database has been published previously by the Market Information Branch in Dairy Programs either on its web site or in the Dairy Market News Report. New users are encouraged to use the "User Guide" to learn how to navigate the search screens. If you are interested in a description of the Federal milk order statistics program, or want current data, in ready made table form, use the "Current Information" link.

This table contains information on the balance sheet of the general government sector. The information is limited to financial assets and liabilities. For each reporting period the opening and closing stocks, financial transactions and other changes are shown. Transactions are economic flows that are the result of agreements between units. Other changes are changes in the value of assets or liabilities that do not result from transactions such as revaluations or reclassifications. The figures are consolidated which means that flows between units that belong to the same sector are eliminated. As a result, assets and liabilities of subsectors do not add up to total assets or liabilities of general government. For example, loans of the State provided to social security funds are part of loans of the State. However, these are not included in the consolidated assets of general government, because it is an asset of a government unit with a government unit as debtor. Financial assets and liabilities in this table are presented at market value. The terms and definitions used are in accordance with the framework of the Dutch national accounts. National accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date. Data available from: Yearly figures from 1995, quarterly figures from 1999. Status of the figures: The figures for the period 1995-2022 are final. The figures for 2023 and 2024 are provisional. Changes as of 10 April 2025: Due to an error made while processing the data, the initial preliminary figures for the government financial balance sheet in 2024 were calculated incorrectly. This causes a downward revision in other accounts payable. Changes as of 26 March 2025: The figures for the fourth quarter of 2024 and annual figures for 2024 are available. The figures for the first three quarters of 2024 have been adjusted. When will new figures be published? Provisional quarterly figures are published three months after the end of the quarter. In September the figures on the first quarter may be revised, in December the figures on the second quarter may be revised and in March the first three quarters may be revised. Yearly figures are published for the first time three months after the end of the year concerned. Yearly figures are revised two times: 6 and 18 months after the end of the year. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the most actual figures. Revised yearly figures are published in June each year. Quarterly figures are aligned to the three revised years at the end of June. More information on the revision policy of Dutch national accounts and government finance statistics can be found under 'relevant articles' under paragraph 3.

This release provides the results of questions on animal health and welfare practices adopted by farmers. Link to main notice: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey#publications Survey methodology This release includes the results for the questions asked on business management practices. Comparisons to results from the previous business management practices module conducted in 2007/08 have where possible been included in this publication. Results from IT usage question were released on the 20 March 2013, for the detailed results please see: https://www.gov.uk/government/publications/farm-practices-survey-october-2012-computer-usage The Farm Business Survey (FBS) is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25,000 Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2011 there were just over 56,000 farm businesses meeting this criteria. In the 2011/12 survey, an additional module was included to collect information on business management practices from a sub-sample of farm businesses. The information collected covered (i) business management practices such as benchmarking, risk management, IT usage and management accounting, (ii) practices specific to animal health and welfare e.g. biosecurity, veterinary strategy, animal health plans, (iii) the environmental footprint of farming, GHG abatement, energy use and, (iv) climate change adaptation. When combined with other data from the survey this helps to explain farm businesses’ behaviour and how this varies with parameters such as farm type, farm size and performance. Completion of the business management practices module was voluntary with a response rate of 71% in 2011/12. The farms that responded to the business management practices module had similar characteristics to those farms in the main FBS in terms of farm type and geographical location. There is a smaller proportion of large and very large farms in the module subset than in the main FBS For further information about the Farm Business Survey please see: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey Data analysis The results from the FBS relate to farms which have a standard output of at least 25,000 Euros . Initial weights are applied to the FBS records based on the inverse sampling fraction for each design stratum (farm type by farm size). These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables. Completion of the business management practices module was voluntary and a sample of around 1,350 farms was achieved. In order to take account of non-response, the results have been reweighted using a method that preserves marginal totals for populations according to farm type and farm size groups. As such, farm population totals for other classifications (e.g. regions) will not be in-line with results using the main FBS weights, nor will any results produced for variables derived from the rest of the FBS (e.g. farm business income). Comparisons between 2007/08 and 2011/12 Results from the 2007/08 and 2011/12 business management practices modules are not directly comparable due to changes in the coverage of the survey and changes in the classification of farms for the 2010/11 campaign. In 2010/11 the survey was restricted to include farms which have at least 25,000 Euros of standard output; prior to this the survey was restricted to farms with ½ Standard Labour Requirement or more. The classification of farms into farm types was also revised for the 2010/11 Farm Business Survey, to bring the classification in line with European guidelines. Equivalent results from 2007/08 have been presented alongside 2011/12 results in many of the charts and tables; however comparisons should be treated with extreme caution due to the reasons given above. To enable more robust comparisons between the 2007/08 and 2011/12 business management practices module, we have examined the subset of farms that participated in both years (approximately 770 farms). For this subset of farms we have carried out significance testing using McNemar’s test to determine whether the differences observed between the two time periods are statistically significant. The McNemar’s test is applied to 2x2 contingency tables, with matched pairs of subjects, to determine whether the row and column marginal frequencies are equal. Where a statistically significant difference has been observed this has been indicated on the tables and charts for the full module results with a *. Commentary alongside the charts and tables will refer to this analysis rather than make comparisons with the 2007/08 data displayed. Accuracy and reliability of the results Where possible, we have shown 95% confidence intervals against the figures. These show the range of values that may apply to the figures. They mean that we are 95% confident this range contains the true value . They are calculated as the standard errors (se) multiplied by 1.96 to give the 95% confidence interval (95% CI). The standard errors only give an indication of the sampling error. They do not reflect any other sources of survey errors, such as non-response bias. The confidence limits shown are appropriate for comparing groups within the same year; they should not be used for comparing, different years’ results from the Farm Business Survey since they do not allow for the fact that in the FBS many of the same farms contributed in both years. We have also shown error bars on the figures in this notice. These error bars represent the 95% confidence intervals for the figures (as defined above).. Estimates based on less than 5 observations have been suppressed to prevent disclosure of the identity of the contributing farms. Estimates based on less than 15 observations have been highlighted in italics in the tables and should be treated with caution as they are likely to be less precise. Definitions Economic performance for each farm is measured as the ratio between economic output (mainly sales revenue) and inputs (costs+ unpaid labour). The higher the ratio, the higher the economic efficiency and performance. Performance bands based on economic performance percentiles are as follows: Low performers - farmers who took part in the Business Management Practices survey and were in the bottom 25% of economic performers in this sample Medium performers -farmers who took part in the Business Management Practices survey and were in the middle 50% of performers in this sample High performers - farmers who took part in the Business Management Practices survey and were in the top 25% of performers in this sample. These are based on economic performance in 2011/12. Availability of results Defra statistical notices can be viewed on the Food and Farming Statistics pages on the Defra website at https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/about/statistics. This site also shows details of future publications, with pre-announced dates.

"The Statistical Abstract is the nation's best known and most popular single source of statistics on the social, political, and economic organization of the country. The print version has been published since 1878, and a compact disc version has been available since 1993. Both are designed to serve as a convenient, easy-to-use statistical reference source and guide to statistical publications and sources. The extensive selection of statistics is provided for the United States, with selected d ata for regions, divisions, states, metropolitan areas, cities, and foreign countries from reports and records of government and private agencies. Software on the disc can be used to perform full-text searches, view official statistics, open tables as Lotus worksheets or Excel workbooks, and link directly to source agencies and organizations for supporting information. The disc contains over 1,500 tables from over 250 different governmental, private, and international organizations. Some of the topics are population; vital statistics; health and nutrition; education; law enforcement, courts and prison; geography and environment; elections; state and local government; federal government finances and employment; national defense and veterans affairs; social insurance and human services; labor force, employment, and earnings; income, expenditures, and wealth; prices; business enterprise; science and technology; agriculture; natural resources; energy; construction and housing; manufactures; domestic trade and services; transportation; information and communication; banking, finance, and insurance; arts, entertainment, and recreation; accommodation, food services, and other services; foreign commerce and aid; outlying areas; and comparative international statistics. Significant changes in the 2002 data include new data from the 2000 census and new tables that include data covering resident population's migration status, educational attainment, disability status, ancestry, place of birth, and language spoken at home as well as househol d income, poverty, and selected housing characteristics from the sample portion of the 2000 census. New tables cover topics such as unmarried households, state children's health insurance programs, limitation of activity level caused by chronic conditions, characteristics of homeschooled children, firearm-use offenders, home- based work and flexible work by workers, computer use in the workplace, employee benefits, and computer and Internet use." Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income (LMI) persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency. Most activities funded by the CDBG program are designed to benefit low- and moderate-income (LMI) persons. That benefit may take the form of housing, jobs, and services. Additionally, activities may qualify for CDBG assistance if the activity will benefit all the residents of a primarily residential area where at least 51 percent of the residents are low- and moderate-income persons, i.e. area-benefit (LMA). [Certain exception grantees may qualify activities as area-benefit with fewer LMI persons than 51 percent.]The Office of Community Planning and Development (CPD) provides estimates of the number of persons that can be considered Low-, Low- to Moderate-, and Low-, Moderate-, and Medium-income persons based on special tabulations of data from the 2016-2020 ACS 5-Year Estimates and the 2020 Island Areas Census. The Low- and Moderate-Income Summary Data may be used by CDBG grantees to determine whether or not a CDBG-funded activity qualifies as an LMA activity. The LMI percentages are calculated at various principal geographies provided by the U.S. Census Bureau. CPD provides the following datasets:Geographic Summary Level "150": Census Tract-Block Group.The block groups are associated with the HUD Unit-of-Government-Identification-Code for the CDBG grantee jurisdiction by fiscal year that is associated with each block group.Local government jurisdictions include; Summary Level 160: Incorporated Cities and Census-Designated Places, i.e. "Places", Summary Level 170: Consolidated Cities, Summary Level 050: County, and Summary Level 060: County Subdivision geographies.In the data files, these geographies are identified by their Federal Information Processing Standards (FIPS) codes and names for the place, consolidated city, or block group, county subdivision, county, and state.The statistical information used in the calculation of estimates identified in the data sets comes from the 2016-2020 ACS, 2020 Island Areas Census, and the Income Limits for Metropolitan Areas and for Non Metropolitan Counties. The data necessary to determine an LMI percentage for an area is not published in the publicly-available ACS data tables. Therefore, the Bureau of Census matches family size, income, and the income limits in a special tabulation to produce the estimates.Estimates are provided at three income levels: Low Income (up to 50 percent of the Area Median Income (AMI)); Moderate Income (greater than 50 percent AMI and up to 80 percent AMI), and Medium Income (greater than 80 percent AMI and up to 120 AMI). HUD is publishing the margin of error (MOE) data for all block groups and all places in the 2020 ACS LMISD. These data are provided within the LMISD tables.The MOE does not provide an expanded range for compliance. For example, a service area of 50 percent LMI with a 2 percent MOE would still be just 50 percent LMI for compliance purposes. However, the 2 percent MOE would inform the grantee about the accuracy of the ACS data before undergoing the effort and cost of conducting a local income survey, which is the alternative to using the HUD-provided data.CPD Notice 24-04 announced the publication of LMISD based on the 2020 ACS, and updated CPD Notice 19-02 as well as explains policy about the accuracy of surveys conducted pursuant to CPD Notice 14-013.Questions about the calculation of the estimates may be directed to Formula Help Desk.Questions about the use of the data should be directed to the staff of the CPD Field Office.

Background:
A household food consumption and expenditure survey has been conducted each year in Great Britain (excluding Northern Ireland) since 1940. At that time the National Food Survey (NFS) covered a sample drawn solely from urban working-class households, but this was extended to a fully demographically representative sample in 1950. From 1957 onwards the Family Expenditure Survey (FES) provided information on all household expenditure patterns including food expenditure, with the NFS providing more detailed information on food consumption and expenditure. The NFS was extended to cover Northern Ireland from 1996 onwards. In April 2001 these surveys were combined to form the Expenditure and Food Survey (EFS), which completely replaced both series. From January 2008, the EFS became known as the Living Costs and Food (LCF) module of the Integrated Household Survey (IHS). As a consequence of this change, the questionnaire was altered to accommodate the insertion of a core set of questions, common to all of the separate modules which together comprised the IHS. Some of these core questions are simply questions which were previously asked in the same or a similar format on all of the IHS component surveys. For further information on the LCF questionnaire, see Volume A of the LCF 2008 User Guide, held with SN 6385. Further information about the LCF, including links to published reports based on the survey, may be found by searching for 'Living Costs and Food Survey' on the ONS website. Further information on the NFS and Living Costs and Food Module of the IHS can be found by searching for 'Family Food' on the GOV.UK website.

History:
The LCF (then EFS) was the result of more than two years' development work to bring together the FES and NFS; both survey series were well-established and important sources of information for government and the wider community, and had charted changes and patterns in spending and food consumption since the 1950s. Whilst the NFS and FES series are now finished, users should note that previous data from both series are still available from the UK Data Archive, under GNs 33071 (NFS) and 33057 (FES).

Purpose of the LCF
The Office for National Statistics (ONS) has overall project management and financial responsibility for the LCF, while the Department for Environment, Food and Rural Affairs (DEFRA) sponsors the food data element. As with the FES and NFS, the LCF continues to be primarily used to provide information for the Retail Prices Index, National Accounts estimates of household expenditure, analysis of the effect of taxes and benefits, and trends in nutrition. The results are multi-purpose, however, providing an invaluable supply of economic and social data. The merger of the two surveys also brings benefits for users, as a single survey on food expenditure removes the difficulties of reconciling data from two sources.

Design and methodology The design of the LCF is based on the old FES, although the use of new processing software by the data creators has resulted in a dataset which differs from the previous structure. The most significant change in terms of reporting expenditure, however, is the introduction of the European Standard Classification of Individual Consumption by Purpose (COICOP), in place of the codes previously used. An additional level of hierarchy has been developed to improve the mapping to the previous codes. The LCF was conducted on a financial year basis from 2001, then moved to a calendar year basis from January 2006 (to complement the IHS) until 2015-16, when the financial year survey was reinstated at the request of users. Therefore, whilst SN 5688 covers April 2005 - March 2006, SN 5986 covers January-December 2006. Subsequent years cover January-December until 2014. SN 8210 returns to the financial year survey and currently covers April 2015 - March 2016.

Northern Ireland sample
Users should note that, due to funding constraints, from January 2010 the Northern Ireland (NI) sample used for the LCF was reduced to a sample proportionate to the NI population relative to the UK.

Family Food database:
'Family Food' is an annual publication which provides detailed statistical information on purchased quantities, expenditure and nutrient intakes derived from both household and eating out food and drink. Data is collected for a sample of households in the United Kingdom using self-reported diaries of all purchases, including food eaten out, over a two week period. Where possible quantities are recorded in the diaries but otherwise estimated. Energy and nutrient intakes are calculated using standard nutrient composition data for each of some 500 types of food. Current estimates are based on data collected in the Family Food Module of the LCFS. Further information about the LCF food databases can be found on the GOV.UK Family Food Statistics web pages.

Secure Access version
A Secure Access version of the LCF from 2006 onwards is available from the UK Data Archive under SN 7047, subject to stringent access conditions. The Secure Access version includes variables that are not included in the standard End User Licence (EUL) version, including geographical variables with detail below Government Office Region, to postcode level; urban/rural area indicators; other sensitive variables; raw diary information files (derived variables are available in the EUL) and the family expenditure codes files. Users are strongly advised to check whether the EUL version is sufficient for their needs before considering an application for the Secure Access version.

Occupation data for 2021 and 2022 data files
The ONS have 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. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.

For the fourth edition (May 2008), the value labels in the 'School' variable (in 'rawper' file) were amended. Users whose analysis includes this variable are advised to download a replacement version of the dataset. See study READ file (link below) for a full edition history.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income (LMI) persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency. Most activities funded by the CDBG program are designed to benefit low- and moderate-income (LMI) persons. That benefit may take the form of housing, jobs, and services. Additionally, activities may qualify for CDBG assistance if the activity will benefit all the residents of a primarily residential area where at least 51 percent of the residents are low- and moderate-income persons, i.e. area-benefit (LMA). [Certain exception grantees may qualify activities as area-benefit with fewer LMI persons than 51 percent.]The Office of Community Planning and Development (CPD) provides estimates of the number of persons that can be considered Low-, Low- to Moderate-, and Low-, Moderate-, and Medium-income persons based on special tabulations of data from the 2016-2020 ACS 5-Year Estimates and the 2020 Island Areas Census. The Low- and Moderate-Income Summary Data may be used by CDBG grantees to determine whether or not a CDBG-funded activity qualifies as an LMA activity. The LMI percentages are calculated at various principal geographies provided by the U.S. Census Bureau. CPD provides the following datasets:Geographic Summary Level "150": Census Tract-Block Group.The block groups are associated with the HUD Unit-of-Government-Identification-Code for the CDBG grantee jurisdiction by fiscal year that is associated with each block group.Local government jurisdictions include; Summary Level 160: Incorporated Cities and Census-Designated Places, i.e. "Places", Summary Level 170: Consolidated Cities, Summary Level 050: County, and Summary Level 060: County Subdivision geographies.In the data files, these geographies are identified by their Federal Information Processing Standards (FIPS) codes and names for the place, consolidated city, or block group, county subdivision, county, and state.The statistical information used in the calculation of estimates identified in the data sets comes from the 2016-2020 ACS, 2020 Island Areas Census, and the Income Limits for Metropolitan Areas and for Non Metropolitan Counties. The data necessary to determine an LMI percentage for an area is not published in the publicly-available ACS data tables. Therefore, the Bureau of Census matches family size, income, and the income limits in a special tabulation to produce the estimates.Estimates are provided at three income levels: Low Income (up to 50 percent of the Area Median Income (AMI)); Moderate Income (greater than 50 percent AMI and up to 80 percent AMI), and Medium Income (greater than 80 percent AMI and up to 120 AMI). HUD is publishing the margin of error (MOE) data for all block groups and all places in the 2020 ACS LMISD. These data are provided within the LMISD tables.The MOE does not provide an expanded range for compliance. For example, a service area of 50 percent LMI with a 2 percent MOE would still be just 50 percent LMI for compliance purposes. However, the 2 percent MOE would inform the grantee about the accuracy of the ACS data before undergoing the effort and cost of conducting a local income survey, which is the alternative to using the HUD-provided data.CPD Notice 24-04 announced the publication of LMISD based on the 2020 ACS, and updated CPD Notice 19-02 as well as explains policy about the accuracy of surveys conducted pursuant to CPD Notice 14-013.Questions about the calculation of the estimates may be directed to Formula Help Desk.Questions about the use of the data should be directed to the staff of the CPD Field Office.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

This table shows the net transactions for the current month, and the current and prior fiscal year-to-date, as well as account balances for the beginning of the current fiscal year and current accounting month and the close of the current accounting month. This activity is related to the means used to finance the budget deficit or to dispose of a budget surplus. An asset account would represent an asset to the United States Government, for example United States Treasury Operating Cash. A liability account would represent a liability to the United States Government, for example Borrowing from the Public. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

Road Safety Statistics releases and guidance about the data collection. Collision analysis tool for bespoke breakdowns of our data. STATS19 R package developed independently of DfT, offering an alternative way to access this data for those familiar with the R language. Latest data Provisional data for the first 6 months of 2024 published 28 November 2024. These are provisional un-validated data. Data included These files provide detailed road safety data about the circumstances of personal injury road collisions in Great Britain from 1979, the types of vehicles involved and the consequential casualties. The statistics relate only to personal injury collisions on public roads that are reported to the police, and subsequently recorded, using the STATS19 collision reporting form. This data contains all the non-sensitive fields that can be made public. Sensitive data fields, for example contributory factors data, can be requested by completing the sensitive data form and contacting the road safety statistics team at roadacc.stats@dft.gov.uk All the data variables are coded rather than containing textual strings. The lookup tables are available in the supporting documents section towards the bottom of the table. Data relating to the casualty and collision severity adjustment to account for changes in police reporting of severity is provided in separate files and can be joined using the appropriate record identifiers. Timing of data release Final annual data is released annually in late September following the publication of the annual reported road casualties Great Britain statistical publication. Individual years data is available for each of the last 5 years, with earlier years available as part of a single download. In addition, un-validated provisional mid-year data (covering January to June) is released at end November, to provide more up to date information Data revisions Except for the severity adjustments, data are not routinely revised those occasionally minor amendments to previous years can be made. Details of recent revisions are available, together with a request for any feedback on the approach to revising the data. The files published here represent the latest data.

Information on farm household income and farm household composition. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Farm Household Income and Household Composition, England

If you require the datasets in a more accessible format, please contact fbs.queries@defra.gsi.gov.uk

Background and guidance on the statistics

Information on farm household income and farm household composition was collected in the Farm Business Survey (FBS) for England for the first time in 2004/05. Collection of household income data is restricted to the household of the principal farmer from each farm business. For practical reasons, data is not collected for the households of any other farmers and partners. Two-thirds of farm businesses have an input only from the principal farmer’s household (see table 5). However, details of household composition are collected for the households of all farmers and partners in the business, but not employed farm workers.

Data on the income of farm households is used in conjunction with other economic information for the agricultural sector (e.g. farm business income) to help inform policy decisions and to help monitor and evaluate current policies relating to agriculture in the United Kingdom by Government. It also informs wider research into the economic performance of the agricultural industry.

This release gives the main results from the income and composition of farm households and the off-farm activities of the farmer and their spouse (Including common law partners) sections of the FBS. These sections include information on the household income of the principal farmer’s household, off-farm income sources for the farmer and spouse and incomes of other members of their household and the number of working age and pensionable adults and children in each of the households on the farm (the information on household composition can be found in Appendix B).

This release provides the main results from the 2013/14 FBS. The results are presented together with confidence intervals.

Survey content and methodology

The Farm Business Survey (FBS) is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25 thousand Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2013 there were just over 58 thousand farm businesses meeting this criteria.

Since 2009/10 a sub-sample of around 1,000 farms in the FBS has taken part in both the additional surveys on the income and composition of farm households and the off-farm activities of the farmer and their spouse. In previous years, the sub-sample had included over 1,600 farms. As such, caution should be taken when comparing to earlier years.

The farms that responded to the additional survey on household incomes and off-farm activities of the farmer and spouse had similar characteristics to those farms in the main FBS in terms of farm type and geographical location. However, there is a smaller proportion of very large farms in the additional survey than in the main FBS. Full details of the characteristic of responding farms can be found at Appendix A of the notice.

For further information about the Farm Business Survey please see: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey

Data analysis

The results from the FBS relate to farms which have a standard output of at least 25,000 Euros. Initial weights are applied to the FBS records based on the inverse sampling fraction for each design stratum (farm type by farm size). These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables. Completion of the additional survey on household incomes and off-farm activities of the farmer and spouse was voluntary and a sample of around 1,000 farms was achieved. In order to take account of non-response, the results have been reweighted using a method that preserves marginal totals for populations according to farm type and farm size groups. As such, farm population totals for other classifications (e.g. regions) will not be in-line with results using the main FBS weights, nor will any results produced for variables derived from the rest of the FBS (e.g. farm business income).

Accuracy and reliability of the results

We show 95% confidence intervals against the results. These show the range of values that may apply to the figures. They mean that we are 95% confident that this range contains the true value. They are calculated as the standard errors (se) multiplied by 1.96 to give the 95% confidence interval. The standard errors only give an indication of the sampling error. They do not reflect any other sources of survey errors, such as non-response bias. For the Farm Business Survey, the confidence limits shown are appropriate for comparing groups within the same year only; they should not be used for comparing with previous years since they do not allow for the fact that many of the same farms will have contributed to the Farm Business Survey in both years.

Availability of results

This release contains headline results for each section. The full set of results can be found at: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey#publications

Defra statistical notices can be viewed on the on the statistics pages of the Defra website at https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/about/statistics. This site also shows details of future publications, with pre-announced dates.

Data Uses

Data from the Farm Business Survey (FBS) are provided to the EU as part of the Farm Accountancy Data Network (FADN). The data have been used to help inform policy decisions (e.g. Reform of Pillar 1 and Pillar 2 of Common Agricultural Policy) and to help monitor and evaluate current policies relating to agriculture in England (and the EU). It is also widely used by the industry for benchmarking and informs wider research into the economic performance of the agricultural industry.

User engagement

As part of our ongoing commitment to compliance with the Code of Practice for Official Statistics http://www.statisticsauthority.gov.uk/assessment/code-of-practice/index.html, we wish to strengthen our engagement with users of these statistics and better understand the use made of them and the types of decisions that they inform. Consequently, we invite users to make themselves known, to advise us of the use they do, or might, make of these statistics, and what their wishes are in terms of engagement. Feedback on this notice and enquiries about these statistics are also welcome.

Definitions

Household income of the principal farmer Principal farmer’s household income has the following components: (1) The share of farm business income (FBI) (including income from farm diversification) attributable to the principal farmer and their spouse. (2) Principal farmer’s and spouse’s off farm income from employment and self-employment, investment income, pensions and social payments. (3) Income of other household members. The share of farm business income and all employment and self-employment incomes, investment income and pension income are recorded as gross of income tax payments and National Insurance contributions, but after pension contributions. In addition, no deduction is made for council tax.

Household A household is defined as a single person or group of people living at the same address as their only or main residence, who either share one meal a day together or share the living accommodation. A household must contain at least one person who received drawings from the farm business or who took a share of the profit from the business.

Drawings Drawings represent the monies which the farmer takes from the business for their own personal use. The percentage of total drawings going to each household is collected and is used to calculate the total share of farm business income for the principal farmer’s household.

Mean Mean household income of individuals is the ”average”, found by adding up the weighted household incomes for each individual farm in the population for analysis and dividing the result by the corresponding weighted number of farms. In this report average is usually taken to refer to the mean.

Percentiles These are the values which divide the population for analysis, when ranked by an output variable (e.g. household income or net worth), into 100 equal-sized groups. E.g. twenty five per cent of the population would have incomes below the 25th percentile.

Median Median household income divides the population, when ranked by an output variable, into two equal sized groups. The median of the whole population is the same as the 50th percentile. The term is also used for the midpoint of the subsets of the income distribution

Quartiles Quartiles are values which divide the population, when ranked by an output variable, into four equal-sized groups. The lowest quartile is the same as the 25th percentile. The divisions of a population split by quartiles are referred to as quarters in this publication.

Quintiles Quintiles are values which divide the population, when ranked by an output variable, into five equal-sized groups. The divisions of a population split by quintiles are referred to as fifths in this publication.

Assets Assets include

This release provides the results of questions on animal health and welfare practices adopted by farmers. Link to main notice: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey#publications

Survey methodology

This release includes the results for the questions asked on business management practices. Comparisons to results from the previous business management practices module conducted in 2007/08 have where possible been included in this publication. Results from IT usage question were released on the 20 March 2013, for the detailed results please see: https://www.gov.uk/government/publications/farm-practices-survey-october-2012-computer-usage

The Farm Business Survey (FBS) is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25,000 Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2011 there were just over 56,000 farm businesses meeting this criteria.

In the 2011/12 survey, an additional module was included to collect information on business management practices from a sub-sample of farm businesses. The information collected covered (i) business management practices such as benchmarking, risk management, IT usage and management accounting, (ii) practices specific to animal health and welfare e.g. biosecurity, veterinary strategy, animal health plans, (iii) the environmental footprint of farming, GHG abatement, energy use and, (iv) climate change adaptation.

When combined with other data from the survey this helps to explain farm businesses’ behaviour and how this varies with parameters such as farm type, farm size and performance.

Completion of the business management practices module was voluntary with a response rate of 71% in 2011/12. The farms that responded to the business management practices module had similar characteristics to those farms in the main FBS in terms of farm type and geographical location. There is a smaller proportion of large and very large farms in the module subset than in the main FBS

For further information about the Farm Business Survey please see: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey

Data analysis

The results from the FBS relate to farms which have a standard output of at least 25,000 Euros . Initial weights are applied to the FBS records based on the inverse sampling fraction for each design stratum (farm type by farm size). These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables. Completion of the business management practices module was voluntary and a sample of around 1,350 farms was achieved. In order to take account of non-response, the results have been reweighted using a method that preserves marginal totals for populations according to farm type and farm size groups. As such, farm population totals for other classifications (e.g. regions) will not be in-line with results using the main FBS weights, nor will any results produced for variables derived from the rest of the FBS (e.g. farm business income).

Comparisons between 2007/08 and 2011/12

Results from the 2007/08 and 2011/12 business management practices modules are not directly comparable due to changes in the coverage of the survey and changes in the classification of farms for the 2010/11 campaign. In 2010/11 the survey was restricted to include farms which have at least 25,000 Euros of standard output; prior to this the survey was restricted to farms with ½ Standard Labour Requirement or more. The classification of farms into farm types was also revised for the 2010/11 Farm Business Survey, to bring the classification in line with European guidelines. Equivalent results from 2007/08 have been presented alongside 2011/12 results in many of the charts and tables; however comparisons should be treated with extreme caution due to the reasons given above.

To enable more robust comparisons between the 2007/08 and 2011/12 business management practices module, we have examined the subset of farms that participated in both years (approximately 770 farms). For this subset of farms we have carried out significance testing using McNemar’s test to determine whether the differences observed between the two time periods are statistically significant. The McNemar’s test is applied to 2x2 contingency tables, with matched pairs of subjects, to determine whether the row and column marginal frequencies are equal. Where a statistically significant difference has been observed this has been indicated on the tables and charts for the full module results with a *. Commentary alongside the charts and tables will refer to this analysis rather than make comparisons with the 2007/08 data displayed.

Accuracy and reliability of the results

Where possible, we have shown 95% confidence intervals against the figures. These show the range of values that may apply to the figures. They mean that we are 95% confident this range contains the true value . They are calculated as the standard errors (se) multiplied by 1.96 to give the 95% confidence interval (95% CI). The standard errors only give an indication of the sampling error. They do not reflect any other sources of survey errors, such as non-response bias. The confidence limits shown are appropriate for comparing groups within the same year; they should not be used for comparing, different years’ results from the Farm Business Survey since they do not allow for the fact that in the FBS many of the same farms contributed in both years.

We have also shown error bars on the figures in this notice. These error bars represent the 95% confidence intervals for the figures (as defined above)..

Estimates based on less than 5 observations have been suppressed to prevent disclosure of the identity of the contributing farms. Estimates based on less than 15 observations have been highlighted in italics in the tables and should be treated with caution as they are likely to be less precise.

Definitions

Economic performance for each farm is measured as the ratio between economic output (mainly sales revenue) and inputs (costs+ unpaid labour). The higher the ratio, the higher the economic efficiency and performance. Performance bands based on economic performance percentiles are as follows:

Low performers - farmers who took part in the Business Management Practices survey and were in the bottom 25% of economic performers in this sample Medium performers -farmers who took part in the Business Management Practices survey and were in the middle 50% of performers in this sample High performers - farmers who took part in the Business Management Practices survey and were in the top 25% of performers in this sample.

These are based on economic performance in 2011/12.

Availability of results

Defra statistical notices can be viewed on the Food and Farming Statistics pages on the Defra website at https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/about/statistics. This site also shows details of future publications, with pre-announced dates.

This dataset is the result of a full-population crawl of the .gov.uk web domain, aiming to capture a full picture of the scope of public-facing government activity online and the links between different government bodies. Local governments have been developing online services, aiming to better serve the public and reduce administrative costs. However, the impact of this work, and the links between governments’ online and offline activities, remain uncertain. The overall research question for this research examines whether local e-government has met these expectations, of Digital Era Governance and of its practitioners. Aim was to directly analyse the structure and content of government online. It shows that recent digital-centric public administration theories, typified by the Digital Era Governance quasi-paradigm, are not empirically supported by the UK local government experience. The data consist of a file of individual Uniform Resource Locators (URLs) fetched during the crawl, and a further file containing pairs of URLs reflecting the Hypertext Markup Language (HTML) links between them. In addition, a GraphML format file is presented for a version of the data reduced to third-level-domains, with accompanying attribute data for the publishing government organisations and calculated webometric statistics based on the third-level-domain link network.This project engages with the Digital Era Governance (DEG) work of Dunleavy et. al. and draws upon new empirical methods to explore local government and its use of Internet-related technology. It challenges the existing literature, arguing that e-government benefits have been oversold, particularly for transactional services; it updates DEG with insights from local government. The distinctive methodological approach is to use full-population datasets and large-scale web data to provide an empirical foundation for theoretical development, and to test existing theorists’ claims. A new full-population web crawl of .gov.uk is used to analyse the shape and structure of online government using webometrics. Tools from computer science, such as automated classification, are used to enrich our understanding of the dataset. A new full-population panel dataset is constructed covering council performance, cost, web quality, and satisfaction. The local government web shows a wide scope of provision but only limited evidence in support of the existing rhetorics of Internet-enabled service delivery. In addition, no evidence is found of a link between web development and performance, cost, or satisfaction. DEG is challenged and developed in light of these findings. The project adds value by developing new methods for the use of big data in public administration, by empirically challenging long-held assumptions on the value of the web for government, and by building a foundation of knowledge about local government online to be built on by further research. This is an ESRC-funded DPhil research project. A web crawl was carried out with Heritrix, the Internet Archive's web crawler. A list of all registered domains in .gov.uk (and their www.x.gov.uk equivalents) was used as a set of start seeds. Sites outside .gov.uk were excluded; robots.txt files were respected, with the consequence that some .gov.uk sites (and some parts of other .gov.uk sites) were not fetched. Certain other areas were manually excluded, particularly crawling traps (e.g. calendars which will serve infinite numbers of pages in the past and future and those websites returning different URLs for each browser session) and the contents of certain large peripheral databases such as online local authority library catalogues. A full set of regular expressions used to filter the URLs fetched are included in the archive. On completion of the crawl, the page URLs and link data were extracted from the output WARC files. The page URLs were manually examined and re-filtered to handle various broken web servers and to reduce duplication of content where multiple views were presented onto the same content (for example, where a site was presented at both http://organisation.gov.uk/ and http://www.organisation.gov.uk/ without HTTP redirection between the two). Finally, The link list was filtered against the URL list to remove bogus links and both lists were map/reduced to a single set of files. Also included in this data release is a derived dataset more useful for high-level work. This is a GraphML file containing all the link and page information reduced to third-level domain level (so darlington.gov.uk is considered as a single node, not a large set of pages) and with the links binarised to present/not present between each node. Each graph node also has various attributes, including the name of the registering organisation and various webometric measures including PageRank, indegree and betweenness centrality.