The tables presents indices (2005=100) and changes on twelve months previously (%) of production, turnover and orders in industry (excl. construction), by sector of industry.

Data available : January 2000 till December 2012

Table has been discontinued as from 22 March 2013 due to change of the base year from 2005 to 2010. Statistics Netherlands has started a new table, Industry; production, sales and orders, changes and index (2010 = 100). For more information see sections 3 and 4.

Status of the figures: Production: three most recent months: provisional. The figures within a reporting year are revised provisional figures until publication in December of the year concerned. Turnover: three most recent months: provisional. Orders: three most recent months: provisional.

Changes as of 8 July 2011. Due to new regulations (European System for National Accounts, 2010, Balance of Payments Manual 6) for National Accounts and Balance of Payment, the turnover definition has been adapted. This results in adjustments in production index and other short term statistics. The adaptation of the turnover definition is related to a change in registration of enterprises that (partially) contract out their production abroad. The adjustment means that goods dealt with by foreign subsidiaries of Dutch parent companies do count for Dutch production. Goods dealt with in the Netherlands by Dutch subsidiaries of foreign parent companies that remain property of these parent companies do no longer count as Dutch production. However, they count as export of services for the sum that has been added to value in the Netherlands. Until December 2009, index figures for manufacturing turnover are based on the previous turnover definition. From January 2010 onwards, the turnover figures are based on the new turnover definition. Therefore, turnover changes 2010 on 2009 are not accurate.

To allow us empirically test whether harnessing meaning at work can improve outcomes in a blue-collar manufacturing firm, we collaborated with a small and medium sized enterprise (SME) to run a field experiment. The SME is a US owned electronic manufacturing service industry, who employees 39 people on their production floor. Although a small number of workers, we are able to gather outcome data on a daily basis. Full description is available https://www.iza.org/publications/dp/15183/can-meaning-make-cents-making-the-meaning-of-work-salient-for-us-manufacturing-workersWe conducted a field experiment in a small electronics manufacturing firm in the US with the specific aim to improve minutes worked, punctuality, tardiness and safety checks. Our intervention was to put posters on the production floor on a random day, which made salient to the blue-collar employees the meaning and importance of their job, which comprised of routine repetitive tasks, in a before and after design. Overall, the intervention was a success with positive and significant effects consistently found for the outcomes both immediately after the experiment finished (+3 days) and also more than two weeks after (+15 days). Our study highlights it is possible to motivate blue collar manual workers intrinsically by drawing attention to the meaning of their work. These data were collected by the human resources manager and handed over to the researchers for analysis.

This table has been discontinued due to a shift in the base year.

This table presents information about developments in production and turnover in industry (excl. construction), SIC 2008 sections B - E. The data can be divided by a number of branches according to Statistics Netherlands' Standard Industrial Classification of all Economic Activities 2008 (SIC 2008). Developments are presented as percentage changes compared to a previous period and by means of indices. In this table, the base year is updated to 2015, in previous publications the base year was 2010.

Developments in turnover and volume are published in two formats. Firstly, in the form of year-on-year changes relative to the same period in the preceding year. These figures are shown both unadjusted and adjusted for calendar effects. The second format pertains to period-on-period changes, for example quarter-on-quarter. Period-on-period changes are calculated by applying seasonal adjustment.

Data available from January 2005 up and until December 2023.

Status of the figures: The figures of a calendar year will become definite no later than five months after the end of that calendar year. Until then, the figures in this table will be “provisional” and can still be adjusted as a result of delayed response. Currently, the monthly turnover figures of 2022 are definitive. Once definitive figures have been published, Statistics Netherlands will only revise the results if significant adjustments and/or corrections are necessary. Since this table has been discontinued, the data will not be finalized.

Changes as of 14 February 2024: The figures of December 2023 have been added to the table and those of September up to and including November 2023 have been adjusted and this table has been discontinued.

Changes as of 9 June 2023: The figures of April 2023 have been added to the table and those of January 2022 up to and including March 2023 have been adjusted. This month the annual update of the seasonal-adjustment models has taken place. All figures of 2022 have been revised for the final time and set to ''definitive'' status.

Changes as of 10 June 2021: The figures of April 2021 have been added to the table. The figures of January 2020 up to and including March 2021 have been adjusted. This month the annual update of the seasonal-adjustment models has taken place. Because of additional changes that have been made due to Covid-19 the adjustments are a bit larger than in other years. All figures of 2020 have been revised for the final time and set to ''definitive'' status.

The underlying coding of the following classifications used in this table has been adjusted: - Manufacture of capital goods - Manufacture of consumer goods - Manufacture of durable consumer goods - Manufacture of intermediate goods - Manufacture of non-durable consumergoods

It is now in line with the standard encoding defined by CBS. The structure and data of the table have not been adjusted.

When will new figures be published? No longer applicable.

This table is succeeded by "Industry; production and sales, changes and index, 2021=100". See Section 3.

Key Table Information.Table Title.Manufacturing: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2231BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesProduction and/or development and exploration workers annual wages ($1,000)Production workers first-quarter payroll ($1,000)Production and/or development and exploration workers for pay period including March 12Construction, production and/or development and exploration workers annual hours (1,000)Other employees annual wages ($1,000)Other employees first-quarter payroll ($1,000)Other employees for pay period including March 12Total fringe benefits ($1,000)Employer's cost for health insurance ($1,000)Employer's cost for defined benefit pension plans ($1,000)Employer's cost for defined contribution plans ($1,000)Employer's cost for other fringe benefits ($1,000)Total cost of supplies and/or materials ($1,000)Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of resales ($1,000)Cost of contract work ($1,000)Cost of purchased fuels consumed ($1,000)Cost of purchased electricity ($1,000)Quantity of electricity purchased for heat and power ($1,000)Quantity of generated electricity ($1,000)Quantity of electricity sold or transferred ($1,000)Value added ($1,000)Total inventories, beginning of year ($1,000)Finished goods or minerals products, crude petroleum, and natural gas liquids inventories, beginning of year ($1,000)Work-in-process inventories, beginning of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, beginning of year ($1,000)Total inventories, end of year ($1,000)Finished goods or minerals products, crude petroleum, and natural gas liquids inventories, end of year ($1,000)Work-in-process inventories, end of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, end of year ($1,000)Gross value of depreciable assets (acquisition costs), beginning of year ($1,000)Gross value of depreciable assets (acquisition costs) for buildings and other structures, beginning of year ($1,000)Gross value of depreciable assets (acquisition costs) for machinery and equipment, beginning of year ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Capital expenditures for buildings and other structures ($1,000)Capital expenditures for machinery and equipment ($1,000)Capital expenditures for automobiles, trucks, etc. for highway use ($1,000) Capital expenditures for computers and peripheral data processing equipment ($1,000)Capital expenditures for all other machinery and equipment ($1,000)Total retirements ($1,000)Retirements for buildings and other structures ($1,000)Retirements for machinery and equipment ($1,000)Gross value of depreciable assets (acquisition costs, end of year) ($1,000)Gross value of depreciable assets (acquisition costs) for buildings and other structures, end of year ($1,000)Gross value of depreciable assets (acquisition costs) for machinery and equipment, end of year ($1,000)Total depreciation during year ($1,000)Total rental payments or lease payments ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Total other operating expenses ($1,000)Temporary staff and leased employee expenses ($1,000)Expensed computer hardware and other equipment ($1,000)Expensed purchases of software ($1,000)Data processing and other purchased computer services ($1,000)Communication services ($1,000)Repair and maintenance services of buildings and/or machinery ($1,000) Refuse removal (including hazardous ...

Annual cotton production in the United States grew from just a few thousand tons at the turn of the 19th century, to fluctuating between 1.6 million and 4.3 million tons throughout most of the 20th century. The amount of space used to produce cotton also grew from three to almost 18 million hectares of land between 1866 and the 1920s, before dropping to around four or five million hectares between the 1960s and 1980s. Despite this drop in land usage, advancements in agricultural technology meant that output remained relatively constant in the 20th century, meaning that output per hectare actually increased significantly.

The mechanical cotton gin's invention in 1793 revolutionized the U.S. cotton industry, which grew exponentially in the early 19th century. Cotton was the U.S.' primary export in these years, and its production was driven by slave labor in the southern states (particularly South Carolina). For the first time, output exceeded one million tons in 1859, and again in 1861, however, the disruption of the American Civil War caused cotton output to drop by over 93 percent in the next three years, to just 68 thousand tons by 1864. Production resumed upon its previous trajectory following the war's end, and many of the former-slaves forced to work on cotton plantations continued to work in the cotton industry, but as sharecroppers who worked the land in exchange for a share of the harvest, as well as housing and facilities (this was similar to tenant farming, although sharecroppers received a smaller share of the crop and had fewer legal protections).

The delineation of spatial ecological and management units is an important aspect of effective ecosystem-based fisheries management. Research at the Northeast Fisheries Science Center (NEFSC) was conducted to define ecological units, or geographic areas which are characterized by similar patterns in depth, bottom type, basic oceanographic conditions related to temperature, salinity, and stratification (layering) of the water column, major marine community and food web patterns control the production potential of a region, . This work provides an objective definition of ecological regions of the shelf system, and are the geographic/ecological scales best suited for development of integrated ecosystem assessments.

Description:From an economic point of view the production encompasses manufacturing, including related ‘industrial services’ as long as they are provided in the production industry. After the guidelines of the official statistics on the measurement of production, all products produced to be sold including repair works, montages and contract processing should be captured. Own consumption and wage work is included. For the calculation of the production indices the primary used data are the monthly production surveys. For this surveys reports of chosen local units of enterprises in the production, in the mining sector and extraction of stones and earth with 50 or more employees are used. Until 2006 the reporting threshold was fixed for 20 or more employees. The manufacturing trade is always included. The production index should demonstrate the development of the quantitative production of the production industry and its sub-areas in Germany, adjusted for chances in prices and structures to provide continuous data. Differences in size and changes in structures can be avoided, by presenting the production output not in total numbers, but in from of index number series orientated towards a basis year. For the calculation of production index numbers, current monthly production values (quantity of sales or sale values) are presented as a ratio of the monthly averages of the base year. Until 1993 the Federal Statistical Office calculated two types of production indices: gross-production indices and net-production indices. From the index system 1991=100 on there is only one production index, defined as e net production index. Both index types differ from one another among other things by the definition of the performance dimensions (value added or value of gross production) and by the way it is structured (net production index by economic sectors, gross production index by types of commodities). Indices of net production in the Federal Republic of Germany exist since 1950. During the past decades the base year changed several times and also the content wise classification economic sectors changed repeatedly trough the introduction of new classification systems. The series with different base years overlap, which gives the opportunity to calculate a continuous series with one single base, if the classification of economic sectors did not change in the entire period. Content-related interlinking of indices with different bases is controversial and the results can only be interpreted with care and under certain assumptions. The net production indices are also used to measure productivity in the production industry. Labor productivity (of a local unit, an enterprise, an economic sector or of the entire national economy) can be defined as the ratio of quantity of production and labor input in a certain period. Interpreting this coefficient, it is important to note that labor productivity also depends on the use of other production factors. The index for labor productivity is defined as the “production results per input component of the working volume”. Two different manifestations of the working volume are used for the calculation of the index: (1) hours of work by employees and (2) number of hours worked. Until 1994 in addition a distinction between “number of workers” and “number of employees” was made. The total national working productivity serves as an indicator for economic performance and competitiveness of an economic sector or of the entire national economy with regard to the entire labor input. Labor productivity (after the results of the national accounts) is apparently the most used productivity notion for the entire economy. It shows how effective the input labor is used in the production process. Anyway, it is important to note that the partial productivity indicator not only depends on the factor work but also on the endowment of a certain sector or the entire economy with machines and their degree of modernity and on the infrastructure, which also has an impact on the production result.Productivity can be measured regarding the following two aspects: production result per worker (per capita productivity) and production result per working hour (hourly productivity). For the entire national economy the labor productivity is measured as the ratio of the gross national product (in constant prices) and the average number of employees. To look at the development of labor productivity of an entire national economy, usually the real gross domestic product is used. When comparing economic sectors within a country, the added values of the economic sectors can be used in the respective prices with regard to one employee or one hour of work. Data tables in HISTAT:A. Index for the industrial net production A.01 Index for the industrial net production by industry groups, monthly data (1950-1994)A.02 Production index for the production industry (1991-2014) B. Index for the industrial gross production B.01 Index for t...

Abstract The activity, in digital networks, of several actors who disseminate false content and news through social networking sites and applications, has mobilized the studies on the interaction practices and meaning production in journalism. The 2018 presidential campaign in Brazil has generated a great amount of events that provide materials for this type of investigation. From the purpose of abstracting a scale on the meanings produced by the press around a political figure and, later, to cross the inferences obtained with the meanings produced by the consumers of these journalistic contents, a methodological exercise was undertaken in the site of the Brazilian version of the Spanish newspaper El País and on Facebook. Data collection and analysis observe and evaluate journalistic practice in relation to the production of meanings in the networks.

Key Table Information.Table Title.Selected Sectors: Industry by Products for the U.S. and States: 2022.Table ID.ECNNAPCSIND2022.EC2200NAPCSINDPRD.Survey/Program.Economic Census.Year.2022.Dataset.ECN Multi-Sector Statistics Product Statistics.Source.U.S. Census Bureau, 2022 Economic Census.Release Date.2025-05-29.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of establishmentsQuantity produced for the NAPCS collection code (sectors 21 and 31-33 only, units defined by Unit of Measurement column)Quantity shipped for the NAPCS collection code (sectors 21 and 31-33 only, units defined by Unit of Measurement column)Sales, value of shipments, or revenue of NAPCS collection code ($1,000)NAPCS collection code sales, value of shipments, or revenue as % of industry sales, value of shipments, or revenue (%)NAPCS collection code sales, value of shipments, or revenue as % of total sales, value of shipments, or revenue of establishments with the NAPCS collection code (%)Number of establishments with NAPCS collection code as % of industry establishments (%)Coefficient of variation for number of establishments (%)Coefficient of variation for quantity produced for the NAPCS collection code (%)Coefficient of variation for quantity shipped for the NAPCS collection code (%)Coefficient of variation for NAPCS collection code sales, value of shipments, or revenue (%)Standard error of NAPCS collection code sales, value of shipments, or revenue as % of industry sales, value of shipments, or revenue (%)Standard error of NAPCS collection code sales, value of shipments, or revenue as % of total sales, value of shipments, or revenue of establishments with the NAPCS collection code (%)Standard error of number of establishments with NAPCS collection code as % of industry establishments (%)Range indicating imputed percentage of total NAPCS collection code sales, value of shipments, or revenueDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. level for all sectors and at the U.S. and state levels for sectors 44-45, 61, 62, 71, 72, and 81. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for all sectors except Agriculture and for selected 7- and 8-digit 2022 NAICS-based code levels for various sectors. For information about NAICS, see Economic Census Code Lists..Business Characteristics.For Wholesale Trade (42), data are presented by Type of Operation (All establishments; Merchant Wholesalers, except Manufacturers’ Sales Branches and Offices; and Manufacturers’ Sales Branches and Offices).For selected Services sectors, data are presented by Tax Status (All establishments, Establishments subject to federal income tax, and Establishments exempt from federal income tax)..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For some data on this table, estimates come only from the establishments selected into the sample. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review ...

Key Table Information.Table Title.Mining: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2221BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesProduction and/or development and exploration workers annual wages ($1,000)Production and/or development and exploration workers for pay period including March 12Construction, production and/or development and exploration workers annual hours (1,000)Other employees annual wages ($1,000)Other employees for pay period including March 12Total fringe benefits ($1,000)Employer's cost for health insurance ($1,000)Employer's cost for defined benefit pension plans ($1,000)Employer's cost for defined contribution plans ($1,000)Employer's cost for other fringe benefits ($1,000)Total cost of supplies and/or materials ($1,000)Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of resales ($1,000)Cost of contract work ($1,000)Cost of purchased fuels consumed ($1,000)Cost of purchased electricity ($1,000)Quantity of electricity purchased for heat and power (1,000 kWh)Quantity of generated electricity (1,000 kWh)Quantity of electricity sold or transferred (1,000 kWh) Value added ($1,000)Total inventories, beginning of year ($1,000)Finished goods or minerals products, crude petroleum, and natural gas liquids inventories, beginning of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, beginning of year ($1,000)Total inventories, end of year ($1,000)Finished goods or minerals products, crude petroleum, and natural gas liquids inventories, end of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, end of year ($1,000)Capital expenditures (except land and mineral rights) ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Capital expenditures for mineral exploration and development ($1,000)Capital expenditures for mineral land and rights ($1,000)Lease rents ($1,000)Expensed mineral exploration, development, land, and rights ($1,000)Current operating expenses for exploration, development, and mineral land and rights ($1,000)Current operating expenses for royalty payments ($1,000)Total rental payments or lease payments ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Total other operating expenses ($1,000)Temporary staff and leased employee expenses ($1,000)Expensed computer hardware and other equipment ($1,000)Expensed purchases of software ($1,000)Data processing and other purchased computer services ($1,000)Communication services ($1,000)Repair and maintenance services of buildings and/or machinery ($1,000) Refuse removal (including hazardous waste) services ($1,000)Advertising and promotional services ($1,000)Purchased professional and technical services ($1,000) Taxes and license fees ($1,000)All other operating expenses ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization...

Canada GDP: 2017p: saar: Industrial Production (1950 Definition) data was reported at 367,339.000 CAD mn in Dec 2024. This records an increase from the previous number of 366,774.000 CAD mn for Nov 2024. Canada GDP: 2017p: saar: Industrial Production (1950 Definition) data is updated monthly, averaging 344,700.500 CAD mn from Jan 2007 (Median) to Dec 2024, with 216 observations. The data reached an all-time high of 375,071.000 CAD mn in May 2019 and a record low of 285,996.000 CAD mn in Aug 2009. Canada GDP: 2017p: saar: Industrial Production (1950 Definition) data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.A026: CSMA: GDP by Industry: 2017 Price: saar.

Canada GDP: CL 2017p: saar: Industrial Production (1950 Definition) data was reported at 374,203.000 CAD mn in Feb 2025. This records a decrease from the previous number of 376,523.000 CAD mn for Jan 2025. Canada GDP: CL 2017p: saar: Industrial Production (1950 Definition) data is updated monthly, averaging 344,318.500 CAD mn from Jan 2007 (Median) to Feb 2025, with 218 observations. The data reached an all-time high of 377,577.000 CAD mn in Sep 2022 and a record low of 283,862.000 CAD mn in Aug 2009. Canada GDP: CL 2017p: saar: Industrial Production (1950 Definition) data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.A030: CSMA: GDP by Industry: Chain Linked 2017 Price: saar.

Release Date: 2017-12-15...Table Name.Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2016 and 2015....ReleaseSchedule.Data are scheduled for release in December 2017.......Universe.The universe is a sample of manufacturing establishments classified in sectors 31-33 with one or more paid employees at any time during the year.....GeographyCoverage.Data are shown at the U.S. level.....IndustryCoverage.Data are shown at the two- through six-digit North American Industry Classification System (NAICS) levels.....Data ItemsandOtherIdentifyingRecords.This file contains data on:..Number of employees.Relative standard error for estimate of number of employees (%).Annual payroll ($1,000).Relative standard error for estimate of annual payroll (%).Total fringe benefits ($1,000).Relative standard error for estimate of total fringe benefits (%).Employer's cost for health insurance ($1,000).Relative standard error for estimate of employer's cost for health insurance (%).Employer's cost for defined benefit pension plans ($1,000).Relative standard error for estimate of employer's cost for defined benefit pension plans (%).Employer's cost for defined contribution plans ($1,000).Relative standard error for estimate of employer's cost for defined contribution plans (%).Employer's cost for other fringe benefits ($1,000).Relative standard error for estimate of employer's cost for other fringe benefits (%).Production workers average for year.Relative standard error for estimate of production workers average for year (%).Production workers annual hours (1,000).Relative standard error for estimate of production workers annual hours (%).Production workers annual wages ($1,000).Relative standard error for estimate of production workers annual wages (%).Total cost of materials ($1,000).Relative standard error for estimate of total cost of materials (%).Cost of materials, parts, containers, packaging, etc. used ($1,000).Relative standard error for estimate of cost of materials, parts, containers, packaging, etc. used (%).Cost of resales ($1,000).Relative standard error for estimate of Cost of resales (%).Cost of purchased fuels consumed ($1,000).Relative standard error for cost of purchased fuels consumed (%).Cost of purchased electricity ($1,000).Relative standard error for estimate of cost of purchased electricity (%).Cost of contract work ($1,000).Relative standard error for estimate of cost of contract work (%).Quantity of electricity purchased for heat and power (1,000 kWh).Relative standard error for estimate of quantity of electricity purchased for heat and power (%).Quantity of generated electricity (1,000 kWh).Relative standard error for estimate of quantity of generated electricity (%).Quantity of electricity sold or transferred (1,000 kWh).Relative standard error for estimate of quantity of electricity sold or transferred (%).Total value of shipments and receipts for services ($1,000).Relative standard error for estimate of total value of shipments and receipts for services (%).Value of primary products shipments and receipts for services made in all industries ($1,000).Relative standard error for estimate of value of primary products shipments and receipts for services made in all industries (%).Value of interplant transfers for shipments and receipts for services ($1,000).Relative standard error for estimate of value of interplant transfers for shipments and receipts for services (%).Total miscellaneous receipts ($1,000).Relative standard error for estimate of total miscellaneous receipts (%).Value of resales ($1,000).Relative standard error for estimate of value of resales (%).Contract receipts ($1,000).Relative standard error for estimate of contract receipts (%).Other miscellaneous receipts ($1,000).Relative standard error for estimate of other miscellaneous receipts (%).Value added ($1,000).Relative standard error for estimate of value added (%).Total inventories, beginning of year ($1,000).Relative standard error for estimate of total inventories, beginning of year (%).Finished goods inventories, beginning of year ($1,000).Relative standard error for estimate of finished goods inventories, beginning of year (%).Work-in-process inventories, beginning of year ($1,000).Relative standard error for estimate of work-in-process inventories, beginning of year (%).Materials and supplies inventories, beginning of year ($1,000).Relative standard error for estimate of materials and supplies inventories, beginning of year (%).Total inventories, end of the year ($1,000).Relative standard error for estimate of total inventories, end of the year (%).Finished goods inventories, end of year ($1,000).Relative standard error for estimate of finished goods inventories, end of year (%).Work-in-process inventories, end of year ($1,000).Relative standard error for estimate of work-in-process inventories, end of year (%).Materials and supplies inventories, end of year ($1,000).Relative stan...

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Veteran Status for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESD2022.AB00MYNESD01D.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2023 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2023 ABS collection year produces statistics for the 2022 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Veteran Status (defined as having served in any branch of the U.S. Armed Forces) Veteran Equally veteran/nonveteran Nonveteran Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business inco...

** percent of German managers from the chemical and pharmaceutical industries consider big data to already have a central meaning for their companies. The figures are based on a survey conducted in Germany in 2018.

Smásvæði

Ísland er dreifbýlt land, en þéttbýlt á höfuðborgarsvæðinu. Hefðbundin skipting landsins í landsvæði og sveitarfélög býður ekki upp á samanburðarhæf svæði hvað varðar hagskýrslugerð. Hagstofan hefur því aukið við flokkunarkerfi fyrir hagskýrslusvæði með því að bæta við smásvæðum sem hafa að meðaltali 1.700 til 1.800 íbúa. Smásvæðin mynda fimmta stigið í flokkunarkerfi, með því að hluta talningarsvæðin frekar niður. Alls eru smásvæðin 206 með rúmlega 1.700 manns meðalíbúafjölda, og íbúafjöldanum haldið á bilinu 900 til 3.500 manns, en sem næst meðaltalinu.

Þrepin í flokkunarkerfinu eru þessi.

1 - Ísland allt 2 - Tvö hagskýrslusvæði (NUTS3) – höfuðborgarsvæði og landsbyggð 3 - Fjórir landshlutar – tveir á höfuðborgarsvæðinu og tveir á landsbyggðinni 4 - Alls 42 talningarsvæði – 13 í Reykjavík, 11 í Nágrenni Reykjavíkur, 9 á Suðursvæði og 9 á Norðursvæði. 5 - Alls 206 smásvæði – hverju talningarsvæði skipt upp í 2 til 11 smásvæði.

Með smásvæðaskiptingunni verður mögulegt að birta ítarlegar hagskýrslur fyrir smærri svæði en áður án þess að þurfa að sleppa úr svæðum eða eyða tölum vegna fámennis.

Smásvæðin eru skilgreind vegna þarfa manntalsins 2021, en einnig hefur verið gerð sérstök útgáfa fyrir manntalið 2011, með 183 svæðum sem fylgja að mestu sömu mörkum.

Smásvæðaflokkunin var unnin í samstarfi við Byggðastofnun, með fjárhagslegum stuðningi frá Evrópusambandinu. Þá naut Hagstofan aðstoðar Náttúrufræðistofnun ásamt afnotum af gögnum frá Landmælingum og Mílu ehf.

Minor Statistical Output Areas (MSOA)

Due to the sparsely populated country and huge differences in the population sizes of the administrative units, Statistics Iceland has added a new small area level to the hierarchical regional classification in preparation for the 2021 Census. The new level is labeled as Minor Statistical Output Areas (MSOA). There are in total 206 MSOA defined, with an average population in the range 1,700 to 1,800 persons and no area having less population than 900 persons, and no area exceeding 3,500.

There are 5 steps in the Regional classification:

1 - Iceland 2 - Two NUTS3 Statistical regions 3 - Four Statistical Regions – 2 in the capital region and 2 in the rural areas 4 - Forty-two (42) Statistical Output Areas (SOA) – 42 areas, 13 in Reykjavik, 11 in the Reykjavik surrounding areas, 9 in the South Region, and 9 in the North region 5 - Two hundred and six (206) Minor Statistical Output Areas (MSOA) –each SOA partitioned further into 2 to 11 MSOA.

With the help of the Minor Statistical Output Areas Statistics Iceland is able to publish detailed statistics for smaller areas than previously possible without skipping areas or deleting data due to disclosure concerns.

While the MSOA are defined in preparation for the 2021 Census of the Population and Housing, a special version has been developed for classifying data in the 2011 Census, with 183 MSOA, which are but for 23 areas identical to the 2021 version.

The MSOA were developed with financial aid from the European Commission, in cooperation with the Icelandic Regional Development Institution and the assistance of the Institute of Nature Research and Míla ehf.

Abstract

Economic Affairs Sector in the Ministry of Agriculture and Land Reclamation is keen to develop and update statistical data continuously and available in appropriate time to assist decision makers to shape and adopt agricultural policies that ensure the optimal available agricultural resources based on economic fundamentals. The agricultural census is a comprehensive survey for the economic structure of the National Agricultural during specified periods and is conducted in Egypt on a regular basis every ten years since 1929. The AC 2009-2010 is the eighth agricultural census and comes after the variables within the agricultural sector in recent years. That census should reflect these changes and deal with comprehensive survey data about new land in a form that can be classified and published separately from the old lands which have an important part in building statistical database.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as an economic and technical unit comprising all livestock kept and all land used wholly or partly for agricultural production purposes. A holding was defined as being within a single district (administrative unit). A certain threshold was applied to establish the census universe

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the AC 2009/2010.

ii. Frame During the first phase of the census fieldwork, a complete list of the agricultural holdings in the household sector was established. The administrative data sources owned by different institutions were used to build the census frame for the non-household sector (the Real Estate Tax Authority, directorates of agriculture, agricultural associations, veterinary units, mechanization units, the Land Reclamation Sector of the MALR, etc.).

ii. Complete and/or sample enumeration methods All holdings in scope were covered by complete enumeration.

iii. Sample design Not applied.

Mode of data collection

Face-to-face [f2f]

Research instrument

Paper-based forms were used for both phases of the census fieldwork:

(i) a listing form to identify the holdings in the household sector;

(ii) a detailed questionnaire for census data collection.

The AC 2010 covered 14 of the 16 core items recommended in the WCA 2010. All questionnaires are attached to external documents.

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING The census questionnaires were first checked and verified by supervisors. After initial quality procedures were applied, the questionnaires were sent to the computer centre for manual data entry, processing and production of final results.

(b) CENSUS DATA QUALITY Significant emphasis was placed on data quality throughout the entire census exercise, including training, supervision of fieldwork, data entry, editing and validation. A comparison between the census results and some administrative data sources (such as the total area of holdings and cultivated area, livestock numbers and agricultural machinery) was made for validation purposes.

Statistics illustrates production of phosphides; whether or not chemically defined, excluding ferrophosphorus in Haiti from 2007 to 2024.

Description: From an economic point of view the production encompasses manufacturing, including related ‘industrial services’ as long as they are provided in the production industry. After the guidelines of the official statistics on the measurement of production, all products produced to be sold including repair works, montages and contract processing should be captured. Own consumption and wage work is included. For the calculation of the production indices the primary used data are the monthly production surveys. For this surveys reports of chosen local units of enterprises in the production, in the mining sector and extraction of stones and earth with 50 or more employees are used. Until 2006 the reporting threshold was fixed for 20 or more employees. The manufacturing trade is always included. The production index should demonstrate the development of the quantitative production of the production industry and its sub-areas in Germany, adjusted for chances in prices and structures to provide continuous data. Differences in size and changes in structures can be avoided, by presenting the production output not in total numbers, but in from of index number series orientated towards a basis year. For the calculation of production index numbers, current monthly production values (quantity of sales or sale values) are presented as a ratio of the monthly averages of the base year. Until 1993 the Federal Statistical Office calculated two types of production indices: gross-production indices and net-production indices. From the index system 1991=100 on there is only one production index, defined as e net production index. Both index types differ from one another among other things by the definition of the performance dimensions (value added or value of gross production) and by the way it is structured (net production index by economic sectors, gross production index by types of commodities). Indices of net production in the Federal Republic of Germany exist since 1950. During the past decades the base year changed several times and also the content wise classification economic sectors changed repeatedly trough the introduction of new classification systems. The series with different base years overlap, which gives the opportunity to calculate a continuous series with one single base, if the classification of economic sectors did not change in the entire period. Content-related interlinking of indices with different bases is controversial and the results can only be interpreted with care and under certain assumptions. The net production indices are also used to measure productivity in the production industry. Labor productivity (of a local unit, an enterprise, an economic sector or of the entire national economy) can be defined as the ratio of quantity of production and labor input in a certain period. Interpreting this coefficient, it is important to note that labor productivity also depends on the use of other production factors. The index for labor productivity is defined as the “production results per input component of the working volume”. Two different manifestations of the working volume are used for the calculation of the index: (1) hours of work by employees and (2) number of hours worked. Until 1994 in addition a distinction between “number of workers” and “number of employees” was made. The total national working productivity serves as an indicator for economic performance and competitiveness of an economic sector or of the entire national economy with regard to the entire labor input. Labor productivity (after the results of the national accounts) is apparently the most used productivity notion for the entire economy. It shows how effective the input labor is used in the production process. Anyway, it is important to note that the partial productivity indicator not only depends on the factor work but also on the endowment of a certain sector or the entire economy with machines and their degree of modernity and on the infrastructure, which also has an impact on the production result. Productivity can be measured regarding the following two aspects: production result per worker (per capita productivity) and production result per working hour (hourly productivity). For the entire national economy the labor productivity is measured as the ratio of the gross national product (in constant prices) and the average number of employees. To look at the development of labor productivity of an entire national economy, usua...

Abstract

The National Institute of Statistics and Geography (INEGI) carried out the National Agricultural Survey 2019 (ENA 2019) to offer statistics on the production of crops and livestock species that are characterized by being the ones that mostly participate in the Gross Domestic Product of the primary sector in Mexico and which, according to the Sustainable Rural Development Law, are those products for which the State seeks the supply, promoting their access to less favored social groups. Likewise, the Food and Agriculture Organization of the United Nations (FAO) considers them essential for food security, agricultural sustainability and rural development.

The ENA 2019 allows to continue obtaining basic and structural statistics of the agricultural and livestock sector, as it is the fourth version of a series of National Agricultural Surveys that INEGI carried out in the years 2012, 2014 and 2017. This survey, in addition to allowing to know The current characteristics of the agricultural production units has been enriched in terms of the results achieved, because, for some priority crops in the Federal Government programs, data was obtained from the small and medium-sized units that have the smallest area planted in the country.

Geographic coverage

National and by Federative Entity.

Analysis unit

For ENA 2019, the Observation Unit is defined as the economic unit made up of one or more pieces of land located in the same municipality, where at least some of them carry out agricultural or forestry activities, under the control of the same administration. If the administration has land located in another municipality, it is considered as another production unit; that is, there will be as many production units as municipalities occupying their land.

Universe

The universe selected for the ENA 2019 was 79,252 production-product units, equivalent to 69,124 production units from which information of interest was obtained. These units come from the Update of the 2016 Agricultural Census Framework (AMCA 2016) and updated with information from the 2017 National Agricultural Survey (ENA 2017). This universe was defined from the 28 products of national interest, 5 of these livestock products being of economic importance for the country.

The products selected for the conformation of the universe of work of the ENA 2019 are 29 products, 24 agricultural: Avocado, Alfalfa, Amaranth, Rice, Cocoa, Coffee, Pumpkin, Sugar Cane, Onion, Chile, Strawberry, Bean, Tomato (Tomato Red), Lemon, White Corn, Yellow Corn, Mango, Apple, Orange, Banana, Sorghum, Soya, Wheat and Grape; while the five species and livestock products were made up of Bovines, Porcine, Poultry, Milk and Egg.

Kind of data

Sample Survey Data [ssd]

Sampling procedure

SAMPLE DESIGN The elements considered for the definition and construction of the sampling scheme of the 2019 National Agricultural Survey (ENA 2019), help determine the size, selection and distribution of the sample; Necessary and substantial elements to define the precision of the information, as well as the analysis of the uptake for the evaluation of the final estimates, through calculations such as the variance and the coefficient of variation.

TARGET POPULATION It is defined by all production units captured in the 2016 Agricultural Census Framework Update (AMCA 2016), updated with information from the 2017 National Agricultural Survey (ENA 2017) for the part of agricultural products and for the part of livestock producers it is taken of the 2007 Agricultural, Livestock and Forestry Census updated with the 2017 ENA that reported, at that time, producing any of the products of interest, classified according to their importance of national and/or state interest.

GEOGRAPHICAL AND SECTOR COVERAGE The survey was designed to obtain information at the national level for the products of interest and for each of the states for their main products.

DOMAIN OF STUDY It refers to subsets of the population under study for which it is intended to obtain information and for which a sample is designed independently for each of them. In this regard, it is worth mentioning that of the 29 products of the ENA 2019 work universe, 26 had a stratified probabilistic design (for purposes of the sample design, corn counts as a single product regardless of whether it is white grain corn or yellow grain corn , reason for which there are 26 and not 27 products); while for poultry and egg products, a non-probabilistic design was considered. The subsets under study are presented below: A. NATIONAL DOMAIN. Each of the 26 products by producer size (large and small and medium producers), obtaining a total of 52 domains, the products considered (Avocado, Alfalfa, Amaranth, Rice, Cattle, Cocoa, Coffee, Pumpkin, Sugarcane, Onion, Chile, Strawberry, Bean, Tomato (Red tomato), Milk, Lemon, Corn, Mango, Apple, Orange, Banana, Pork, Sorghum, Soy, Wheat, Grape). B. PRODUCT-FEDERAL ENTITY DOMAIN. For the main federal entities by producer size, for this case 60 product-federal entity domains were considered. C. DOMAIN PRODUCT-FEDERAL ENTITY-SIZE OF PRODUCTION UNIT BY AREA. (For ten products, the federative entity domain-size of production unit per area is necessary) for this case, 384 domains were considered.

SAMPLING UNIT The observation unit is the Production Unit (UDP), defined as: The economic unit made up of one or more pieces of land located in the same municipality, where at least some of them carry out agricultural or forestry activities, under the control of the same administration. Under this context, the sampling unit is the production-product unit. If the production unit has more than one product or crop, it will be included in two or more study domains.

SAMPLING FRAME It was integrated from two different sources: A. AGRICULTURAL PRODUCTS: the framework derived from the AMCA 2016, updated with the results of the ENA 2017, was the input for determining the sampling framework of the ENA 2019. B. LIVESTOCK PRODUCTS: the 2007 Agricultural, Livestock and Forestry Census, updated with the results of the 2017 ENA.

STRATIFICATION For agricultural products, the variable of interest for stratification was the planted area in hectares (ha), depending on the characteristics of the crop, from four to six strata. The determination of the ranges of the strata is obtained by the Dalenius-Hodges method. According to William G. Cochran (1977), "for a single feature or variable, the best feature is, of course, the frequency distribution. The next best is probably the frequency distribution, given the number of strata, the equations for determining the best limits between them under Neyman proportional assignment, have been obtained by Dalenius (1957)". For livestock products, the number of heads variable was used.

SAMPLING SCHEME For the products of interest, both large and small and medium producers, the sampling design is stratified probabilistic with simple random selection within each study domain: A. PROBABILISTIC. The selection units had a known, non-zero probability of being selected. B. STRATIFIED. Sampling units with similar characteristics were grouped to form strata. The results of the sample are generalized to the entire population and it is possible to know the precision of the results.

SAMPLE SIZE Different sample sizes were calculated for: A. SAMPLE SIZE FOR DOMAINS AT THE NATIONAL LEVEL (PRODUCT). For products of national interest, the sample size obtained for these domains is 19,320 production-product units; 10,968 for large producers and 8,352 for small and medium producers. B. SAMPLE SIZES FOR DOMAINS AT THE PRODUCT-FEDERAL ENTITY LEVEL. For products of state interest, the sample size obtained for these domains is 19,320 production-product units; 10,968 for large producers and 8,352 for small and medium producers. C. SAMPLE SIZES FOR DOMAINS AT THE PRODUCT-FEDERAL ENTITY-SIZE OF PRODUCTION UNIT LEVEL BY AREA. In this case, the calculation differentiated by producer size was made, in such a way that the sample size for small and medium-sized producers was strengthened, according to the following considerations: Yo. DOMAIN OF LARGE PRODUCERS. The sample size obtained for these domains is 3,255 production-product units. ii. DOMAIN OF SMALL AND MEDIUM PRODUCERS. The sample size obtained for these domains is 7,355 production-product units. D. SAMPLE SIZES FOR LIVESTOCK PRODUCTS Yo. DOMAIN OF LARGE PRODUCERS. For the bovine product, a relative error of 14% was considered for the national design sample. ii. The sample size obtained for these domains is 10,554 units. The interest of bovines is both the number of stocks and milk production.

SAMPLE ALLOCATION. For the three large levels of interest, (National (product), Product-federative entity and Product-federative entity-size of production unit by area). The sample was assigned in each stratum by the Neyman method according to the planted area or number of heads. Except for small and medium-sized producers in the domains at the product-federative entity-size of production unit per area level.

SAMPLE SELECTION It is performed randomly and independently for each study domain. The sample selected for the design is 79,252 production-product units, equivalent to 69,124 production units in which information of interest is obtained.

CALCULATION OF EXPANSION FACTORS Three different types of expansion factors were calculated, which are: A. Production-product unit expansion factors (for each production-product unit) B. Production unit expansion factors (based on design expansion factors for production-product units) C. Producer expansion factors (for each producer, based on design expansion factors for production-product units)

ADJUSTMENT TO EXPANSION FACTORS A. Expansion