AHA Annual Survey Database™ for Fiscal Year 2022 is a comprehensive hospital database for peer comparisons, market analysis, and health services research. It is produced primarily from the AHA Annual Survey of Hospitals, which has been administered by the American Hospital Association (AHA) since 1946. The survey responses are supplemented by data drawn the U.S. Census Bureau, hospital accrediting bodies, and other organizations.

This page provides information for the Civil Division Annual Survey performance measure.

The Annual Survey of Jails (ASJ) is the only data collection effort that provides an annual source of data on local jails and jail inmates. Data on the size of the jail population and selected inmate characteristics are obtained every five to six years from the Census of Jails. In each of the years between the full censuses, a sample survey of jails is conducted to estimate baseline characteristics of the nation's jails and inmates housed in these jails. The 2011 Annual Survey of Jails is the 24th such survey in a series begun in 1982. The ASJ supplies data on characteristics of jails such as admissions and releases, growth in the number of jail facilities, changes in their rated capacities and level of occupancy, growth in the population supervised in the community, changes in methods of community supervision, and crowding issues. The ASJ also provides information on changes in the demographics of the jail population, supervision status of persons held, and a count of non-citizens in custody. Starting in 2010, BJS enhanced the ASJ survey instruments to address topics on the number of convicted inmates that are unsentenced or sentenced and the number of unconvicted inmates awaiting trial/arraignment, or transfers/holds for other authorities. In order to reduce respondent burden, the ASJ no longer collects data on conviction status by sex. Also new to the survey, data are collected on jails' operational capacity and design capacity. Incorporating enhanced capacity measurements enables BJS to describe more accurately the variation and volatility of inmate bed space and crowding, especially as they relate to safety and security in jails. To address more directly issues related to overcrowding and safety and security in jails, BJS started collecting data on staff and assaults against staff from the largest jails. In the modifications to the ASJ, starting in 2010, 335 jail jurisdictions (370 respondents) included with certainty in the ASJ sample survey were asked to provide additional information (forms CJ-5D or CJ-5DA) on the flow of inmates going through jails and the distribution of time served, staff characteristics and assaults on staff resulting in death, and inmate misconduct. The data presented in this study were collected in the Annual Survey of Jails, 2011. These data are used to track growth in the number of jails and the capacities nationally, changes in the demographics of the jail population and supervision status of persons held, the prevalence of crowding issues, and a count of non-United States citizens within the jail population. The data are intended for a variety of users, including federal and state agencies, local officials in conjunction with jail administrators, researchers, planners, and the public. The reference date for the survey is June 30, 2011.

The Annual Survey of Hours and Earnings (ASHE) is one of the largest surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete.

While limited in terms of personal characteristics compared to surveys such as the Labour Force Survey, the ASHE is useful not only because of its larger sample size, but also the responses regarding wages and hours are considered to be more accurate, since the responses are provided by employers rather than from employees themselves. A further advantage of the ASHE is that data for the same individuals are collected year after year. It is therefore possible to construct a panel dataset of responses for each individual running back as far as 1997, and to track how occupations, earnings and working hours change for individuals over time. Furthermore, using the unique business identifiers, it is possible to combine ASHE data with data from other business surveys, such as the Annual Business Survey (UK Data Archive SN 7451).

The ASHE replaced the New Earnings Survey (NES, SN 6704) in 2004. NES was developed in the 1970s in response to the policy needs of the time. The survey had changed very little in its thirty-year history. ASHE datasets for the years 1997-2003 were derived using ASHE methodologies applied to NES data.

The ASHE improves on the NES in the following ways:

• the NES questionnaire allowed too much variation in employer responses, leading to wide variations in the data
• weightings have been introduced to take account of the population size (significant biases were a known problem in NES data)
• the significant numbers of employees who change jobs between the sample selection and survey reference dates are retained in the ASHE sample, whereas these were dropped from the NES

Linking to other business studies
These data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Observations from Northern Ireland
The ASHE data held by the UK Data Archive include very few observations from Northern Ireland. Users requiring access to Northern Ireland data are advised to contact the Northern Ireland Statistics and Research Agency, who administer this aspect of the survey.

Local unit reference variable, luref
The local unit reference variable 'luref', is generated to indicate multiple occurrences of the same local unit for disclosure checking purposes. It is inconsistent across years and is not an IDBR reference number. It should not be used to link ASHE with other business datasets.

For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available.

Latest Edition Information
For the twenty-sixth edition (February 2025), the data file 'ashegb_2023r_2024p_pc' has been added, along with the accompanying data dictionary.

This collection provides annual data on jail populations across the nation and examines the "spillover" on local jails resulting from the dramatic growth in federal and state prison populations. These data are used to track growth in the number of jails and their capacities nationally, changes in the demographics of the jail population (including sex, race, and adult or juvenile status), supervision status of persons held, prevalence of crowding issues, and a count of non-United States citizens within the jail population.

Provides estimates of revenue and other measures for most traditional service industries. The Bureau of Economic Analysis uses these data in its preparation of the Gross Domestic Product (GDP), national income and product accounts, and its benchmark and annual input-output tables. The Bureau of Labor Statistics uses the data as input to its producer price indexes and in developing productivity measurements. The Centers for Medicare and Medicaid Services (CMS) uses the data to estimate expenditures for the National Health Accounts. The Coalition of Service Industries uses data for general research and planning. Trade and professional organizations use the estimates to analyze industry trends and benchmark their own statistical programs, develop forecasts, and evaluate regulatory requirements. The media use estimates for news reports and background information. Private businesses use the estimates to measure market share; analyze business potential; and plan investment decisions.

The Annual Survey of Manufactures (ASM) provides key intercensal measures of manufacturing activity, products, and location for the public and private sectors. The ASM provides the best current measure of current U.S. manufacturing industry outputs, inputs, and operating status, and is the primary basis for updates of the Longitudinal Research Database (LRD). Census Bureau staff and academic researchers with sworn agent status use the LRD for micro data analysis.

The Department of Health requires adult care facilities (ACFs) to complete an electronic filing of each facility's licensed adult home and enriched housing program bed census on an annual basis. These facilities include adult homes (AHs), enriched housing programs (EHPs), assisted living programs (ALPs), assisted living residences (ALRs), special needs assisted living residences (SNALR), and enhanced assisted living residences (EALR). Available bed and occupancy information in ACFs are self-reported and is not audited by the NYSDOH. This dataset is refreshed on a annual basis. For more information, check out http://www.health.ny.gov/facilities/adult_care/. The "About" tab contains additional details concerning this dataset.

Key Table Information.Table Title.Capital Outlay and Other Expenditure of Public Elementary-Secondary School Systems: U.S. and State: 2012 - 2023.Table ID.GOVSTIMESERIES.GS00SS06.Survey/Program.Public Sector.Year.2024.Dataset.PUB Public Sector Annual Surveys and Census of Governments.Source.U.S. Census Bureau, Public Sector.Release Date.2025-05-01.Release Schedule.The Annual Survey of School System Finances occurs every year. Data are typically released in early May. There are approximately two years between the reference period and data release..Dataset Universe.Census of Governments - Organization (CG):The universe of this file is all federal, state, and local government units in the United States. In addition to the federal government and the 50 state governments, the Census Bureau recognizes five basic types of local governments. The government types are: County, Municipal, Township, Special District, and School District. Of these five types, three are categorized as General Purpose governments: County, municipal, and township governments are readily recognized and generally present no serious problem of classification. However, legislative provisions for school district and special district governments are diverse. These two types are categorized as Special Purpose governments. Numerous single-function and multiple-function districts, authorities, commissions, boards, and other entities, which have varying degrees of autonomy, exist in the United States. The basic pattern of these entities varies widely from state to state. Moreover, various classes of local governments within a particular state also differ in their characteristics. Refer to the Individual State Descriptions report for an overview of all government entities authorized by state.The Public Use File provides a listing of all independent government units, and dependent school districts active as of fiscal year ending June 30, 2024. The Annual Surveys of Public Employment & Payroll (EP) and State and Local Government Finances (LF):The target population consists of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Survey of Public Pensions (PP):The target population consists of state- and locally-administered defined benefit funds and systems of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Surveys of State Government Finance (SG) and State Government Tax Collections (TC):The target population consists of all 50 state governments. No local governments are included. For the purpose of Census Bureau statistics, the term "state government" refers not only to the executive, legislative, and judicial branches of a given state, but it also includes agencies, institutions, commissions, and public authorities that operate separately or somewhat autonomously from the central state government but where the state government maintains administrative or fiscal control over their activities as defined by the Census Bureau. Additional details are available in the survey methodology description.The Annual Survey of School System Finances (SS):The Annual Survey of School System Finances targets all public school systems providing elementary and/or secondary education in all 50 states and the District of Columbia..Methodology.Data Items and Other Identifying Records.Total capital outlay expenditureCapital outlay expenditure - ConstructionCapital outlay expenditure - Land and existing structuresCapital outlay expenditure - Equipment - InstructionalCapital outlay expenditure - Equipment - OtherOther expenditure - Interest on debtOther expenditure - Payments to other governmentsDefinitions can be found by clicking on the column header in the table or by accessing the Glossary.For detailed information, see Government Finance and Employment Classification Manual..Unit(s) of Observation.The basic reporting unit is the governmental unit, defined as an organized entity which in addition to having governmental character, has sufficient discretion in the management of its own affairs to distinguish it as separate from the administrative structure of any other governmental unit.The reporting units for the Annual Survey of School System Finances are public school systems that provide elementary and/or secondary education. The term "public school systems" includes t...

This annual statistical survey provides profile and trend data on undergraduate and freshmen enrollment at the thousands of two-year and four-year colleges and universities in the United States which responded to a College Board questionnaire. The survey provides basic general data about the school, admissions requirements and procedures, accreditation information, degree and program information, foreign student information, enrollment data, and financial aid and expenses data.

This year, the Orange County Annual Survey highlights growth and its effects on the quality of local life. This topic moves to the forefront in 1987 and joins traffic congestion among the most discussed issues in the county. The sample size is 1,010 Orange County adult residents.Online data analysis & additional documentation in Link below.

Abstract

Since the 1980s, the Office of Refugee Resettlement (ORR) has conducted the Annual Survey of Refugees (ASR), which collects information on refugees during their first five years after arrival in the U.S. The ASR is the only scientifically-collected source of national data on refugees’ progress toward self-sufficiency and integration. ORR uses the ASR results alongside other information sources to fulfill its Congressionally-mandated reporting following the Refugee Act of 1980. Historically, the microdata from these surveys have generally been unavailable to researchers.

In the spring of 2018, ORR completed its 51st Annual Survey of Refugees (ASR). The data from the ASR offer a window into respondents’ first five years in the United States and show the progress that refugee families made towards learning English, participating in the workforce, and establishing permanent residence.

Geographic coverage

National coverage

Analysis unit

Households and individuals

Universe

The population of interest – the study population – for the 2017 ASR is defined as refugees entering the U.S. between FY 2012 and FY 2016, inclusive, who are at ages 16 and over at the time of the 2017 ASR interview3. Because the interviews were conducted in early 2018, the population includes a small number of refugees younger than 16 at the time of arrival to the U.S.

While this covers five distinct fiscal years of refugee entrants, there is special policy/analytic interest in collapsing years into three domains as follows:

• Cohort 1 – Refugees entering FY 2012 and FY 2013,

• Cohort 2 – Refugees entering FY 2014 and FY 2015, and

• Cohort 3 – Refugees entering FY 2016

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 ASR employed a stratified probability sample design of refugees. The first stage of selection was the household (PA) and the second stage was the selection of persons within households. Principal features of the sample design are highlighted below.

The 2017 ASR design replicated the 2016 ASR design, which used a full cross-sectional national sample of refugees entering within the past five years. This section documents the research design, data collection and data processing protocols. It also presents outcomes (e.g., sample sizes) and paradata results such as response rates.

The population of interest - the study population - for the 2017 ASR is defined as refugees entering the U.S. between FY 2013 and FY 2017, inclusive, who are at ages 16 and over at the time of the 2018 ASR interview. Because the interviews were conducted in early 2018, the population includes a small number of refugee respondents younger than 16 at the time of arrival to the U.S.

The 2017 ASR targeted 1,500 completed interviews from refugee households entering the U.S. between FY 2012-2016. The sample was designed to allow for separate estimates and analyses from each of the three designated cohorts. Moreover, the design needed to accommodate both household- and person-level analyses.

The sample was drawn as fresh cross sections by cohort; there was no longitudinal component. The survey objectives required that – in addition to primary stratification by cohort – the sample of households (i.e., PAs) be stratified at least by year of entry and geographic region of origin.

The 2017 ASR sampling frame was ORR’s Refugee Arrivals Data System (RADS) dataset.

The ASR design targeted equal numbers of household interviews by cohort. This means that there was an oversample of households for FY 2016, the most recent year of entry. This allocation prioritizes the statistical precision to cohorts.

Within each of the three cohort strata, the following factors were used for stratification: year of arrival (for cohorts 1 and 2 only), geographic region, native language, age group, gender, and family size at arrival (1, 2, 3+ persons). Missing contact information status was also used as a stratification variable for cohort 3 due to an unusual degree of missing contact information among FY 2017 arrivals. Proportionate stratified samples were drawn independently within cohort.

Sampling deviation

The 2017 ASR employed a sample management plan integrating the sample design and field protocols to include locating subjects, contacting them and conducting telephone interviews. A sample of 6,006 PAs was released at the start of data collection. A reserve sample of about 4,500 was held in case some portion was needed to meet the interview target of 1,500.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Response rate

An overall response rate of 25 percent was achieved. The response rate was driven by the ability to locate and speak to (1515+534)/6006 = 32 percent of the sample, meaning that two thirds of the sample could neither be located nor (if located) successfully contacted.

The overall response rates decreased with time since arrival to the U.S., varying from 18 percent for FY 2012-13 refugees to 26 percent for FY 2014-15 refugees and a high of 34 percent for FY 2016 refugees.

UK product proportions by industry for the Annual Survey of Goods and Services (ASGS)

Abstract

Introduction

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess and evaluate, objectively and realistically, the changes in the growth, composition and structure of organized manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. The survey has so far been conducted annually under the statutory provisions of the Collection of Statistics (COS) Act, 1953 and the rules framed there-under in 1959 except in the State of Jammu & Kashmir where it is conducted under the J&K Collection of Statistics Act, 1961 and rules framed there under in 1964. From ASI 2010-11 onwards, the survey is to be conducted annually under the statutory provisions of the Collection of Statistics (COS) Act, 2008 and the rules framed there-under in 2011except in the State of Jammu & Kashmir where it is to be conducted under the J&K Collection of Statistics Act, 1961 and rules framed there under in 1964.

ASI schedule is the basic tool to collect required data for the factories registered under Sections 2(m)(i) and 2(m)(ii) of the Factories Act, 1948. In addition to Sections 2(m)(i) & 2(m)(ii) of the Factories Act, 1948, bidi & cigar units, employing 10 or more workers with the aid of power and 20 or more workers without the aid of power and registered under the Bidi & Cigar Workers (Conditions of Employment) Act, 1966 are also covered in ASI. Although the scope of the ASI is extended to all registered manufacturing establishments in the country, establishments under the control of the Defence Ministry, oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI.

The schedule for ASI, at present, has two parts. Part-I of ASI schedule, processed at the CSO (IS Wing), Kolkata, aims to collect data on assets and liabilities, employment and labour cost, receipts, expenses, input items: indigenous and imported, products and by-products, distributive expenses, etc. Part-II of ASI schedule is processed by the Labour Bureau. It aims to collect data on different aspects of labour statistics, namely, working days, man-days worked, absenteeism, labour turnover, man-hours worked etc. The concepts and definition of various terms used in collection of ASI data are given in Chapter Two, and the details of the schedule, item descriptions and procedures for collecting information for each item.

Geographic coverage

The ASI extends its coverage to the entire country upto state level.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to same scheme (census or sample) is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design adopted in ASI has undergone considerable changes from time to time, taking into account the technical and other requirements. The earlier sampling design had been adopted from ASI 2007-08 to ASI 2011-12. From ASI 2012-13, a new sampling design has been adopted following the recommendation of Dr. S. L.Shetty Committee and approved by the SCIS subsequently. According to the new sampling design, all the factories in the updated frame are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector consists of the following units: a) All industrial units belonging to the six less industrially developed states/ UT's viz.Manipur, Meghalaya, Nagaland, Sikkim, Tripura and Andaman & Nicobar Islands. b) For the rest of the twenty-six states/ UT's., (i) units having 100 or more employees, and (ii) all factories covered under Joint Returns. c) After excluding the Census scheme units, as defined above, all units belonging to the strata (State x District x Sector x 4 digit NIC 2008) having less than or equal to 4 units are also considered under Census Scheme. It may be noted that in the formation of stratum, the sectors are considered as Bidi, Manufacturing and Electricity.

Sample Sector All the remaining units in the frame are considered under Sample Scheme. For all the states, strata are formed for each State x District x Sector x 4 digit NIC2008 factories. The units are arranged in descending order of their number of employees. Samples are drawn as per Circular Systematic Sampling technique for this scheme. An even number of units with a minimum of 4 units are selected and distributed in four sub-samples. It may be noted that all the 4 sub-samples from a particular stratum may not have equal number of units. Out of these 4 sub-samples, two pre-assigned sub-samples are given to NSSO (FOD) and the other two-subsamples are given to State/UT for data collection.

The entire census units plus all the units belonging to the two sub-samples given to NSSO (FOD) are treated as the Central Sample.

The units belonging to the two sub-samples allocated to States/UTs are to be canvassed by the respective States/UTs. Hence, State/UT has to use the data (collected by NSSO (FOD) and processed by CSO (IS Wing)) along with the state sample data while deriving the district level estimates for their respective State/UT.

The entire census units plus all the units belonging to the two sub-samples given to NSSO (FOD) plus all the units belonging to the two sub-samples given to State/UT are required for pooling of Central and State Samples.

Sampling deviation

The sampling design adopted in ASI has undergone considerable changes from time to time, taking into account the technical and other requirements. The present sampling design has been adopted from ASI 2007-08. All the factories in the updated frame are divided into two sectors, viz., Census and Sample.

Mode of data collection

Statutory return submitted by factories as well as Face to Face

Research instrument

Annual Survey of Industries Questionnaire is divided into different blocks:

BLOCK A.IDENTIFICATION BLOCK - This block has been designed to collect the descriptive identification of the sample enterprise. The items are mostly self-explanatory.

BLOCK B. TO BE FILLED BY OWNER OF THE FACTORY - This block has been designed to collect the particulars of the sample enterprise. This point onwards, all the facts and figures in this return are to be filled in by owner of the factory.

BLOCK C: FIXED ASSETS - Fixed assets are of a permanent nature having a productive life of more than one year, which is meant for earning revenue directly or indirectly and not for the purpose of sale in ordinary course of business. They include assets used for production, transportation, living or recreational facilities, hospital, school, etc. Intangible fixed assets like goodwill, preliminary expenses including drawing and design etc are excluded for the purpose of ASI. The fixed assets have, at the start of their functions, a definite value, which decreases with wear and tear. The original cost less depreciation indicates that part of value of fixed assets, which has not yet been transferred to the output. This value is called the residual value. The value of a fixed asset, which has completed its theoretical working life should always be recorded as Re.1/-. The revalued value is considered now. But depreciation will be taken on original cost and not on revalued cost.

BLOCK D: WORKING CAPITAL & LOANS - Working capital represents the excess of total current assets over total current liabilities.

BLOCK E : EMPLOYMENT AND LABOUR COST - Particulars in this block should relate to all persons who work in and for the establishment including working proprietors and active business partners and unpaid family workers. However, Directors of incorporated enterprises who are paid solely for their attendance at meeting of the Board of Directors are to be excluded.

BLOCK F : OTHER EXPENSES - This block includes the cost of other inputs as both the industrial and nonindustrial service rendered by others, which are paid by the factory and most of which are reflected in the ex-factory value of its production during the accounting year.

BLOCK G : OTHER INCOMES - In this block, information on other output/receipts is to be reported.

BLOCK H: INPUT ITEMS (indigenous items consumed) - This block covers all those goods (raw materials, components, chemicals, packing material, etc.), which entered into the production process of the factory during the accounting year. Any material used in the production of fixed assets (including construction work) for the factory's own use should also be included. All intermediate products consumed during the year are to be excluded. Intermediate products are those, which are produced by the factory but are, subjected to further manufacture. For example, in a cotton textile mill, yarn is produced from raw cotton and the same yarn is again used for manufacture of cloth. An intermediate product may also be a final product in the same factory. For example, if the yarn produced by the factory is sold as yarn, it becomes a final product

The purpose of the Survey of Jails in Indian Country is an enumeration of all known adult and juvenile facilities -- jails, confinement facilities, detention centers, and other correctional facilities operated by tribal authorities or the Bureau of Indian Affairs (BIA), U.S. Department of the Interior. For the purpose of this collection, Indian country includes reservations, pueblos, rancherias, and other Native American and Alaska Native communities throughout the United States. The survey collects data on the number of adults and juveniles held on the last weekday in June 2016, type of offense, average daily population in June, most crowded day in June, admissions and releases in June, number of inmate deaths and suicide attempts, rated capacity, and jail staffing.

Situation, behavior and structural changes of the manufacturing sector, includes export maquiladoras and non-maquiladoras. Base year 2018.

India Textile: Annual Survey of Industry: Percentage of Total Manufacturing Industry: Number of Factories data was reported at 7.500 % in 2017. This records a decrease from the previous number of 7.600 % for 2016. India Textile: Annual Survey of Industry: Percentage of Total Manufacturing Industry: Number of Factories data is updated yearly, averaging 8.300 % from Mar 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 8.800 % in 2011 and a record low of 7.500 % in 2017. India Textile: Annual Survey of Industry: Percentage of Total Manufacturing Industry: Number of Factories data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Textile Sector – Table IN.RSJ001: Textile: Overview of Annual Survey Industry.

Release Date: 2023-03-23.Release Schedule:.The data in this file come from the 2021 Annual Survey of Manufactures data files released in March 2023. For more information about the Annual Survey of Manufactures data, see About: Annual Survey of Manufactures...Key Table Information:.Includes only establishments of firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records: ..Total inventories, end of year ($1,000) .Relative standard error for estimate of total inventories, end of year (%) .Last-in, first-out (LIFO) inventory costing, end of year ($1,000) .Relative standard error for estimate of Last-in, first-out (LIFO) inventory costing, end of year (%) .LIFO reserve, end of year ($1,000) .Relative standard error for estimate of LIFO reserve, end of year (%) .Total inventories by valuation method (non-LIFO methods), end of year ($1,000) .Relative standard error for estimate of total inventories by valuation method (non-LIFO methods), end of year (%) .Total inventories by valuation method (LIFO gross amount plus amount not subject to LIFO), end of year ($1,000) .Relative standard error for estimate of total inventories by valuation method (LIFO gross amount plus amount not subject to LIFO), end of year (%) .First-in, first-out (FIFO) inventory costing, end of year ($1,000) .Relative standard error for estimate of First-in, first-out (FIFO) inventory costing, end of year (%) .Average cost inventory valuation, end of year ($1,000) .Relative standard error for estimate of average cost inventory valuation, end of year (%) .Standard cost inventory valuation, end of year ($1,000) .Relative standard error for estimate of standard cost inventory valuation, end of year (%) .Other non-LIFO inventory, end of year ($1,000) .Relative standard error for estimate of other non-LIFO inventory, end of year (%) .Total inventories, beginning of year ($1,000) .Relative standard error for estimate of total inventories, beginning of year (%) .Last-in, first-out (LIFO) inventory costing, beginning of year ($1,000) .Relative standard error for estimate of last-in, first-out (LIFO) inventory costing, beginning of year (%) .LIFO reserve, beginning of year ($1,000) .Relative standard error for estimate of LIFO reserve, beginning of year (%) .Total inventories by valuation method (non-LIFO methods), beginning of year ($1,000) .Relative standard error for estimate of total inventories by valuation method (non-LIFO methods), beginning of year (%) .Total inventories by valuation method (LIFO gross amount plus amount not subject to LIFO), beginning of year ($1,000) .Relative standard error for estimate of total inventories by valuation method (LIFO gross amount plus amount not subject to LIFO), beginning of year (%) .First-in, first-out (FIFO) inventory costing, beginning of year ($1,000) .Relative standard error for estimate of first-in, first-out (FIFO) inventory costing, beginning of year (%) .Average cost inventory valuation, beginning of year ($1,000) .Relative standard error for estimate of average cost inventory valuation, beginning of year (%) .Standard cost inventory valuation, beginning of year ($1,000) .Relative standard error for estimate of standard cost inventory valuation, beginning of year (%) .Other non-LIFO inventory, beginning of year ($1,000) .Relative standard error for estimate of other non-LIFO inventory, beginning of year (%) ..Geography Coverage:.The data are shown for employer establishments and firms for the U.S. level that vary by industry..For information about 2021 Annual Survey of Manufactures, see About: Annual Survey of Manufactures...Industry Coverage:.The data are shown at the 2- through 6-digit 2017 NAICS code levels for the U.S. For information about NAICS, see Annual Survey of Manufactures (ASM): Technical Documentation: ASM Product Class Codes and Descriptions...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/asm/data/2021/AM1831IVAL.zip..API Information:.Annual Survey of Manufactures API data are housed in the Census Bureau API. For more information, see ASM API..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only..To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, co...

Statistical overview of the structural behavior of the transportation sector in Mexico. Base year 2013.

ASI: Number of Workers: Daily Average: Madhya Pradesh data was reported at 339,435.000 Person in 2023. This records an increase from the previous number of 322,247.000 Person for 2022. ASI: Number of Workers: Daily Average: Madhya Pradesh data is updated yearly, averaging 232,834.500 Person from Mar 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 339,435.000 Person in 2023 and a record low of 156,565.000 Person in 2003. ASI: Number of Workers: Daily Average: Madhya Pradesh data remains active status in CEIC and is reported by Labour Bureau. The data is categorized under India Premium Database’s Labour Market – Table IN.GBA051: Annual Survey of Industries: Number of Workers: Daily Average: by States.