Release Date: 2016-09-01..Table Name. . Statistics for U.S. Employer Firms by Sector, Gender, Ethnicity, Race, and Veteran Status for the U.S., States, and Top 50 MSAs: 2014. ..Release Schedule. . This file was released in September 2016.. ..Key Table Information. . These data are related to all other 2014 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2014 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2014 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The top fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for U.S. Employer Firms by Sector, Gender, Ethnicity, Race, and Veteran Status for the U.S., States, and Top 50 MSAs: 2014 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. . The data are shown for:. . All firms classifiable by gender, ethnicity, race, and veteran status. . Gender. . Female-owned. Male-owned. Equally male-/female-owned. . . Ethnicity. . Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. . . Race. . White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Equally minority/nonminority. Nonminority. . . Veteran Status. . Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. . . . . Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Gender (SEX). Ethnicity (ETH_GROUP). Race (RACE_GROUP). Veteran Status (VET_GROUP). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SE1400CSA01 table at: https://www2.census.gov/programs-surveys/ase/data/2014/SE1400CSA01.zip. ..Contact Information. . To contact the Annual Survey of Entrepreneurs staff:. . Visit the website at www.census.gov/programs-surveys/ase.html.. Email general, nonsecure, and unencrypted messages to ewd.annual.survey.of.entrepreneurs@census.gov.. Call 301.763.1546 between 7 a.m. and 5 p.m. (EST), Monday through Friday.. Write to:. U.S. Census Bureau. Annual Survey of Entrepreneurs. 4600 Silver Hill Road. Washington, DC 20233. . . ...[NOTE: Includes firms with payroll at any time during 2014. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2014 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.].Symbols:S - Withheld because estimate did not meet publication standardsN - Not available or not comparableX - Not applicableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2014 Annual Survey of EntrepreneursNote: The data in ...

For information on economic census geographies, including changes for 2012, see the economic census Help Center...Table Name. All sectors: Geographic Area Series: Economy-Wide Key Statistics: 2012. . .Release Schedule. The data in this file are scheduled for release starting in March 2014 and ending in June 2016.. . .Key Table Information. The data in this file come from separate 2012 Economic Census of the U.S., Economic Census of Island Areas, and Nonemployer Statistics data files released on a flow basis from March 2014 through June 2016. As such, these data are subject to change and will be replaced when updated data are added from more recent data files. Users should be aware that during the release of this consolidated file, data at more detailed NAICS and geographic levels may not add to higher-level totals. However, at the completion of the economic census (once all the component files have been released), the detailed data in this file will add to these totals.. . .Universe. The universe of this file is all operating establishments with one or more paid employees (employers) as well as all operating establishments with no paid employees (nonemployers). This universe includes all establishments classified in the North American Industry Classification System (NAICS) Codes 21 through 813990.. . .Geographic Coverage. The data are shown for employer establishments at the US, State, Combined Statistical Area, Metropolitan and Micropolitan Statistical Area, Metropolitan Division, Consolidated City, County (and equivalent), and Economic Place (and equivalent; incorporated and unincorporated) levels for the U.S. and the Island Areas. Data for nonemployer establishments are shown for the U.S. for all levels except Economic Places and only for Puerto Rico for the Island Areas.. . .Industry Coverage. The data are shown at the 2- through 6-digit NAICS code levels for all economic census sectors and at the 7- and 8-digit NAICS code levels for selected economic census sectors.. . .Data Items and Other Identifying Records. This file contains data on:.. Number of employer establishments. Sales, receipts, revenue, shipments, or value of business done for employer establishments. Annual payroll of employer establishments. Total employment of employer establishments. Number of nonemployer establishments. Receipts for nonemployer establishments. Relative standard errors for the first 4 employer data items (Construction industries only).. .Data are also published by Type of Operation or Tax Status for selected sectors. For Wholesale Trade, data are published for Total Wholesale Trade and for Merchant Wholesalers. For the Services sectors, data are published for All Establishments, as well as Taxable and Tax Exempt Establishments...For additional statistics not shown in this file, see the individual data files from the Economic Census of the U.S. Industry, Geographic Area, Subjects, and Summary Series and the Economic Census of Island Areas Geographic Area Series.. . .Sort Order. Data are presented in ascending geography (GEO_ID) by NAICS code (NAICS2012) by Type of Operation or Tax Status (OPTAX) sequence.. . .FTP Download.Download the entire table athttps://www2.census.gov/econ2012/EC/sector00/EC1200A1.zip. . .Contact Information.U.S. Census Bureau, Economic Management Division.Dissemination Branch.Tel: (301)763-9560.econ.dissemination@census.gov. . .The data in this file come from separate 2012 Economic Census of the U.S., Economic Census of Island Areas, and Nonemployer Statistics data files released on a flow basis from March 2014 through June 2016. As such, these data are subject to change and will be replaced when updated data are added from more recent data files. See the Table Notes for more information on this and for related additivity and comparability issues. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census, 2012 Economic Census of Island Areas, and 2012 Nonemployer Statistics..Note: The data in this file are based on the 2012 Economic Census, and the related programs listed above. To maintain confidentiality, the Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and nonsampling error. Data users who create their own estimates using data from this file should cite the Census Bureau as the source of the original data only. For the full technical documentation, see Methodology link in headnote above.

This dataset represents an archived record of annual California sea otter surveys from 1985-2014. Survey procedures involve counting animals during the "spring survey" -- generally beginning in late April or early May and usually ending in late May early June but may extend into early July, depending on weather conditions. Annual surveys are organized by survey year and within each year, three shapefiles are included: census summary of southern sea otter, extra limit counts of southern sea otter, and range extent of southern sea otter. The surveys, conducted cooperatively by scientists of the U.S. Geological Survey, California Department of Fish and Wildlife, U.S. Fish and Wildlife Service and Monterey Bay Aquarium with the help of experienced volunteers, cover about 375 miles of California coast, from Half Moon Bay south to Santa Barbara. The information gathered may be used by federal and state wildlife agencies in making decisions about the management of this threatened marine mammal. These data, in conjunction with findings from several more in-depth studies, may also provide the necessary information to assess female reproductive rates and changes in reproductive success of the California sea otter population through time. For more information on annual California sea otter surveys, including most current surveys and associated data and corresponding USGS Data Series reports, go to: https://www.sciencebase.gov/catalog/item/5601b6dae4b03bc34f5445ec The GIS shapefile "Census summary of southern sea otter" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring range-wide census. This census has been undertaken each year using consistent methodology involving both ground-based and aerial-based counts. This range-wide census provides the primary basis for gauging population trends by State and Federal management agencies. This shapefile includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California. Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al. 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California. The GIS shapefile "Extra limit counts of southern sea otters" is a point layer representing the locations of sea otter sightings that fall outside the officially recognized range of the southern sea otter in mainland California. These data were collected during the spring range-wide census. Sea otter distribution in California (the mainland range) is considered to comprise a band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits as defined above. However, a few individual sea otters (almost always males) can frequently be found outside this officially recognized range, and these "extra-limital" animals are also counted during the census. The GIS shapefile "Range extent of southern sea otters" is a simple polyline representing the geographic distribution of the southern sea otter in mainland California, based on data collected during the spring range-wide census. The spring 2011 survey was incomplete due to weather conditions and there were no “extra-limital” sightings of otters during the spring 1992 survey, hence no data or shapefile for “Extra limit counts 1992.” For ease of access, an additional CSV file of the census summary from 1985-2014 is provided: "AnnualCaliforniaSeaOtter_Census_summary_1985_2014.csv" References: Tinker, M. T., Doak, D. F., Estes, J. A., Hatfield, B. B., Staedler, M. M. and Bodkin, J. L. (2006), INCORPORATING DIVERSE DATA AND REALISTIC COMPLEXITY INTO DEMOGRAPHIC ESTIMATION PROCEDURES FOR SEA OTTERS. Ecological Applications, 16: 2293–2312, https://doi.org/10.1890/1051-0761(2006)016[2293:IDDARC]2.0.CO;2 Tinker, M. T. , D. P. Costa , J. A. Estes , and N. Wieringa . 2007. Individual dietary specialization and dive behaviour in the California sea otter: using archival time–depth data to detect alternative foraging strategies. Deep Sea Research II 54: 330–342, https://doi.org/10.1016/j.dsr2.2006.11.012

description: The 2014 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Combined Statistical Areas (CSAs) are defined by the Office of Management and Budget (OMB) and consist of two or more adjacent Core Based Statistical Areas (CBSAs) that have significant employment interchanges. The CBSAs that combine to create a CSA retain separate identities within the larger CSA. Because CSAs represent groupings of CBSAs, they should not be ranked or compared with individual CBSAs.; abstract: The 2014 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Combined Statistical Areas (CSAs) are defined by the Office of Management and Budget (OMB) and consist of two or more adjacent Core Based Statistical Areas (CBSAs) that have significant employment interchanges. The CBSAs that combine to create a CSA retain separate identities within the larger CSA. Because CSAs represent groupings of CBSAs, they should not be ranked or compared with individual CBSAs.

Abstract

Geostat conducted Census of Agriculture 2014 in accordance with the World Programme of Agricultural Censuses 2006-2015 recommended by the Food and Agriculture Organization (FAO). The census was based on the FAO methodology. Statistics experts of FAO and the United States Department of Agriculture (USDA) were actively engaged at every stage of the census process. At the first stage, in November 2014, together with Population Census there was conducted Census of Agriculture for households. In addition to this, in spring 2015 there was conducted Census of Agriculture for legal entities. As a result, the census covered all agricultural holdings in the country (on the territory controlled by the Government of Georgia) – all households and legal entities, who, as of October 1, 2014, were owning or temporarily operating agricultural land, livestock, poultry, beehive or permanent crop (agricultural), regardless the fact whether there was produced any kind of agricultural product or not during the reference year. The census provided diverse information about agriculture of Georgia such is structure and use of land operated by holdings, livestock, poultry and beehive numbers.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The main statistical unit was the agricultural holding, defined as an economic unit engaged in agricultural production under single management without regard to size and legal status. An economic unit that operates agricultural land or permanent crop trees, but that during the reference year has no agricultural production, is also considered an agricultural holding. As the AC 2014 data collection for the agricultural holdings in the household sector was carried out jointly with the GPC, the common statistical unit was the agricultural production household. Two types of agricultural holdings were distinguished: family holdings and agricultural enterprises.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Frame In 2013, Geostat conducted preliminary fieldwork to establish the list of dwellings and households existing in Georgia. The information received from the preliminary fieldwork was used to update and finalize the census frame for data collection. For agricultural enterprises, to ensure full coverage of the list of potential agricultural enterprises, all existing reliable sources in the country were used.

Mode of data collection

Face-to-face [f2f]

Research instrument

One questionnaire was used for the AC 2014 data collection, in both paper and electronic format covering:

• Household roster
• Number of Holdings
• Structure of the Land Operated by Holdings
• Agricultural Land and Its Use
• Arable Land
• Permanent Crops
• Orchards
• Vineyards
• Livestock and Poultry

The AC 2014 questionnaire covered 15 of the 16 core items recommended for the WCA 2010 round. The following item was not covered: "Other economic production activities of the holding's enterprise".

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING For several months after the census enumeration, approximately 300 people worked on the digitalization of census data. They were permanently supervised by IT and other technical staff. In parallel, digitized questionnaires were compared with paper questionnaires by editors. Finally, data were cleaned by the appropriate division at the central office of Geostat. The data cleaning process used several methods. Data relating to large holdings were verified by telephone calls. In addition, different reliable sources (registers) were used to fill in missing data. Furthermore, donor imputation was used to fill in the missing values. For tabulation, a special software was prepared by Geostat. Geostat implemented a microdata archiving system to save the census data.

(b) CENSUS DATA QUALITY Geostat conducted a PES to assess the quality of the AC. During the fieldwork, Geostat used a six-level control system, which involved the following categories of census staff: field work coordinator, regional coordinator, municipal supervisor, sector supervisor, instructor-coordinator and enumerator.

description: Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2010 Census blocks nest within every other 2010 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.; abstract: Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2010 Census blocks nest within every other 2010 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.

Abstract (en): The data contain records of sentenced offenders in the custody of the Bureau of Prisons (BOP) at year-end of fiscal year 2008. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or supervised release violators), offenders convicted in other courts (e.g., military or District of Columbia courts), and persons admitted to prison as material witnesses or for purposes of treatment, examination, or transfer to another authority. These data include variables that describe the offender, such as age, race, citizenship, as well as variables that describe the sentences and expected prison terms. The data file contains original variables from the Bureau of Prisons' SENTRY database, as well as "SAF" variables that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 7.9-7.16. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. Offenders in the custody of the United States Bureau of Prisons at year-end of fiscal year 2008. 2014-03-11 AGE variable has been relabeled based on when age was computed.2011-03-08 All parts are being moved to restricted access and will be available only using the restricted access procedures. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics.

This data provides estimates of Internet, broadband, and mobile use at the subnational level from 1997-2014. While the U.S. Bureau of the Census has collected data on Internet use over the years, estimates below the state level did not exist until the introduction of the new American Community Survey in 2013. The datasets here fill these gaps with estimates over time for cities, counties, metropolitan areas and states. They also provide demographic breakdowns for the 2013 and 2014 American Community Survey data, beyond what is available on the census website. The datasets can be used to draw comparisons across geographic locations and across time, to track inequality, change, and the impact of Internet use. Collectively, they show major differences across cities, as well as between urban and rural counties. Time series data indicate the flattening of growth in recent years, leading to the persistence of inequalities across places and demographic groups. Multilevel models are used to estimate the percentage of Internet use across counties, principal cities, and metropolitan areas with the CPS and ACs data. A group of random intercept logistic regressions (a type of multilevel model) are constructed for each of the Internet-related variables, namely, home Internet access, home broadband, mobile Internet, and fully-connected household (with broadband and mobile). Estimates are based on the U.S. Bureau of the Census Current Population Survey data for 1997, 2998, 200, 2001, 2003, 2007, 2009, 2010, 2011, and 2012 and the U.S. Bureau of the Census American Community Survey 2013 and 2014, with estimates for missing years imputed via linear interpolation. Estimates for home Internet access are available for 1997-2014, home broadband use for 2000-2014, and mobile use and fully-connected Internet use for 2011-2014. Data available for different geographies is described below. Current Population Survey Data, 1997-2012: Internet use time series, three-year averages, time series for rate of change in Internet use, three-year averages for the rate of change, and yearly summary statistics are available for approximately 330 counties (with some variation over years), the 50 largest Metropolitan Statistical Areas (MSAs), principal cities in the 50 largest MSAs, and the 50 states. American Community Survey Data, 2013-2014: Using Summary Tables of the American Community Survey available in FactFinder, estimates for home Internet access and home broadband are provided by race, ethnicity, education, age, and employment status for 50 states, 817 counties, 381 MSAs, 383 principal cities in 2013 and 387 principal cities in 2014. Using microdata, estimates are developed for home Internet access, home broadband, mobile Internet, and fully connected households broken down by race, ethnicity, education, age, family income, and language skill. The microdata estimates are available for 50 states, 417 counties, 260 MSAs and 102 principal cities in 2013. See Codebook for a more complete description of the datasets, data sources, survey questions, and methods. See the Center for Policy Informatics at Arizona State University website at policyinformatics.asu.edu/broadband-data-portal/home for visualization (maps and graphs) and for further information about this project.

Abstract

The Sudan Multiple Indicator Cluster Survey (MICS), was conducted from August to December 2014 at national level covering all eighteen states. The MICS was designed to collect information on a variety of socioeconomic and health indicators required to inform the planning, implementation and monitoring of national policies and programs for the enhancement of the welfare of women and children.

The survey was carried out by the Central Bureau of Statistics (CBS) in collaboration with the ministries of health, welfare, general education, national environment, and national water cooperation as part of the global MICS program. Technical support was provided by the United Nations Children's Fund (UNICEF). UNICEF, World Health Organization (WHO), United Nations Population Fund (UNFPA), World Food Program (WFP) and the Department for International Development (DfID) UK, provided financial support.

MICS surveys measure key indicators that allow countries to generate accurate evidence for use in policies and programs, and to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments. The Sudan Multiple Indicator Survey is a nationally representative sample survey. Interviews were successfully completed in 15,801 households drawn from a sample of 18,000 households in all 18 states of Sudan with an overall response rate of 98 percent. 20,327 women in the 15-49 years age group, and 14,751 children under 5 years of age. The specific objectives of the survey is to:

1. Update information for assessing the situation of children and women in Sudan based on MICS5 modules and geographical coverage of the 18 States in Sudan.
2. Measure the trend towards achievement of the MDGs and the goals of a World Fit For Children Plan of Action and other internationally agreed upon indicators related to children and women.
3. Furnish data needed for the indicators as per the global review of the Millennium Development Goals.
4. Contribute to the improvement of data and monitoring systems in Sudan and to strengthen technical expertise, national capacity building in the design, implementation, and analysis of such systems.
5. Update Census indicators and provide solid evidence for decentralization (planning and measure of progress).
6. Provide key evidence for social sector programming and the Poverty Reduction Strategy Paper (PRSP) under development and accountabilities for sector strategic plans and UNDAF 2013-2016.

Results presented in this survey have been reviewed by the national MICS Technical Committee and approved by the national MICS Steering Committee. The results are not expected to change and are considered final.

Geographic coverage

National

Analysis unit

• Individuals
• Households

Universe

The survey covered all women aged between 15-49 years and all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Sudan MICS 2014 was to produce statistically reliable estimates for a large number of indicators at the national level. This included urban and rural areas and the eighteen states of the country namely: Northern, River Nile, Red Sea, Kassala, Gadaraf, Khartoum, Gezira, Sinnar, Blue Nile, White Nile, North Kordofan, South Kordofan, North Darfur, West Darfur, South Darfur, and the recent established West Kordofan, Eastern Darfur and Central Darfur.

In order to produce state level estimates of moderate precision, a minimum of 30 enumeration areas (EAs) were selected in each state, resulting in a sample that was not self-weighting. Urban and rural areas in each of the eighteen states were defined as the sampling strata and a multi two-stage, stratified cluster sampling approach was used for the selection of the survey sample.

In the first stage within each stratum, a specified number of EAs were selected systematically with probability proportional to size. In the second stage, after a household listing was carried out within the selected enumeration areas, a systematic sample of 25 households was drawn in each selected EA.

Out of the 18,000 households selected in the sample, 17,142 were found to be occupied. Of these 16,801 were successfully interviewed for a household response rate of 98 percent. In the interviewed households 20,327 women (age 15-49 years) were identified. Of these 18,302 were successfully interviewed, yielding a response rate of 90 percent. In addition to the women 14,751 children under the age of five years were listed in the household questionnaires. Questionnaires were completed for 14,081 of these children, corresponding to the under-5 response rates of 95.5 percent within the interviewed households. The highest response rates at state level for households was in South Darfur at 99.3 percent, while the lowest response rates were in West Kordofan at 93.4 percent. Response rates were slightly higher in rural areas at 98.5 percent than in urban areas at 96.8 percent. The highest response rates among eligible women between 15-49 years was 96.6 percent in Giezera State while the lowest response rates of 78.1 percent were in North Darfur. Similarly, the highest response rates among eligible children under-5 was recorded for Giezera which was 96.9 percent and the lowest response rates was also in North Darfur at 87.9 percent.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three types of questionnaires were used in the survey: 1. Household Questionnaire: It was used to collect information on all de jure household members, the household, and the dwelling 2. Women Questionnaire: It was administered in each household to all women aged 15-49 years 3. Children under five Questionnaire: It was administered to mothers or caretakers of all children under 5 years living in the household.

Cleaning operations

Data were entered into the computers using the Census and Surveys Processing System (CSPro) software package, Version 5.0. The data were entered on 32 desktop computers by 40 data entry operators and 9 data entry supervisors. For quality assurance purposes, all questionnaires were double-entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS programs and adapted to the Sudan questionnaires were used throughout. Data of entry started on September 14 and was completed on November 27 2014. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software, Version 21. Model syntax and tabulation plans developed by the Global MICS team were customized and used for this purpose.

Response rate

The Sudan MICS 2014 was based on a representative sample of 15,801 households drawn from a sample 18,000 households. All 18 states of Sudan with an overall response rate of 98 percent.

Sampling error estimates

MICS 2014 was conducted in a very challenging context of ongoing long term armed conflicts and many displacements of populations prevailing in Darfur and Kordofan states as well as the outstanding high risk mining areas. A very large sample design was defined for MICS 2014 in Sudan. It comprised of 720 Clusters (40 per state), 18,000 Households (1,000 per state) in order to ensure adequate representation of statistical estimation by each state.

During the implementation of the field data collection, the Central Bureau of Statistics (CBS) was constrained to proceed to the replacement of 22 clusters among 720 sampled for the survey (which represented 3%).The maximum number of clusters were replaced within states in four clusters in the Red Sea, West Kordofan, East Darfur and Central Darfur. This was in addition to the two clusters in Kassala and one cluster each in South Darfur, West Darfur, Khartoum and Gedaref. The main reason for the replacement of clusters was as follows: 1. Insecurity in Darfur States 2. Mining area in Kassala State 3. The displacement of population in the Red Sea 4. The rainy season in Gadaref State

CBS benefited from solid expertise of consulting in sampling and developed adequate technical measures by providing the field work team leader. Clear instructions enabled to perform the replacement in close compliance to the statistical practice of replacement of the enumeration area by choosing the nearest accessible area using a list of frame in respect to urban and rural areas. Taking into account the provisional measure of sample design which included 10 percent of “non-respondents rate” and the expansion of initial calculated required sample from 930 clusters to 1,000. Any anticipated error which may have emerged from the replacements was fully absorbed. Indicators measured for MICS 2014 in Sudan were not affected by the replacement of 22 clusters (from 1 to 4 into some states).

Abstract (en): The data contain records of arrests and bookings for federal offenses in the United States during fiscal year 2014. The data were constructed from the United States Marshals Service (USMS) Prisoner Tracking System database. Records include arrests made by federal law enforcement agencies (including the USMS), state and local agencies, and self-surrenders. Offenders arrested for federal offenses are transferred to the custody of the USMS for processing, transportation, and detention. The Prisoner Tracking System contains data on all offenders within the custody of the USMS. The data file contains variables from the original USMS files as well as additional analysis variables, or "SAF" variables, that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 1.1-1.3. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by Abt and the Bureau of Justice Statistics. Data and documentation were prepared by Abt. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. Federal offenders in the custody of the United States Marshals Service during fiscal year 2014.

Abstract (en): The United States Census Bureau conducts a Census of Governments every five years -- in years ending in "2" or "7" -- to collect information about governments in the United States. The Government Organization branch of the 1997 Census of Governments describes the organization and activities of local governments. The 1997 Local Government Directory Survey covered all county, municipal, town or township, school district, special district governments, school systems, and education service agencies that met the Census Bureau criteria for independent governments. The counts of local governments reflect those in operation in June 1997. This collection includes eight parts, each including information regarding a different type of government: (1) county governments, (2) municipal governments, (3) township governments, (4) special district governments, (5) school district governments, (6) state dependent school systems, (7) local dependent school systems, and (8) education service agencies. The data include information on various codes used to identify the government unit, government name, population in 1996 (or enrollment in 1996 for data collected from schools), and government functions. Census statistics on governments are designed to account for the totality of public sector activity without omission or duplication. There are no weight variables included in this collection. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: The final response rate was 83.4 percent. Local governments in the United States. Data that are derived from a census are not subject to sampling variability. 2014-06-20 SPSS, SAS, and Stata setup files, as well as SPSS and Stata system files, a SAS transport (CPORT) file, a tab-delimited data file, and an R data file have been added to the collection. Additionally, a codebook has been created. mail questionnaire For additional information on the Census of Governments, 1997, please refer to the United States Census Bureau Web site.

Abstract (en): The data contain records of defendants in federal criminal cases filed in United States District Court during fiscal year 2014. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central System file. According to the EOUSA, the United States attorneys conduct approximately 95 percent of the prosecutions handled by the Department of Justice. The Central System data contain variables from the original EOUSA files as well as additional analysis variables, or "SAF" variables, that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by Abt and the Bureau of Justice Statistics. Data and documentation were prepared by Abt. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. Defendants in federal criminal cases filed in United States District Court during fiscal year 2014.

Abstract (en): This dataset contains county-level totals for the years 2002-2014 for eight types of crime: murder, rape, robbery, aggravated assault, burglary, larceny, motor vehicle theft, and arson. These crimes are classed as Part I criminal offenses by the United States Federal Bureau of Investigations (FBI) in their Uniform Crime Reporting (UCR) program. Each record in the dataset represents the total of each type of criminal offense reported in (or, in the case of missing data, attributed to) the county in a given year. All counties in the United States, excluding US island territories.Smallest Geographic Unit: county

(CLOB) ..Table Name.Manufacturing: Geographic Area Series: Detailed Statistics for the State: 2012....ReleaseSchedule.Data are scheduled for release on a flow basis beginning in December 2014 and ending in February 2015.......Universe.The universe includes all establishments classified in manufacturing sectors 31-33 with one or more paid employee at any time during the year......GeographyCoverage.Data are shown at the state level.....IndustryCoverage.Data are shown at the two-digit North American Industry Classification System (NAICS) level.....Data ItemsandOtherIdentifyingRecords.This file contains data on:..Number of companies.Number of establishments.Establishments with 0 to 19 employees.Establishments with 20 to 99 employees.Establishments with 100 employees or more.Number of employees.Annual payroll ($1,000).Total 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).Production workers average for year.Production workers for pay period including March 12.Production workers for pay period including June 12.Production workers for pay period including September 12.Production workers for pay period including December 12.Production workers annual hours (1,000).Production workers annual wages ($1,000).Total cost of materials ($1,000).Cost of materials, parts, containers, packaging, etc. used ($1,000).Cost of resales ($1,000).Cost of purchased fuels consumed ($1,000).Cost of purchased electricity ($1,000).Cost of contract work ($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).Total value of shipments and receipts for services ($1,000).Value of resales ($1,000).Value added ($1,000).Total inventories, beginning of year ($1,000).Finished goods inventories, beginning of year ($1,000).Work-in-process inventories, beginning of year ($1,000).Materials and supplies inventories, beginning of year ($1,000).Total inventories, end of year ($1,000).Finished goods inventories, end of year ($1,000).Work-in-process inventories, end of year ($1,000).Materials and supplies inventories, end of year ($1,000).Gross value of depreciable assets (acquisition costs), beginning of year ($1,000).Total capital expenditures ($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).Gross value of depreciable assets (acquisition costs), 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 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). .....Sort Order.Data are presented in ascending state sequence.....FTP Download.Download the entire table at https://www2.census.gov/econ2012/EC/sector31/EC1231A2.zip....ContactInformation. U.S. Census Bureau, Economy Wide Statistics Division. Data User Outreach and Education Staff . Washington, DC 20233-6900. Tel: (800) 242-2184 . Tel: (301) 763-5154. ewd.outreach@census.gov. ..For information on economic census geographies, including changes for 2012, see the economic census Help Center..Data based on the 2012 Economic Census. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology. Data in this file represent those available when this file was created; data may not be available for all NAICS industries or geographies. Data in this table may be subject to employment- and/or sales-size minimums that vary by industry..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census.Note: The data in this file are based on the 2012 Economic Census. To maintain confidentiality, t...

We complied district- and country-level crop area and production data for India from 1947 to 2014. Country level data covering 31 crops for 1947 to 1960 were obtained from a combination of Indian Statistical Abstracts (Statistical abstract, India, 1951-1961) and the Food and Agriculture Organization of the United Nations (FAO) Statistical Yearbooks (FAO, 1949-1962). For 1961 to 2014, data covering 80 crops from FAOSTAT (2017) were used. District level data were compiled from (i) the India Agriculture and Climate (IAC) data set, which covers 20 crops for 1956 to 1987 (Sanghi et al., 1998) and (ii) the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), which covers 24 crops for 19 states for the period from 1966 to 2011 (ICRISAT, 2013).

description: States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.; abstract: States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

description: The 2014 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.; abstract: The 2014 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Abstract

The Survey of Income and Living Conditions (EU-SILC) is the European Union reference source for comparative statistics on income distribution and social exclusion at the European level, particularly in the context of the 'Programme of Community action to encourage cooperation between Member States to combat social exclusion' and for producing key policy indicators on social cohesion for the follow up of the EU2020 main target on poverty and social inclusion and flagship initiatives in related domains, e.g. in the context of the European Semester. It provides two types of annual data: Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions, and Longitudinal data pertaining to individual-level changes over time, observed periodically over a four-year period. The first priority is to be given to the delivery of comparable, timely and high quality data. The cross-sectional data is collected in two stages: An early subset of variables collected by register or interview to assess as early as possible poverty trends. A full set of variables provided along with the longitudinal data to produce main key policy indicators on social cohesion.

Geographic coverage

National

Universe

The reference population of EU-SILC is all private households and their current members residing in the territory of the Member States (MS) at the time of data collection. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements: For all components of EU-SILC (whether survey or register based), the cross-sectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.

Mode of data collection

Face-to-face [f2f]

description: The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.; abstract: The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.

Abstract

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 2011 except 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. 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.

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 (District x 4 digit NIC 2008) having less than or equal to 4 units are also considered under Census Scheme.

Sample Sector Remaining units, excluding those of Census Sector, called the sample sector, are arranged in order of their number of employees and samples are then drawn circular systematically considering sampling fraction, say 20%, within each stratum (District X Sector X 4-digit NIC) in the form of 4 independent subsamples. This will be done for each district and thus, for each State/UT. An even number of units with a minimum of 4 are selected from each stratum and evenly distributed in four subsamples. The sectors considered here are 'Bidi', 'Manufacturing' and 'Electricity'.

Allocation of Samples: All the units belonging to the Census Sector together with selected units of 2 sub-samples, say, of sub-samples 1 and 3 will form the central sample and information for these units will be collected and processed by the Central Agency (i.e., NSSO and CSO(ISW)). After selecting the central sample in the way mentioned above, the units selected for the remaining 2 sub-samples, say, of sub-samples 2 and 4 will be allocated for each State/UT separately. Validated state-wise unit-level data of the central sample will also be sent to the states for pooling this data with their surveyed data to get a combined estimate at the sub-state level

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 and not an intermediate product. If however, a part of the yarn produced by a factory is consumed by it for manufacture of cloth, that part of the yarn so used will be an intermediate product.

BLOCK I: INPUT ITEMS - directly imported items only (consumed) - Information in this block is to be reported for all imported items consumed. The items are to be imported by the factory directly or otherwise. The instructions for filling up of this block are same as those for Block H. All imported goods irrespective of whether they are imported directly by the unit or not, should be recorded in Block I. Moreover, any imported item, irrespective of whether it is a basic item for manufacturing or not, should be recorded in Block I. Hence 'consumable stores' or 'packing items', if imported, should be recorded in Block I and not in Block H.

BLOCK J: PRODUCTS AND BY-PRODUCTS (manufactured by the unit) - In this block information like quantity manufactured, quantity sold, gross sale value, excise duty, sales tax paid and other distributive expenses, per unit net sale value and ex-factory value of output will be furnished by the factory item by item. If the distributive expenses are not available product-wise, the details may be given on the basis of reasonable estimation.

Cleaning operations

Data submitted by the factories undergo manual scrutiny at different stages.

1) They are verified by field staff of NSSO from factory