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

Description

From website:

The Statistical Abstract of the United States, published since 1878, is the authoritative and comprehensive summary of statistics on the social, political, and economic organization of the United States.

Use the Abstract as a convenient volume for statistical reference, and as a guide to sources of more information both in print and on the Web

Sources of data include the Census Bureau, Bureau of Labor Statistics, Bureau of Economic Analysis, and many other Federal agencies and private organizations

Sections include:

• Accommodation, Food, & Other Services
• Agriculture
• Arts, Recreation, & Travel
• Banking, Finance, & Insurance
• Births, Deaths, Marriages, & Divorces
• Business Enterprise
• Construction & Housing
• Education
• Elections
• Energy & Utilities
• Federal Gov’t Finances & Employment
• Foreign Commerce & Aid
• Geography & Environment
• Health & Nutrition
• Income, Expenditures, Poverty, & Wealth
• Information & Communications
• International Statistics
• Labor Force, Employment, & Earnings
• Law Enforcement, Courts, & Prisons
• Manufactures
• National Security & Veterans Affairs
• Natural Resources
• Population
• Prices
• Puerto Rico & the Island Areas
• Science & Technology
• Social Insurance & Human Services
• State & Local Gov’t Finances & Employment
• Transportation
• Wholesale & Retail Trade

Openness: OPEN

• License: US government so public domain.
• Access: ok. Most data provided as xls or pdf but no bulk.
  • www: yes. Browse and then get individual items as xls or pdf. Note one can get some of this information in more convenient form from http://www.usgovernmentspending.com/ (download series page).
  • bulk: no.

First published in 1878, the Statistical Abstract serves as the official federal summary of statistics and provides over 1,400 tables of benchmark measures on the demographic, housing, social, political, and economic condition of the United States.

Kenya’s Statistical Abstract is the single source of data covering a series of years. The Abstract enables you to get complete time series data of the Kenyan Economy from one SINGLE official source. The issue is published by the Kenya National Bureau of Statistics (KNBS).

Kenya’s Statistical Abstract is the single source of data covering a many areas of Kenya’s Economic, Political, Geographic, Financial and Educational Data. It is a compilation of statistical information from KNBS Censuses and Surveys. Broadly the topics covered in this publication touch on the Constitution, land, climate, population, migration, tourism, national accounts(GDP), External trade, domestic exports, imports, agriculture, forestry, fishing, manufacturing, building, construction, housing, mining engery , electricity, fuel, currency, banking, insurance, stock exchange, transportation and telecommunications, public health, public finance and retail sectors.

SitAn

Children's rights are enshrined in the UN Convention on the Rights of the Child (UNCRC) and the African Charter on the Rights and Welfare of the Child (ACRWC), to which Uganda is a signatory, and are also recognised in the Constitution of Uganda and the Children Act. However, despite sustained and substantial reductions in the proportion of Ugandans living below the poverty line over the past two decades, and not withstanding significant progress in improving the lives of children, 55% of children under the age of five years are deprived of two or more of their rights (MoGLSD et al., 2014).

This analysis aims to provide a robust and comprehensive understanding of the situation of children to identify broad areas of intervention in the national development agenda within which government and key stakeholders can situate emerging opportunities for programming, policy advocacy and research activities aimed at improving the lives of children. While focusing on children's rights in four key dimensions - survival, education and development, protection, and participation - the report also explores the crosscutting issues of inequality and gender, to give a holistic view of the potential for policies, programmes and practices to yield positive change in children's lives in the short, medium and long term.

Statistical Abstract

For children, equity refers to the equal opportunity to survive, develop and reach their full potential without discrimination, bias or favouritism, including children in the most disadvantaged segments of society. This appendix provides a detailed and comprehensive statistical overview of the inequities that affect the wellbeing of children in Uganda. The aim is to identify key priority areas of intervention in the national development agenda to influence more equitable programming, policy advocacy and research initiatives aimed at improving the lives of all children in Uganda.

Data was sourced from: National Surveys, Sector Management Information Systems, Annual Performance Reports or Programme Reports

The Idaho Statistics Update project is made possible by a 1997/98 Seed Grant from the University of Idaho Research Office. The grant was used to hire three student assistants to input the data and to convert the data to a usable format for the Web. The undertaking of this project is possible to accomplish only with the assistance of several librarians at the University of Idaho. Some of the original chapters included here were published as volume one of the Idaho Statistical Abstract, 4th edition, by University of Ida ho, Center for Business Development and Research. Efforts were made to use the sources listed in the original chapters to update the data when available. The chapters intended for volume 2 of Idaho Statistical Abstract, 4th edition, are new data collected from various sources by Lily Wai, the Compiler-in-Chief. The Idaho Department of Commerce also contributed some funds for this project. This is an on-going project with periodic updates planned when funding becomes available. In the interest of improving the quality and coverage of future updates, users of this site are encouraged to address suggestions to Lily Wai, Head of Government Documents, University of Idaho Library, Moscow, Idaho 83844-2353.

Statistical Abstract For Kampala City
This dataset falls under the category Planning & Policy Planning.
It contains the following data: The statistical abstract is a standard summary of statistics on the social, political, and economic organisation of Kampala City. It is designed to serve as a guide to other statistical publications and sources
This dataset was scouted on as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.kcca.go.ug/media/docs/Statistical-Abstract-2019.pdf URL for data access and license information.

This data layer shows the size classification of each county based on population statistics published in the most recent edition of the Florida Statistical Abstract.Section 420.5087(1), Florida Statutes, requires State Apartment Incentive Loan (SAIL) program funds to be made available based on the need in each of the following categories of counties as determined by using the population statistics published in the most recent edition of the Florida Statistical Abstract:County population equal to or greater than 825,000 (classified as Large Counties)County population greater than 100,000 but less than 825,000 (classified as Medium Counties); and County population less than or equal to 100,000 (classified as Small Counties).This data layer shows the size classification of each county in Florida and is updated annually.

The Idaho Statistics Update project is made possible by a 1997/98 Seed Grant from the University of Idaho Research Office. The grant was used to hire three student assistants to input the data and to convert the data to a usable format for the Web. The undertaking of this project is possible to accomplish only with the assistance of several librarians at the University of Idaho. Some of the original chapters included here were published as volume one of the Idaho Statistical Abstract, 4th edition, by University of Id aho, Center for Business Development and Research. Efforts were made to use the sources listed in the original chapters to update the data when available. The chapters intended for volume 2 of Idaho Statistical Abstract, 4th edition, are new data collected from various sources by Lily Wai, the Compiler-in-Chief. The Idaho Department of Commerce also contributed some funds for this project. This is an on-going project with periodic updates planned when funding becomes available. In the interest of improving the quality and coverage of future updates, users of this site are encouraged to address suggestions to Lily Wai, Head of Government Documents, University of Idaho Library, Moscow, Idaho 83844-2353.

The Idaho Statistics Update project is made possible by a 1997/98 Seed Grant from the University of Idaho Research Office. The grant was used to hire three student assistants to input the data and to convert the data to a usable format for the Web. The undertaking of this project is possible to accomplish only with the assistance of several librarians at the University of Idaho. Some of the original chapters included here were published as volume one of the Idaho Statistical Abstract, 4th edition, by U niversity of Idaho, Center for Business Development and Research. Efforts were made to use the sources listed in the original chapters to update the data when available. The chapters intended for volume 2 of Idaho Statistical Abstract, 4th edition, are new data collected from various sources by Lily Wai, the Compiler-in-Chief. The Idaho Department of Commerce also contributed some funds for this project. This is an on-going project with periodic updates planned when funding becomes available. In the interest of improving the quality and coverage of future updates, users of this site are encouraged to address suggestions to Lily Wai, Head of Government Documents, University of Idaho Library, Moscow, Idaho 83844-2353.

The Idaho Rangeland Atlas is a collaboration of the University of Idaho Library and the University of Idaho Rangeland Center. Its purpose is to provide simple, clear information about Idaho's rangelands using open, accessible web technologies. Leveraging the University of Idaho's investements in geospatial data and infrastructure enable us to present this information. We believe that if an Idaho citizen wants to understand the basic facts of rangeland ecology and space in our state, those facts should be available without the need to engage in advanced analysis or obtain new skills.The lack of an aggregating resource, like a statistical abstract, adds time to process of discovery and delays the ability of users to move on, either to advanced research questions, as they have to answer and prove more fundamental ones first, or to other tasks based on the information that they now have. Given the increasing accessibility of web-based geospatial processing, and the improvement in technology to provide rich, informative, web-based queries of spatial data, the opportunity exists to re-invent the statistical abstract for natural resource and agricultural questions, providing a simple interface to gather facts about the state of Idaho’s rangelands.

The lack of an aggregating resource, like a statistical abstract, adds time to process of discovery and delays the ability of users to move on, either to advanced research questions, as they have to answer and prove more fundamental ones first, or to other tasks based on the information that they now have. Given the increasing accessibility of web-based geospatial processing, and the improvement in technology to provide rich, informative, web-based queries of spatial data, the opportunity exists to re-invent the statistical abstract for natural resource and agricultural questions, providing a simple interface to gather facts about the state of Idaho’s rangelands.

This page provides a range of data about the staff who work for the London Fire Brigade. There is the LFB Human Resources (HR) Statistical Abstract which provides a range of data from 2014 onwards, and is updated quarterly. There are also bi-annual narrative People Services Performance Reports, which include a commentary about performance along with appendices containing the supporting data. The data covers workforce composition, including senior management/top earners data, recruitment outcomes, leavers, sickness and absence (including levels of stress, anxiety and depression (SAD), grievance, discipline, and includes breakdowns for gender, disability, ethnicity, sexual identity (orientation) and age (where such data is available). Data is shown for the organisation as a whole and by occupational group. Our occupational groups are: Operational (firefighters and operational managers, 84% of the organisation) Fire & Rescue Service staff (FRS staff) – non-uniformed support staff Control - 999 call handlers and their managers. The data will be updated quarterly or annually depending on the area. Further information about our people can be found in Our Performance report, People and Resources section . Pay gap data and action plans can be found here .

description: The Bureau of Labor Statistics (BLS) collects and publishes statistical information to compare labor conditions and developments in the United States and selected foreign economies. All of the measures are based on data from the statistical agencies of the foreign economies covered or from international organizations. The Bureau does not initiate surveys or data collection programs abroad. The statistical concepts and methods used in different countries are developed primarily to meet domestic rather than international needs. When there are substantial conceptual differences, BLS adjusts the data to improve comparability or describes the differences so users will not draw misleading conclusions. In adjusting data for greater comparability, BLS must depend on the availability of relevant information, and in some instances it is necessary to make estimates based on incomplete data. Therefore it is possible to achieve only approximate statistical comparability among countries.; abstract: The Bureau of Labor Statistics (BLS) collects and publishes statistical information to compare labor conditions and developments in the United States and selected foreign economies. All of the measures are based on data from the statistical agencies of the foreign economies covered or from international organizations. The Bureau does not initiate surveys or data collection programs abroad. The statistical concepts and methods used in different countries are developed primarily to meet domestic rather than international needs. When there are substantial conceptual differences, BLS adjusts the data to improve comparability or describes the differences so users will not draw misleading conclusions. In adjusting data for greater comparability, BLS must depend on the availability of relevant information, and in some instances it is necessary to make estimates based on incomplete data. Therefore it is possible to achieve only approximate statistical comparability among countries.

Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National

Analysis unit

Households

Universe

All Household members. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING DESIGN

Sample size is 9,000 households. The sample design adopted for the study was a two-stage self-weighting design in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households. The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained, and weights had to be added.

(b) SAMPLE FRAME

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups. In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one. In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases, questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

Data about China's Economy and Society, includes: statistical yearbook, census data, survey data, data compilation, analysis reports, and Statistical Abstracts and other data, a total of more than 20 million data items can be searched. Open to the 2nd National University Data Driven Innovation Research Competition, 10 concurrent users allowed. The deadline for data use is April 1, 2019.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the downloads tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

With some exceptions, all samples are probability based and nationally representative of the resident adult population. The coverage area is the entire country including rural areas, and the sampling frame represents the entire civilian, non-institutionalized, aged 15 and older population. For more details on the overall sampling and data collection methodology, see the World poll methodology attached as a resource in the downloads tab. Specific sampling details for each country are also attached as technical documents in the downloads tab. Exclusions: Due to ongoing conflict and security issues, Tigray, Gambella, Harari regions were excluded. The excluded areas represent approximately 7% of the total population of Ethiopia. Design effect: 1.52

Mode of data collection

Face-to-Face [f2f]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 3.8. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Data appraisal

The variable FEWFOOD was not considered in the computation of the published FAO food insecurity indicator based on FIES due to the results of the validation process.

Abstract

The 2011 Population and Housing Census of Samoa was taken on the midnight of November the 7th 2011. It counted every person in the country on that night and collected a wide range of social, economic and demographic information about each individual and their housing status.

The information were used to develop statistical indicators to support national plannning and policy-making and also to monitor MDG indicators and all other related conventions. This included population growth rates, educational attainment, employment rates, fertility rates, mortality rates, internal movements, household access to water supply, electricity, sanitation, and many other information. The full report is available at SBS website: http://www.sbs.gov.ws under the section on Population statistics and demography.

Geographic coverage

National coverage Regions Districts Village Enumeration areas

Analysis unit

Private households Institutional households Individuals Women 15-49 Housing/Buildings

Universe

The PHC 2011 covered all de facto household members, institutional households such as boarding schools, hospitals, prison inmates and expatriates residing in Samoa for more than 3 months. The PHC excluded all tourists visiting Samoa during the enumeration period and all Samoans residing overseas.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable to a complete enumeration census.

Mode of data collection

Face-to-face [f2f]

Research instrument

Users' consultation seminars were conducted for three consecutive days (June 8th -10th, 2010) with financial support provided by the office of UNFPA in Suva via the Samoa Parliamentary Group for Population Development (SPGPD) annual programs. For the first time in census history, the SPGPD or members of parliament have become the target group of users to get involved in any census questionnaire consultations.

All government ministries and non-governmental organizations were invited to the consultation seminars and each was asked to make a presentation of data needs for consideration in the final census 2011 questionnaire. To avoid re-inventing the wheel in the compilation of the list of census questions for census 2011, the questionnaire from the census 2006 was reprinted and distributed to all participants and presenters to select questions that they would consider again for the census 2011 in addition to their new data needs. Users were also advised that any new question would need good justifications of how it links to national interests.

At the end of the three days seminar, all new questions were compiled for final selection by Samoa Bureau of Statistics. Not all the users' data needs have been included in the final 2011 census questionnaire due mainly to the cost involved and limited time for census enumeration. Therefore, the final selection of questions was purely based on the linkage of the data being requested to the list of statistical indicators in the 'Strategy for the Development of Samoa 2008-2012' (SDS) and the 'Millennium Development Goals' (MDGs) 2015. All data requests outside of the two frameworks were put aside to be integrated in other more appropriate survey activities by the bureau.

From July 2010-December 2010, the questionnaire was formatted using the In-Design CS4 software. It is important to note that the PHC 2011 was the first ever census using the scanning technology to process data from the census questionnaires as a replacement of the usual manual data entry process. The scanning was pilot tested in April 2011, before it was finally used for final census enumeration.

The questionnaire was designed using A3 paper size.

The Population questionnaire was administered in each household, which collected various information on household members including age, sex, citizenship, disability, orphanhood, marital status, residence (birth, usual, previous), religion, education and employment.

In the Population questionnaire, a special section was administered in each household for women age 15-49, which also asked information on their children ever born still living, died or living somewhere else. Mothers of children under one year were also asked whether their last born children were still living at the time of the census.

The Housing questionnaire was also administered in each household which collected information on the types of building the household lived, floor materials, wall materials, roof materials, land tenure, house tenure, water supply, drinking water, lighting, cooking fuel, toilet facility, telephone, computer, internet, refrigerator, radio, television and others.

Cleaning operations

Data editing was done in several stages. 1. Office manual editing and coding 2. Automatic scanning data entry edits 3. Visual verification questionnaire edits 3. Structure checking and completeness 4. Structure checks of the CSPro data files Editing program can be enquired at the Division of IT and Data Processing at email address: info.stats@sbs.gov.ws

Sampling error estimates

The census is a full-coverage of the population, therefore it is not a sample where sampling errors can be estimated.

Data appraisal

There was no post-enumeration in the census 2011. One of the normal practices by the bureau to validate the total population counts from all villages, districts and regions of Samoa in any census is the manual count of the population in all areas during the on-going census enumeration.That information is collected by the enumerators and field supervisors during the enumeration using the Enumerators and Supervisors control forms. At the end of the enumeration, the control forms which mainly contained the number of males and females per enumeration area will be collected and compiled by the Census and Survey division as the first preliminary count of the census. In the census 2011, the preliminary population counts were compiled and launched as the 'Village Directory 2011' report after 4 weeks from end of the enumeration period.

The significance of the Village Directory report is it helps to provide a qiuick overall picture of the population growth and population distribution in all villages of the country relative to previous censuses. Most important of all is that the preliminary count will provide the basis for a decision whether a post-enumeration is warrant or otherwise. If the preliminary country is close to the projected population then the post-enumeration is assumed not worth the cost because it is expensive and it will delay all other census processes. In the census 2011, the preliminary count arrived at 186,340 which was more than the projected population of 184,032 as depicted in the Statistical Abstract 2009. Therefore the decision was made that post-enumeration was not worth it.

Statistical abstract relating to British India from 1840 to 1865