This dataset is the proportion of people in the 2018 Census dataset and data sources used to count them by territorial authority / local board (TALB) geography. Coverage compares the proportion of people counted in the census with the number of people that should have been counted on census night. It is usually expressed as a percentage. We are using interim coverage at this time, with the official post-enumeration survey (PES) coverage rates for the 2018 Census being published by March 2020. The dataset shows that whilst there is variation across territorial authority / local board areas in the proportions of people we counted (from 96.3 to >100%), the interim coverage rates for local areas are generally very high. Understanding how we counted people will help you to use the census dataset because it provides more information about the data sources used and the composition of the data in terms of the proportion of: census individual forms received. partial census form responses (via the paper dwelling form or online household summary form but did not receive an individual form for the person). admin enumerations (the use of administrative data to add people to the usually resident census population when a census response has not been received). The higher the proportion of census forms received, the more robust the characteristic data, for example income, occupation, will be within the dataset for variables. This is especially the case where characteristic information cannot be provided from alternative sources such as historic census data, administrative data or imputation. A csv version of this table is also available (attachment below).

Abstract

The primary mission of the 2006 Population and Housing Census (PHC) of Nigeria was to provide data for policy-making, evidence-based planning and good governance. The Government at all tiers, researchers, the academia, civil society organizations and the international agencies will find the sets of socio-demographic data useful in formulating developmental policies and planning. The 2006 data will certainly provide benchmarks for monitoring the Millennium Development Goals (MDGs). Enumeration in the 2006 PHC was conducted between March 21st and 27th 2006. It was designed to collect information on the quality of the population and housing, under the following broad categories: demographic and social, education, disability, household composition, economic activity, migration, housing and amenities, mortality and fertility. The results of the exercise are being released as per the Commission's Tabulation Plan which began with the release of the total enumerated persons by administrative areas in the country in the Official Gazette of the Federal Republic of Nigeria No.2, Vol 96 of February 2,2009 and followed with the release of Priority Tables that provide some detailed characteristics of the population of Nigeria by State and LGA.

Geographic coverage

National

Analysis unit

Individuals Households

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Census 2006 Processing: The Technology and Methodology:-

Unlike the data capture method used for the country’s previous censuses, where information from the census forms are typed into the computer system, data capture for census 2006 was carried out by OMR/OCR/ICR systems where questionnaires are scanned through high speed optical scanners. The choice of the scanning system was because it is faster and more accurate than the data keying method.

OMR/OCR/ICR Technology

Definition of terms

• OMR (Optical Mark Recognition) - This means the ability of the scanning machine to detect pencil marks made on the questionnaires by the Enumerators in accordance with the responses given by the respondents.
• OCR (Optical Character Recognition) - This means the ability of the scanning machine to recognize machine printed characters on the questionnaires.
• ICR (Intelligent Character Recognition) - This means the ability of the scanner to recognize characters hand written by the Enumerators in accordance with the responses given by the respondents.

Processing Procedures of Census 2006 at the DPCs:- Data processing took place in the Commission’s seven (7) Data Processing Centres located in different geographical zones in the country. There was absolute uniformity in the processing procedures in the seven DPCs.

(a) Questionnaire Retrieval/Archiving Questionnaires from the fields were taken directly from the Local Government Areas to designated DPCs. The forms on arrival at the DPCs were counted, archived and labeled. Retrieval of the questionnaires at the DPCs were carried out based on the EA frame received from the Cartography Department. Necessary Transmittal Forms are completed on receipt of the Forms at the DPCs. The Transmittal Forms are also used to keep track of questionnaires movement within the DPC.

(b) Forms Preparation The scanning machine has been designed to handle A4 size paper. And the Census form being twice that size has to be split into two through the dotted lines at the middle of the form. This forms preparation procedure is to get the questionnaires, for each Enumeration Areas (EAs), ready for scanning. There is a Batch Header to identify each batch.

(c) Scanning Each Batch on getting to the Scanning Room was placed on joggers (a vibrating machine)to properly align the forms, and get rid of dust or particles that might be on the forms.

The forms are thereafter fed into the scanner. There were security codes in form of bar codes on each questionnaire to identify its genuineness. There was electronic editing and coding for badly coded or poorly shaded questionnaires by the Data Editors. Torn, stained or mutilated forms are rejected by the scanner. These categories of forms were later manually keyed into the system.

Re-archiving of Scanned Forms:- Scanned forms were placed in their appropriate marked envelopes in batches, and thereafter returned to the Archiving Section for re-archiving.

Data Output from the Scanning Machine:- The OMR/OCR Software interprets the output from the scanner and translates it into an XML file from where it is further translated into the desired ASCII output that is compatible for use by the CSPro Package for further processing and tabulation.

Data back-up and transfer:- After being sure that the data are edited for each EA batch in an LGA, data then was exported to the SAN (Storage Area Network) of the Server. Two copies of images of the questionnaires for each EA copied to the LTO tapes as backup and then transferred to the Headquarters. The ASCII data files for each LGA are zipped and encrypted, and thereafter transfer to the Data Validation Unit (DVU) at the Headquarters in Abuja.

Data appraisal

Data collation and validation:- The Data Validation Unit at the Headquarters was responsible for collating these data into EAs, LGAs, States and National levels. The data are edited/validated for consistency errors and invalid entries. The Census and Survey Processing (CSPro) software is used for this process. The edited, and error free data are thereafter processed into desired tables.

Activities of the Data Validation unit (DVU):-

Decryption of each LGA Data File Concatenation/merging of Data Files Check each EA batch file for EA completeness within an LGA and State Check for File/Data Structure Check for Range and Invalid Data items Check for Blank and empty questionnaire Check for inter and intra record consistency Check for Skip Patterns Perform Data Validation and Imputation Generate Statistics Report of each function/activity Generate Statistical Tables on LGA, State and National levels.

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Summary

See story map for coverage and composition rates.

Download lookup file from Stats NZ map hub or Stats NZ geographic data service.

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Interim coverage rates

Coverage rates use dual system estimation (DSE) benchmarks as the denominator to calculate interim coverage rates. Dataset contains interim coverage rates for the usually resident population and for people of Māori descent, and for Māori, Pacific, and Asian ethnic groups.

Composition rates

Dataset contains composition rates (data sources used to count the census usually resident population) for the usually resident population and for each of the six ethnic groups (European; Māori; Pacific; Asian; Middle Eastern, Latin American, and African (MELAA); and Other).

Data sources used to count the census usually resident population:

1. Proportion individual response – census individual forms received.
2. Proportion partial response – partial census form responses (from the paper dwelling form or online household set-up form but where an individual form for the person was not received).
3. Proportion admin enumeration – the use of admin data to add people to the usually resident census population when a census response was not received.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023.

Abstract

There is a long history to the agricultural census in the Netherlands. From 1934 onwards a census has been carried out (almost) every year. In recent years it is no longer purely a statistical project, but serves several purposes: on the one hand production of statistics by Statistics Netherlands and creating a frame for sampling, on the other hand providing data on individual holdings for administrative purposes by the Ministry of Economic Affairs, Agriculture and Innovation (the Ministry). Since the Ministry and Statistics Netherlands have a common interest in the census, it is held as a joint effort. In 1990, it was the last time special meeting days were organised to assess the data from the farmers. On these meeting days, farmers and enumerators jointly filled in the questionnaire manually. In the period 1991 – 1995, these sessions still took place, but the manual procedure was gradually replaced by filling in the information in a computer file. In 1996, the farmer could make a choice between coming to a special meeting place or filling in the survey form himself and returning it by postal mail. From 1997 on, a complete census was organised by postal mail every year. The year 2003 was a pilot year in which respondents had the opportunity to supply the census information through an internet application. In recent years the information is predominantly supplied via the internet. Since the statistical year 2002 the questionnaire of the agricultural census is combined with the application for animal, crop and arable land subsidies (in 2006 also for the single payment scheme). In 2007 data collection for the enforcement of the manure law is also combined in this questionnaire. This is done for efficiency reasons, both for farmers, and for administration and processing of data.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as a single unit, both technically and economically, which has a single management and which undertakes agricultural activities listed in Annex Ito the European Parliament and Council Regulation (EC) No. 1166/2008 within the economic territory of the EU, either as its primary or secondary activity.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Frame Statistics Netherlands has a business register of all industrial and non-industrial commercial establishments, but the agricultural holdings are not yet fully covered in this register. The agricultural census therefore relies on the administrative farm register (AFR) of the Ministry held by NSIR, an executive service of the Ministry. By law farmers have to register with NSIR. The AFR contains names, addresses and a few other characteristics of holders or holdings and a unique registration number. With the census information of several years Statistics Netherlands has built up a statistical farm register (SFR). Relevant characteristics from the AFR (a.o. identification number, addresses, legal status) are also stored in the SFR. Changes in addresses are entered into the AFR throughout the year, changes in the SFR of course only once a year. The SFR provides a magnificent basis for stratification and efficient sampling of subsequent agricultural statistics. An annual census may seem expensive (even when only half of the cost is looked upon as expenses for statistics). But the excellent quality of the sample frame allows for relative small samples in related agricultural statistics and thus reduction of costs.

Mode of data collection

Computer Assisted Web Interview (CAWI)

Research instrument

One questionnaire was used, integrating both the 2010 AC and the SAPM, and presented to respondents as a single statistical inquiry. The questionnaire covered all 16 core items recommended in the WCA 2010.

Questionnaire:

1 Work and education 2 Number of animals and housing 3 Horticulture under glass 4 Mushrooms, bulb growing, chicory growing 5 Crops on open land and land use 6 Agricultural land area 7 Subsidies 8 Farm data 9 Livestock manure 10 Excavation notification (WION) 11 Signature

Cleaning operations

a. Data collection and data entry About 85% of the questionnaires was filled in and returned using the web application, which already contained a lotof c hecks and validations. Paper forms were digitized by a data-entry firm and processed by NSIR in the same way as the online questionnaires. There were several quality controls to ensure correct digitization.

b. Data processing, estimation and analysis Data processing, estimation and analysis were performed in two successive stages:

1. Pre-processing at NSIR After data collection and data entry the input data go through an extensive error control phase. In this phase checks are made on missing values, valid values, unlikely values, range checks, checks of correlation in the data, checks of totals and so on. When necessary additional information is collected from the farmers by phone. Data that is checked and accepted by NSIR is forwarded to Statistics Netherlands.

2. Processing at Statistics Netherlands Processing at Statistics Netherlands involves additional error control, enrichment with additional information, such as total SO and typology, imputation for non-response and analysis. Analyses are made at several levels of aggregation and comprise comparison with previous results and agricultural data from other sources.

Data appraisal

Checking the information in the questionnaires took place using a special control programme. Data were checked for hard and soft errors. Hard errors are non-valid values. Soft errors are unlikely values. If necessary, the checking personnel contacted the respondent to correct for errors. Approximately 85 percent of the questionnaires were completed online. The online questionnaire application contained extensive interactive controls and edits.

Dissemination: Dissemination is done via the Statline database, which is available on the Internet (www.cbs.nl ). In this database, Internet users may select their own indicators and information topics. Short publications on specific subjects are presented in the form of newspaper or Internet articles. Safe access to census microdata is also provided.

Abstract (en): To reduce respondent burden for the 2013 collection, the Census of Jails was combined with the Deaths in Custody Reporting Program (DCRP). The census provides the sampling frame for the nationwide Survey of Inmates in Local Jails (SILJ) and the Annual Survey of Jails (ASJ). Previous jail enumerations were conducted in 1970 (ICPSR 7641), 1972 (ICPSR 7638), 1978 (ICPSR 7737), 1983 (ICPSR 8203), 1988 (ICPSR 9256), 1993 (ICPSR 6648), 1999 (ICPSR 3318), 2005 (ICPSR 20367), and 2006 (ICPSR 26602). The RTI International collected the data for the Bureau of Justice Statistics in 2013. The United States Census Bureau was the collection agent from 1970-2006. The 2013 Census of Jails gathered data from all jail detention facilities holding inmates beyond arraignment, a period normally exceeding 72 hours. Jail facilities were operated by cities and counties, by private entities under contract to correctional authorities, and by the Federal Bureau of Prisons (BOP). Excluded from the census were physically separate temporary holding facilities such as drunk tanks and police lockups that do not hold persons after being formally charged in court. Also excluded were state-operated facilities in Connecticut, Delaware, Hawaii, Rhode Island, Vermont, and Alaska, which have combined jail-prison systems. Fifteen independently operated jails in Alaska were included in the Census. The 2013 census collected facility-level information on the number of confined and nonconfined inmates, number of inmates participating in weekend programs, number of confined non-U.S. citizens, number of confined inmates by sex and adult or juvenile status, number of juveniles held as adults, conviction and sentencing status, offense type, number of inmates held by race or Hispanic origin, number of inmates held for other jurisdictions or authorities, average daily population, rated capacity, number of admissions and releases, program participation for nonconfined inmates, operating expenditures, and staff by occupational category. 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.; Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Datasets:DS1: Census of Jails, 2013 All locally, regionally, and federally administered jails in the United States. The respondent universe was derived from a facility list maintained by the Census Bureau for BJS, from correctional association directories, and from other secondary sources. Census forms were sent to each jail jurisdiction. In addition to a paper form, BJS offered respondents an electronic version via the internet, allowing them to complete and submit their completed questionnaires on-line. 2018-04-25 The dataset and the codebook have been updated2016-03-25 Two records needed to be updated. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. mail questionnaire web-based survey

Abstract

The 2009 Agricultural Census was undertaken by the Samoa Bureau of Statistics in collaboration with the Ministry of Agriculture and Fisheries. The Census collected a large volume of information pertaining to the agricultural activities of households. Enumeration was carried out for 5 weeks in November/December 2009 by enumerators selected from the villages through interview and a basic test. The test included basic mathematical skills, knowledge of agricultural practices and map reading. This was to ensure that the enumerators are of high quality. The officers of the Samoa Bureau of Statistics and the Ministry of Agriculture and Fisheries were allocated to specified areas as supervisors.

Geographic coverage

National

Analysis unit

Households (Agricultural and non-Agricultural) Agricultural Holdings

Kind of data

Census/enumeration data [cen]

Sampling procedure

For any census to be successfully carried out, good household lists and enumeration area maps are pre-requisites. A list of households in respect of each enumeration block in the country was prepared in 2005 for the 2006 Population Census. The updated household list from the 2006 Population Census was used as a frame for the Agricultural Census.

Mode of data collection

Face-to-face [f2f]

Research instrument

The methodology for carrying out the census of Agriculture in Samoa was a combination of complete count and sample survey. Thus the census was basically two part operation. The first part involved all households who were required to complete the Household Form. The households identified as agriculturally active from the Household Forms (Subsistence, Subsistence and Cash and Commercial) were required to complete the Holding Form for every holding operated.

The second part of the questionnaire was designed to cover 25 percent of all agricultural holdings as identified in the first part, with selection made on systematic sample basis (every fourth holding selected). Thus while the Household Form was canvassed in respect of all households, the Holding Form was to be completed by agriculturally active Households only and the Parcel Form was completed in respect of 25 percent of the agricultural holdings.

Printing of Questionnaires and Instruction Manuals In all there were three questionnaires and two instruction manuals one in Samoan and one in English. The three questionnaires were printed on different coloured paper for ease of identification. All census documents were printed and distributed well in advance of the start of the field work.

Cleaning operations

The Secretariat of Pacific community (SPC) provided technical assistance for data processing. The TA was delivered in two separate missions, first to implement data entry, and the second mission was to perform data editing and generate final tabulation for final report. Prior to the start of data entry, Siaumau Misela of Samoa Bureau of Statistics was invited to SPC in December 2009 for a two weeks attachment. Misela worked closely with the SPC data processing specialist in developing the data entry system using CSPro (Census and Survey Processing System). The first mission of the data processing specialist in January 2010 was to finalize and implement data entry. The second mission in October 2010 concentrated mainly on data editing, data recode and generating final tables. The data processing (manual and computer) was done in the Data Processing Section of the Samoa Bureau of Statistics. To facilitate the manual and machine processing of the forms, questionnaires from the same enumeration area were bound together in a batch / folio and assigned a batch id. This id consists of the District, Village and the enumeration area codes. These forms were subjected to manual data scrutiny and corrections. The data entry was implemented using ENTRY of CSPro, and BATCH EDIT for the validation of encoded data items. Data entry was run through a network, which link all data entry work station to a server. A team of 6 staff (1 permanent and 5 temporary) were assigned to do the data processing.

Data appraisal

Fifty percent key verification was done on all the batches, and questionnaires with key verification error rate higher than the tolerance limit was subjected to 100 percent key verification. Additional checks were added in the validation program. Detected errors and inconsistencies were corrected in the batch files.

Abstract

The agricultural census and the survey on agricultural production methods were conducted jointly, i.e. within the same organisational structure, at the same time, and using a single electronic questionnaire and the same methods of data collection and processing. The agricultural census covered about 1.8 million of agricultural holdings. At all farms participating in the census, respondents were asked about the "other gainful activities carried out by the labour force" (OGA). The frame for the full survey was prepared on the basis of the list of holdings prepared for the census. When creating the list, an object-oriented approach was adopted for the first time, which meant that at the first stage the holdings (objects) were identified, their coordinates defined (they were located spatially) and their holders were identified on the basis of data from administrative sources. For domestic purposes, the farms with the smallest area, as well as those of little economic importance (meeting very low national thresholds) were included in the sample survey carried out jointly with the census. The survey on agricultural production methods was conducted on a sample of approximately 200 thousand farms in respect of the precision requirements set out in Regulation (EC) 1166/2008. The frame prepared for the agricultural census was used as the sampling frame.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as "an agricultural area, including forest land, buildings or their parts, equipment and stock if they constitute or may constitute an organized economic unit as well as rights related to running the farm". Two types of holding were distinguished (i) the natural persons' holdings (to which thresholds were applied) and (ii) legal persons holdings (no threshold applied).

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Frame The frame for the agricultural census and the survey on agricultural production methods was based on the list of agricultural holdings. In the process of the list of farms creation for the needs of AC and SAPM 2010 the objective approach was used for the first time, which meant that on the first stage of work agricultural holdings were identified, its coordinates were defined (farms were located in space), and its holder was determined according to administrative data as described below. The list creation started from identification of all land parcels used for agricultural purposes. The land parcels found in the set of the Agency for Restructuring and Modernisation of Agriculture (including the Records of holdings and Records of producers) were combined into holding and had their holders defined. For the rest of land parcels, the holders were defined from the Records of Land and Buildings, afterwards the data concerning users were updated by the set of Real Property Tax Record.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A single electronic questionnaire was used for data collection, combining information related to both the AC 2010 and the SAPM. The census covered all 16 core items recommended in the WCA 2010.

Questionnaire:

Section 0. Identifying characters Section 1. Land use Section 2. Economic activity Section 3. Income structure Section 4. Sown and other area Section 5. Livestock Section 6. tractor, machines and equipment Section 7. Use of fertilizers Section 8. Labour force Section 9. Agricultural production methods

Cleaning operations

a. DATA PROCESSING AND ARCHIVING The data captured through the CAPI, CATI and CAWI channels were gathered in the Operational Microdata Base (OMB) built for the AC 2010 and processed there (including control and correction of data, as well as completing the file obtained in the AC with the data obtained from administrative sources, imputed units and estimation for the SAPM). The data, depersonalized and validated in the OMB, were exported to an Analytical Microdata Base (AMB) to conduct analyses, prepare the data set for transmission to Eurostat and develop multidimensional tables for internal and external users.

b. CENSUS DATA QUALITY Except for a few isolated cases, the CAPI and CATI method resulted in fully completed questionnaires. The computer applications used enabled controls for completeness and correctness of the data already at the collection stage, also facilitating the use of necessary definitions and clarifications during the questionnaire completion process. A set of detailed questionnaire completion guidelines was developed and delivered during training sessions.

Data appraisal

The preliminary results of the agricultural census were published in February 2011 (basic data at the national level), and then in July 2011 in the publication entitled "Report on the Results of the 2010 Agricultural Census" (in a broader thematic scope, at NUTS3 2 level). The final results of the AC 2010 were disseminated by a sequence of publications, covering the main thematic areas of the census. The reference publications were released in paper form, and are available online (www.stat.gov.pl http://www.stat.gov.pl), and on CD-ROMs.

Modeled on the Community Survey of the Project on Human Development in Chicago Neighborhoods, the proposed project collected survey data from random samples of individuals from a random sample of “neighborhood clusters” in El Paso County, Texas. Neighborhood clusters consist of geographically contiguous and socially similar census tracts and for El Paso will be determined by a combination of the local knowledge possessed by the project’s researchers, preliminary analyses of the most recent census data regarding the distributions of immigrant status, language use, year of entry, and aspects of economic disadvantage as well as obvious boundaries (such as Interstates, major roads, mountains, and military installations). The project used a sampling frame of neighborhood clusters in El Paso County stratified by measures of immigrant concentration (e.g., generational status, length of time since immigration) and socio-economic status. The project then employed Cole Lists, a company that provides consumer information for direct marketers, to obtain a list of all residential addresses in El Paso County by census tract. From each sampled neighborhood cluster, 30 residences were selected using a systematic random sampling procedure (a random start determined from a table of random numbers and then selecting every kth address. Each selected residence was mailed a notification letter, printed in English and in Spanish, regarding participation in the project and which specified that a trained interviewer will personally visit to determine which adult resident(s), if any, are willing to participate. For residences that agreed to participate, the adult resident who had the most recent birthday was selected for actual participation. These respondents received an incentive of $20. In face-to-face interviews, trained interviewers recorded each respondent’s answers on a paper form and later manually entered this information into a computer file using spreadsheet software.

Modeled on the Community Survey of the Project on Human Development in Chicago Neighborhoods, the project collected survey data from random samples of individuals from a random sample of "neighborhood clusters" in El Paso County, Texas. Neighborhood clusters consist of geographically contiguous and socially similar census tracts and for El Paso were determined by a combination of the local knowledge possessed by the project's researchers, preliminary analyses of the most recent census data regarding the distributions of immigrant status, language use, year of entry, and aspects of economic disadvantage as well as obvious boundaries (such as Interstates, major roads, mountains, and military installations). The project used a sampling frame of neighborhood clusters in El Paso County stratified by measures of immigrant concentration (e.g., generational status, length of time since immigration) and socio-economic status. The project then employed Cole Lists, a company that provides consumer information for direct marketers, to obtain a list of all residential addresses in El Paso County by census tract. From each sampled neighborhood cluster, 30 residences were selected using a systematic random sampling procedure (a random start determined from a table of random numbers and then selecting every kth address. Each selected residence was mailed a notification letter, printed in English and in Spanish, regarding participation in the project and which specified that a trained interviewer will personally visit to determine which adult resident(s), if any, are willing to participate. For residences that agreed to participate, the adult resident who had the most recent birthday was selected for actual participation. These respondents received an incentive of $20. In face-to-face interviews, trained interviewers recorded each respondent's answers on a paper form and later manually entered this information into a computer file using spreadsheet software.

Poverty and empowerment impacts of the Bihar Rural Livelihoods Project: Evidence from a Mixed-Methods Cluster-Randomized Trial Jeevika is a World Bank assisted project focussed (now under the umbrella of the NRLM) on building networks of women's self-help credit and savings groups,and then using them as a base of other "vertical" interventions. This houshold and village survey data was collected over two rounds to conduct an impact evaluation of Phase 2 of the project with random assignment of the project over a two year period. Collaboration: World Bank Social Observatory team with Government of Bihar. Evaluation design, methods and implementation In order to evaluate the impacts of Jeevika, 180 panchayats were randomly selected from within 16 blocks in seven districts where scale-up of the project was planned but had not yet occurred. Some of these blocks were in districts relatively far from Patna, which had not yet been entered by the project (Madhepura, Saharsa, Supaul), while others were within the larger districts within which Jeevika was already operating (Gaya, Nalanda, Madhubani, Muzaffarpur). The project had already entered these districts in Phase 1, but had not yet expanded to all blocks due to (project) capacity constraints. Within each of the study villages, hamlets (tolas) in which the majority of the population belonged to a scheduled caste or scheduled tribe were identified. This was the same procedure as used by Jeevika to identify the target population (of poor women) for mobilization into the project. Tolas were identified through a focus group discussion held in each village, along with the population of target castes (SC/STs) within each. In Bihar, tola boundaries are easily distinguishable. Field teams would enter the tola at a random point, determine the skip pattern based on the population size and target sample size, and select households through a random walk. Survey staff aimed to include 70% SC/ST households, and 30% households from other castes in each village, in order to ensure variation in socio-economic status within the sample. If the households in selected tolas included fewer SC/ST households than this, households from nearby non-SC/ST majority tolas were also included in the sample. Interviews for the quantitative study were conducted using a structured paper survey form. Baseline and follow up surveys included detailed questions on debt, asset holdings, consumption expenditures, livelihood activities, and women’s mobility, role in household decisions, and aspirations. In addition, in each village, a focus group discussion was conducted, through which data were collected on village level attributes such as local sources of credit, interest rates from each source, local wage rates, and the presence of or distance to markets and other institutions and amenities. Respondents were not compensated for their time. If a respondent was unavailable during initial field visit, the supervisor recorded contact details and returned with interviewers at a later date. As long as the survey team was in that district, repeat visits were undertaken, keeping attrition to a minimum. If a household could not be re-surveyed at endline, it was replaced with another household in the same village. Short re-surveys containing a subset of questions from the main survey were conducted by supervisors for 10% of the sample. Staff from the project also conducted occasional visits after the survey was completed in a village to confirm that all modules had been covered by survey staff. Data was entered in duplicate using CSPro and any discrepancies were corrected based on the paper form. Following the baseline survey, panchayats were stratified on the 16 administrative blocks in the sample and the panchayat-level mean of outstanding high cost (monthly interest rate of 4% or higher) debt held by households at baseline. They were then randomly assigned to an early rollout group or a late rollout group using the random number generator within the Stata statistical analysis software package. The baseline survey was administered to 8988 households across 333 villages in 179 panchayats. The target number of households per panchayat was 50, but there was some variation around this in reality. The lowest number of households in a given panchayat was 49 (9 panchayats), and the largest number was 53 households (3 panchayats). To ensure that control panchayats were not entered by the project, Jeevika held a quarterly ""evaluation panchayat"" meeting, which block project managers of the 16 blocks were required to attend. At these meetings the project M&E team checked whether any village in a control panchayat had been entered, and received an update on progress in treatment panchayats. This procedure was successful in maintaining adherence to randomized treatment assignment throughout the evaluation period. Of the 4,472 households in the sample across 89 panchayats allocated to receive the SHG intervention, 2,722 reported that one of their members belonged to an SHG by endline, constituting 61% of the sample. Since SHG membership was optional, approximately 38% of households in treatment group panchayats had no member in an SHG by endline. The remaining 56 households (across 39 panchayats) did not answer this question or were lost to follow-up (only one such household was not replaced). Although it was possible for those residing in control areas to join (non-Jeevika) SHGs, the proportion of households group in this area containing SHG members remained minimal at endline, with only 460 households (just over 10% of the total sample) reporting SHG membership. Attrition (and replacement) were similar in control and treatment arms, with 132 treatment group baseline households not reached for a follow-up interview and all but one of these replaced, and 128 not reached and thus replaced in the control group. The qualitative evaluation draws on data collected from 2011 to early 2015 in six villages, two where Jeevika had been operating since 2006, two it entered during Phase II, and two where it had not yet intervened by the end of data collection. The Phase I treatment villages were selected at random from the set of previously entered villages in two different districts – Muzaffarpur and Madhubani. Each treatment village was then matched with a set of control villages using propensity score matching methods (Imbens and Rubin 2015) on the basis of village level data from the 2001 government census on literacy, caste composition, landlessness, levels of outmigration, and the availability of infrastructure. In order to find the closest treatment-control match, field investigators then visited the set of possible controls for two days for visual inspection and qualitative assessment. This combined quantitative and qualitative matching method yielded three matched pairs of phase I treatment, phase II treatment, and control villages, with each pair located within the same district. This method of sample selection allows comparison of villages receiving the intervention at each stage with their statistical clones that received it at a different stage or had not received it at all, allowing us to draw causal inferences about the effects induced by Jeevika during the different phases of its expansion. For the purpose of keeping their identity anonymous, we refer to the villages in Madhubani district as Ramganj (Phase I treatment), Nauganj (Phase II treatment) and Virganj (control) and the villages in Muzaffarpur district Saifpur (Phase I treatment), Raipur (Phase II treatment) and Bhimpur (Control). Villages in Madhubani are divided into segregated and caste-homogenous tolas. Brahmins are a majority in these villages, and their tolas are located close to the main resources of the village: the temple, pond and school. All other tolas extend southwards in decreasing order of status in the caste hierarchy, with the Schedule Caste (SC) communities being located farthest south. Each of these communities is also spatially segregated. The SC communities of these villages are mainly comprised of Musahar, Pasi, Ram, and Dhobi subcastes, and the other backward caste communities are comprised of Yadav, Mandal, Badhai, Hajaam, and Teli subcastes. The only big difference between Ramganj and Virganj is that the former has a sizeable Muslim population, comprising Sheikhs, Ansaris, Nutts and Pamariyas, while in the latter, there is only one Muslim (Sheikh) family in the entire village. Inhabitants of these villages primarily depend on agriculture and related activities for their livelihood. The villages in Muzaffarpur district are largely similar to the ones in Madhubani with the important differences being that they are primarily bazaar (market)-centric and the dominant caste is the Chaudhury, who belong to the business community. In each of these villages, first, preliminary studies were conducted using several participatory rural appraisal methods to gain an understanding of the layout of the village. Following this, a team of four field investigators (recruited from a local research-based NGO) accompanied by one of the three principal researchers would visit the villages every three to four months for a cycle of data collection (11 in total over the study period). During every cycle, the ethnographers would enter a different tola in the village for a week (there are roughly 10 tolas in each village). The ethnographers spoke to as many respondents as possible across the village and also returned to the first few respondents in the concluding cycles of data collection. These repeat interviews allowed us to see how respondents reflected on changes experienced as a result of the project [or otherwise] over the four-year period. The first set of participants was selected to be representative of different socioeconomic strata in the village, and subsequent participants were selected via a mixture of purposive and snowball sampling. We

This dataset contains replication files for "The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility" by Raj Chetty, John Friedman, Nathaniel Hendren, Maggie R. Jones, and Sonya R. Porter. For more information, see https://opportunityinsights.org/paper/the-opportunity-atlas/. A summary of the related publication follows. We construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children’s earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby tracts: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, we show that the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods’ causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low-income families, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets. Click here to view the Opportunity Atlas Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The statistical summaries reported in this paper have been cleared by the Census Bureau’s Disclosure Review Board release authorization number CBDRB-FY18-319.

Abstract

Tanzania Mainland through the National Bureau of Statistics (NBS) has been conducting the household budget surveys (HBSs) since 1969 to collect data on consumption, expenditure and the poverty situation in the country. The first round of scientific HBSs that represented urban and rural areas was conducted in 1991. Since then NBS has successfully completed five rounds of scientific HBS including the 2017- 18 HBS. The HBS data series is the major sources of information for estimation of poverty and its associated characteristics. It provides empirical evidence for users to understand the income (using the consumption expenditure as proxy to income) dimension of poverty.

Objectives of the Survey: The main objective of the 2017-18 HBS was to obtain current information on poverty estimation and its associated characteristics and to assess the progress made in improving the living standards of the people. The result will be used for monitoring the implementation of national, regional and global commitments such as Tanzania Development Vision 2025, national Second Five Year Development Plan (FYDP-II 2016/17 2020/21), East Africa Community Vision 2050 (EAC 2050), Africa Development Agenda 2063 (ADA 2063) and Global Agenda 2030 on Sustainable Development Goals (2030 SDGs). Specifically, the 2017-18 HBS aimed at: - Providing series of data for assessing poverty and changes in the households' living standards over time; and for monitoring and evaluation of the impacts of socio-economic policies and programs on the welfare of people; - Providing baseline data for compiling household accounts such as the Private Final Consumption Expenditure (PFCE) component of the demand side of Gross Domestic Product (GDP) as recommended in the System of National Accounts (SNA); and - Rebasing of GDP and Consumer Price Indices (CPI).

Geographic coverage

• National coverage for Tanzania Mainland
• Rural and urban areas
• Regions: Dodoma, Arusha, Kilimanjaro, Tanga, Morogoro, Pwani, Dar es Salaam, Lindi, Mtwara, Ruvuma, Iringa, Mbeya, Singida, Tabora, Rukwa, Kigoma, Shinyanga, Kagera, Mwanza, Mara, Manyara, Njombe, Katavi, Simiyu, Geita, and Songwe.

Analysis unit

• Individuals
• Households
• Communities

Universe

The survey covered all members residing in private households in Tanzania Mainland.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2017-18 HBS covered the population residing in private households in Tanzania Mainland. A representative probability sample of 9,552 households was selected. This sample was designed to allow separate estimates for each of the 26 regions of the Tanzania Mainland, also urban and rural areas separately at the national level.

The 2017-18 HBS adopted a two-stage cluster sample design. The first stage involved selection of enumeration areas (primary sampling units - PSUs) from the 2012 Population and Housing Census (2012 PHC) Frame. A total of 796 PSUs (69 from Dar es Salaam, 167 from Other Urban Areas and 560 from Rural Areas) was selected. The NBS carried out listing exercise in which households residing in selected PSUs were freshly listed to update the 2012 PHC list before selecting households.

The second stage of sampling involved systematic sampling of households from the updated PSUs list. A sample of 12 households was selected from each selected PSU. All household members regardless of their age, who were usual members of the selected households and all visitors who were present in the household on the night before the survey interview, were eligible for the survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2017-18 HBS was implemented using six electronic questionnaires (Forms I - V and VII) and a paper questionnaire (Form VI). The information collected was the following: - Form I: Demographics; parents' survivorship; birth delivery and breast feeding; citizenship and migration; education; literacy; health; disability; insurances, individual asset ownership and identification documents; labour market indicators; non-farm household businesses; and individual non-wage income; - Form II: Dwellings; utility; water and sanitation; transport and communications; tourism; investments; banking; and households’ recall expenditures; children and adult mortality. The form also contained the TASAF and food security modules; - Form III: Crops, livestock and food security; - Form IV: Time use (5+ years Household members); - Form V: Household diary for recording daily household consumption and expenditure over a 14-days period; - Form VI: Individual diary for recording daily consumption and expenditure for each household member age five years or more; and - Form VII: Access to community services (selected communities).

The questionnaires are in English, and provided as external resources.

Response rate

Out of 9,552 selected households, 9,465 households participated in the survey yielding a response rate of 99 percent.

Abstract

The Central Statistical Organization (CSO) conducted fifth Economic Census in 2005 in all the States/UTs in collaboration with State Directorates of Economics and Statistics. The first Economic Census was conducted in 1977 covering only non- agricultural establishments and the three Economic Censuses subsequently carried out in 1980, 1990 and 1998 covered all agricultural and non-agricultural enterprises excepting those engaged in crop production and plantation. There was no change in the coverage of the fifth Economic Census as compared to the fourth Economic Census. Economic Census not only provides updated frame for detailed follow-up surveys but also gives basic entrepreneurial data for planning and development specially for unorganized sector of the economy.

There are certain new features in the fifth Economic Census. Addresses of the enterprises employing 10 workers or more were collected for the first time in the fifth Economic Census through Address Slip. At present the country does not maintain a Business Register. The directory of enterprises to be generated from the Address Slip would be the basic input for preparation of a Business Register. For the first time, data collected in the fifth Economic Census are processed through Intelligent Character Recognition (ICR) Technology.

The results of EC-2005 "ALL INDIA REPORT" contains the all India figures on the number of enterprises and their employment, cross-classified according to their locations, major activity groups, type of the establishments, size-class of the employment, etc. The disaggregated data for States/UTs are also included in the report.

Geographic coverage

All the States/UTs. in the country

Analysis unit

Establishment

Universe

Economic Census (EC) is the complete count of all entrepreneurial units located within the geographical boundaries of the country. All units engaged in the production or distribution of goods or services other than for the sole purpose of own consumption are counted. While all units engaged in nonagricultural activities are covered, in the agricultural sector units in crop production and plantation activities are excluded.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

All questionaires are provided as external resources

Cleaning operations

Intelligent Character Recognition (ICR) technology, which is also known as Automated Forms Processing, was used to process the EC-2005 data. Automated Forms Processing technology enables the user to process documents from their images or directly from paper and convert them to computer readable data.

The schedules of the Fifth EC were scanned/digitized at the fifteen regional Data Processing Centres of Registrar General of India (RGI). After running the edit programme, the error list files were handed over to the State Governments for corrections. The DES officials of the State Government corrected the error files in two/three cycles and then sent the data files to RGI Headquarters to give final touch before sending to Computer Centre, MOSPI. The data files were made further error free by applying auto corrections at the Computer Centre.

Abstract

The Household Consumption and Expenditure (HCE) survey is administered by the Central Statistical Agency every five years, most recently in 2010/11. The core objective of the HCE survey is to provide data that enable to understand the income dimension of poverty and the major objectives are to: • Assess the level, extent and distribution of income dimension of poverty. • Provide data on the levels, distribution and pattern of household expenditure that will be used for analysis of changes in the households' living standard level over time in various socio-economic groups and geographical areas. • Provide basic data that enables to design, monitor and evaluate the impact of socio- economic policies and programs on households/individuals living standard. • Furnish series of data for assessing poverty situations, in general, and food security, in particular. • Provide data for compiling household accounts in the system of national accounts, especially in the estimation of private consumption expenditure. • Obtain weights and other useful information for the construction and /or rebasing of consumer price indices at various levels and geographical areas.

Geographic coverage

The 2010/11 HCE survey covered all rural and urban areas of the country except the non-sedentary populations in Afar (three zones) and Somali (six zones).

Analysis unit

• Household
• Individual
• Consumption expenditure items

Universe

The survey covered households in the selected samples except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The 2007 Population and Housing Census served as the sampling frame from which the rural and urban EAs were selected. A fresh list of households for each selected EA was collected at the beginning of the survey period. Households were then selected for inclusion in the survey by choosing a random number as the starting point in the list and selecting every nth household (n being the necessary number to achieve the desired number of households in each EA).

Sample Design & Selection In order to produce a representative sample, the country was stratified into the following four categories: rural, major urban centers, medium towns, and small towns.

a. Category I - Rural This category consists of the rural areas of 68 zones and special weredas, which are considered zones, in 9 regions of the country. This category also includes the rural areas of the Dire Dawa City Administration. A stratified two-stage cluster sample design was used, with the primary sampling unit being the EAs. Sample EAs were selected using Probability Proportional to Size, with size being the number of households identified in the 2007 Population and Housing Census. Twelve households were randomly selected from each sample rural EA for survey administration. The total sample for this category is 864 EAs and 10,368 households.

b. Category II - Major Urban Centers This category includes all regional capitals as well as five additional major urban centers with large populations, for a total of 15 major urban centers. These 15 urban centers were broken down into the 14 regional capitals and the 10 sub-cities of Addis Ababa City Administration resulting in a total of 24 represented urban domains. A stratified two-stage sample design was also used for this category as in the rural sample with EAs as the primary sampling unit. For this category, however, 16 households were randomly selected in each EA. In total, 576 EAs and 9,216 households were selected for this category.

c. Categories III & IV - Other Urban Centers These two categories capture other urban areas not included in Category II. A domain of other urban centers was formed from 8 regions (all except Harari, Addis Ababa, and Dire Dawa where all urban centers are included in Category II). Unlike the other categories, a three-stage sample design was used. However, sampling was still conducted using probability proportionate to size. The urban centers were the primary sampling units and the EAs were secondary sampling units. Sixteen households were randomly selected from each of the selected EAs. A total sample of 112 urban centers, 528 EAs, and 8,448 households were selected for these two categories.

Mode of data collection

Face-to-face [f2f]

Research instrument

A hard copy (Paper print) booklet type questioner has been used for data collection. The design of the questionnaire has structured/organized into five main parts (forms).

The main components of the survey questionnaire are: Form 0: is used together basic household information that could help to assess the general livelihood nature of a household and its members, such as: source of household income, status and scope of agriculture engagement (diversity and specialization), safety net/asset accumulation participation, participation in micro and small scale business enterprise, accessibility and/or credit facility status from micro-finance institution, …etc.,

Form 1: has been used to collect data on demographic characteristics and economic activity of household members, such as: age, sex, marital status, education, income contribution status, economic activity and other related variables.

Form 2 (2A & 2B): is used to collect actual consumption (quantity consumed) and equivalent expenditure of food, beverages and tobacco items, that would have been actually consumed by the household (members of the household) within the reference period of the survey. Note that the first three consecutive day's consumption being collected in Form 2A and 2B is used to collect the second phase (consecutive 4 days) of the survey week.

Form 3 (3A, 3B & 3C): Household consumption and expenditure data on non-durable goods and frequent services has been collected using three segments of form 3. Of which 3A and 3B are designed to handle three and four day's data, respectively; while 3C has been used to capture a full month reference data.

Form 4 (4A-4E): Household expenditure data of durable goods and Less-Frequent services was administered in form 4. In order to facilitate a systematic way of data collection approach, these goods and services are grouped into classes and data were collected using five chapters of the main module in such a way that expenditure data on: • Clothing and footwear was collected in 4A; • Dwelling rent, water, fuel and energy, furniture's & furnishing, household equipment and operation were collected by use of form 4B; • Health, transport and communication goods and services has been collected in form 4C; • Education, recreation, entertainment, cultural and sport goods and services were collected by the use of 4D; and • Personal goods and services, financial services, and others including operational cost of production with respect to unincorporated household economic enterprises;

Dairy book: Consumption expenditure of food and beverages data are collected, at first on daily basis, by listing every consumed item by the household (every household member) in each day in a dairy book, to facilitate exhaustiveness of consumption. And, then a summary of attributes are transferred to the main questionnaire.

Measuring tools: Kitchen balance (digital type in urban and analog type in rural areas) and measuring type are used for consumption/quantity data collection.

Cleaning operations

Data Processing All data processing was undertaken at the head office. Completed questionnaires were returned to the CSA data processing department from the field periodically. Data processing activities included cleaning, coding, and verifying data as well as checking for consistency. These activities were carried out on a quarterly basis after entering three months of data. Further processing, including the estimation of sampling weights, was carried out at the close of data entry.

Data Entry and Coding Manual editing and coding of data began as early as August 2010, when the first round of completed questionnaires was received at the head office. A team of 21 editors, 5 verifiers, and 4 supervisors carried out these activities. Subject matter experts provided a 5-day intensive training for this team to equip them with the necessary skills. Additionally, a team of 12 encoders was trained to enter the data. A double-entry system was used, wherein two separate encoders manually entered each survey. Any discrepancies between the two entries were flagged automatically and the physical survey was reviewed to correct the errors. Data entry was completed in October 2011.

Data Validation and Cleaning Data validation and cleaning was carried out by subject matter experts and data programmers. Systematic validity checks were completed at the commodity, household and visit levels. Activities related to consistency, validity, and completeness included the following: a. Imputation of missing observations on consumption goods (in quantity or value) using the market price survey that was collected at the time of the HCE. b. Validity and consistency of quantity and value of consumption items was checked by comparing the figures across both household visits (using the household-provided prices and/or the market price survey). c. Estimation of the value of consumption of own production using the household-provided quantities and market survey prices. d. Comparison of household expenditure on durable goods using different recall periods (i.e., 3 and 12 months). After analyzing the annualized values using each reference period, it was decided to use whichever period resulted in the largest expenditure, which was often the