NOTE: For information on confidentiality protection, nonsampling error, and definitions, see http://www.census.gov/prod/cen2010/doc/cd113.pdf..Source: U.S. Census Bureau, 2010 Census.

Upper limits on the production cross-section times the HH to bbtautau branching ratio for non-resonant HH at 95% CLS and...

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of American Samoa, data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on American Samoa's data products, see the 2020 Island Areas Censuses Technical Documentation..Note: For information on the codes used when processing the data in this table, see the 2020 Island Areas Censuses Technical Documentation..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "N" means data are not displayed for the selected geographic area due to concerns with statistical reliability or an insufficient number of cases.5. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, American Samoa.

Upper limits at the 95% CL for nonresonant HH production with anomalous lambda_HHH and yt couplings, compared for the decay...

The instrument maps and data tables aim to give an insight into the mapping process and datasets used to reconcile the historical data for the households and NPISH financial categories AF.6, AF.7 and AF.8 assets and liabilities.

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to operational changes for military installation enumeration, the 2020 Census of Guam data tables reporting housing, social, and economic characteristics do not include housing units or populations living on Guam's U.S. military installations in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about operational changes and the impacts on Guam's data products, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of Guam, data users should consider the following when using Guam's data products: 1) Data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on Guam's data products, see the 2020 Island Areas Censuses Technical Documentation. 2) Cells in data tables will display the letter "N" when those data are not statistically reliable. A list of the geographic areas and data tables that will not have data displayed due to data quality concerns can be found in the 2020 Island Areas Censuses Technical Documentation. 3) The Census Bureau advises that data users consider high allocation rates while using the 2020 Census of Guam's available characteristics data. Allocation rates -- a measure of item nonresponse -- are higher than past censuses. Final counts can be adversely impacted when an item's allocation rate is high, and bias can be introduced if the characteristics of the nonrespondents differ from those reported by respondents. Allocation rates for Guam's key population and housing characteristics can be found in the 2020 Island Areas Censuses Technical Documentation. .Note: For information on the codes used when processing the data in this table, see the 2020 Island Areas Censuses Technical Documentation..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "N" means data are not displayed for the selected geographic area due to concerns with statistical reliability or an insufficient number of cases.5. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, Guam.

All HH-extraction values were normalized to the initial bulk sample weight. All fish tooth values were normalized to the measured weight of the teeth.

Abstract

The Nauru Population and Housing Census 2011 is funded by UNFPA and AusAID. Technical assistant was provided by the SPC/SDP from Noumea. The Census night took place on 30th October 2011 at 12 midnight. The fieldwork was scheduled to complete in 2 weeks and the final schedule was given 1-2 weeks extension time for supervisor's editing of forms.

Geographic coverage

• National
• District
• Enumeration area
• Household members

Analysis unit

Region/EA Identity Household questionnaire Person questionnaire

Universe

The survey covered all de jure household members (usual residents), all household, all population, all age, all sex, all nationality

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable to a full enumeration census

Sampling deviation

Not applicable to a full enumeration census

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing

Response rate

96%

Sampling error estimates

Not applicable to a full enumeration census

Data appraisal

Data quality tables are available to review the quality of the data and include the following:

Table H1. District by Type of building Table H2. District by Materials of Outerwalls Table H3. District by Period building first constructed Table H4. District by type of tenure Table H5A. District by Number of household rooms Table H5B. District by Number of household dining rooms Table H5C. District by Number of household kitchen Table H5D. District by Number of household total rooms Table H6. District by Shared bathroom Table H7. District by Shared kitchen Table H8A. District by Materials of roofing Table H8B. District by Roofing condition Table H9A. District by Materials of guttering Table H9B. District by Condition of guttering Table H10A. District by Materials for downpipe Table H10B. District by Condition for downpipe Table H11. District by Downpipe connected to storage Table H12. District by Main source of drinking water Table H13. District by Main source of water in general Table H14. District by Water storage capacity Table H15. District by Material of water storage Table H16. District by water availability during 'dry' periods Table H17. District by household sharing main water supply Table H18. District by Source of water when scarce Table H19A. District by purpose of underground water usage Table H19B. District by Abstraction of underground water usage Table H20. District by Main toilet facility Table H21. District by Toilet flushed with water sources Table H22. District by Toilet flushed drainage system type Table H23A.District by items working order (For sustaining quality of life) Table H23B. District by items working order (ICT and communications) Table H23C. District by items working order (Commercial or subsistence value) Table H24. District by Main source of lighting Table H25. District by main fuel for cooking Table H26. District by main source of electricity Table H27. District by Household subsistence activities Table H28. District by household have a kitchen garden Table H29. District by Agricultural Activities Table H30. District by Livestock Table H31. District by Cash Inflow during last three months Table H32. District by any household member died last 3 years

Table 1 . Total Households and Population by District Nauru:2011 Table 2. District by Broad Age Group and P2. Sex, Nauru:2011 Table 3. Population by single age by sex, Nauru:2011 Table 4. Population by District and 5 year age group and sex, NAURU:2011 Table 5. Population by District, Relationship to head of household by sex, NAURU:2011 Table 6 . Population by 5 year age group, and relationship to head of household by sex, NAURU:2011 Table 7. Population by District and Religion, Nauru:2011 Table 8. Population by religion, 5 year age group,Nauru:2011 Table 9. Population by district by country of birth, Nauru:2011 Table 10. Population by country of birth, 5 year age group and sex, Nauru:2011 Table 11. Population by district, whether mother still alive and living in the household, Nauru:2011 Table 12. Population by district and whether father is still alive, Nauru:2011 Table 13. Population by district, marital status and sex, Nauru:2011 Table 14. Population by 5 year age group, marital status and sex, Nauru:2011 Table 15. Population by district, mothers local tribe and sex, Nauru:2011 Table 16. Population by 5 year age group, mothers local tribe and sex, Nauru:2011 Table 17. Population by district, whether married to a Nauruan and sex, Nauru:2011 Table 18. Population by 5 year age group whether married to Nauruan and sex, Nauru:2011 Table 19. Population by district, nationality and sex, Nauru:2011 Table 20. Population by 5year age group, nationality and sex, Nauru:2011 Table 21. Population by district, citizenship and sex Table 22. Population by 5 year age group, citizenship and sex, Nauru:2011 Table 23. Population by district and difficulties, Nauru:2011 Table 24. Male population by district and difficulties, Nauru:2011 Table 25. Female population by district and difficulties, Nauru:2011 Table 26. Population by 5 year age group and difficulties, Nauru:2011 Table 27. Male Population by 5 year age group and difficulties, Nauru:2011 Table 28. Female Population by 5 year age group and difficulties, Nauru:2011 Table 29. Population by district, currently attending school and sex, Nauru:2011 Table 30. Popualtion 15 years and over, school attendace and sex, Nauru:2011 Table 31. Population 15 years and over by district , type of education institution attending, Nauru:2011 Table 32. Population 15 years and over by type of education institution attending and sex, Nauru:2011 Table 33. Population 15 years and over by highest qualification completed and sex, Nauru:2011 Table 34. Population 15 years and over by main activity and sex, Nauru:2011 Table 35. Population 15 years and over by labour force participation and sex, Nauru:2011 Table 36. Female population aged 15 years and over ever given birth, Nauru:2011 Table 37. Female population aged 15 years and over by total Children ever born Table 38. Female population aged 15 years and over by total male born, Nauru:2011 Table 39. Female population aged 15 years and over by total female born, Nauru:2011 Table 40. Female population aged 15 years and over and total children ever born, Nauru:2011 Table 41. Female population aged 15 years and over and total male ever born, Nauru:2011 Table 42. Female population aged 15 years and over and total female ever born, Nauru:2011

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Note: For information on the codes used when processing the data in this table, see the 2020 Island Areas Censuses Technical Documentation..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "N" means data are not displayed for the selected geographic area due to concerns with statistical reliability or an insufficient number of cases.5. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, Commonwealth of the Northern Mariana Islands.

Abstract

During the second half of 2008, the Government of Pakistan established the Benazir Income Support Program (BISP) as the country's main safety net mechanism. The short term objective of the program is to cushion the adverse impact of the food, fuel and financial crises on the poor, but its broader objective is to meet the redistributive goals of the country by providing a minimum income support package to the chronic poor and those affected by future shocks. The Program provides cash transfers of Rs.1000 [$12] per month to eligible families. In order to quickly launch the program, parliamentarians (Members of the National Assembly and Senators) were asked to identify 8,000 beneficiary households each on a prescribed form, which collected information on names, national ID card, and household income. BISP is a national program that covers all provinces of the country, Azad Jamnu and Kashmir (AJK) and Northern Areas.

In December 2008, the Government of Pakistan (GoP) decided to improve the existing targeting mechanism of BISP through the adoption of a poverty scorecard (“proxy means test” based targeting) that would be used for the selection of beneficiaries. The scorecard roll-out started during April 2009 in 16 districts. The GoP intended to complete the national roll-out by the end of 2010.

Expecting there would be a transition from the Parliamentarian selected beneficiaries to poverty scorecard identified beneficiaries after the poverty scorecard information was collected and cut-off scores were identified, the World Bank team and the GoP decided to launch a BISP rapid assessment survey with the primary objective to examine the overlap between the poverty scorecard identified beneficiaries and Parliamentarian identified beneficiaries in order to inform the policy makers on the potential transition mechanisms. In addition, the rapid assessment survey would also inform the BISP application process and challenges in delivery mechanisms as well as the how BISP beneficiaries tend to spend the cash transfer received.

Geographic coverage

The main survey was conducted in 15 randomly selected districts of Pakistan

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling Survey for the Rapid Assessment of Parliamentarian selected beneficiaries was conducted in the two stages: a test phase stages and the main survey.

1.Test Phase.A test of the planned survey was conducted by IDS in May 2009. The test was based on a random sample of 101 beneficiaries in two districts Nowshera (NWFP) and Rajanpur (Punjab). The 101 beneficiaries came from 101 households composed of 613 household members. The results and lessons from the test-phase were used to refine the final survey questionnaires.

2.Main Survey. The main survey was conducted in 15 randomly selected districts of Pakistan, during the months of August and September before Ramadan.

The sample beneficiaries were selected from the list of the Parliamentarian identified beneficiaries provided by the National Database and Registration Authority's (NADRA). The sample was drawn from the NADRA computer records in April, 2009. There were 1,660,584 existing beneficiaries in that record, composed of 46.23 percent from Punjab, 26.04 from Sindh, 18.35 percent from NWFP, 6.08 percent from Baluchistan, 2.38 percent from FATA, 0.73 percent from Islamabad and 0.14 percent from Northern areas.

The study used the probability to Proportion to Size method to obtain a sample as representative as possible with the sample size of 2,500 households. For this purpose a randomly selected sample of 5680 beneficiaries' names, addresses, NIC numbers and names of spouses/fathers was provided by NADRA for the 15 selected Tehsils in the 13 districts of the four Provinces, and Islamabad in the Federal Capital and Muzafarabad in the Azad Jammu and Kashmir.

The total number of Districts/Tehsils was purposively decided on the basis of an eventually representative sample size to capture the large diversity in levels of development across districts and the resources available. The 13 districts from the four Provinces were then allocated to roughly represent the relative share of the beneficiaries from each Province in the NADRA records on that day. As such 7 districts on Punjab, 3 in Sindh, 2 in NWFP and 1 in Baluchistan were selected.

The literature on Poverty in Pakistan divides provinces into distinct poverty or deprivation bands. For example, Punjab, is looked upon as three regional disparity bands i.e. Northern Punjab (best off), central Punjab (moderately well off) and southern Punjab (worst off). The seven districts of Punjab represent this classification with the number of districts selected in each poverty band being selected on the basis of the share of each region in the population of Punjab. As such, Attack was chosen from the North Punjab, Rajanpur was chosen from the South, and the remaining was chosen from the Center to represent the region as moving from North to South i.e from Sargodha to Faisalabad to Vehari to Multan to Bahawalpur. The same rationale applies to other provinces. For example, in Sindh province, the selected districts ranged from Karachi (the most developed) to Larkana (deprived but politically privileged) to Sanghar (most deprived). In NWFP province, Nowshera is amongst the least deprived in NWFP and Karak is one of the most deprived. Similarly, Muzafarabad was chosen to represent AJK and Islamabad to represent the Federal Area.

In Baluchistan, the unrest and law and order situation made survey almost impossible except in Quetta where a small sample was selected.

The 13 districts in the four provinces for which this list of beneficiaries was selected were chosen to represent 1) the total number of districts in each province, 2) the regional level of deprivation in each province (based on existing literature, districts in each province in Pakistan are grouped by levels of poverty - for example Punjab has three groups namely North (low poverty), central (higher poverty) and Southern (highest poverty) and 3) the share of beneficiaries in each district. One district was selected in Azad Jammu and Kashmir and Islamabad Capital territory was selected as a stand alone.The selected districts represent 70 Members of the National Assembly and 143 members of the Provincial Assembly. There are 2 designated Senate seats for Islamabad while the other Senate seats are allocated on no regional criteria. Having selected the districts, one tehsil in each district was drawn randomly. However, the list of beneficiaries provided by NADRA did not include any coding of addresses by Union Council. The Unions Council names were therefore manually coded into the information on the beneficiaries. From this list sample UCs were designated in each tehsil in such a way that at least a minimum of 10 beneficiaries would be covered in a particular sample UC.

Sample UCs were selected using Probability proportionate to Size (PPS) method of selection. Beneficiaries in each UC will be treated as measure of size. Due to nature of the Survey and to avoid unnecessary field problems as already stated UCs with 10+ beneficiaries were selected in the sample. However, in case if in any Tehsil where the number of UCs/Beneficiaries is small, the UCs with less than 10 beneficiaries were also selected. Sample beneficiaries to be covered from each sample UC in a Tehsil will be in proportion to the size of the sample UCs. Beneficiaries in a sample UC were selected by Simple Random Sample method of selection. In case if a sample beneficiary is not traceable then subsequent beneficiary in the list of beneficiaries of that UC will be selected as replacement.

The issue of replacement HHs in case a HH could not be traced due to incorrect address required clarification. After exhaustive deliberations with the World Bank team, it was agreed that the sample size would be increased to 2595. Out of the sample of 2,595 households (HHs), a total of 2,540 HHs were initially successfully enumerated before the Eid break. The enumeration teams could not reach 55 HHs. These 55 HHs were later verified / enumerated after the break due to Ramazan and Eid holidays. Post Eid all 2595 HHs were successfully enumerated. Out of the 2,595 HHs enumerated, the enumeration teams, in consultation with their field supervisors and the designated IDS supervisors, had to drop 347 HHs. The reasons for dropping these HHS are discussed in detail subsequently. In addition to these 347 dropped households (for whom both female and male questionnaires were not filled), there are 78 households for whom only female questionnaires were filled as either there were no male member in the HH or male members, even after three visits to the HH (refer to table 7 of the BISP Rapid Assessment Report for sample summary).

Sampling deviation

Out of the sample of 2,595 households (HHs), a total of 2,540 HHs were initially successfully enumerated before the eid break. The enumeration teams could not reach 55 HHs. These 55 HHs were later verified / enumerated after the break due to Ramadan and Eid holidays. Post Eid all 2595 HHs were successfully enumerated. Out of the 2,595 HHs enumerated, the enumeration teams, in consultation with their field supervisors and the designated Innovative Development Strategies (Pvt.) Ltd.(IDS) supervisors, had to drop 347 HHs. The reasons for dropping these HHS are discussed in detail subsequently. In addition to these 347 dropped households (for whom both female and male questionnaires were not filled), there are 78 households for whom only female questionnaires were filled as either there were no male member in the HH or male members, even after three visits to the