6 datasets found
  1. w

    Demographic and Health Survey 2017-2018 - Pakistan

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Feb 26, 2019
    + more versions
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    National Institute of Population Studies (NIPS) (2019). Demographic and Health Survey 2017-2018 - Pakistan [Dataset]. https://microdata.worldbank.org/index.php/catalog/3411
    Explore at:
    Dataset updated
    Feb 26, 2019
    Dataset authored and provided by
    National Institute of Population Studies (NIPS)
    Time period covered
    2017 - 2018
    Area covered
    Pakistan
    Description

    Abstract

    The Pakistan Demographic and Health Survey PDHS 2017-18 was the fourth of its kind in Pakistan, following the 1990-91, 2006-07, and 2012-13 PDHS surveys.

    The primary objective of the 2017-18 PDHS is to provide up-to-date estimates of basic demographic and health indicators. The PDHS provides a comprehensive overview of population, maternal, and child health issues in Pakistan. Specifically, the 2017-18 PDHS collected information on:

    • Key demographic indicators, particularly fertility and under-5 mortality rates, at the national level, for urban and rural areas, and within the country’s eight regions
    • Direct and indirect factors that determine levels and trends of fertility and child mortality
    • Contraceptive knowledge and practice
    • Maternal health and care including antenatal, perinatal, and postnatal care
    • Child feeding practices, including breastfeeding, and anthropometric measures to assess the nutritional status of children under age 5 and women age 15-49
    • Key aspects of family health, including vaccination coverage and prevalence of diseases among infants and children under age 5
    • Knowledge and attitudes of women and men about sexually transmitted infections (STIs), including HIV/AIDS, and potential exposure to risk
    • Women's empowerment and its relationship to reproductive health and family planning
    • Disability level
    • Extent of gender-based violence
    • Migration patterns

    The information collected through the 2017-18 PDHS is intended to assist policymakers and program managers at the federal and provincial government levels, in the private sector, and at international organisations in evaluating and designing programs and strategies for improving the health of the country’s population. The data also provides information on indicators relevant to the Sustainable Development Goals.

    Geographic coverage

    National coverage

    Analysis unit

    • Household
    • Individual
    • Children age 0-5
    • Woman age 15-49
    • Man age 15-49

    Universe

    The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-49 years resident in the household.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sampling frame used for the 2017-18 PDHS is a complete list of enumeration blocks (EBs) created for the Pakistan Population and Housing Census 2017, which was conducted from March to May 2017. The Pakistan Bureau of Statistics (PBS) supported the sample design of the survey and worked in close coordination with NIPS. The 2017-18 PDHS represents the population of Pakistan including Azad Jammu and Kashmir (AJK) and the former Federally Administrated Tribal Areas (FATA), which were not included in the 2012-13 PDHS. The results of the 2017-18 PDHS are representative at the national level and for the urban and rural areas separately. The survey estimates are also representative for the four provinces of Punjab, Sindh, Khyber Pakhtunkhwa, and Balochistan; for two regions including AJK and Gilgit Baltistan (GB); for Islamabad Capital Territory (ICT); and for FATA. In total, there are 13 secondlevel survey domains.

    The 2017-18 PDHS followed a stratified two-stage sample design. The stratification was achieved by separating each of the eight regions into urban and rural areas. In total, 16 sampling strata were created. Samples were selected independently in every stratum through a two-stage selection process. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at different levels, and by using a probability-proportional-to-size selection at the first stage of sampling.

    The first stage involved selecting sample points (clusters) consisting of EBs. EBs were drawn with a probability proportional to their size, which is the number of households residing in the EB at the time of the census. A total of 580 clusters were selected.

    The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters, and a fixed number of 28 households per cluster was selected with an equal probability systematic selection process, for a total sample size of approximately 16,240 households. The household selection was carried out centrally at the NIPS data processing office. The survey teams only interviewed the pre-selected households. To prevent bias, no replacements and no changes to the pre-selected households were allowed at the implementing stages.

    For further details on sample design, see Appendix A of the final report.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Six questionnaires were used in the 2017-18 PDHS: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, Biomarker Questionnaire, Fieldworker Questionnaire, and the Community Questionnaire. The first five questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Pakistan. The Community Questionnaire was based on the instrument used in the previous rounds of the Pakistan DHS. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. The survey protocol was reviewed and approved by the National Bioethics Committee, Pakistan Health Research Council, and ICF Institutional Review Board. After the questionnaires were finalised in English, they were translated into Urdu and Sindhi. The 2017-18 PDHS used paper-based questionnaires for data collection, while computerassisted field editing (CAFE) was used to edit the questionnaires in the field.

    Cleaning operations

    The processing of the 2017-18 PDHS data began simultaneously with the fieldwork. As soon as data collection was completed in each cluster, all electronic data files were transferred via IFSS to the NIPS central office in Islamabad. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing was carried out in the central office, which involved resolving inconsistencies and coding the openended questions. The NIPS data processing manager coordinated the exercise at the central office. The PDHS core team members assisted with the secondary editing. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage as it maximised the likelihood of the data being error-free and accurate. The secondary editing of the data was completed in the first week of May 2018. The final cleaning of the data set was carried out by The DHS Program data processing specialist and completed on 25 May 2018.

    Response rate

    A total of 15,671 households were selected for the survey, of which 15,051 were occupied. The response rates are presented separately for Pakistan, Azad Jammu and Kashmir, and Gilgit Baltistan. Of the 12,338 occupied households in Pakistan, 11,869 households were successfully interviewed, yielding a response rate of 96%. Similarly, the household response rates were 98% in Azad Jammu and Kashmir and 99% in Gilgit Baltistan.

    In the interviewed households, 94% of ever-married women age 15-49 in Pakistan, 97% in Azad Jammu and Kashmir, and 94% in Gilgit Baltistan were interviewed. In the subsample of households selected for the male survey, 87% of ever-married men age 15-49 in Pakistan, 94% in Azad Jammu and Kashmir, and 84% in Gilgit Baltistan were successfully interviewed.

    Overall, the response rates were lower in urban than in rural areas. The difference is slightly less pronounced for Azad Jammu and Kashmir and Gilgit Baltistan. The response rates for men are lower than those for women, as men are often away from their households for work.

    Sampling error estimates

    The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017-18 Pakistan Demographic and Health Survey (2017-18 PDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

    Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017-18 PDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

    Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that

  2. Pakistan Tehsil District Census

    • kaggle.com
    zip
    Updated Dec 16, 2017
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    kashif (2017). Pakistan Tehsil District Census [Dataset]. https://www.kaggle.com/kashifkaleem/pakistan-tehsil-district-census
    Explore at:
    zip(27421 bytes)Available download formats
    Dataset updated
    Dec 16, 2017
    Authors
    kashif
    Area covered
    Pakistan
    Description

    Context

    Extracted from http://www.pbscensus.gov.pk and some minor cleanup done.

    Content

    Lists tehsil and district level population from the 2017 and 1998 censuses.

    Acknowledgements

    http://www.pbscensus.gov.pk

  3. d

    Census 2011: State wise Area, Households, Female and Male Population

    • dataful.in
    Updated May 22, 2025
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    Dataful (Factly) (2025). Census 2011: State wise Area, Households, Female and Male Population [Dataset]. https://dataful.in/datasets/173
    Explore at:
    csv, xlsx, application/x-parquetAvailable download formats
    Dataset updated
    May 22, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Time period covered
    2011
    Area covered
    States of India
    Variables measured
    Area, Population
    Description

    This Dataset contains statistics related to State wise Area, Households, Female and Male Population of Census 2011.

    Note:The population figures exclude the population of the area under unlawful occupation of Pakistan and China where Census could not be taken.

    Area figures include the area under unlawful occupation of Pakistan and China. The area includes 78,114 Sq.km under illegal occupation of Pakistan, 5180 Sq.km illegally handed over by Pakistan to China and 37,555 Sq.km under illegal occupation of China.

  4. d

    Decade and State wise Urban, Rural, Total Population and Decadal Growth Rate...

    • dataful.in
    Updated Jul 1, 2025
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    Dataful (Factly) (2025). Decade and State wise Urban, Rural, Total Population and Decadal Growth Rate [Dataset]. https://dataful.in/datasets/21431
    Explore at:
    application/x-parquet, xlsx, csvAvailable download formats
    Dataset updated
    Jul 1, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Area covered
    India
    Variables measured
    Population
    Description

    The dataset contains Decade and State wise Urban, Rural, Total Population and Decadal Growth Rate

    Note: 1. The Population figures exclude population of areas under unlawful occupation of Pakistan and China, where Census could not be taken. 2. In Arunachal Pradesh, the census was conducted for the first time in 1961. 3. Population data of Assam include Union Territory of Mizoram, which was carved out of Assam after the 1971. 4. The 1981 Census could not be held in Assam. Total Population for 1981 has been worked out by Interpolation. 5. The 1991 Census could not be held in Jammu & Kashmir. Total Population for 1991 has been worked out by Interpolation. 6. India and Manipur figures include estimated Population for those of the three sub-divisions viz., Mao Maram,Paomata and Purul of Senapati district of Manipur as census result of 2001 in these three sub-divisions were cancelled due to technical and administrative reasons

  5. A

    Pakistan - Demographic, Health, Education and Transport indicators

    • data.amerigeoss.org
    • cloud.csiss.gmu.edu
    • +1more
    csv
    Updated Apr 22, 2020
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    UN Humanitarian Data Exchange (2020). Pakistan - Demographic, Health, Education and Transport indicators [Dataset]. https://data.amerigeoss.org/es/dataset/unhabitat-pk-indicators
    Explore at:
    csv(67236)Available download formats
    Dataset updated
    Apr 22, 2020
    Dataset provided by
    UN Humanitarian Data Exchange
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    Pakistan
    Description

    The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.

  6. Pakistan Average Monthly Wages

    • ceicdata.com
    Updated Jan 15, 2025
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    CEICdata.com (2025). Pakistan Average Monthly Wages [Dataset]. https://www.ceicdata.com/en/pakistan/average-monthly-wages-by-industry/average-monthly-wages
    Explore at:
    Dataset updated
    Jan 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jun 1, 2008 - Jun 1, 2021
    Area covered
    Pakistan
    Variables measured
    Wage/Earnings
    Description

    Pakistan Average Monthly Wages data was reported at 24,028.000 PKR in 2021. This records an increase from the previous number of 21,326.000 PKR for 2019. Pakistan Average Monthly Wages data is updated yearly, averaging 12,636.500 PKR from Jun 2008 (Median) to 2021, with 10 observations. The data reached an all-time high of 24,028.000 PKR in 2021 and a record low of 6,612.000 PKR in 2008. Pakistan Average Monthly Wages data remains active status in CEIC and is reported by Pakistan Bureau of Statistics. The data is categorized under Global Database’s Pakistan – Table PK.G004: Average Monthly Wages: by Industry. No data for 2016-2017 as per source. Labour Force Survey has not been conducted for these two years due to Population Census.

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National Institute of Population Studies (NIPS) (2019). Demographic and Health Survey 2017-2018 - Pakistan [Dataset]. https://microdata.worldbank.org/index.php/catalog/3411

Demographic and Health Survey 2017-2018 - Pakistan

Explore at:
10 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Feb 26, 2019
Dataset authored and provided by
National Institute of Population Studies (NIPS)
Time period covered
2017 - 2018
Area covered
Pakistan
Description

Abstract

The Pakistan Demographic and Health Survey PDHS 2017-18 was the fourth of its kind in Pakistan, following the 1990-91, 2006-07, and 2012-13 PDHS surveys.

The primary objective of the 2017-18 PDHS is to provide up-to-date estimates of basic demographic and health indicators. The PDHS provides a comprehensive overview of population, maternal, and child health issues in Pakistan. Specifically, the 2017-18 PDHS collected information on:

  • Key demographic indicators, particularly fertility and under-5 mortality rates, at the national level, for urban and rural areas, and within the country’s eight regions
  • Direct and indirect factors that determine levels and trends of fertility and child mortality
  • Contraceptive knowledge and practice
  • Maternal health and care including antenatal, perinatal, and postnatal care
  • Child feeding practices, including breastfeeding, and anthropometric measures to assess the nutritional status of children under age 5 and women age 15-49
  • Key aspects of family health, including vaccination coverage and prevalence of diseases among infants and children under age 5
  • Knowledge and attitudes of women and men about sexually transmitted infections (STIs), including HIV/AIDS, and potential exposure to risk
  • Women's empowerment and its relationship to reproductive health and family planning
  • Disability level
  • Extent of gender-based violence
  • Migration patterns

The information collected through the 2017-18 PDHS is intended to assist policymakers and program managers at the federal and provincial government levels, in the private sector, and at international organisations in evaluating and designing programs and strategies for improving the health of the country’s population. The data also provides information on indicators relevant to the Sustainable Development Goals.

Geographic coverage

National coverage

Analysis unit

  • Household
  • Individual
  • Children age 0-5
  • Woman age 15-49
  • Man age 15-49

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2017-18 PDHS is a complete list of enumeration blocks (EBs) created for the Pakistan Population and Housing Census 2017, which was conducted from March to May 2017. The Pakistan Bureau of Statistics (PBS) supported the sample design of the survey and worked in close coordination with NIPS. The 2017-18 PDHS represents the population of Pakistan including Azad Jammu and Kashmir (AJK) and the former Federally Administrated Tribal Areas (FATA), which were not included in the 2012-13 PDHS. The results of the 2017-18 PDHS are representative at the national level and for the urban and rural areas separately. The survey estimates are also representative for the four provinces of Punjab, Sindh, Khyber Pakhtunkhwa, and Balochistan; for two regions including AJK and Gilgit Baltistan (GB); for Islamabad Capital Territory (ICT); and for FATA. In total, there are 13 secondlevel survey domains.

The 2017-18 PDHS followed a stratified two-stage sample design. The stratification was achieved by separating each of the eight regions into urban and rural areas. In total, 16 sampling strata were created. Samples were selected independently in every stratum through a two-stage selection process. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at different levels, and by using a probability-proportional-to-size selection at the first stage of sampling.

The first stage involved selecting sample points (clusters) consisting of EBs. EBs were drawn with a probability proportional to their size, which is the number of households residing in the EB at the time of the census. A total of 580 clusters were selected.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters, and a fixed number of 28 households per cluster was selected with an equal probability systematic selection process, for a total sample size of approximately 16,240 households. The household selection was carried out centrally at the NIPS data processing office. The survey teams only interviewed the pre-selected households. To prevent bias, no replacements and no changes to the pre-selected households were allowed at the implementing stages.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were used in the 2017-18 PDHS: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, Biomarker Questionnaire, Fieldworker Questionnaire, and the Community Questionnaire. The first five questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Pakistan. The Community Questionnaire was based on the instrument used in the previous rounds of the Pakistan DHS. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. The survey protocol was reviewed and approved by the National Bioethics Committee, Pakistan Health Research Council, and ICF Institutional Review Board. After the questionnaires were finalised in English, they were translated into Urdu and Sindhi. The 2017-18 PDHS used paper-based questionnaires for data collection, while computerassisted field editing (CAFE) was used to edit the questionnaires in the field.

Cleaning operations

The processing of the 2017-18 PDHS data began simultaneously with the fieldwork. As soon as data collection was completed in each cluster, all electronic data files were transferred via IFSS to the NIPS central office in Islamabad. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing was carried out in the central office, which involved resolving inconsistencies and coding the openended questions. The NIPS data processing manager coordinated the exercise at the central office. The PDHS core team members assisted with the secondary editing. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage as it maximised the likelihood of the data being error-free and accurate. The secondary editing of the data was completed in the first week of May 2018. The final cleaning of the data set was carried out by The DHS Program data processing specialist and completed on 25 May 2018.

Response rate

A total of 15,671 households were selected for the survey, of which 15,051 were occupied. The response rates are presented separately for Pakistan, Azad Jammu and Kashmir, and Gilgit Baltistan. Of the 12,338 occupied households in Pakistan, 11,869 households were successfully interviewed, yielding a response rate of 96%. Similarly, the household response rates were 98% in Azad Jammu and Kashmir and 99% in Gilgit Baltistan.

In the interviewed households, 94% of ever-married women age 15-49 in Pakistan, 97% in Azad Jammu and Kashmir, and 94% in Gilgit Baltistan were interviewed. In the subsample of households selected for the male survey, 87% of ever-married men age 15-49 in Pakistan, 94% in Azad Jammu and Kashmir, and 84% in Gilgit Baltistan were successfully interviewed.

Overall, the response rates were lower in urban than in rural areas. The difference is slightly less pronounced for Azad Jammu and Kashmir and Gilgit Baltistan. The response rates for men are lower than those for women, as men are often away from their households for work.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017-18 Pakistan Demographic and Health Survey (2017-18 PDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017-18 PDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that

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