Dataset

This dataset was created by Ahsaan F.

Released under CC0: Public Domain

Contents

Description This dataset contains demographic information from the Pakistan Population Census conducted in 2017. It provides detailed population data at various administrative levels within Pakistan, including provinces, divisions, districts, and sub-divisions. The dataset also includes information on urban and rural populations, gender distribution, transgender individuals, sex ratios, population figures from the 1998 census, and annual growth rates.

Features Province: The administrative provinces or regions of Pakistan where the census data was collected.

Division: The divisions within each province. Divisions are the second level of administrative divisions in Pakistan.

District: Districts within each division, representing larger administrative units.

Sub-Division: Sub-divisions or tehsils within each district, providing more localized data.

Area: The land area (in square kilometers) of each sub-division.

Urban Population 2017: The population of urban areas within each sub-division for the year 2017.

Rural Population 2017: The population of rural areas within each sub-division for the year 2017.

Male Population 2017: The male population within each sub-division for the year 2017.

Female Population 2017: The female population within each sub-division for the year 2017.

Transgender Population 2017: The population of transgender individuals within each sub-division for the year 2017.

Sex Ratio 2017: The sex ratio, calculated as the number of females per 1000 males, within each sub-division for the year 2017.

Population in 1998: The total population of each sub-division as recorded in the 1998 census.

Annual Growth Rate: The annual growth rate of the population in each sub-division, calculated as the percentage increase from 1998 to 2017.

Data Source The data in this dataset was collected from official Pakistan Population Census reports and may include data from various government sources. It is essential to provide proper attribution and reference the original sources when using this dataset for analysis or research.

Data Usage Researchers and analysts can use this dataset to explore demographic trends, population growth, urbanization rates, gender distribution, and more within Pakistan at different administrative levels. Ensure compliance with ethical and legal guidelines when using this data for research or public sharing.

Please note that this description is a template, and you should adapt it based on the actual data sources and specific details of your dataset when creating it for Kaggle or any other platform.

The catalog contains the data related to number of male, female and total population (rural and urban) in Punjab.

Census: Population: Punjab data was reported at 27,743,338.000 Person in 03-01-2011. This records an increase from the previous number of 24,358,999.000 Person for 03-01-2001. Census: Population: Punjab data is updated decadal, averaging 10,367,652.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 27,743,338.000 Person in 03-01-2011 and a record low of 6,731,510.000 Person in 03-01-1911. Census: Population: Punjab data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.

The Jat Sikh population is the largest endogamous group of Punjab, a state in north-west India, and has not yet been explored for genetic polymorphism based on X-STR genetic markers. In India, which is the second most populous country in the world, only two population studies based on X-STR markers have been reported so far. To explore the genetic diversity of 12 X chromosomal STR genetic markers in the Jat Sikh population of Punjab and expand the X-STR polymorphism database. In this study, a total of 200 Jat Sikh individuals (100 males and 100 females) residing in Punjab were investigated for 12 X-STR markers using the Investigator Argus X-12 QS Kit. The highest power of discrimination (PD) in females (PDf) and males (PDm) was observed to be 0.965 (DXS10135) and 0.929 (DXS10135 and DXS10148), respectively. DXS10135 was found to be the most polymorphic and discriminating locus among all the studied loci in both males and females with highest values of power of discrimination (PD) and polymorphic information content (PIC) as well. Overall, the studied markers of the Argus 12 X-STR kit provide high polymorphic information which may prove to be an important tool in resolving issues such as missing person identification, incest, immigration disputes, kinship analysis and genealogical studies. The dataset obtained from this study will add to the present database of X-STRs.

Census: Population: Punjab: Zirakpur data was reported at 95,553.000 Person in 03-01-2011. This records an increase from the previous number of 25,022.000 Person for 03-01-2001. Census: Population: Punjab: Zirakpur data is updated decadal, averaging 25,022.000 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 95,553.000 Person in 03-01-2011 and a record low of 25,022.000 Person in 03-01-2001. Census: Population: Punjab: Zirakpur data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC: Census: Population: by Towns and Urban Agglomerations: Punjab.

Census: Population: Punjab: Amritsar data was reported at 1,159,227.000 Person in 03-01-2011. This records an increase from the previous number of 1,003,917.000 Person for 03-01-2001. Census: Population: Punjab: Amritsar data is updated decadal, averaging 336,114.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 1,159,227.000 Person in 03-01-2011 and a record low of 152,756.000 Person in 03-01-1911. Census: Population: Punjab: Amritsar data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC: Census: Population: by Towns and Urban Agglomerations: Punjab.

Pakistan Religion Distribution Dataset (1901 to 2023)

Dataset Description

This dataset contains religious distribution data for Pakistan from 1901 to 2023, sourced from various census data and official reports. The dataset provides a comprehensive overview of the population breakdown by religious groups across different decades. It includes historical data on major religions such as Islam, Hinduism, Sikhism, Christianity, and others, along with population percentages for each group at different points in time.

The dataset spans over a century and serves as a valuable resource for understanding the demographic and religious shifts in Pakistan's population. This data can be useful for researchers, policymakers, and educators interested in the sociological and historical trends of religious communities in Pakistan.

File Information

• File Name: Pakistan_Religion_Distribution_1901_to_2023.csv
• File Format: CSV
• Columns: The file contains data for each religious group across the years 1901, 1911, 1921, 1931, 1941, 1951, 1961, 1972, 1981, 1998, 2017, and 2023.

Column Descriptors

Data Provenance

• Source: Census data from Pakistan's historical records, including census reports from 1901, 1911, 1921, 1931, 1941, 1951, 1961, 1972, 1981, 1998, 2017, and 2023.
• Collection Method: Data was compiled from official records, government publications, and historical census reports. The figures represent the religious distribution of the population across various administrative divisions, including Punjab, Sindh, Khyber Pakhtunkhwa, Balochistan, Azad Jammu and Kashmir, and Gilgit–Baltistan.
• Note: The 1901, 1911, 1931, and 1941 data include information for all administrative divisions that composed the region of contemporary Pakistan, while 1951 and 1961 data represent the total population of the former administrative division of West Pakistan.

Use Cases

This dataset is ideal for: - Studying demographic and religious trends in Pakistan - Researching the impact of religious distribution on social policies - Understanding historical changes in religious communities

Pakistani Cities and Their Provinces Dataset Description This dataset contains a comprehensive list of cities from Pakistan, along with their corresponding provinces. It serves as a valuable resource for anyone seeking geographical insights into Pakistan’s urban areas. The dataset covers major cities from all provinces, including Sindh, Punjab, Khyber Pakhtunkhwa, and Balochistan, making it suitable for various applications such as urban planning, population studies, and regional analysis.

Key Features:

City Names Province Names Country: Pakistan Potential Use Cases Geographical Analysis: Ideal for researchers and students performing geographical, demographic, or regional studies of Pakistan's urban landscape. Data Science Projects: Can be used for machine learning projects involving geospatial analysis, regional clustering, and city-level modeling. Visualization Projects: Helpful for creating maps, charts, and visual representations of Pakistan’s provinces and cities in tools like Power BI or Tableau. Business Insights: Useful for businesses analyzing market expansion strategies, targeting regional demographics, or performing location-based analysis. Education: A helpful resource for students and educators in geography, data science, and economics to understand the distribution of cities across provinces. Applications Machine Learning (Geospatial data, clustering models) Data Visualization (Map plotting, heatmaps) Policy Making (Urban development, resource allocation) Educational Projects (Geography, demographics) Feel free to download, explore, and incorporate this dataset into your projects. I welcome any feedback or suggestions to improve its utility!

The catalog contains the data related to number of main workers (male, female and total), marginal workers (male, female and total), non-workers (male, female and total) and main workers percentage to total population in Punjab.

This polygon dataset shows village boundaries with socio-demographic and economic Census data for 1991 for the State of Punjab, India linked to the 1991 Census. Includes village socio-demographic and economic Census attribute data such as total population, population by sex, household, literacy and illiteracy rates, and employment by industry. This layer is part of the VillageMap dataset which includes socio-demographic and economic Census data for 1991 at the village level for all the states of India. This data layer is sourced from secondary government sources, chiefly Survey of India, Census of India, Election Commission, etc.

Autosomal STR typing using capillary electrophoresis is a reliable method for establishing parentage and for deciphering genomic ancestry. This study was planned to show the genetic diversity of the Jat Sikh population, which is a widespread community of the Punjab region, and to assess its genetic relationship with existing Indian populations. Blood samples of unrelated healthy individuals of the Jat Sikhs (n = 123) were used in this study. Fifteen autosomal STR markers along with the sex determination genetic marker Amelogenin were amplified using AmpFlSTR®Identifiler® Plus kit, and genetic analyser 3100 was used for genotyping. A total of 246 alleles were observed with allele frequencies ranging from 0.004 to 0.447. The heterozygosity ranged from 0.659 to 0.886, and all studied loci were in Hardy–Weinberg Equilibrium (HWE). Fibrinogen A alpha (Aα) chain (FGA) was found to be the most polymorphic and also the most discriminating locus in the studied population. Neighbor-joining (NJ) tree, principal component analysis (PCA) plot, and Nei’s Distance matrix revealed genetic affinity with the previously reported Jatt Sikh (Punjab) population and showed the outlier nature of this population compared with other Indian populations. The data generated by this study enhance the database of Indian populations to be used in civil and forensic cases and also in other population-based genetic studies.

Supplementary information files for article A susceptibility putative haplotype within NLRP3 inflammasome gene influences ischaemic stroke risk in the population of Punjab, India Despite strong genetic implications of NLRP3 inflammasome, its examination as genetic determinant of ischaemic stroke (IS) remains to be done in Punjab, which has been investigated in this study. In this case control study, 400 subjects (200 IS patients, 200 stroke free controls) were included. Contributions of 5 single nucleotide polymorphisms (SNPs) including a functional SNP within NLRP3 gene (rs10754558, rs4612666, rs2027432, rs3738488 and rs1539019) for the risk of IS were investigated through genetic models after correcting the effect of significant variables. Plasma levels of three pro-inflammatory markers, that is, C-reactive protein (CRP), interleukin-1beta (IL-1β) and interleukin-18 (IL-18) were measured by enzyme-linked immunosorbent assays (ELISA). Minor alleles of 3 out of 5 SNPs (rs10754558, rs4612666 and rs1539019) exhibited association with IS risk in additive, recessive and multiplicative models. Multivariable regression analysis confirmed that higher levels of systolic blood pressure (β ± SE: 1.42 ± 0.57, p = .013), CRP (β ± SE: 1.22 ± 0.41, p = .003), IL-1β (β ± SE: 1.78 ± 0.88, p = .043) and IL-18 (β ± SE: 1.13 ± 0.49, p = .021) were independent risk predictors for IS. Haplotype analysis revealed a susceptibility putative haplotype GTGTA, which approximately doubled the IS risk (OR: 1.98, 95% CI: 1.12–3.78, p = .04) in dominant mode after adjusting the effect with confounding variables. This susceptibility putative haplotype GTGTA was significantly associated with increased concentrations of CRP (β = 1.21, p = .014) and IL-1β (β = 1.53, p = .034) in dose-dependent manner (less in carriers of 1 copy than those who had 2 copies of GTGTA). The present study has revealed a susceptibility putative haplotype GTGTA within NLRP3 gene, carriers of which have double the risk of IS by having increased plasma levels of CRP and IL-1β in a dose-dependent manner.

Census: Population: Punjab: Bathinda data was reported at 285,788.000 Person in 03-01-2011. This records an increase from the previous number of 217,256.000 Person for 03-01-2001. Census: Population: Punjab: Bathinda data is updated decadal, averaging 34,991.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 285,788.000 Person in 03-01-2011 and a record low of 13,185.000 Person in 03-01-1901. Census: Population: Punjab: Bathinda data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC: Census: Population: by Towns and Urban Agglomerations: Punjab.

The second National Family Health Survey (NFHS-2), conducted in 1998-99, provides information on fertility, mortality, family planning, and important aspects of nutrition, health, and health care. The International Institute for Population Sciences (IIPS) coordinated the survey, which collected information from a nationally representative sample of more than 90,000 ever-married women age 15-49. The NFHS-2 sample covers 99 percent of India's population living in all 26 states. This report is based on the survey data for 25 of the 26 states, however, since data collection in Tripura was delayed due to local problems in the state. IIPS also coordinated the first National Family Health Survey (NFHS-1) in 1992-93. Most of the types of information collected in NFHS-2 were also collected in the earlier survey, making it possible to identify trends over the intervening period of six and one-half years. In addition, the NFHS-2 questionnaire covered a number of new or expanded topics with important policy implications, such as reproductive health, women's autonomy, domestic violence, women's nutrition, anaemia, and salt iodization. The NFHS-2 survey was carried out in two phases. Ten states were surveyed in the first phase which began in November 1998 and the remaining states (except Tripura) were surveyed in the second phase which began in March 1999. The field staff collected information from 91,196 households in these 25 states and interviewed 89,199 eligible women in these households. In addition, the survey collected information on 32,393 children born in the three years preceding the survey. One health investigator on each survey team measured the height and weight of eligible women and children and took blood samples to assess the prevalence of anaemia. SUMMARY OF FINDINGS POPULATION CHARACTERISTICS Three-quarters (73 percent) of the population lives in rural areas. The age distribution is typical of populations that have recently experienced a fertility decline, with relatively low proportions in the younger and older age groups. Thirty-six percent of the population is below age 15, and 5 percent is age 65 and above. The sex ratio is 957 females for every 1,000 males in rural areas but only 928 females for every 1,000 males in urban areas, suggesting that more men than women have migrated to urban areas. The survey provides a variety of demographic and socioeconomic background information. In the country as a whole, 82 percent of household heads are Hindu, 12 percent are Muslim, 3 percent are Christian, and 2 percent are Sikh. Muslims live disproportionately in urban areas, where they comprise 15 percent of household heads. Nineteen percent of household heads belong to scheduled castes, 9 percent belong to scheduled tribes, and 32 percent belong to other backward classes (OBCs). Two-fifths of household heads do not belong to any of these groups. Questions about housing conditions and the standard of living of households indicate some improvements since the time of NFHS-1. Sixty percent of households in India now have electricity and 39 percent have piped drinking water compared with 51 percent and 33 percent, respectively, at the time of NFHS-1. Sixty-four percent of households have no toilet facility compared with 70 percent at the time of NFHS-1. About three-fourths (75 percent) of males and half (51 percent) of females age six and above are literate, an increase of 6-8 percentage points from literacy rates at the time of NFHS-1. The percentage of illiterate males varies from 6-7 percent in Mizoram and Kerala to 37 percent in Bihar and the percentage of illiterate females varies from 11 percent in Mizoram and 15 percent in Kerala to 65 percent in Bihar. Seventy-nine percent of children age 6-14 are attending school, up from 68 percent in NFHS-1. The proportion of children attending school has increased for all ages, particularly for girls, but girls continue to lag behind boys in school attendance. Moreover, the disparity in school attendance by sex grows with increasing age of children. At age 6-10, 85 percent of boys attend school compared with 78 percent of girls. By age 15-17, 58 percent of boys attend school compared with 40 percent of girls. The percentage of girls 6-17 attending school varies from 51 percent in Bihar and 56 percent in Rajasthan to over 90 percent in Himachal Pradesh and Kerala. Women in India tend to marry at an early age. Thirty-four percent of women age 15-19 are already married including 4 percent who are married but gauna has yet to be performed. These proportions are even higher in the rural areas. Older women are more likely than younger women to have married at an early age: 39 percent of women currently age 45-49 married before age 15 compared with 14 percent of women currently age 15-19. Although this indicates that the proportion of women who marry young is declining rapidly, half the women even in the age group 20-24 have married before reaching the legal minimum age of 18 years. On average, women are five years younger than the men they marry. The median age at marriage varies from about 15 years in Madhya Pradesh, Bihar, Uttar Pradesh, Rajasthan, and Andhra Pradesh to 23 years in Goa. As part of an increasing emphasis on gender issues, NFHS-2 asked women about their participation in household decisionmaking. In India, 91 percent of women are involved in decision-making on at least one of four selected topics. A much lower proportion (52 percent), however, are involved in making decisions about their own health care. There are large variations among states in India with regard to women's involvement in household decisionmaking. More than three out of four women are involved in decisions about their own health care in Himachal Pradesh, Meghalaya, and Punjab compared with about two out of five or less in Madhya Pradesh, Orissa, and Rajasthan. Thirty-nine percent of women do work other than housework, and more than two-thirds of these women work for cash. Only 41 percent of women who earn cash can decide independently how to spend the money that they earn. Forty-three percent of working women report that their earnings constitute at least half of total family earnings, including 18 percent who report that the family is entirely dependent on their earnings. Women's work-participation rates vary from 9 percent in Punjab and 13 percent in Haryana to 60-70 percent in Manipur, Nagaland, and Arunachal Pradesh. FERTILITY AND FAMILY PLANNING Fertility continues to decline in India. At current fertility levels, women will have an average of 2.9 children each throughout their childbearing years. The total fertility rate (TFR) is down from 3.4 children per woman at the time of NFHS-1, but is still well above the replacement level of just over two children per woman. There are large variations in fertility among the states in India. Goa and Kerala have attained below replacement level fertility and Karnataka, Himachal Pradesh, Tamil Nadu, and Punjab are at or close to replacement level fertility. By contrast, fertility is 3.3 or more children per woman in Meghalaya, Uttar Pradesh, Rajasthan, Nagaland, Bihar, and Madhya Pradesh. More than one-third to less than half of all births in these latter states are fourth or higher-order births compared with 7-9 percent of births in Kerala, Goa, and Tamil Nadu. Efforts to encourage the trend towards lower fertility might usefully focus on groups within the population that have higher fertility than average. In India, rural women and women from scheduled tribes and scheduled castes have somewhat higher fertility than other women, but fertility is particularly high for illiterate women, poor women, and Muslim women. Another striking feature is the high level of childbearing among young women. More than half of women age 20-49 had their first birth before reaching age 20, and women age 15-19 account for almost one-fifth of total fertility. Studies in India and elsewhere have shown that health and mortality risks increase when women give birth at such young ages?both for the women themselves and for their children. Family planning programmes focusing on women in this age group could make a significant impact on maternal and child health and help to reduce fertility. INFANT AND CHILD MORTALITY NFHS-2 provides estimates of infant and child mortality and examines factors associated with the survival of young children. During the five years preceding the survey, the infant mortality rate was 68 deaths at age 0-11 months per 1,000 live births, substantially lower than 79 per 1,000 in the five years preceding the NFHS-1 survey. The child mortality rate, 29 deaths at age 1-4 years per 1,000 children reaching age one, also declined from the corresponding rate of 33 per 1,000 in NFHS-1. Ninety-five children out of 1,000 born do not live to age five years. Expressed differently, 1 in 15 children die in the first year of life, and 1 in 11 die before reaching age five. Child-survival programmes might usefully focus on specific groups of children with particularly high infant and child mortality rates, such as children who live in rural areas, children whose mothers are illiterate, children belonging to scheduled castes or scheduled tribes, and children from poor households. Infant mortality rates are more than two and one-half times as high for women who did not receive any of the recommended types of maternity related medical care than for mothers who did receive all recommended types of care. HEALTH, HEALTH CARE, AND NUTRITION Promotion of maternal and child health has been one of the most important components of the Family Welfare Programme of the Government of India. One goal is for each pregnant woman to receive at least three antenatal check-ups plus two tetanus toxoid injections and a full course of iron and folic acid supplementation. In India, mothers of 65 percent of the children born in the three years preceding NFHS-2 received at least one antenatal

punjab_dryad_cleanerThis is a Stata version 15.1 dta file that contains data from the 2013-2014 Punjab (India) population serosurvey on hepatitis C virus. Note that variables have been cut from the dataset due to Dryad's rules on data confidentiality.

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

Census: Number of Migrants: Punjab data was reported at 13,735,616.000 Person in 03-01-2011. This records an increase from the previous number of 9,189,438.000 Person for 03-01-2001. Census: Number of Migrants: Punjab data is updated decadal, averaging 9,189,438.000 Person from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 13,735,616.000 Person in 03-01-2011 and a record low of 6,960,431.000 Person in 03-01-1991. Census: Number of Migrants: Punjab data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAG001: Census of India: Migration: Number of Migrants: by States.

Census: Population: Punjab: Zirakpur: Female data was reported at 45,056.000 Person in 03-01-2011. This records an increase from the previous number of 10,823.000 Person for 03-01-2001. Census: Population: Punjab: Zirakpur: Female data is updated decadal, averaging 10,823.000 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 45,056.000 Person in 03-01-2011 and a record low of 10,823.000 Person in 03-01-2001. Census: Population: Punjab: Zirakpur: Female data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC: Census: Population: by Towns and Urban Agglomerations: Punjab.