This statistic shows the biggest cities in Pakistan as of 2023. In 2023, approximately ***** million people lived in Karāchi, making it the biggest city in Pakistan.

Major Cities in Pakistan by Population

Population in largest city in Pakistan was reported at 17648555 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Pakistan PK: Population in Largest City data was reported at 15,020,931.000 Person in 2017. This records an increase from the previous number of 14,650,981.000 Person for 2016. Pakistan PK: Population in Largest City data is updated yearly, averaging 6,793,799.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 15,020,931.000 Person in 2017 and a record low of 1,853,325.000 Person in 1960. Pakistan PK: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Digital point dataset of Major Cities of Pakistan. This dataset is Basic Vector layer derived from ESRI Map & Data 2001.

Population in the largest city (% of urban population) in Pakistan was reported at 18.31 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Pakistan PK: Population in Largest City: as % of Urban Population data was reported at 20.922 % in 2017. This records a decrease from the previous number of 20.928 % for 2016. Pakistan PK: Population in Largest City: as % of Urban Population data is updated yearly, averaging 21.610 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 23.038 % in 1980 and a record low of 18.670 % in 1960. Pakistan PK: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Major Cities Population

Graph and download economic data for Geographical Outreach: Number of Automated Teller Machines (ATMs) in 3 Largest Cities for Pakistan (PAKFCACLNUM) from 2004 to 2015 about ATM, Pakistan, banks, and depository institutions.

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Commercial Banks for Pakistan (PAKFCBODCLNUM) from 2004 to 2015 about branches, Pakistan, banks, and depository institutions.

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the 26 largest cities (>250k habitants) in the country.

The following cities are included: City - Population

Chiniot 278,528

Nawabshah 279,338

Mingora 331,377

Okara 358,146

Kasur 358,296

Mardan 359,024

Wah Cantonment 379,534

Sahiwal 388,795

Gujrat 390,758

Dera Ghazi Khan 397,362

Rahimyar Khan 420,963

Sheikhūpura 473,269

Larkana 488,006

Sukkur 500,401

Sialkot 656,730

Sargodha 658,208

Bahawalpur 762,774

Quetta 999,385

Hyderabad 1,733,622

Multan 1,872,641

Peshawar 1,969,823

Gujranwala 2,028,421

Rawalpindi Islamabad 3,106,827

Faisalabad 3,210,158

Lahore 11,119,985

Karachi 14,884,402

This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location).

Data publication: 2021-10-18

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Dariia Nesterenko

Data lineage:

Produced using OpenStreetMap data for roads, railways, rivers; UN Map country border; The HydroSHEDS 15' resolution GRID for the DEM, GHSL - Global Human Settlement Layer. For the city population - http://www.citypopulation.de/

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped raster TIF file for Accessibility: Travel Time-Cost to Major Cities (Pakistan - ~ 500m)

This dataset contains daily historical weather data for six major cities in Pakistan — Karachi, Lahore, Islamabad, Quetta, Peshawar, and Gilgit — covering the period from January 1, 2000 to 2024.

The data was collected using a reliable weather API and includes key meteorological parameters such as:

• Daily temperature (max/min)

• Humidity

• Wind speed

• Precipitation

• Weather conditions (where available)

This dataset is ideal for:

• Climate and environmental analysis

• Time series forecasting

• Machine learning model training

• Academic or educational research

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Deposit Taking Microfinance Institutions (MFIs) for Pakistan (PAKFCBODMFLNUM) from 2004 to 2015 about microfinance, branches, Pakistan, and deposits.

The dataset is of 10,782 children and adolescents, aged 2 to 19 years, who belonged to four major cities of Pakistan including Lahore, Multan, Rawalpindi/Islamabad. The dataset consists of data about variables age (years), gender status (boys/girls), residential city (Multan/ Lahore/ Rawalpindi or Islamabad) and anthropometric measurements i.e., height (0.1cm), weight (kg.), WC (0.1cm), HpC (0.1cm), MUAC (0.1cm), NC (0.1cm) and WrC (0.1cm).

Abstract

The major aim of the survey is to collect a set of comprehensive statistics on the various dimensions of country’s civilian labour force as a means to pave the way for skill development, planning, employment generation, assessing the role and importance of the informal sector and, sizing up the volume, characteristics and contours of employment. The broad objectives of the survey are as follows: - To collect data on the socio-demographic characteristics of the total population i.e. age, sex, marital status, level of education, current enrolment and migration etc; - To acquire current information on the dimensions of national labour force; i.e. number of persons employed, unemployed, and underemployed or out of labour market; - To gather descriptive facts on the engagement in major occupational trades and the nature of work undertaken by the institutions/organizations; - To profile statistics on employment status of the individuals, i.e. whether they are employers, own account workers, contributing family workers or paid employees (regular/casual); - To classify non-agricultural enterprises employing household member(s) as formal and informal; - To quantify the hours worked at main/subsidiary occupations; - To provide data on wages and mode of payment for paid employees; - To make an assessment of occupational health and safety of employed persons by causes, type of treatment, conditions that caused the accident/injury and time of recovery; and - To collect data on the characteristics of unemployed persons i.e. age, sex, level of education, previous experience if any, occupation, industry, employment status related to previous job, waiting time invested in the quest for work, their availability for work and expectations for future employment.

Geographic coverage

National coverage.

The survey covers all urban and rural areas of the four provinces of Pakistan defined as such by1998 Population Census, excluding Federally Administered Tribal Areas (FATA) and military restricted areas. The population of excluded areas constitutes about 2% of the total population.

All sample enumeration blocks in urban areas and mouzas/dehs/villages in rural areas were enumerated except three sample areas (PSUs), due to law & order and recent flood. However, the number of sample households enumerated (36420) is high (equivalent) 99.9% of the total sample size) to the estimated sample size (36464).

Analysis unit

• Individual aged 10 years and above
• Household

Universe

The universe for Labour Force Survey consistsed of all urban and rural areas of the four provinces of Pakistan defined as such by 1998 Population Census excluding FATA and military restricted areas. The population of excluded areas constitutes about 2% of the total population. The following groups were also excluded non-settled population, persons living in institutions and foreigners.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Quarterly.

Sampling procedure

Sample Design: A stratified two-stage sample design is adopted for the survey.

Sampling Frame: Federal Bureau of Statistics (FBS) has developed its own sampling frame for urban areas. Each city/town is divided into enumeration blocks. Each enumeration block is comprised of 200 to 250 households on the average with well-defined boundaries and maps. The list of enumeration blocks as updated through Economic Census 2003 and the list of villages/mouzas/dehs of 1998 Population Census are taken as sampling frames. Enumeration blocks & villages are considered as Primary Sampling Units (PSUs) for urban and rural domains respectively.

Stratification Plan - Urban Domain: Large cities Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Sialkot, Sargodha, Bahawalpur, Hyderabad, Sukkur, Peshawar, Quetta and Islamabad are considered as large cities. Each of these cities constitutes a separate stratum, further sub-stratified according to low, middle and high income groups based on the information collected in respect of each enumeration block at the time of demarcation/ updating of urban area sampling frame.

• Remaining Urban Areas: In all the four provinces after excluding the population of large cities from the population of an administrative division, the remaining urban population is grouped together to form a stratum.

• Rural Domain: Each administrative district in the Punjab, Sindh and Khyber Pakhtunkhwa (KP) is considered an independent stratum whereas in Balochistan, each administrative division constitutes a stratum.

• Selection of primary sampling units (PSUs): Enumeration blocks in urban domain and mouzas/dehs/villages in rural are taken as Primary Sampling Units (PSUs). In the urban domain, sample PSUs from each ultimate stratum/sub-stratum are selected with probability proportional to size (PPS) method of sampling scheme. In urban domain, the number of households in an enumeration block as updated through Economic Census 2003 and village population of 1998 Census for rural domain is considered as measure of size.

• Selection of secondary sampling units (SSUs): The listed households of sample PSUs are taken as Secondary Sampling Units (SSUs). A specified number of households i.e. 12 from each urban sample PSU, 16 from rural sample PSU are selected with equal probability using systematic sampling technique with a random start.

• Sample Size and Its Allocation: A sample of 36,464 households is considered appropriate to provide reliable estimates of key labour force characteristics at National/Provincial level. The entire sample of households (SSUs) is drawn from 2580 Primary Sampling Units (PSUs) out of which 1204 are urban and 1376 are rural. The overall sample has been distributed evenly over four quarters independently. As urban population is more heterogeneous therefore, a higher proportion of sample size is allocated to urban domain. To produce reliable estimates, a higher proportion of sample is assigned to Khyber Pk and Balochistan in consideration to their smallness. After fixing the sample size at provincial level, further distribution of sample PSUs to different strata in rural and urban domains in each province is made proportionately.

Mode of data collection

Face-to-face [f2f]

Research instrument

Structured questionnaire.

Cleaning operations

Editing and coding is done at headquarter by the subject matter section. Computer edit checks are applied to get even with errors identified at the stage of data entry. The relevant numerical techniques are used to eliminate erroneous data resulting from mistakes made during coding. The survey records are further edited and rectified through a series of computer processing stages.

Response rate

99.9%

Data appraisal

Notwithstanding complete observance of the requisite codes to ensure reliability of data, co-efficient of variations, computed in the backdrop of 5% margin of error exercised for determining sample size, are also given below to affirm the reliability of estimates.

Credit report of Two Star Industries Private Ltd City Tower 9th Floor 6 K Main Boulevard Gulberg Ii Lahore 54660 Pakistan contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Abstract

A primary objective of the national Time Use Survey in Pakistan is to account for the 24 hours time in term of the full spectrum of activities carried out during the duration. The objectives of the survey are specified as under:- - To profile the quantum and distribution of paid/unpaid work as a means to infer policy/programme implications from the perspective of gender equity. - To collect and analyze the time use pattern of the individuals in order to help draw inferences for employment and welfare programmes. - To collect and analyze the comprehensive information about the time spent by people on marketed and non-marketed economic activities covered under the 1993-SNA, non-marketed non-SNA activities within the General Production Boundary and personal care and related activities that cannot be delegated to others. - To use the data in generating more reliable estimates on work force.

Geographic coverage

The survey covers all urban and rural areas of the four provinces of Pakistan defined as such by 1998 Population Census excluding Federally Administered Tribal Areas (FATA) and certain administrative areas of NWFP. The population of geographic areas excluded from the survey constitutes about 2 percent of the total population as enumerated in 1998 Population Census. The population excluded is located in difficult terrain and its enumeration through personal interview is not possible within the given constraints of time, access and cost.

Analysis unit

Households Individuals

Universe

The universe consists of all urban and rural areas of the four provinces of Pakistan, defined as such by Population Census 1998, excluding FATA & Military Restricted Areas. The population of excluded area constitutes about 3% of the total population and is located in different terrain.

Sampling procedure

Sampling Frame Federal Bureau of Statistics has developed its own sampling frame for all urban areas of the country. Each city/town has been divided into a number of enumeration blocks. Each enumeration block consists of 200-250 households on the average with well-defined boundaries and maps. The sampling frame i.e. lists of enumeration blocks as up-dated through Economic Census 2003-04 and the lists of villages/mouzas/dehs published by Population Census Organization as a result of 1998 Population Census have been taken as sampling frame. Enumeration blocks and villages are considered as primary sampling unites (PSUs) for urban and rural domain respectively.

Stratification a) Urban Domain i) Large Sized Cities Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Sialkot, Sargodha, Bahawapur, Hyderabad, Sukkur, Peshawar, Quetta and Islamabad are considered as large sized cities. Each of these cities constitutes a separate stratum which is further sub-stratified according to low, middle, high income groups based on the information collected in respect of each enumeration block at the time of demarcation/up-dating of urban area sampling frame. ii) Remaining urban areas After excluding the population of large sized cities from the population of respective administrative division, the remaining urban population of administrative division of four provinces is grouped together to form a stratum called other urban. Thus ex-division in remaining urban areas in the four provinces constitutes a stratum. b) Rural Domain In rural domain, each administrative district in the Punjab, Sindh and NWF Provinces is considered as independent and explicit stratum whereas, in Balochistan, each administrative division constitutes a stratum.

Sample size and its Allocation Keeping in view the resources available, a sample size of 19600 sample households has been considered appropriate to provide estimates of key characteristics at the desired level. The entire sample of households (SSUs) has been drawn from 1388 Primary Sampling Units (PSUs) out of which 652 are urban and 736 are rural. In order to control seasonal variation etc. sample has been distributed evenly over four quarters. This has facilitated to capture the variation due to any seasonal activity as urban population is more heterogeneous therefore, a higher proportion of sample size has been allocated to urban domain. Similarly NWFP and Balochistan being the smaller province, have been assigned higher proportion of sample in order to get reliable estimates. After fixing the sample size at provincial level, further distribution of sample PSUs to different strata in rural and urban domains in each province has been made proportionately.

Sample Design A three-stage stratified sample design has been adopted for the survey. Sample Selection Procedure a) Selection of Primary Sampling Unites (PSUs) Enumeration blocks in urban domain and mouzas/dehs/villages in rural domain are taken as primary sampling unites (PSUs). In the urban domain, sample PSUs from each ultimate stratum/sub-stratum is selected with probability proportional to size (PPS) method of sampling scheme. In urban domain, the number of households in enumeration block as up-dated through Economic Census 2003-04 and population of 1998 Census for each village/mouza/deh are considered as measure of size. b) Section of Secondary Sampling Units (SSUs) Households within sample PSUs are taken as secondary sampling unites (SSUs). A specified number of households i.e. 12 from each urban sample PSU and 16 from each rural sample PSU are selected with equal probability using systematic sampling technique with a random start. Different households are selected in each quarter. c) Selection of Third Stage Sampling Units i.e. Individuals/Persons (TSUs) From the sample households, individuals/persons aged 10+ years within each sample households (SSUs) have been taken as third stage sampling units (TSUs). Two individuals aged 10 years and above among the eligible individuals/persons from each sample household have been interviewed using a selection grid.The grid and selection steps are detailed on p13 of the survey report available under external resources.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire has been framed in the light of contemporary precedents and practices in vogue in the developing countries. The recommendations of Gender Responsive Budgeting Initiatives (GRBI) expert who visited Pakistan in June 2006 have been taken into account. Further, the advice of local experts hailing both from data producing and using agencies has also been considered. Survey Questionnaire and Manual of Instructions, for the Supervisors & Enumerators, was finalized jointly by Federal Bureau of Statistics and GRBI Project staff. The questionnaire was also pre-tested and reviewed accordingly. The questionnaire adopted for the survey is given at Annexure-A. All the households selected in the sample stand interviewed. Diary part of the questionnaire is filled-in from two respondents selected from each of the enumerated households. The questionnaire consists of the following six parts. Section-1: Identification of the area, respondents, detail of field visits and staff entrusted with supervision, editing and coding. Section-2: Detailed information about the socio-economic and demographic particulars of the selected households and individuals. Some of the important household characteristics i.e. ownership status and type of the household, earthquake damage, household items, sources of energy, drinking water, transport, health & education facilities, sources of income, monthly income, age and sex composition of the population. Section-3: Demographic detail such as age, sex, marital status, educational level, having children, employment status, source of income etc. of the selected respondent of that household Section-4: Comprised of diary to record the activities performed by the first selected respondent through the 24 hours period between 4.00 a.m. of the day preceding the day of interview and 3.00 a.m. on the day of the interview. Section-5 and 6 pertain to the second selected respondent of the selected household. The diary which is the core instrument of the time use study is divided into forty eight half-hour slots. An open ended question about the activities performed during the thirty minutes was asked from the respondent. Provision for minimum of recording three activities through half hour slot was made. In case of reporting more than one activity, the respondent was probed whether these activities were carried out simultaneously or one after the other. Similarly, the two locations of performing the activities were also investigated in the diary part of the questionnaire. The activities recorded in the diary are then coded by the field enumerator according to the activity classification given at Annex-B.

Cleaning operations

Soon after data collection, the field supervisors manually clean, edit and check the filled in questionnaire and refer back to field where necessary. This does not take much time since most of the manual editing is done in the field. Further editing is done by the subject matter section at the Headquarter. Also during data entry, further editing of error identified by applying computer edit checks is done. In edit checks, data ranges in numerical values are used to eliminate erroneous data as a result of mistakes made during coding. Thus, the survey records are edited and corrected through a series of computer processing stages.

Abstract

The Pakistan Demographic and Health Survey (PDHS) was fielded on a national basis between the months of December 1990 and May 1991. The survey was carried out by the National Institute of Population Studies with the objective of assisting the Ministry of Population Welfare to evaluate the Population Welfare Programme and maternal and child health services. The PDHS is the latest in a series of surveys, making it possible to evaluate changes in the demographic status of the population and in health conditions nationwide. Earlier surveys include the Pakistan Contraceptive Prevalence Survey of 1984-85 and the Pakistan Fertility Survey of 1975.

The primary objective of the Pakistan Demographic and Health Survey (PDHS) was to provide national- and provincial-level data on population and health in Pakistan. The primary emphasis was on the following topics: fertility, nuptiality, family size preferences, knowledge and use of family planning, the potential demand for contraception, the level of unwanted fertility, infant and child mortality, breastfeeding and food supplementation practices, maternal care, child nutrition and health, immunisations and child morbidity. This information is intended to assist policy makers, administrators and researchers in assessing and evaluating population and health programmes and strategies. The PDHS is further intended to serve as a source of demographic data for comparison with earlier surveys, particularly the 1975 Pakistan Fertility Survey (PFS) and the 1984-85 Pakistan Contraceptive Prevalence Survey (PCPS).

MAIN RESULTS

Until recently, fertility rates had remained high with little evidence of any sustained fertility decline. In recent years, however, fertility has begun to decline due to a rapid increase in the age at marriage and to a modest rise in the prevalence of contraceptive use. The lotal fertility rate is estimated to have fallen from a level of approximately 6.4 children in the early 1980s to 6.0 children in the mid-1980s, to 5.4 children in the late 1980s. The exact magnitude of the change is in dispute and will be the subject of further research. Important differentials of fertility include the degree ofurbanisation and the level of women's education. The total fertility rate is estimated to be nearly one child lower in major cities (4.7) than in rural areas (5.6). Women with at least some secondary schooling have a rate of 3.6, compared to a rate of 5.7 children for women with no formal education.

There is a wide disparity between women's knowledge and use of contraceptives in Pakistan. While 78 percent of currently married women report knowing at least one method of contraception, only 21 percent have ever used a method, and only 12 percent are currently doing so. Three-fourths of current users are using a modem method and one-fourth a traditional method. The two most commonly used methods are female sterilisation (4 percent) and the condom (3 percent). Despite the relatively low level of contraceptive use, the gain over time has been significant. Among married non-pregnant women, contraceptive use has almost tripled in 15 years, from 5 percent in 1975 to 14 percent in 1990-91. The contraceptive prevalence among women with secondary education is 38 percent, and among women with no schooling it is only 8 percent. Nearly one-third of women in major cities arc current users of contraception, but contraceptive use is still rare in rural areas (6 percent).

The Government of Pakistan plays a major role in providing family planning services. Eighty-five percent of sterilised women and 81 percent of IUD users obtained services from the public sector. Condoms, however, were supplied primarily through the social marketing programme.

The use of contraceptives depends on many factors, including the degree of acceptability of the concept of family planning. Among currently married women who know of a contraceptive method, 62 percent approve of family planning. There appears to be a considerable amount of consensus between husbands and wives about family planning use: one-third of female respondents reported that both they and their husbands approve of family planning, while slightly more than one-fifth said they both disapprove. The latter couples constitute a group for which family planning acceptance will require concerted motivational efforts.

The educational levels attained by Pakistani women remain low: 79 percent of women have had no formal education, 14 percent have studied at the primary or middle school level, and only 7 percent have attended at least some secondary schooling. The traditional social structure of Pakistan supports a natural fertility pattern in which the majority of women do not use any means of fertility regulation. In such populations, the proximate determinants of fertility (other than contraception) are crucial in determining fertility levels. These include age at marriage, breastfeeding, and the duration of postpartum amenorrhoea and abstinence.

The mean age at marriage has risen sharply over the past few decades, from under 17 years in the 1950s to 21.7 years in 1991. Despite this rise, marriage remains virtually universal: among women over the age of 35, only 2 percent have never married. Marriage patterns in Pakistan are characterised by an unusually high degree of consangninity. Half of all women are married to their first cousin and an additional 11 percent are married to their second cousin.

Breasffeeding is important because of the natural immune protection it provides to babies, and the protection against pregnancy it gives to mothers. Women in Pakistan breastfeed their children for an average of20months. Themeandurationofpostpartumamenorrhoeais slightly more than 9 months. After tbebirth of a child, women abstain from sexual relations for an average of 5 months. As a result, the mean duration of postpartum insusceptibility (the period immediately following a birth during which the mother is protected from the risk of pregnancy) is 11 months, and the median is 8 months. Because of differentials in the duration of breastfeeding and abstinence, the median duration of insusceptibility varies widely: from 4 months for women with at least some secondary education to 9 months for women with no schooling; and from 5 months for women residing in major cities to 9 months for women in rural areas.

In the PDHS, women were asked about their desire for additional sons and daughters. Overall, 40 percent of currently married women do not want to have any more children. This figure increases rapidly depending on the number of children a woman has: from 17 percent for women with two living children, to 52 percent for women with four children, to 71 percent for women with six children. The desire to stop childbearing varies widely across cultural groupings. For example, among women with four living children, the percentage who want no more varies from 47 percent for women with no education to 84 percent for those with at least some secondary education.

Gender preference continues to be widespread in Pakistan. Among currently married non-pregnant women who want another child, 49 percent would prefer to have a boy and only 5 percent would prefer a girl, while 46 percent say it would make no difference.

The need for family planning services, as measured in the PDHS, takes into account women's statements concerning recent and future intended childbearing and their use of contraceptives. It is estimated that 25 percent of currently married women have a need for family planning to stop childbearing and an additional 12 percent are in need of family planning for spacing children. Thus, the total need for family planning equals 37 percent, while only 12 percent of women are currently using contraception. The result is an unmet need for family planning services consisting of 25 percent of currently married women. This gap presents both an opportunity and a challenge to the Population Welfare Programme.

Nearly one-tenth of children in Pakistan die before reaching their first birthday. The infant mortality rate during the six years preceding the survey is estimaled to be 91 per thousand live births; the under-five mortality rate is 117 per thousand. The under-five mortality rates vary from 92 per thousand for major cities to 132 for rural areas; and from 50 per thousand for women with at least some secondary education to 128 for those with no education.

The level of infant mortality is influenced by biological factors such as mother's age at birth, birth order and, most importantly, the length of the preceding birth interval. Children born less than two years after their next oldest sibling are subject to an infant mortality rate of 133 per thousand, compared to 65 for those spaced two to three years apart, and 30 for those born at least four years after their older brother or sister.

One of the priorities of the Government of Pakistan is to provide medical care during pregnancy and at the time of delivery, both of which are essential for infant and child survival and safe motherhood. Looking at children born in the five years preceding the survey, antenatal care was received during pregnancy for only 30 percent of these births. In rural areas, only 17 percent of births benefited from antenatal care, compared to 71 percent in major cities. Educational differentials in antenatal care are also striking: 22 percent of births of mothers with no education received antenatal care, compared to 85 percent of births of mothers with at least some secondary education.

Tetanus, a major cause of neonatal death in Pakistan, can be prevented by immunisation of the mother during pregnancy. For 30 percent of all births in the five years prior to the survey, the mother received a tetanus toxoid vaccination. The differentials are about the same as those for antenatal care generally.

Eighty-five percent of the

The Pakistan Demographic and Health Survey (PDHS) was fielded on a national basis between the months of December 1990 and May 1991. The survey was carried out by the National Institute of Population Studies with the objective of assisting the Ministry of Population Welfare to evaluate the Population Welfare Programme and maternal and child health services. The PDHS is the latest in a series of surveys, making it possible to evaluate changes in the demographic status of the population and in health conditions nationwide. Earlier surveys include the Pakistan Contraceptive Prevalence Survey of 1984-85 and the Pakistan Fertility Survey of 1975.

The primary objective of the Pakistan Demographic and Health Survey (PDHS) was to provide national- and provincial-level data on population and health in Pakistan. The primary emphasis was on the following topics: fertility, nuptiality, family size preferences, knowledge and use of family planning, the potential demand for contraception, the level of unwanted fertility, infant and child mortality, breastfeeding and food supplementation practices, maternal care, child nutrition and health, immunisations and child morbidity. This information is intended to assist policy makers, administrators and researchers in assessing and evaluating population and health programmes and strategies. The PDHS is further intended to serve as a source of demographic data for comparison with earlier surveys, particularly the 1975 Pakistan Fertility Survey (PFS) and the 1984-85 Pakistan Contraceptive Prevalence Survey (PCPS).

MAIN RESULTS

Until recently, fertility rates had remained high with little evidence of any sustained fertility decline. In recent years, however, fertility has begun to decline due to a rapid increase in the age at marriage and to a modest rise in the prevalence of contraceptive use. The lotal fertility rate is estimated to have fallen from a level of approximately 6.4 children in the early 1980s to 6.0 children in the mid-1980s, to 5.4 children in the late 1980s. The exact magnitude of the change is in dispute and will be the subject of further research. Important differentials of fertility include the degree ofurbanisation and the level of women's education. The total fertility rate is estimated to be nearly one child lower in major cities (4.7) than in rural areas (5.6). Women with at least some secondary schooling have a rate of 3.6, compared to a rate of 5.7 children for women with no formal education.

There is a wide disparity between women's knowledge and use of contraceptives in Pakistan. While 78 percent of currently married women report knowing at least one method of contraception, only 21 percent have ever used a method, and only 12 percent are currently doing so. Three-fourths of current users are using a modem method and one-fourth a traditional method. The two most commonly used methods are female sterilisation (4 percent) and the condom (3 percent). Despite the relatively low level of contraceptive use, the gain over time has been significant. Among married non-pregnant women, contraceptive use has almost tripled in 15 years, from 5 percent in 1975 to 14 percent in 1990-91. The contraceptive prevalence among women with secondary education is 38 percent, and among women with no schooling it is only 8 percent. Nearly one-third of women in major cities arc current users of contraception, but contraceptive use is still rare in rural areas (6 percent).

The Government of Pakistan plays a major role in providing family planning services. Eighty-five percent of sterilised women and 81 percent of IUD users obtained services from the public sector. Condoms, however, were supplied primarily through the social marketing programme.

The use of contraceptives depends on many factors, including the degree of acceptability of the concept of family planning. Among currently married women who know of a contraceptive method, 62 percent approve of family planning. There appears to be a considerable amount of consensus between husbands and wives about family planning use: one-third of female respondents reported that both they and their husbands approve of family planning, while slightly more than one-fifth said they both disapprove. The latter couples constitute a group for which family planning acceptance will require concerted motivational efforts.

The educational levels attained by Pakistani women remain low: 79 percent of women have had no formal education, 14 percent have studied at the primary or middle school level, and only 7 percent have attended at least some secondary schooling. The traditional social structure of Pakistan supports a natural fertility pattern in which the majority of women do not use any means of fertility regulation. In such populations, the proximate determinants of fertility (other than contraception) are crucial in determining fertility levels. These include age at marriage, breastfeeding, and the duration of postpartum amenorrhoea and abstinence.

The mean age at marriage has risen sharply over the past few decades, from under 17 years in the 1950s to 21.7 years in 1991. Despite this rise, marriage remains virtually universal: among women over the age of 35, only 2 percent have never married. Marriage patterns in Pakistan are characterised by an unusually high degree of consangninity. Half of all women are married to their first cousin and an additional 11 percent are married to their second cousin.

Breasffeeding is important because of the natural immune protection it provides to babies, and the protection against pregnancy it gives to mothers. Women in Pakistan breastfeed their children for an average of20months. Themeandurationofpostpartumamenorrhoeais slightly more than 9 months. After tbebirth of a child, women abstain from sexual relations for an average of 5 months. As a result, the mean duration of postpartum insusceptibility (the period immediately following a birth during which the mother is protected from the risk of pregnancy) is 11 months, and the median is 8 months. Because of differentials in the duration of breastfeeding and abstinence, the median duration of insusceptibility varies widely: from 4 months for women with at least some secondary education to 9 months for women with no schooling; and from 5 months for women residing in major cities to 9 months for women in rural areas.

In the PDHS, women were asked about their desire for additional sons and daughters. Overall, 40 percent of currently married women do not want to have any more children. This figure increases rapidly depending on the number of children a woman has: from 17 percent for women with two living children, to 52 percent for women with four children, to 71 percent for women with six children. The desire to stop childbearing varies widely across cultural groupings. For example, among women with four living children, the percentage who want no more varies from 47 percent for women with no education to 84 percent for those with at least some secondary education.

Gender preference continues to be widespread in Pakistan. Among currently married non-pregnant women who want another child, 49 percent would prefer to have a boy and only 5 percent would prefer a girl, while 46 percent say it would make no difference.

The need for family planning services, as measured in the PDHS, takes into account women's statements concerning recent and future intended childbearing and their use of contraceptives. It is estimated that 25 percent of currently married women have a need for family planning to stop childbearing and an additional 12 percent are in need of family planning for spacing children. Thus, the total need for family planning equals 37 percent, while only 12 percent of women are currently using contraception. The result is an unmet need for family planning services consisting of 25 percent of currently married women. This gap presents both an opportunity and a challenge to the Population Welfare Programme.

Nearly one-tenth of children in Pakistan die before reaching their first birthday. The infant mortality rate during the six years preceding the survey is estimaled to be 91 per thousand live births; the under-five mortality rate is 117 per thousand. The under-five mortality rates vary from 92 per thousand for major cities to 132 for rural areas; and from 50 per thousand for women with at least some secondary education to 128 for those with no education.

The level of infant mortality is influenced by biological factors such as mother's age at birth, birth order and, most importantly, the length of the preceding birth interval. Children born less than two years after their next oldest sibling are subject to an infant mortality rate of 133 per thousand, compared to 65 for those spaced two to three years apart, and 30 for those born at least four years after their older brother or sister.

One of the priorities of the Government of Pakistan is to provide medical care during pregnancy and at the time of delivery, both of which are essential for infant and child survival and safe motherhood. Looking at children born in the five years preceding the survey, antenatal care was received during pregnancy for only 30 percent of these births. In rural areas, only 17 percent of births benefited from antenatal care, compared to 71 percent in major cities. Educational differentials in antenatal care are also striking: 22 percent of births of mothers with no education received antenatal care, compared to 85 percent of births of mothers with at least some secondary education.

Tetanus, a major cause of neonatal death in Pakistan, can be prevented by immunisation of the mother during pregnancy. For 30 percent of all births in the five years prior to the survey, the mother received a tetanus toxoid vaccination. The differentials are about the same as those for

This collection contains two datasets: one, data used in TI-City model to predict future urban expansion in Accra, Ghana; and two, residential electricity consumption data used to map intra-urban living standards in Karachi, Pakistan. The TI-City model data are ASCII files of infrastructure and amenities that affect location decisions of households and developers. The residential electricity consumption data consist of average kilowatt hours (kw/h) of electricity consumed per month by ~ 2 million households in Karachi. The electricity consumption data is aggregated into 30m grid cells (count = 193050), with centroids and consumption values provided. The values of the points (centroids), captured under the field "Avg_Avg_Cs", represents the median of average monthly consumption of households within the 30m grid cells.Our project addresses a critical gap in social research methodology that has important implications for combating urban poverty and promoting sustainable development in low and middle-income countries. Simply put, we're creating a low-cost tool for gathering critical information about urban population dynamics in cities experiencing rapid spatial-demographic and socioeconomic change. Such information is vital to the success of urban planning and development initiatives, as well as disaster relief efforts. By improving the information base of the actors involved in such activities we aim to improve the lives of urban dwellers across the developing world, particularly the poorest and most vulnerable. The key output for the project will be a freely available 'City Sampling Toolkit' that provides detailed instructions and opensource software tools for replicating the approach at various spatial scales. Our research is motivated by the growing recognition that cities are critical arenas for action in global efforts to tackle poverty and transition towards more environmentally sustainable economic growth. Between now and 2050 the global urban population is projected to grow by over 2 billion, with the overwhelming majority of this growth taking place in low and middle-income countries in Africa and Asia. Developing evidence-based policies for managing this growth is an urgent task. As UN Secretary General Ban Ki Moon has observed: "Cities are increasingly the home of humanity. They are central to climate action, global prosperity, peace and human rights...To transform our world, we must transform its cities." Unfortunately, even basic data about urban populations are lacking in many of the fastest growing cities of the world. Existing methods for gathering vital information, including censuses and sample surveys, have critical limitations in urban areas experiencing rapid change. And 'big data' approaches are not an adequate substitute for representative population data when it comes to urban planning and policymaking. We will overcome these limitations through a combination of conceptual innovation and creative integration of novel tools and techniques that have been developed for sampling, surveying and estimating the characteristics of populations that are difficult to enumerate. This, in turn, will help us capture the large (and sometimes uniquely vulnerable) 'hidden populations' in cities missed by traditional approaches. By using freely available satellite imagery, we can get an idea of the current shape of a rapidly changing city and create a 'sampling frame' from which we then identify respondents for our survey. Importantly, and in contrast with previous approaches, we aren't simply going to count official city residents. We are interested in understanding the characteristics of the actually present population, including recent migrants, temporary residents, and those living in informal or illegal settlements, who are often not considered formal residents in official enumeration exercises. In other words, our 'inclusion criterion' for the survey exercise is presence not residence. By adopting this approach, we hope to capture a more accurate picture of city populations. We will also limit the length of our survey questionnaire to maximise responses and then use novel statistical techniques to reconstruct a rich statistical portrait that reflects a wide range of demographic and socioeconomic information. We will pilot our methodology in a city in Pakistan, which recently completed a national census exercise that has generated some controversy with regard to the accuracy of urban population counts. To our knowledge this would be the first project ever to pilot and validate a new sampling and survey methodology at the city scale in a developing country. The TI-City data was accessed from institutions responsible for land use and planning in Ghana as well as secondary sources (See the the underlying paper for more https://doi.org/10.1177/23998083211068843). The residential electricity consumption data was provided by K-Electric (KE), the monopoly provider of electricity in Karachi. The data pertains to ~2 million households aggregated into 30m grid cells (see the underlying paper for more https://dx.doi.org/10.2139/ssrn.4154318).