Pakistan PK: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 33.500 % in 2015. This records an increase from the previous number of 30.700 % for 2013. Pakistan PK: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 32.050 % from Dec 1987 (Median) to 2015, with 12 observations. The data reached an all-time high of 33.500 % in 2015 and a record low of 28.700 % in 1996. Pakistan PK: Gini Coefficient (GINI Index): World Bank Estimate 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: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

The sample excludes the Federally Administered Northern Areas (FANA) and Federally Administered Tribal Areas (FATA) because of security risks. The excluded area represents less than 5% of the total adult population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in the majority of economies was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Poverty is a big threat to prosperity in developing countries like Pakistan. Alleviating poverty needs concerted efforts including how to measure and analyze poverty. Therefore, this paper employs synthetic panel technique and uses repeated cross-sections household survey dataset (Household Integrated and Economic Survey (HIES)) of Pakistan for 2010–11 and 2015–16, to derive poverty bounds for Pakistan. The findings of the paper suggest that 17% of population still remains in poverty in 2015–16 as they were in 2010–11. They don’t move in or out of poverty. In the same periods 19% population affected by poverty. The 2.5% poor’s of 2010–11 moves out of poverty in 2015–16. This constitutes the first attempt to provide an insight into poverty dynamics in Pakistan using the available survey data.

Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Urban data deficits in developing countries impede evidence-based planning and policy. Could energy data be used to overcome this challenge by serving as a local proxy for living standards or economic activity in large urban areas? To answer this question, we examine the potential of georeferenced residential electricity meter data and night-time lights (NTL) data in the megacity of Karachi, Pakistan. First, we use nationally representative survey data to establish a strong association between electricity consumption and household living standards. Second, we compare gridded radiance values from NTL data with a unique dataset containing georeferenced median monthly electricity consumption values for over 2 million individual households in the city. Finally, we develop a model to explain intra-urban variation in radiance values using proxy measures of economic activity from Open Street Map. Overall, we find that NTL data are a poor proxy for living standards but do capture spatial variation in population density and economic activity. By contrast, electricity data are an excellent proxy for living standards and could be used more widely to inform policy and support poverty research in cities in low- and middle-income countries.

Tariff Structure announced by Government of Pakistan.

Abstract

The PSLM Project is designed to provide Social & Economic indicators in the alternate years at provincial and district levels. The project was initiated in July 2004 and will continue up to June 2015. The data generated through surveys is used to assist the government In formulating the poverty reduction strategy as well as development plans at district level and for the rapid assessment of program in the overall context of MDGs. As such this survey is one of the main mechanisms for monitoring MDGs indicators. It provides a set of representative, population-based estimates of social indicators and their progress under the PRSP/MDGs. For Millennium Development Goals (MDGs), UN has set 18 targets for 48 indicators for its member countries to achieve by 2015. Pakistan has committed to implement 16 targets and 37 indicators out of which 6 targets and 13 indicators are monitored through PSLM Surveys. The PSLM surveys are conducted at district level and at Provincial level respectively at alternate years. PSLM District level survey collects information on key Social indicators whereas through provincial level surveys (Social & HIES) collects information on social indicators as well as on Income and Consumption while in specific sections also information is also collected about household size; the number of employed people and their employment status, main sources of income; consumption patterns; the level of savings; and the consumption of the major food items. However, Planning Commission also uses this data for Poverty analysis.

Another important objective of the PSLM Survey is to try to establish the distributional impact of development programs; whether the poor have benefited from the program or whether increased government expenditure on the social sectors has been captured by the better off. The sample size of PSLM surveys district level is approximately 80000 households and approximately 18000 at Provincial level.

Main Indicators: Indicators on Demographic characteristics, Education, Health, Employment, Household Assets, Household Amenities, Population Welfare and Water Supply & Sanitation are developed at National/Provincial /District levels.

Geographic coverage

National coverage

Analysis unit

Households and Individuals

Universe

The universe of this survey consists of all urban and rural areas of all four provinces, AJK and Gilgit Baltistan. FATA and Military restricted areas have been excluded from the scope of the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: Pakistan Bureau of statistics PBS has developed its own urban area frame. Each city/town is divided into enumeration blocks. Each enumeration block is comprised to 200-250 households on the average with well-defined boundaries and maps .The list of enumeration blocks as updated from field on the prescribed Performa by Quick Count Technique in 2013 for urban and the list of villages/mouzas/dehs or its part (block), updated during House listing in 2011 for conduct of Population Census, are taken as sampling frame. Enumeration blocks and villages are considered as Primary Sampling Units (PSUs) for urban and rural domains respectively. A project to update the rural blocks is currently in hand.

Stratification Plan

Urban Areas: Large sized cities having population five laces and above have been treated as independent stratum. Each of these cities has further been sub-stratified into low, middle and high income groups. The remaining cities/towns within each defunct administrative division have been grouped together to constitute an independent stratum.

Rural Areas: The entire rural domain of a district for Khyber Pakhtunkhwa, Punjab, and Sindh provinces has been considered as independent stratum, whereas in Balochistan province defunct administrative division has been treated as stratum.

Sample Size and its Allocation: To determine optimum sample size for this survey, 6 indicators namely Literacy rate, Net enrolment rate at primary level, Population 10+ that ever attended school, Contraceptive prevalence of women age 15-49 years, Children age 12-23 months who are fully immunized and post natal consultation for ever married women aged 15-49 years were taken into consideration. Keeping in view the prevalence of these indicators at different margin of errors, reliability of estimates and field resources available a sample of size 19620 households distributed over 1368 PSUs (567 urban and 801 rural) has been considered sufficient to produce reliable estimates in respect of all four provinces with urban rural breakdown, however data was collected from 1307 PSU’S by covering 17989 household.

Sample Design: A two-stage stratified sample design has been adopted for this survey.

Selection of primary sampling Units (PSUs): Enumeration blocks in urban and rural domains have been taken as PSUs. In urban and rural domains sample PSUs from each stratum have been selected by PPS method of sampling scheme; using households in each block as Measure of size (MOS).

Selection of Secondary Sampling Units (SSUs): Households within PSU have been considered as SSUs. 16 and 12 households have been selected from urban/rural domains respectively by systematic sampling scheme with a random start.

Sampling deviation

Out of 1368 PSUs, of all four provinces 61 PSUs (11 urban and 50 rural PSUs) of Balochistan were dropped due to bad law and order situation and the remaining 1307 PSUs (556 urban and 751 rural) comprising 17989 households were covered.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

At both individual and household level, the PSLM Survey collects information on a wide range of topics using an integrated questionnaire. The questionnaire comprises a number of different sections, each of which looks at a particular aspect of household behavior or welfare. Data collected under Round IX includes education, diarrhea, immunization, reproductive health, pregnancy history, maternity history, family planning, pre and post-natal care and access to basic services.

Cleaning operations

Data quality in PSLM Survey has been ensured through a built in system of checking of field work by the supervisors in the field and by the in charge of the concerned Regional/Field offices. Teams from the headquarters also pay surprise visits and randomly check the work done by the enumerators. Regional/ Field offices ensured the data quality through preliminary editing at their office level. The entire data entry was carried at the PBS headquarter Islamabad and specially designed data entry programme had a number of built in consistency checks.

Data appraisal

To determine the reliability of the estimates confidence interval and Standard error of important key indicators have been worked out and are attached at the end of each section of the survey report, provided under the 'Related Materials' tab

75,8 (Millionen) in 2018. Number of people living on less than $3.20 a day at 2011 international prices. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).

Whether one is in favor of private education or not, it is here to stay and there is a critical need to understand this new environment. Unfortunately, little is known about the private sector and what its growth implies for the provision of education. There are important questions we need to answer before engaging in productive debate about how education can be best provided in the Pakistani context. For instance: a. Where are private schools setting up? Are they only being established in urban areas and only for the elite? b. What is the quality of education in private sector schools? How does it compare to public schools? c. Are the poor being left out? Is the private sector creating two classes of people in Pakistan—those who can afford private education and those who cannot? d. What is the effect of private schools on government schools?

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.

An upsurge in global food prices in 2008 led to significantly higher food prices across the developing world. Global commodity prices have since declined but still remain volatile, but at the same time local food prices remain high in many countries. This study examines the potential impacts of the rise in food prices on poverty—income based poverty and calorie-based poverty- focusing on Pakistan, and its rural and urban areas. For this purpose, we used HIES data collected in three waves 2005–06, 2007–08 and 2010–11. Price elasticities are computed using binary Logistic regression method. The study results show that price of wheat, rice, milk, meat, fruit, pulses appear to distinguish the status of a household. Price elasticities shows that urban households are hit harder than rural households in calorie-poverty model. Overall, rising food prices are likely to lead higher poverty in Pakistan, as the negative impact on net consumers outweighs the benefits to producers. Therefore, effective strategy for eliminating poverty is far more concerned with price increases. Safety net programs can be more effective, but geographic targeting and other investments to strengthen safety nets are necessary to ensure that fewer people are affected by future crises. Government policies oriented towards relieving the food price pressure on the Pakistani poor should aim at lowering the prices of wheat, rice, eggs, oil, milk, and chicken.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: No - Individuals: Yes - Group quarters: No

UNIT DESCRIPTIONS: - Dwellings: Housing units means such a residential place which has separate building structure and is separate housing unit. These could be one or more than one housing units in a building. Housing unit and house are the same by definition in population and housing census. - Households: Households consisting of more than one person living together under common cooking arrangements (i.e., they use one burner for cooking). However if a person lives alone, he shall also be considered a household. These persons are generally relatives but these could also be friends, servants of the household and other non relatives residing in them. In such a case if the members of household do not eat at the place where they live, then they will be counted at the place where they live rather than at a place where they take their meals. - Group quarters: Housing unit which has been constructed for collective residence in connection with semi-government or trading purpose. e.g. hotel , hostel , residential barracks of Armed or semi Armed forces, residential camps, jail, Sanitarium, Mental hospital, Disabled, poor , orphans, paupers and special institutions for residences of other such people.

Universe

All the people who are residing in the boundaries of Pakistan on the Census Day, which include all types of persons (i.e., infants or babies, adults or old, males or females, landlords or tenants, Pakistanis or foreigners). The staff members of diplomats and their families are exempted.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Population Census Organization

SAMPLE DESIGN: Systematic sample of every 10th person with a random start, drawn from a 38% sample containing a weight variable (Short form data) by the Minnesota Population Center. Persons were not organized into households.

SAMPLE UNIT: Person

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 8,433,058

Mode of data collection

Face-to-face [f2f]

Research instrument

There are two Population Census forms. The short form contains a few question on demographic characteristics including name, relationship, residential status, sex, age, marital status, religion, ability to read Quran, literacy, education level, and language used in the household. These questions were asked from about ninety percent of the population. The long form will be asked of the rest of the population, and it contains all the questions asked in the short form and additional questions on higher education, field of education, migration, economic characteristics, number of children, disability, and household members living abroad.

PK：覆盖：社会安全网计划：Poorest Quintile：占人口百分比在12-01-2013达19.703%，相较于12-01-2009的13.402%有所增长。PK：覆盖：社会安全网计划：Poorest Quintile：占人口百分比数据按年更新，12-01-2007至12-01-2013期间平均值为13.402%，共3份观测结果。该数据的历史最高值出现于12-01-2013，达19.703%，而历史最低值则出现于12-01-2007，为1.144%。CEIC提供的PK：覆盖：社会安全网计划：Poorest Quintile：占人口百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的巴基斯坦 – 表 PK.世界银行：社会保护。

Abstract

The Pakistan Social and Living Standards Measurement (PSLM) Survey is one of the main mechanisms for monitoring the implementation of the Poverty Reduction Strategy Paper (PRSP). It provides a set of representative, population-based estimates of social indicators and their progress under the PRSP. These include intermediate as well as 'output' measures, which assess what is being provided by the social sectors - enrolment rates in education, for example. They include a range of 'outcome' measures, which assess the welfare of the population - Immunisation Rate, for example.

An important objective of the PSLM Survey is to try to establish what the distributional impact of PRSP has been. Policymakers need to know, for example, whether the poor have benefited from the programme or whether increased government expenditure on the social sectors has been captured by the better off.

Geographic coverage

National, excluding military restricted areas

Analysis unit

Individual, Household, Children Under Five-Years of Age, Ever Married Women 15-49 Years of Age

Universe

The universe of this survey consists of all urban and rural areas of the four provinces and Islamabad excluding military restricted areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: Federal Bureau of Statistics has developed its own urban area frame, which was up-dated in 2003. Each city/town has been divided into enumeration blocks consisting of 200-250 households identifiable through sketch map. Each enumeration block has been classified into three categories of income groups i.e. low, middle and high keeping in view the living standard of the majority of the people. List of villages published by Population Census Organization obtained as a consequence of Population Census 1998 has been taken as rural frame.

Stratification Plan: A. Urban Domain: Islamabad, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Bahawalpur, Sargodha, Sialkot, Karachi, Hyderabad, Sukkur Peshawar and Quetta, have been considered as large sized cities. Each of these cities constitutes a separate stratum and has further been sub-stratified according to low, middle and high-income groups. After excluding population of large sized city (s), the remaining urban population in each district in all the provinces has been grouped together to form a stratum. B. Rural Domain: Each district in the four provinces of Pakistan has been treated as an independent stratum.

Sample Size and Its Allocation: Keeping in view the objectives of the survey the sample size for the four provinces has been fixed at 74420 households comprising 5204 sample village/enumeration blocks, which is expected to produce reliable results at each district. However, the total sample size including Azad Jammu and Kashmir (AJK), Northern Area (NA) and FATA is 76520 households.

Sample Design: A two-stage stratified sample design has been adopted in this survey.

Selection of Primary Sampling Units (PSUs): Villages and enumeration blocks in urban and rural areas respectively have been taken as Primary Sampling Units (PSUs). Sample PSUs have been selected from strata/sub-strata with PPS method of sampling technique. Selection of Secondary Sampling Units (SSUs): Households within sample PSUs have been taken as Secondary Sampling Units (SSUs). A specified number of households i.e 16 and 12 from each sample PSU of rural & urban area have been selected respectively using systematic sampling technique with a random start.

Detailed sampling plan is attached as Appendix A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

At both individual and household level, the PSLM Survey collects information on a wide range of topics using an integrated questionnaire. The questionnaire comprises a number of different sections, each of which looks at a particular aspect of household behaviour or welfare. Data collected under Round I include education, health, immunisation, diarrhoea, its treatment, and pre and post-natal care, housing conditions and access to basic services and amenities. Information on utilisation of Health and Educational facilities in rural PSUs has also been collected and outcome of which will be made part of provincial/district level reports.

Cleaning operations

Regional/Field Offices ensured the data quality through preliminary editing at their office level. Data entry programme used had a number of built-in consistency checks.

Response rate

Non-response in the entire survey is negligible.

Data appraisal

To determine the reliability of the estimates, Coefficient of Variations (CVs) and Confidence Limits of important key indicators have been worked out and are attached as Appendix B of the survey report.