In 2016, there were approximately 19.2 thousand millionaires in Pakistan. The number of individuals owning one million U.S. dollars or more in Pakistan is expected to rise to 26.9 thousand by 2026.

HNWI forecast in Pakistan

Individuals with investible assets of at least one million U.S. dollars in current exchange rate terms are considered high net worth. The number of high-net-worth individuals in Pakistan is expected to rise overall between 2022 and 2028, settling at just under eight thousand individuals.

Countries with the highest millionaire rate

In 2021, Switzerland had the highest rate of millionaires in the world, with 16.4 percent of the adult population owning assets worth more than one million U.S. dollars. Luxembourg came in second, with 16.2 percent of the population being millionaires, and Iceland came in third. Furthermore, over 22 million people in the United States were among the world's top one percent of ultra-high net-worth individuals in 2021. China came second, with over five million top one percent wealth holders worldwide.

The number of billionaires in Pakistan is forecast to reach four in 2026. In 2016, there were just three individuals whose net worth exceeded one billion U.S. dollars in Pakistan.

Leading billionaire cities

According to the Hurun Global Rich List 2022, Beijing had the most billionaires in 2022. In total, 144 billionaires lived in China's capital. Furthermore, 121 billionaires resided in Shanghai, while 110 were in New York. Many of the world's billionaires are concentrated in a few megacities. A look at the primary industries of billionaires globally helps to explain the importance of traditional global business capitals such as New York, London, and Hong Kong. The inclusion of Chinese cities on the list can be explained partly by the country's industrial conglomerates' strong performance in recent years.

The effect of COVID-19 on the wealth of billionaires

Elon Musk was the billionaire whose fortune grew the most due to the COVID-19 pandemic. From September 2019 to September 2022, Elon Musk increased his net worth by 231.1 billion US dollars. Google’s Larry Page added the second highest value to his net worth during the period under consideration, with an increase of 37.5 billion dollars. In contrast, Facebook founder Mark Zuckerberg’s net worth decreased by nearly 12 billion US dollars during the same time.

This statistic depicts the number of multi-millionaires living in Pakistan from 2006 to 2016 with a forecast for 2026. In 2016, Pakistan had approximately 1.05 thousand multi-millionaires.

Pakistan PK: Surface Area data was reported at 796,100.000 sq km in 2017. This stayed constant from the previous number of 796,100.000 sq km for 2016. Pakistan PK: Surface Area data is updated yearly, averaging 796,100.000 sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 796,100.000 sq km in 2017 and a record low of 796,100.000 sq km in 2017. Pakistan PK: Surface Area 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: Land Use, Protected Areas and National Wealth. Surface area is a country's total area, including areas under inland bodies of water and some coastal waterways.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

This statistic depicts the number of centa-millionaires living in Pakistan from 2006 to 2016 with a forecast for 2026. In 2016, Pakistan had approximately 45 millionaires worth 100 million U.S. dollars or more.

Pakistan PK: Agricultural Irrigated Land: % of Total Agricultural Land data was reported at 0.066 % in 2014. This records a decrease from the previous number of 51.842 % for 2011. Pakistan PK: Agricultural Irrigated Land: % of Total Agricultural Land data is updated yearly, averaging 52.410 % from Dec 2001 (Median) to 2014, with 12 observations. The data reached an all-time high of 56.921 % in 2010 and a record low of 0.066 % in 2014. Pakistan PK: Agricultural Irrigated Land: % of Total Agricultural Land 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: Land Use, Protected Areas and National Wealth. Agricultural irrigated land refers to agricultural areas purposely provided with water, including land irrigated by controlled flooding.; ; Food and Agriculture Organization, electronic files and web site.; Weighted average;

Abstract

In order to elucidate the financial lives of smallholder households and build the evidence base on this important client group, Consultative Group to Assist the Poor (CGAP) of the World Bank launched the year-long Financial Diaries with Smallholder Families (the "Smallholder Diaries"). The study captured the financial and in-kind transactions of 270 households in Tanzania, Pakistan and Mozambique, of which 94 households are in the Punjab province, the breadbasket of Pakistan. The sample was drawn from 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them. Between June 2014 and July 2015, enumerators visited sample families every fortnight to conduct comprehensive face-to-face interviews to track all the money flowing into and out of their households.

Geographic coverage

In Pakistan, the Smallholder Diaries were conducted in Bahawalnagar, southern Punjab, within the country's breadbasket. Rice, wheat, and cotton are commonly grown and typically sold through a network of local commission agents (known as arthis) and village traders. Given the dominance of agricultural middlemen in Pakistan, two villages in the district of Bahawalnagar were selected as representative of an area with relatively looser connections to agricultural value chains and middlemen.

Analysis unit

The main unit for data collection for transactions was the household. However, each income source and financial instrument was ascribed to a specific household member during the initial questionnaire. Thus all transactions associated with that instrument or income source are registered under its owner. Similarly, transactions related to expenses were individually attributed to the member who initiated the respective transaction.

There was a small number of cash flows where the interviewer was not able to unambiguously identify the initiating household member. In these cases, the cash flow was recorded as belonging to the entire household (in the dataset the member ID field would be blank).

Analysis can be performed at two different levels of aggregation: a) The household itself b) Individual household members

In our study the household is defined as including those who consistently share financial resources, live together, share the same cooking arrangement, and report to the same household head. This includes babies, children, people who travel for work or school during the week and consider the household to be their main residence. However, the definition does not include people who are currently spending an extended period of time away from the household, including college students, students away at boarding school, military personnel, people in prison, or people who live in the house but maintain completely separate expenses (e.g. roommates, other families).

Universe

Once the villages for the Smallholder Diaries were selected, the research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research.

In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators, administered to all households in the selected villages. As a supplement to this process, village leaders and community representatives were consulted to help ensure local participation and eliminate households with large landholdings.

Kind of data

Event/Transaction data [evn]

Sampling procedure

The methodology and sample size of the Smallholder Diaries was designed to generate a rich pool of detailed information and insights on a targeted population. The Smallholder Diaries are not intended to be statistically representative of smallholder families in participating countries.

Total number of households in sample: 93 (Mozambique); 86 (Tanzania); 94 (Pakistan). The sample came was drawn from 3 villages in Mozambique, 2 villages in Tanzania, and 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them.

The research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research. In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators, administered to all households in the selected villages. As a supplement to this process, village leaders and community representatives were consulted to help ensure local participation and eliminate households with large landholdings, harvests per year, use of inputs, and integration with local markets and a variety of families were chosen.

In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators. As a supplement to this process, village leaders and community representatives were consulted to help ensure local ownership and eliminate households with large landholdings.

Mode of data collection

Face-to-face [f2f]

Research instrument

Interviewers visited each household and conducted three initial questionnaires. They 1) collected a household roster and demographic information about household members; 2) captured a register of physical assets and income sources for each household member and 3) registered the unique financial instruments used by each household member. This baseline information was then used to generate a custom cash flows questionnaire for each household, built to collect income, expenditure, and financial transactions for each individual. This customized cash flows questionnaire was then used for the collection of cash flows data. During regular visits about every two weeks, interviewers captured a complete set of daily, individual transactions from the preceding two-week period. Households were asked only about transactions using financial instruments and income sources that they actually have, rather than going through a generic list of questions. However, the cash flows questionnaire was continuously updated as new members joined the household, members acquired new financial instruments or income sources, or as the interviewers became aware of previously undisclosed ones.

Cleaning operations

All data editing was done manually.

Response rate

The sample initially included 286 households in all three countries, and the study ended with 273 households in total – an attrition rate similar to what has been observed in the past in similar Financial Diaries exercises. Households left the study due to moving from the study villages, seasonal migration, and occasionally by the prompting of the research team due to concerns about the household’s willingness to be forthcoming about important sources of income.

This statistic depicts the number of Ultra High Net Worth Individuals (UHNWIs) living in Pakistan from 2006 to 2018, with a forecast for 2026. In 2018, Pakistan had approximately 313 UHNWIs. For 2026, the number of UHNWIs was forecasted to reach 550.

Pakistan PK: Oil Rents: % of GDP data was reported at 0.433 % in 2016. This records an increase from the previous number of 0.408 % for 2015. Pakistan PK: Oil Rents: % of GDP data is updated yearly, averaging 0.504 % from Dec 1971 (Median) to 2016, with 46 observations. The data reached an all-time high of 1.108 % in 1990 and a record low of 0.001 % in 1973. Pakistan PK: Oil Rents: % of GDP 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: Land Use, Protected Areas and National Wealth. Oil rents are the difference between the value of crude oil production at regional prices and total costs of production.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted Average;

The Gross Domestic Product (GDP) in Pakistan was worth 373.07 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Pakistan represents 0.35 percent of the world economy. This dataset provides - Pakistan GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Pakistan PK: Coal Rents: % of GDP data was reported at 0.036 % in 2016. This records an increase from the previous number of 0.031 % for 2015. Pakistan PK: Coal Rents: % of GDP data is updated yearly, averaging 0.063 % from Dec 1971 (Median) to 2016, with 46 observations. The data reached an all-time high of 0.153 % in 2008 and a record low of 0.026 % in 1971. Pakistan PK: Coal Rents: % of GDP 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: Land Use, Protected Areas and National Wealth. Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted average;

Abstract

The World Values Survey (WVS) is an international research program devoted to the scientific and academic study of social, political, economic, religious and cultural values of people in the world. The project’s goal is to assess which impact values stability or change over time has on the social, political and economic development of countries and societies. The project grew out of the European Values Study and was started in 1981 by its Founder and first President (1981-2013) Professor Ronald Inglehart from the University of Michigan (USA) and his team, and since then has been operating in more than 120 world societies. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years. Extensive geographical and thematic scope, free availability of survey data and project findings for broad public turned the WVS into one of the most authoritative and widely-used cross-national surveys in the social sciences. At the moment, WVS is the largest non-commercial cross-national empirical time-series investigation of human beliefs and values ever executed.

The project’s overall aim is to analyze people’s values, beliefs and norms in a comparative cross-national and over-time perspective. To reach this aim, project covers a broad scope of topics from the field of Sociology, Political Science, International Relations, Economics, Public Health, Demography, Anthropology, Social Psychology and etc. In addition, WVS is the only academic study which covers the whole scope of global variations, from very poor to very rich societies in all world’s main cultural zones.

The WVS combines two institutional components. From one side, WVS is a scientific program and social research infrastructure that explores people’s values and beliefs. At the same time, WVS comprises an international network of social scientists and researchers from 120 world countries and societies. All national teams and individual researchers involved into the implementation of the WVS constitute the community of Principal Investigators (PIs). All PIs are members of the WVS.

The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. The WVS findings have proved to be valuable for policy makers seeking to build civil society and stable political institutions in developing countries. The WVS data is also frequently used by governments around the world, scholars, students, journalists and international organizations such as the World Bank, World Health Organization (WHO), United Nations Development Program (UNDP) and the United Nations Headquarters in New York (USA). The WVS data has been used in thousands of scholarly publications and the findings have been reported in leading media such as Time, Newsweek, The New York Times, The Economist, the World Development Report, the World Happiness Report and the UN Human Development Report.

The World Values Survey Association is governed by the Executive Committee, the Scientific Advisory Committee, and the General Assembly, under the terms of the Constitution.

Strategic goals for the 7th wave included:

Expansion of territorial coverage from 60 countries in WVS-6 to 80 in WVS-7; Deepening collaboration within the international development community; Deepening collaboration within NGOs, academic institutions and research foundations; Updating the WVS-7 questionnaire with new topics & items covering new social phenomena and emerging processes of value change; Expanding the 7th wave WVS with data useful for monitoring the SDGs; Expanding capacity and resources for survey fieldwork in developing countries. The 7th wave continued monitoring cultural values, attitudes and beliefs towards gender, family, and religion; attitudes and experience of poverty; education, health, and security; social tolerance and trust; attitudes towards multilateral institutions; cultural differences and similarities between regions and societies. In addition, the WVS-7 questionnaire has been elaborated with the inclusion of such new topics as the issues of justice, moral principles, corruption, accountability and risk, migration, national security and global governance.

For more information on the history of the WVSA, visit https://www.worldvaluessurvey.org/WVSContents.jsp ›Who we are › History of the WVSA.

Geographic coverage

Pakistan.

The WVS has just completed wave 7 data that comprises 64 surveys conducted in 2017-2022. With 64 countries and societies around the world and more than 80,000 respondents, this is the latest resource made available for the research community.

The WVS-7 survey was launched in January 2017 with Bolivia becoming the first country to conduct WVS-7. In the course of 2017 and 2018, WVS-7 has been conducted in the USA, Mexico, Brazil, Argentina, Chile, Ecuador, Peru, Andorra, Greece, Serbia, Romania, Turkey, Russia, Germany, Thailand, Australia, Malaysia, Indonesia, China, Pakistan, Egypt, Jordan, Nigeria, Iraq and over dozen of other world countries. Geographic coverage has also been expanded to several new countries included into the WVS for the first time, such as Bolivia, Greece, Macao SAR, Maldives, Myanmar, Nicaragua, and Tajikistan.

Analysis unit

Household, Individual

Sampling procedure

The sample type preferable for using in the World Values Survey is a full probability sample of the population aged 18 years and older. A detailed description of the sampling methodology is provided in the country specific sample design documentation available for download from WVS.

A detailed description of the sampling methodology is provided in the Pakistan 2018 sample design documentation available for download from WVS and also from the Downloads section of the metadata.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey was fielded in the following language(s): Urdu. The questionnaire is available for download from the WVS website.

Abstract

The Pakistan Integrated Household Survey (PIHS), a national sample survey, that provide household and community level data which can be used to monitor, evaluate, and assess the impact of Social Action Program (SAP). An important objective of the PIHS is to try and establish what the distributional impact of SAP 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. In order to do this, a measure of living standards is needed so that benefits from public investment in social services can be compared across different income groups. For this purpose, PIHS includes a measure of household consumption (expenditure on goods and services) against which many of the outcome variables are tabulated. In Round I, the number of items in the consumption module was limited and provided the basis for only a crude measure of household consumption. In Round II, the consumption module has been expanded and refined so that the consumption measure used in report will be more reliable measure of household welfare than Round I.

Geographic coverage

National

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size of the 1996-97 PIHS survey was fixed by the Federal Bureau of Statistics (FBS) at a level high enough to allow most estimates to be obtained for each province and region (urban/rural). In addition, for key variables, the aim of the sampling strategy was to provide estimates with confidence intervals of plus or minus 5 per cent. In the case of the 1991 PIHS, however, the sample size for the survey was considerably smaller than that for the 1995-96 and 1996-97 PIHS surveys.

A two-stage random sampling strategy was adopted for the 1995-96 and 1996-97 PIHS surveys. At the first sampling stage, a number of clusters or Primary Sampling Units (PSUs) were selected from different parts of the country. Enumerators then compiled lists of all households residing in the selected PSUs. At the second sampling stage, these lists were subsequently used to select a fixed number of households from each PSU for interviews using a systematic sampling procedure with a random start. This two-stage sampling strategy was used in order to reduce survey costs, and to improve the efficiency of the sample. The number of PSUs to be drawn from each strata in the first stage was fixed so as to ensure that there were enough observations to allow representative statistics to be derived for each main strata of interest.

Use of this particular sampling procedure means that households residing in different parts of the country have been selected for the PIHS surveys with differing probabilities of selection. In order to derive representative statistics for each of the provinces, as well as for the country as a whole, raising factors (i.e. sampling weights) have been applied to the 1991, 1995-96 and 1996-97 PIHS data sets. These raising factors take into account the sampling strategy adopted in both of these surveys, and result in data for different households being weighted by a factor that is inversely proportional to their probability of selection in the sample.

Under this second round of the PIHS, data was collected from some 12,622 households living in 905 different primary sampling units (PSUs) selected throughout the Punjab, Sindh, NWFP, and Baluchistan (see Table 1.2). The data was collected between July 1996 and June 1997, apart from NWFP where the enumeration was completed in October 1997. All households were therefore not enumerated at the same time of the year. The survey is also being implemented in Azad Jammu and Kashmir, FATA, and FANA, the results of which will be presented in a later report.

In each of the selected PSUs, a fixed number of households were selected at random (12 in each urban PSU, 16 in each rural PSU), and a detailed household questionnaire was administered to each of them. In addition, in each PSU, a community questionnaire was also completed which gathered information on the quality of infrastructure, the provision of services, and consumer prices prevailing in the community.

Before moving on to discuss questionnaire content, two issues are worth elaborating with respect to PIHS sampling in Round II: sample size and household selection. With respect to sample size, it was recognised after Round I that the rural Baluchistan sample was too small to make accurate estimates of key variables. Accordingly, the number of Baluchistan rural households was increased from 950 in Round I to 1,111 in Round II. Regarding household selection, in Round II of the PIHS the enumeration team sought to re-interview two-thirds of the households from Round I, and replace one-third with new households. If the original household from Round I could not be found then they were not replaced. The enumeration teams were successful in locating almost all the original households from Round I.

Mode of data collection

Face-to-face [f2f]

Research instrument

At the individual and household level, the PIHS collects information on a wide range of topics using an integrated questionnaire. The household questionnaire comprises a number of different modules, each of which looks at a particular aspect of household behaviour or welfare. Data collected under Round II included educational attainment and health status of all household members. In addition, information was also sought on the maternity history and family planning practices of all eligible household members. Finally, data was also collected on the household's consumption of goods and services in the last fortnight / month / year, as well as on housing conditions and access to basic services and amenities.

The modules used for Round II differed slightly from those used in Round I. The main changes in the questionnaire included: dropping the sections on vocational education and parental attitudes to education; dropping the sections in the health module covering other illnesses and injuries, and disabilities; revising the consumption module substantially; and including new sections on pre- and post-natal care, and maternal mortality. In addition, many individual questions were revised in several of the sections. Where this has been done, an explanation is provided in the notes under each table.

As the maternity history and family planning sections of the PIHS questionnaire were deemed too sensitive for male interviewers to administer directly to women, female interviewers were included in each of the field teams. This allowed the household questionnaire to be split into two parts. One part was administered to male members 10 years and older and the other to all female members. Children under 10 years were covered in the female questionnaire. Barring exceptional circumstances where individuals were not at home or unable to answer for themselves, all individual level information was obtained directly from each household member.

Data was also collected through the community questionnaire on the quality of infrastructure in the PSU, as well as the range of publicly and privately provided services (education, health, family planning, and water supply and sanitation) in the community. Information was also collected in the community questionnaire on each government health facility and primary school in the PSU. This part included questions on staffing at the facility, the quality of infrastructure, as well as the utilisation of services by members of the community.

PIHS data collected at the household and community level in each PSU provides a very rich source of data. It can be used to assess some of the dimensions of household welfare in different parts of the country, as well as to identify the main beneficiaries of different government policies and programmes.

Money Supply M0 in Pakistan increased to 12859112 PKR Million in May from 12856847 PKR Million in April of 2025. This dataset provides - Pakistan Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Pakistan PK: Mineral Rents: % of GDP data was reported at 0.017 % in 2016. This records a decrease from the previous number of 0.023 % for 2015. Pakistan PK: Mineral Rents: % of GDP data is updated yearly, averaging 0.000 % from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 0.104 % in 2010 and a record low of 0.000 % in 1994. Pakistan PK: Mineral Rents: % of GDP 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: Land Use, Protected Areas and National Wealth. Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted average;

Using a sample of 1,211 households in Pakistan, we examine the effects of COVID-19 on three key domains: education, economic, and health-related. First, during school closures, 66 percent of surveyed households report not using technology for learning at all. Wealth disparities mar access to distance learning, and richer households are 39 percent more likely to use technology for learning compared to the poorest households. This has implications for learning remediation as children head back to school. Second, more than half of the respondents report a reduction in income and one-fifth report being food insecure during the lockdown in the first week of May, 2020. Only one-fifth of households reporting a reduction in income and one-fifth of respondents reporting a reduction in the number of meals consumed report being covered by the federal government’s cash transfer program. Third, while a majority of respondents (90 percent) report adopting precautionary measures such as face masks, a vast majority of respondents (78 percent) underestimate the risk of contracting a COVID-19 infection compared to tuberculosis. With schools reopening in a phased manner since mid-September, most respondents (68 percent) believe that school reopenings will further increase the risk of COVID-19 infections. (2020)

The Gross Domestic Product per capita in Pakistan was last recorded at 1643.68 US dollars in 2024. The GDP per Capita in Pakistan is equivalent to 13 percent of the world's average. This dataset provides - Pakistan GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Introduction

Welcome to the Urdu General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Urdu speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Urdu communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Urdu speech models that understand and respond to authentic Urdu accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Urdu. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Urdu speech and language AI applications:

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden.

The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones.

The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

National excluding the population residing in Fata and Gilgit Baltistan

Analysis unit

Household Individual

Universe

National Population, Both sexes,18 and more years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size: 1200

Step 1: The sample was drawn according to the method of Probability Sampling, Population Proportionate to Size (PPS). Pakistan was divided into 4 provinces. Every province was representative of its actual proportion in the total population. Step 2: The distribution between urban and rural was also proportionate to its proportion as per last census, and re‐weighted accordingly in the tabulated results. Step 3: Within the districts villages and urban circles were selected according to random sampling procedure. 5 Step 4: Within selected villages or an urban circle a household was selected randomly after a start on random walk and proceeding according to a standardized interval. Step 5: Within each Household respondent was selected through KISH method. Step 6: Households with a designated interval was contacted by using “Right hand Rule” technique Two stratifications were used during drawing sample 1. Province and; 2. Urban/Rural

Mode of data collection

Face-to-face [f2f]

Research instrument

For each wave, suggestions for questions are solicited by social scientists from all over the world and a final master questionnaire is developed in English. Since the start in 1981 each successive wave has covered a broader range of societies than the previous one. Analysis of the data from each wave has indicated that certain questions tapped interesting and important concepts while others were of little value. This has led to the more useful questions or themes being replicated in future waves while the less useful ones have been dropped making room for new questions.

The questionnaire is translated into the various national languages and in many cases independently translated back to English to check the accuracy of the translation. In most countries, the translated questionnaire is pre-tested to help identify questions for which the translation is problematic. In some cases certain problematic questions are omitted from the national questionnaire.

WVS requires implementation of the common questionnaire fully and faithfully, in all countries included into one wave. Any alteration to the original questionnaire has to be approved by the EC. Omission of no more than a maximum of 12 questions in any given country can be allowed.

Sampling error estimates

Estimated error: 2.9

Patterns of educational attainment vary greatly across countries, and across population groups within countries. In some countries, virtually all children complete basic education whereas in others large groups fall short. The primary purpose of this database, and the associated research program, is to document and analyze these differences using a compilation of a variety of household-based data sets: Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS); Living Standards Measurement Study Surveys (LSMS); as well as country-specific Integrated Household Surveys (IHS) such as Socio-Economic Surveys.As shown at the website associated with this database, there are dramatic differences in attainment by wealth. When households are ranked according to their wealth status (or more precisely, a proxy based on the assets owned by members of the household) there are striking differences in the attainment patterns of children from the richest 20 percent compared to the poorest 20 percent.In Mali in 2012 only 34 percent of 15 to 19 year olds in the poorest quintile have completed grade 1 whereas 80 percent of the richest quintile have done so. In many countries, for example Pakistan, Peru and Indonesia, almost all the children from the wealthiest households have completed at least one year of schooling. In some countries, like Mali and Pakistan, wealth gaps are evident from grade 1 on, in other countries, like Peru and Indonesia, wealth gaps emerge later in the school system.The EdAttain website allows a visual exploration of gaps in attainment and enrollment within and across countries, based on the international database which spans multiple years from over 120 countries and includes indicators disaggregated by wealth, gender and urban/rural location. The database underlying that site can be downloaded from here.