Context

The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The health dimension is assessed by life expectancy at birth, the education dimension is measured by mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. The standard of living dimension is measured by gross national income per capita. The HDI uses the logarithm of income, to reflect the diminishing importance of income with increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using geometric mean. Refer to Technical notes for more details. The HDI can be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The HDI simplifies and captures only part of what human development entails. It does not reflect on inequalities, poverty, human security, empowerment, etc. The HDRO provides other composite indices as a broader proxy on some of the key issues of human development, inequality, gender disparity, and poverty. A fuller picture of a country's level of human development requires analysis of other indicators and information presented in the HDR statistical annex.

Content

In this Dataset, we have Global, regional, and country/territory-level data on key dimensions of human development with various composite indices. The human development composite indices have been developed to capture broader dimensions of human development, identify groups falling behind in human progress and monitor the distribution of human development. In addition to the HDI, the indices include Multidimensional Poverty Index (MPI), Inequality-adjusted Human Development Index (IHDI), Gender Inequality Index (GII), Gender Development Index (GDI), Planetary pressures-adjusted HDI (PHDI) and Gender Social Norms Index (GSNI).

Dataset Glossary (Alphabetical Order)

• Adolescent Birth Rate - Births per 1000 Women Ages 15 to 19
• Carbon Dioxide Emissions per Capita Production in Tonnes
• Coefficient of Human Inequality
• Expected Years of Schooling - Female
• Expected Years of Schooling - Male
• Expected Years of Schooling
• Gender Development Index
• Gender Inequality Index
• Gross National Income Per Capita - Female
• Gross National Income Per Capita - Male
• Gross National Income Per Capita
• HDI Female
• HDI Male
• Human Development Index
• Inequality Adjusted Human Development Index
• Inequality in Education
• Inequality in Income
• Inequality in Life Expectancy
• Labour Force Participation Rate - Female Percentage Ages 15 and Older
• Labour Force Participation Rate - Male Percentage Ages 15 and Older
• Life Expectancy at Birth - Female
• Life Expectancy at Birth - Male
• Life Expectancy at Birth
• Material Footprint per Capita in Tonnes
• Maternal Mortality Ratio - Deaths per 100000 Live Births
• Mean Years of Schooling - Female
• Mean Years of Schooling - Male
• Mean Years of Schooling
• Planetary Pressures Adjusted Human Development Index

Cover Photo by: pch.vector on Freepik

This dataset provides a country–year panel for OECD countries covering the period 2010–2024. It combines annual data on public, private and total social expenditure as a share of GDP with the World Happiness Index (WHI) and the Human Development Index (HDI).The data are constructed to analyze the relationships between social spending, subjective well-being and human development in OECD countries. The panel structure (one observation per country per year) makes the dataset suitable for descriptive analysis as well as regression-based empirical research.ContentsThe main Excel file contains a single data sheet:Sheet: data_setEach row corresponds to a specific country–year observation for an OECD member state.Variables:Country: Country name (OECD member; e.g., “Australia”, “Türkiye”, “United States”).iso3: ISO 3166-1 alpha-3 country code (e.g., “AUS”, “TUR”, “USA”).year: Calendar year (2010–2024).pub_socexp_gdp: Public social expenditure as a percentage of GDP (%).priv_socexp_gdp: Private (mandatory and voluntary) social expenditure as a percentage of GDP (%).tot_socexp_gdp: Total social expenditure (public + private) as a percentage of GDP (%).WHI: World Happiness Index; average national happiness score on a 0–10 scale based on the Cantril ladder question.HDI: Human Development Index; composite index of three basic dimensions of human development (health, education, and standard of living).income_group: Binary country income group indicator used in the analysis. High‑income OECD countries are coded as 1 (“High”), and all other OECD members (upper‑middle, lower‑middle and low income) are coded as 0 (“NonHigh”). Income groups were constructed using data from the OECD Data Explorer (2024) and the World Bank country income classification for 2024, based on PPP (purchasing power parity) income thresholds.Empty cells indicate that data for the corresponding country–year observation are not available in the original sources or were not included in the analytical sample due to missingness.Data sourcesSocial expenditure (pub_socexp_gdp, priv_socexp_gdp, tot_socexp_gdp)Data are taken from the OECD Social Expenditure Database (SOCX). SOCX provides reliable and internationally comparable statistics on public and mandatory and voluntary private social expenditure at the program level for 38 OECD countries (and some accession countries), with coverage from 1980 and estimates for more recent years.Reference: OECD Social Expenditure Database (SOCX), https://www.oecd.org/en/data/datasets/social-expenditure-database-socx.html.World Happiness Index (WHI)Happiness data are drawn from the World Happiness Report, accessed via HumanProgress.org (World Happiness Report section). The index is based on average national values for answers to the Cantril ladder question, which asks respondents to evaluate their current life on a 0–10 scale, with the worst possible life as 0 and the best possible life as 10.Reference: World Happiness Report; HumanProgress.org, https://humanprogress.org.Human Development Index (HDI)HDI data are drawn from the Human Development Index series compiled by the United Nations Development Programme (UNDP), accessed via HumanProgress.org (Human Development Index section). The HDI measures three basic dimensions of human development: life expectancy at birth; an education component (adult literacy rate and school enrollment); and GDP per capita (purchasing power parity, PPP, in U.S. dollars), combined into a composite index.Reference: United Nations Development Programme (UNDP), Human Development Reports; HumanProgress.org, https://humanprogress.org.Data construction and coverageThe dataset is restricted to OECD member countries and the years 2010–2024.WHI and HDI series are matched to OECD social expenditure data using ISO3 country codes and calendar years.In addition, a binary income group variable (income_group) was created to distinguish high‑income OECD countries from other OECD members, using the World Bank’s 2024 income thresholds (PPP‑based) and country information from the OECD Data Explorer (2024).Some country–year combinations, particularly in later years (e.g., 2022–2024), contain missing values where the original sources do not provide data or only provide partial estimates. These are retained as empty cells.The empirical analyses in the associated study are conducted on subsets of the data restricted to complete cases for the relevant variables.Researchers can use this dataset to replicate the results of the associated study or to conduct additional analyses on the links between social expenditure, happiness and human development within the OECD context.If you use this dataset, please cite both this data file and the original data providers (OECD, World Happiness Report, UNDP, and HumanProgress.org).

Global Economic, Environmental, Health, and Social indicators Ready for Analysis

📝 Description

This comprehensive dataset merges global economic, environmental, technological, and human development indicators from 2000 to 2020. Sourced and transformed from multiple public datasets via Google BigQuery, it is designed for advanced exploratory data analysis, machine learning, policy modeling, and sustainability research.

Curated by combining and transforming data from the Google BigQuery Public Data program, this dataset offers a harmonized view of global development across more than 40 key indicators spanning over two decades (2000–2020). It supports research across multiple domains such as:

• Economic Growth
• Climate Sustainability
• Digital Transformation
• Public Health
• Human Development
• Resilience and Governance

for formulas and more details check: https://github.com/Michael-Matta1/datasets-collection/tree/main/Global%20Development

📅 Temporal Coverage

• Years: 2000–2020

• Includes calculated features:

  • years_since_2000
  • years_since_century
  • is_pandemic_period (binary indicator for pandemic periods)

🌍 Geographic Scope

• Countries: Global (identified by ISO country codes)
• Regions and Income Groups included for aggregated analysis

📊 Key Feature Groups

• Economic Indicators:

  • GDP (USD), GDP per capita
  • FDI, inflation, unemployment, economic growth index

• Environmental Indicators:

  • CO₂ emissions, renewable energy use
  • Forest area, green transition score, CO₂ intensity

• Technology & Connectivity:

  • Internet usage, mobile subscriptions
  • Digital readiness score, digital connectivity index

• Health & Education:

  • Life expectancy, child mortality
  • School enrollment, healthcare capacity, health development ratio

• Governance & Resilience:

  • Governance quality, global resilience
  • Human development composite, ecological preservation

🔍 Use Cases

• Trend analysis over time
• Country-level comparisons
• Modeling development outcomes
• Predictive analytics on sustainability or human development
• Correlation and clustering across multiple indicators

⚠️ Note on Missing Region and Income Group Data

Approximately 18% of the entries in the region and income_group columns are null. This is primarily due to the inclusion of aggregate regions (e.g., Arab World, East Asia & Pacific, Africa Eastern and Southern) and non-country classifications (e.g., Early-demographic dividend, Central Europe and the Baltics). These entries represent groups of countries with diverse income levels and geographic characteristics, making it inappropriate or misleading to assign a single region or income classification. In some cases, the data source may have intentionally left these fields blank to avoid oversimplification or due to a lack of standardized classification.

📋 Column Descriptions

• year: Year of the recorded data, representing a time series for each country.
• country_code: Unique code assigned to each country (ISO-3166 standard).
• country_name: Name of the country corresponding to the data.
• region: Geographical region of the country (e.g., Africa, Asia, Europe).
• income_group: Income classification based on Gross National Income (GNI) per capita (low, lower-middle, upper-middle, high income).
• currency_unit: Currency used in the country (e.g., USD, EUR).
• gdp_usd: Gross Domestic Product (GDP) in USD (millions or billions).
• population: Total population of the country for the given year.
• gdp_per_capita: GDP divided by population (economic output per person).
• inflation_rate: Annual rate of inflation (price level rise).
• unemployment_rate: Percentage of the labor force unemployed but seeking employment.
• fdi_pct_gdp: Foreign Direct Investment (FDI) as a percentage of GDP.
• co2_emissions_kt: Total CO₂ emissions in kilotons (kt).
• energy_use_per_capita: Energy consumption per person (kWh).
• renewable_energy_pct: Percentage of energy consumption from renewable sources.
• forest_area_pct: Percentage of total land area covered by forests.
• electricity_access_pct: Percentage of the population with access to electricity.
• life_expectancy: Average life expectancy at birth.
• child_mortality: Deaths of children under 5 per 1,000 live births.
• school_enrollment_secondary: Percentage of population enrolled in secondary education.
• health_expenditure_pct_gdp: Percentage of GDP spent on healthcare.
• hospital_beds_per_1000...

Estimated number of 3- and 4-y-olds with low development according to the ECDI by region.

If this data set benefit your work, don't hesitate to upvote 😀

This data is gathered from United Nations databases, the following links below is been used.

https://rankedex.com/society-rankings/education-index https://en.wikipedia.org/wiki/Education_Index https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2022_ANNEX.pdf

This data can be used to measure the influence of education or income or both on any variable or vector, for example, ANOVA models.

The Income classification is for year 2021 and the education index is for 2019 to 2023.

The education index (EI) is one of the parameters that is used to calculate the Human Development Index (HDI). It is calculated by this formula: Education Index = (MYS Index + EYS Index) / 2 where MYS is Mean Years of Schooling and EYS is Expected Years of Schooling.

In this data it is assumed that : 1-Countries EI below 0.4 have Very Low Educated population 2-Countries EI between 0.4 and 0.6 have Low to Moderate Educated population 3-Countries EI between 0.6 and 0.8 have High to Moderate Educated population 4-Countries EI above 0.8 have Very Educated Educated population

Context

Every Politician lie but data doesn't. So I collected data of some of the important metrics of all the Indian States to check what is good and bad in all of them. The data is mostly scrapped from Wikipedia so it can be little bit inconsistent however, I will improve that in the subsequent versions.

Content

The contains the data about the metrics like HDI ( Human Development Index), Nominal GDP, Crime Rate, Percentage of population below poverty line and unemployment rate of all the states of India.

Acknowledgements

Most of the data is scrapped from Wikipedia so thanks to them for providing the data however I wish they improve their authenticity.

Inspiration

1. Feel free to play around the data, check where each state stands in all the metrics.
2. Try finding out why some states are top of some of the metrics, while at the bottom in others.
3. See if there's any correlation between different metrics. For example, One I am very interested to if there's any correlation between HDI and unemployment or HDI and nominal GDP or HDI and poverty.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2006/2007. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2012 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2006/2007:
In order to develop an effective poverty reduction policies and programs, Iraqi policy makers need to know how large the poverty problem is, what kind of people are poor, and what are the causes and consequences of poverty. Until recently, they had neither the data nor an official poverty line. (The last national income and expenditure survey was in 1988.)

In response to this situation, the Iraqi Ministry of Planning and Development Cooperation established the Household Survey and Policies for Poverty Reduction Project in 2006, with financial and technical support of the World Bank. The project has been led by the Iraqi Poverty Reduction Strategy High Committee, a group which includes representatives from Parliament, the prime minister's office, the Kurdistan Regional Government, and the ministries of Planning and Development Cooperation, Finance, Trade, Labor and Social Affairs, Education, Health, Women's Affairs, and Baghdad University.

The Project has consisted of three components: - Collection of data which can provide a measurable indicator of welfare, i.e. The Iraq Household Socio Economic Survey (IHSES).

• Establishment of an official poverty line (i.e. a cut off point below which people are considered poor) and analysis of poverty (how large the poverty problem is, what kind of people are poor and what are the causes and consequences of poverty).

• Development of a Poverty Reduction Strategy, based on a solid understanding of poverty in Iraq.

The survey has four main objectives. These are:

• To provide data that will help in the measurement and analysis of poverty. • To provide data required to establish a new consumer price index (CPI) since the current outdated CPI is based on 1993 data and no longer applies to the country's vastly changed circumstances. • To provide data that meet the requirements and needs of national accounts. • To provide other indicators, such as consumption expenditure, sources of income, human development, and time use.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2012 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Total sample size and stratification:

The total effective sample size of the Iraq Household Socio Economic Survey (IHSES) 2007 is 17,822 households. The survey was nominally designed to visit 18,144 households - 324 in each of 56 major strata. The strata are the rural, urban and metropolitan sections of each of Iraq's 18 governorates, with the exception of Baghdad, which has three metropolitan strata. The Iraq Household Socio Economic Survey (IHSES) 2007 and the MICS 2006 survey intended to visit the same nominal sample. Variable q0040 indicates whether this was indeed the case.

----> Sample frame:

The 1997 population census frame was applied to the 15 governorates that participated in the census (the three governorates in Kurdistan Region of Iraq were excluded). For Sulaimaniya, the population frame prepared for the compulsory education project was adopted. For Erbil and Duhouk, the enumeration frame implemented in the 2004 Iraq Living Conditions Survey was updated and used. The population covered by Iraq Household Socio Economic Survey (IHSES) included all households residing in Iraq from November 1, 2006, to October 30, 2007, meaning that every household residing within Iraq's geographical boundaries during that period potentially could be selected for the sample.

----> Primary sampling units and the listing and mapping exercise:

The 1997 population census frame provided a database for all households. The smallest enumeration unit was the village in rural areas and the majal (census enumeration area), which is a collection of 15-25 urban households. The majals were merged to form Primary Sampling Units (PSUs), containing 70-100 households each. In Kurdistan, PSUs were created based on the maps and frames updated by the statistics offices. Villages in rural areas, especially those with few inhabitants, were merged to form PSUs. Selecting a truly representative sample required that changes between 1997 and the pilot survey be accounted for. The names and addresses of the households in each sample point (that is, the selected PSU) were updated; and a map was drawn that defined the unit's borders, buildings, houses, and the streets and alleys passing through. All buildings were renumbered. A list of heads of household in each sample point was prepared from forms that were filled out and used as a frame for selecting the sample households.

----> Sampling strategy and sampling stages:

The sample was selected in two stages, with groups of majals (Census Enumeration Areas) as Primary Sampling Units (PSUs) and households as Secondary Sampling Units. In the first stage, 54 PSUs were selected with probability proportional to size (pps) within each stratum, using the number of households recorded by the 1997 Census as a measure of size. In the second stage, six households were selected by systematic equal probability sampling (seps) within each PSU. To these effects, a cartographic updating and household listing operation was conducted in 2006 in all 3,024 PSUs, without resorting to the segmentation of any large PSUs. The total sample is thus nominally composed of 6 households in each of 3,024 PSUs.

----> Sample Points Trios, teams and survey waves:

The PSUs selected in each governorate (270 in Baghdad and 162 in each of the other governorates) were sorted into groups of three neighboring PSUs called trios -- 90 trios in Baghdad and 54 per governorate elsewhere. The three PSUs in each trio do not necessarily belong to the same stratum. The 12 months of the data collection period were divided into 18 periods of 20 or 21 days called survey waves. Fieldworkers were organized into teams of three interviewers, each team being responsible for interviewing one trio during a survey wave. The survey used 56 teams in total - 5 in Baghdad and 3 per governorate elsewhere. The 18 trios assigned to each team were allocated into survey waves at random. The 'time use' module was administered to two of the six households selected in each PSU: nominally the second and fifth households selected by the seps procedure in the PSU.

----> Time-use sample:

The Iraq Household Socio Economic Survey (IHSES) questionnaire on time use covered all household members aged 10 years and older. A subsample of one-third of the households was selected (the second and fifth of the six households in each sample point). The second and fourth visits were designated for completion of the time-use sheet, which covered all activities performed by every member of the household.

A more detailed description of the allocation of sample across governorates is provided in the tabulation report document available among external resources in both English and Arabic.

Sampling deviation

----> Exceptional Measures

The design did not consider the replacement of any of the randomly selected units (PSUs or households.) However, sometimes a team could not visit a cluster during the allocated wave because of unsafe security conditions. When this happened, that cluster was then swapped with another cluster from a randomly selected future wave that was considered more secure. If none were considered secure, a sample point was randomly selected from among those that had been visited already. The team then visited a new cluster within that sample point. (That is, the team visited six households that had not been previously interviewed.) The original cluster as well as the new cluster were both selected by systematic equal probability sampling.

This explains why the survey datasets only contain data from 2,876 of the 3,024 originally selected PSUs, whereas 55 of the PSUs contain more that the six households nominally dictated by the design.

The wave number in the survey datasets is always the nominal wave number, corresponding to the random allocation considered by the design. The effective interview dates can be found in questions 35 to 39 of the survey questionnaires.

Remarkably few of the original clusters could not be visited during the fieldwork. Nationally, less than 2 percent of the original clusters (55 of 3,024) had to be replaced. Of the original clusters, 20 of 54 (37 percent) could not be visited in the stratum of “Kirkuk/other urban” and

Informations:

This dataset contains informations about the average IQ in countries around the world, with another infos like Nobel Prices won collectively in that specific country. I also added more stats like GNI, HDI and Mean Years of Schooling from another dataset of mine since it provides direct correlation of why some people in a country are more prone to be more intelligent.

Datasets:

avgIQpercountry.csv => Contains data from different measures to measure a country, like GNI, HDI and Mean Years OF Schooling. Some studies suggest that there's a correlation between overall quality of life and average iq per person in a country.

IQ_classification.csv => This table distinguishes an IQ score by classifications, for example, someone might be a genius or a slightly gifted depending in how much IQ points he's got.

This project provides a comprehensive country-level analysis of various economic, social, and environmental metrics using Power BI. The dashboard covers key indicators such as GDP, GDP per Capita, Tourism Revenue, Healthcare and Education Expenditures, Human Development Index (HDI), Renewable Energy Share, Energy Consumption, and CO2 Emissions among several countries.

Key features of the dashboard:

Economic Overview: Visualizes GDP (in trillions USD), GDP per capita trends, and tourism revenue across multiple countries. Social Insights: Shows metrics like HDI, literacy rate, healthcare expenditures, and life expectancy to compare the quality of life across nations. Environmental Metrics: Highlights the renewable energy share and CO2 emissions, reflecting the environmental sustainability efforts by countries. Interactive Slicers: Users can filter by year and country to dynamically analyze trends and comparisons. This project aims to provide a clear and insightful visual representation of the data to help stakeholders make informed decisions and better understand global trends across different dimensions.

1Weighted percentages include 95% confidence intervals that incorporate the effects of stratification and clustering due to the sample design.Ogumaniha Multidimensional Poverty Index (MPI) adapted from the Oxford Poverty and Human Development Initiative (OPHI).

This dataset provides a comprehensive overview of various socio-economic and health metrics related to gender across different countries. The metrics range from life expectancy, schooling, and gross national income per capita to maternal mortality rates, adolescent birth rates, and labor force participation. Such data is vital for researchers, policymakers, and advocates working towards gender equality and understanding the intricate nuances of gender disparities in different regions.

Notably, this dataset has been featured as an example dataset in the R programming language package named genderstat.

Link to CRAN package: https://cran.r-project.org/web/packages/genderstat/index.html

Data for this collection was meticulously extracted from reputable sources to ensure its accuracy and reliability.

Sources:

UNDP Human Development Reports Data Center World Bank Gender Data Portal

Dive into the dataset to explore the varying dimensions of gender disparities and gain insights that can guide interventions and policy decisions.

Early Childhood Development Index items.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

The hypertension sample includes data from 43 countries, and the diabetes sample includes data from 28 countries.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under Safeguarded Licence (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411. Polygenic indices are available under SL SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.

The 2006 Iraq Multiple Indicator Cluster Survey has as its primary objectives: - To provide up-to-date information for assessing the situation of children and women in Iraq; - To furnish data needed for monitoring progress toward goals established by the Millennium Development Goals and the goals of A World Fit For Children (WFFC) as a basis for future action; - To contribute to the improvement of data and monitoring systems in Iraq and to strengthen technical expertise in the design, implementation and analysis of such systems.

Survey Content MICS questionnaires are designed in a modular fashion that was customized to the needs of the country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire.

Survey Implementation The survey was implemented by the Central Organization for Statistics and Information Technology (COSIT), the Kurdistan Region Statistics Office (KRSO) and Suleimaniya Statistical Directorate (SSD), in partnership with the Ministry of Health (MOH). The survey also received support and assistance of UNICEF and other partners. Technical assistance and training for the surveys was provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Geographic coverage

The survey is nationally representative and covers the whole of Iraq.

Analysis unit

Households (defined as a group of persons who usually live and eat together)

De jure household members (defined as memers of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)

Women aged 15-49

Children aged 0-4

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household. The survey also includes a full birth history listing all chuldren ever born to ever-married women age 15-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Iraq Multiple Indicator Cluster Survey was designed to provide estimates on a large number of indicators on the situation of children and women at the national level; for areas of residence of Iraq represented by rural and urban (metropolitan and other urban) areas; for the18 governorates of Iraq; and also for metropolitan, other urban, and rural areas for each governorate. Thus, in total, the sample consists of 56 different sampling domains, that includes 3 sampling domains in each of the 17 governorates outside the capital city Baghdad (namely, a metropolitan area domain representing the governorate city centre, an other urban area domain representing the urban area outside the governorate city centre, and a rural area domain) and 5 sampling domains in Baghdad (namely, 3 metropolitan areas representing Sadir City, Resafa side, and Kurkh side, an other urban area sampling domain representing the urban area outside the three Baghdad governorate city centres, and a sampling domain comprising the rural area of Baghdad).

The sample was selected in two stages. Within each of the 56 sampling domains, 54 PSUs were selected with linear systematic probability proportional to size (PPS).

\After mapping and listing of households were carried out within the selected PSU or segment of the PSU, linear systematic samples of six households were drawn. Cluster sizes of 6 households were selected to accommodate the current security conditions in the country to allow the surveys team to complete a full cluster in a minimal time. The total sample size for the survey is 18144 households. The sample is not self-weighting. For reporting national level results, sample weights are used.

The sampling procedures are more fully described in the sampling appendix of the final report and can also be found in the list of technical documents within this archive.

(Extracted from the final report: Central Organisation for Statistics & Information Technology and Kurdistan Statistics Office. 2007. Iraq Multiple Indicator Cluster Survey 2006, Final Report. Iraq.)

Sampling deviation

No major deviations from the original sample design were made. One cluster of the 3024 clusters selected was not completed all othe clusters were accessed.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires were based on the third round of the Multiple Indicator Cluster survey model questionnaires. From the MICS-3 model English version, the questionnaires were revised and customized to suit local conditions and translated into Arabic and Kurdish languages. The Arabic language version of the questionnaire was pre-tested during January 2006 while the Kurdish language version was pre-tested during March 2006. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires.

In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for iodine content, and measured the weights and heights of children age under-5 years.

Cleaning operations

Data were processed in clusters, with each cluster being processed as a complete unit through each stage of data processing. Each cluster goes through the following steps: 1) Questionnaire reception 2) Office editing and coding 3) Data entry 4) Structure and completeness checking 5) Verification entry 6) Comparison of verification data 7) Back up of raw data 8) Secondary editing 9) Edited data back up

After all clusters are processed, all data is concatenated together and then the following steps are completed for all data files: 10) Export to SPSS in 5 files (hh - household, hl - household members, wm - women age 15-49, ch - children under 5 bh - women age 15-49) 11) Recoding of variables needed for analysis 12) Adding of sample weights 13) Calculation of wealth quintiles and merging into data 14) Structural checking of SPSS files 15) Data quality tabulations 16) Production of analysis tabulations

Detailed documentation of the editing of data can be found in the data processing guidelines in the MICS Manual (http://www.childinfo.org/mics/mics3/manual.php)

Data entry was conducted by 12 data entry operators in tow shifts, supervised by 2 data entry supervisors, using a total of 7 computers (6 data entry computers plus one supervisors computer). All data entry was conducted at the GenCenStat head office using manual data entry. For data entry, CSPro version 2.6.007 was used with a highly structured data entry program, using system controlled approach, that controlled entry of each variable. All range checks and skips were controlled by the program and operators could not override these. A limited set of consistency checks were also included inthe data entry program. In addition, the calculation of anthropometric Z-scores was also included in the data entry programs for use during analysis. Open-ended responses ("Other" answers) were not entered or coded, except in rare circumstances where the response matched an existing code in the questionnaire.

Structure and completeness checking ensured that all questionnaires for the cluster had been entered, were structurally sound, and that women's and children's questionnaires existed for each eligible woman and child.

100% verification of all variables was performed using independent verification, i.e. double entry of data, with separate comparison of data followed by modification of one or both datasets to correct keying errors by original operators who first keyed the files.

After completion of all processing in CSPro, all individual cluster files were backed up before concatenating data together using the CSPro file concatenate utility.

Data editing took place at a number of stages throughout the processing (see Other processing), including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files

Detailed documentation of the editing of data can be found in the data processing guidelines in the MICS Manual (http://www.childinfo.org/mics/mics3/manual.php)

Response rate

Of the 18144 households selected for the sample, 18123 were found to be occupied. Of these, 17873 were successfully interviewed for a household response rate of 98.6 percent. In the interviewed households, 27564 women (age 15-49 years) were identified. Of these, 27186 were successfully interviewed, yielding a

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria. Survey Objectives The 2005 Montenegro Multiple Indicator Cluster Survey has as its primary objectives: To provide up-to-date information for assessing the situation of children and women in Montenegro. To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; To contribute to the improvement of data and monitoring systems in Montenegro and to strengthen technical expertise in the design, implementation, and analysis of such systems. Survey Content MICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire. Survey Implementation The survey was carried out by the Statistical Office of the Republic of Montenegro (MONSTAT) and the Strategic Marketing Research Agency (SMMRI), with the support and assistance of UNICEF and other partners. Technical assistance and training for the survey was provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination. In 2005 Serbia and Montenegro was the State Union composed of the Republic of Serbia (92.5% of population) and the Republic of Montenegro (7.5% of total population). The MICS 2005 survey was planned and implemented on the whole territory of Serbia and Montenegro, and all documents regarding survey plan and contracts with implementing agencies covered the State Union. In May, 2006 the Republic of Montenegro had a referendum of independency and the State Union broke apart. The results of MICS 2005 survey were presented separately for both countries and two separate reports were prepared. The survey was implemented by the Statistical Office of the Republic of Serbia (in Serbia) and the Statistical Office of the Republic of Montenegro (in Montenegro) and the expert research agency Strategic Marketing & Media Research Institute (SMMRI), which covered the survey implementation in both Serbia and Montenegro. Special tasks performed by the Statistical Office of the Republic of Montenegro: Preparation of questionnaire for the survey: Preparation of methodological guidelines for realization of the survey; Updating of lists of households in the selected census block units; Conducting the pilot ; Selection of households to be covered by sample; Coordination of work of their teams in the field; Interviewing of the households; Work control of their teams; Preparation of report. Special tasks performed by the SMMRI: Sample selection; Preparation of survey tools; Organising the training; Conducting the pilot; Updating of lists of households in the selected census block units; Organising field work; Coordination of work of their teams in the field; Interviewing of the households; Work control of their teams; Data processing and analysis.

aηUY = income elasticity of the UBIbn = number of countries of the sample in the income groups.c GNI pc = Gross National Income per capita 2013 (2011, PPP $) (UNDP, 2014), PPP is purchasing power parity.Unhealthy Behaviour Index and components by income groups.

Context

Most countries of the world define poverty as a lack of money. Yet poor people themselves consider their experience of poverty much more broadly. A person who is poor can suffer from multiple disadvantages at the same time – for example they may have poor health or malnutrition, a lack of clean water or electricity, poor quality of work or little schooling. Focusing on one factor alone, such as income, is not enough to capture the true reality of poverty.

Multidimensional poverty measures can be used to create a more comprehensive picture. They reveal who is poor and how they are poor – the range of different disadvantages they experience. As well as providing a headline measure of poverty, multidimensional measures can be broken down to reveal the poverty level in different areas of a country, and among different sub-groups of people.

Content

OPHI researchers apply the AF method and related multidimensional measures to a range of different countries and contexts. Their analyses span a number of different topics, such as changes in multidimensional poverty over time, comparisons in rural and urban poverty, and inequality among the poor. For more information on OPHI’s research, see our working paper series and research briefings.

OPHI also calculates the Global Multidimensional Poverty Index MPI, which has been published since 2010 in the United Nations Development Programme’s Human Development Report. The Global MPI is an internationally-comparable measure of acute poverty covering more than 100 developing countries. It is updated by OPHI twice a year and constructed using the AF method.

The Alkire Foster (AF) method is a way of measuring multidimensional poverty developed by OPHI’s Sabina Alkire and James Foster. Building on the Foster-Greer-Thorbecke poverty measures, it involves counting the different types of deprivation that individuals experience at the same time, such as a lack of education or employment, or poor health or living standards. These deprivation profiles are analysed to identify who is poor, and then used to construct a multidimensional index of poverty (MPI). For free online video guides on how to use the AF method, see OPHI’s online training portal.

To identify the poor, the AF method counts the overlapping or simultaneous deprivations that a person or household experiences in different indicators of poverty. The indicators may be equally weighted or take different weights. People are identified as multidimensionally poor if the weighted sum of their deprivations is greater than or equal to a poverty cut off – such as 20%, 30% or 50% of all deprivations.

It is a flexible approach which can be tailored to a variety of situations by selecting different dimensions (e.g. education), indicators of poverty within each dimension (e.g. how many years schooling a person has) and poverty cut offs (e.g. a person with fewer than five years of education is considered deprived).

The most common way of measuring poverty is to calculate the percentage of the population who are poor, known as the headcount ratio (H). Having identified who is poor, the AF method generates a unique class of poverty measures (Mα) that goes beyond the simple headcount ratio. Three measures in this class are of high importance:

Adjusted headcount ratio (M0), otherwise known as the MPI: This measure reflects both the incidence of poverty (the percentage of the population who are poor) and the intensity of poverty (the percentage of deprivations suffered by each person or household on average). M0 is calculated by multiplying the incidence (H) by the intensity (A). M0 = H x A.

Find out about other ways the AF method is used in research and policy.

Additional data here.

Acknowledgements

Alkire, S. and Robles, G. (2017). “Multidimensional Poverty Index Summer 2017: Brief methodological note and results.” OPHI Methodological Note 44, University of Oxford.

Alkire, S. and Santos, M. E. (2010). “Acute multidimensional poverty: A new index for developing countries.” OPHI Working Papers 38, University of Oxford.

Alkire, S. Jindra, C. Robles, G. and Vaz, A. (2017). ‘Multidimensional Poverty Index – Summer 2017: brief methodological note and results’. OPHI MPI Methodological Notes No. 44, Oxford Poverty and Human Development Initiative, University of Oxford.

Inspiration

• Which countries exhibit the largest subnational disparities in MPI?
• Which countries have high per-capita incomes yet still rank highly in MPI?