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

Structure of the Dataset

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Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: pch.vector on Freepik

The dataset contains year- and country-wise historical data on the human development index, gender development index, and multidimensional index of global countries, together with other components such as child mortality, access to drinking water, electricity, and housing, nutrition and sanitation rate, school attendance rate, assets, etc.

Is the Gini Coefficient Enough? A Microeconomic Data Decomposition StudyIvan Skliarov, Lukasz Goczek (2023).List of data files:1. theil_raw.csv - data obtained from LISSY using the lis_theil.R script.*2. scv_raw.csv - data obtained from LISSY using the scv_theil.R script.*3. hdi.csv - Human Development Index and its components.4. gini.csv - Gini coefficient from SWIID 9.4.5. wdi.csv - World Development Indicators from the World Bank.6. wgi.csv - World Governance Indicators from the World Bank.7. govcon.csv - government consumption (% of GDP) from UNCTAD.8. theil_fin.csv - final dataset (1, 3-7 combined), which is used in lis_analysis.do.9. scv_fin.csv - final dataset (2-7 combined), which is used in lis_analysis.do.10. indexes.csv - only within and between-cohort components of the Theil index and SCV with imputed values (see lis_analysis.do) for Georgia and Lithuania, which is used in lis_plot.R. * LISSY is the remote-execution system allowing access to the Luxembourg Income Study database: https://www.lisdatacenter.org/data-access/lissy/.For questions about this research please contact:Ivan Skliarov, MA: Faculty of Economic Sciences, University of Warsaw, Poland, Długa 44/50, Warsaw 00-241, Poland, i.skliarov@student.uw.edu.pl.Lukasz Goczek, PhD: Faculty of Economic Sciences, University of Warsaw, Poland, Długa 44/50, Warsaw 00-241, Poland, lgoczek@wne.uw.edu.pl.

Context

Brazilian summarized census data for each county and the calculated Human Development Index calculated by United Nations Development Programme (PNUD Brasil).

Content

Census data by municipality for the years 1191, 200, adn 2010. PNUD Brasil also calculates the Human Development Index (IDH in Portuguese).

Acknowledgements

Data was cleaned, summarized and published by the United Nations Development Programme

Inspiration

How to improve Brasil?

Breast cancer is a worldwide threat to female health with patient outcomes varying widely. The exact correlation between global outcomes of breast cancer and the national socioeconomic status is still undetermined. Mortality-to-incidence ratio (MIR) of breast cancer was calculated with the contemporary age standardized incidence and mortality rates for countries with data available at GLOBOCAN 2012 database. The MIR matched national human development indexes (HDIs) and health system attainments were respectively obtained from Human Development Report and World Health Report. Correlation analysis, regression analysis, and Tukey-Kramer post hoc test were used to explore the effects of HDI and health system attainment on breast cancer MIR. Our results demonstrated that breast cancer MIR was inversely correlated with national HDI (r = -.950; P < .001) and health system attainment (r = -.898; P < .001). Countries with very high HDI had significantly lower MIRs than those with high, medium and low HDI (P < .001). Liner regression model by ordinary least squares also indicated negative effects of both HDI (adjusted R2 = .903, standardize β = -.699, P < .001) and health system attainment (adjusted R2 =. 805, standardized β = -.009; P < .001), with greater effects in developing countries identified by quantile regression analysis. It is noteworthy that significant health care disparities exist among countries in accordance with the discrepancy of HDI. Policies should be made in less developed countries, which are more likely to obtain worse outcomes in female breast cancer, that in order to improve their comprehensive economic strength and optimize their health system performance.

ABSTRACT In spite the considerable advance made to the literature on development by the recent United Nations attempt to measuring “human development”, this paper argues that, especially in the context of developing countries, where poverty and inequality are of substantial order, these two dimensions should be integrated and added to the index proposed by that organism. This is accomplished by a new measure that simultaneously take into account indices of poverty, income distribution and human development. Empirical results show that the extension suggested is important and more appropriate for policy purposes.

Results of the Civil Service Development Index (CSDI), obtained from diagnostics of the institutional quality of civil service systems in 16 Latin American countries. The IDB supported the design of a methodology that evaluates critical points to assess the civil services and carried out country evaluations in 2004. Between 2011 and 2013, a second group of diagnostics second group of diagnostics were completed (with the support of the Inter-American Development Bank, and in the case of Central American countries and Dominican Republic with the support of the Spanish Agency for International Cooperation and Development –AECID- and the Central American Integration System-SICA). Scores are available for 2004, 2011, 2012, 2013, 2015, 2017 and 2019 (year of second and/or third measurement varies per country). In 2015, 2017 and 2019, the IDB completed the third series of diagnosis. During the first assessment, 93 critical points were identified; each of those fed a subsystem and an index. In 2010 the methodology was simplified to 33 critical points and the base line was recalibrated to ensure comparability. The methodology is based in the identification of critical points that feed 8 subsystems: 1. Human Resources Planning, 2. Work Organization, 3. Employment management, 4. Performance management, 5. Compensation management, 6. Development management, 7. Human and social relations management, 8. HR Function organization; and 5 indexes: 1. Efficiency, 2. Merit, 3. Structural consistency, 4. Functional capacity, and 5. Integrating capacity.

🧭 Description

This dataset catalogs key development, economic, governance, and policy indicators .These indexes originate from institutions and international organizations, and they are widely used by policymakers, researchers, journalists, and development agencies.

🌍 Context

In the landscape of African development, data is power — yet the origin, ownership, and frequency of key indicators are often overlooked. This dataset was created to map out where the numbers come from, who produces them, how often they are updated, and whether they are homegrown or externally driven. It offers a meta-level view of 60+ indicators — such as the Human Development Index (HDI), and Multidimensional Poverty Index (MPI) — that shape how African progress is measured, debated, and compared globally.

📚 Sources

The data was compiled manually from publicly available information on the websites of:

• UNDP (United Nations Development Programme)
• World Bank
• African Development Bank
• OECD
• Mo Ibrahim Foundation
• And other regional or international publishers

Each row includes:

• Indicator name and abbreviation
• Year of conception
• Status (active/inactive)
• Origin (African vs. Non-African)
• Publishing source
• Release schedule

💡 Inspiration

This dataset was inspired by the need to: * Promote transparency about who defines Africa’s development metrics * Highlight the dependence on external indicators * Encourage discussions on data sovereignty and local capacity-building * Serve as a starting point for researchers, think tanks, and students exploring African data systems

📘 License

This dataset is shared under the CC BY 4.0 License — you are free to use, adapt, and share it with attribution.

📊 Data Set Overview:

Total Rows - 62 Columns - 9 Non-null rows - 62 Empty rows - 0 Data type of columns - Object

Women in all countries of the world suffer different forms of violence, inequality and discrimination, both in the public and private spheres. Facing situations of abuse and unequal treatment.

The inequalities experienced by women occur in all areas of their development: health, education, work, among others, seriously undermining women's rights to a dignified life.

One of the most serious scourges suffered by women in Latin America is femicides.

This dataset will allow research development on gender issues -in latin american countries- in terms of: human development, gender development, gender inequalities, femicides and violence.

This contains official indicators from the United Nations Development Program (UNDP), the Economic Commission for Latin America and the Caribbean (ECLAC) -a dependent body of the United Nations Organization- and the Institute for Economics and Peace (IEP).

This dataset contains 7 indexes, to mention.

From UNDP: -Human Development Index (HDI) -Gender Development Index (GDI) -Inequalities in HDI (IHDI) -Gender Inequality Index (GII) -Planetary pressures–adjusted Human Development Index (PHDI)

From CEPAL: Number of femicides (fem)

From the Institute for Economics and Peace (IEP): Global Peace Index (gpi)

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

The E-Government Development Index presents the state of E-Government Development of the United Nations Member States. Along with an assessment of the website development patterns in a country, the E-Government Development index incorporates the access characteristics, such as the infrastructure and educational levels, to reflect how a country is using information technologies to promote access and inclusion of its people. The EGDI is a composite measure of three important dimensions of e-government, namely: provision of online services, telecommunication connectivity and human capacity.

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.

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.

Cost estimates for national and three regions using the detailed reference method (US$).

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.

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.

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.

The National Child Development Study: Sweeps 3-9, 1974-2013, Townsend Index (LSOA) Linked Data: Secure Access study includes the Towsend Index of Deprivation for Sweeps 3-9 of the NCDS, as well as the variables needed to compose these.

International Data Access Network (IDAN)
These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.

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.

Matrix with collaboration ties and dominance indexes in Tropical Medicine publications, in documents included in the SCI-Expanded database (2011–2015).

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%).