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

Students will explore the spatial patterns of the Human Development Index (HDI) to identifyregional patterns and causal factors in the data. The activity uses a web-based map and is tied to the AP Human Geography benchmarks. Learning outcomes: Students will be able to analyze development statistics and see how development correlates with other APHG topics (for example, fertility and mortality).Find more advanced human geography geoinquiries and explore all geoinquiries at http://www.esri.com/geoinquiries

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).

In 2023 Zurich was both the leading smart city based on the IMD smart city index as well as the city with the highest human development index score, making it one of the premier places on earth to live in. Notable exceptions to the HDI to IMD index score were Beijing, Dubai, and Abu Dhabi. Beijing is a notable outlier because although it ranked 12th on the digital smart cities ranking it was nearly 90 points lower than Zurich on the HDI score. This is compared to Munich, Germany, which was the 20th digital city but had a HDI score of ***. Smart tech is watching. CCTV cameras powered by artificial intelligence have become a significant growing market in the modern city. These are predominantly residential, with half the market catering to residential applications of CCTV cameras. However, commercial and business-related CCTV cameras have also seen significant growth, with the market reaching over *** million U.S. dollars in 2023. Digital cities need data and data needs infrastructure. The leading issue with AI infrastructure is data management. AI is a strong influence on how digital cities work and requires a considerable amount of infrastructure to be effective. Storage of AI software is a minor concern, accounting for less than ** percent of challenges globally in 2023.

BackgroundThe development of cognitive and socioemotional skills early in life influences later health and well-being. Existing estimates of unmet developmental potential in low- and middle-income countries (LMICs) are based on either measures of physical growth or proxy measures such as poverty. In this paper we aim to directly estimate the number of children in LMICs who would be reported by their caregivers to show low cognitive and/or socioemotional development.Methods and FindingsThe present paper uses Early Childhood Development Index (ECDI) data collected between 2005 and 2015 from 99,222 3- and 4-y-old children living in 35 LMICs as part of the Multiple Indicator Cluster Survey (MICS) and Demographic and Health Surveys (DHS) programs. First, we estimate the prevalence of low cognitive and/or socioemotional ECDI scores within our MICS/DHS sample. Next, we test a series of ordinary least squares regression models predicting low ECDI scores across our MICS/DHS sample countries based on country-level data from the Human Development Index (HDI) and the Nutrition Impact Model Study. We use cross-validation to select the model with the best predictive validity. We then apply this model to all LMICs to generate country-level estimates of the prevalence of low ECDI scores globally, as well as confidence intervals around these estimates.In the pooled MICS and DHS sample, 14.6% of children had low ECDI scores in the cognitive domain, 26.2% had low socioemotional scores, and 36.8% performed poorly in either or both domains. Country-level prevalence of low cognitive and/or socioemotional scores on the ECDI was best represented by a model using the HDI as a predictor. Applying this model to all LMICs, we estimate that 80.8 million children ages 3 and 4 y (95% CI 48.1 million, 113.6 million) in LMICs experienced low cognitive and/or socioemotional development in 2010, with the largest number of affected children in sub-Saharan Africa (29.4.1 million; 43.8% of children ages 3 and 4 y), followed by South Asia (27.7 million; 37.7%) and the East Asia and Pacific region (15.1 million; 25.9%). Positive associations were found between low development scores and stunting, poverty, male sex, rural residence, and lack of cognitive stimulation. Additional research using more detailed developmental assessments across a larger number of LMICs is needed to address the limitations of the present study.ConclusionsThe number of children globally failing to reach their developmental potential remains large. Additional research is needed to identify the specific causes of poor developmental outcomes in diverse settings, as well as potential context-specific interventions that might promote children’s early cognitive and socioemotional well-being.

Early Childhood Development Index items.

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

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

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...

Abstract

The objectives of the survey were to provide information regarding the following: a. Household use of, and expenditure patterns for, social services; b. Reasons for low levels of household investment in education and health services for children; c. The distribution of the benefits of public spending for social services and how to improve targeting; d. Households' evaluation of the social services available to them; e. The potential for demand-side interventions to increase human capital investment directly (especially for girls and the poor); and f. The feasibility of repeated national monitoring surveys to assess the impact of future Bank and government projects in the social sectors, and to increase Tanzania's capacity to perform household survey work.

Geographic coverage

National coverage

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 5,184 households

The HRDS is national in scope and uses all the 222 clusters of the National Master Sample (NMS) maintained by the Bureau of Statistics as its sampling frame.4 Two NMS clusters were not surveyed because of weather conditions. For example, Nyamburi village in the Mara region was inaccessible. Heavy rains had washed away a bridge 8 kms (14 miles) from the village. All household surveys conducted by the Bureau of Statistics (e.g. Agricultural Sample Survey since 1986/87, Labor Force Survey in 1990/91) have used the framework of the NMS. This permits obtaining estimates at the national level and by area: rural, Dar es Salaam (DSM), and other urban towns. The current NMS covers 222 clusters: 100 rural villages representing the rural areas, and 122 Enumeration Areas (EAs) representing the urban areas. Fifty-two EAs are from the capital city, itself, 40 EAs are from the nine municipalities (Arusha, Dodoma, Moshi, Tanga, Morogoro, Iringa, Mbeya, Tabora, and Mwanza), and 10 EAs are from the remaining regional headquarters.

Selection of households and non-response.

Household selection was done in the field. In each cluster the team supervisor would first obtain the list of ten-cell leaders from the local authorities, and then, from each ten cell-leader, the list of households belonging to his/her cell. Each household was assigned a unique number, and then, using a table of random numbers, randomly selected. In each cluster, a list of about 30 households was then obtained, the last households in the list being alternates. With the collaboration of local authorities, the field workers were able to have an almost 100 percent reponse rate, except for the cases in which no member of the household was present for intervieing, and returning to the household was not feasible. Refusals to cooperate were rare. In those cases--absent households or refusals--, new households were drawn from the list of alternates.

The survey covered a total of 4,953 households in the 20 regions of Mainland Tanzania: 2,135 rural and 2,818 urban (see Table 1). In a second stage, the survey was extended to Zanzibar, where 230 households, in 24 clusters, were interviewed.

Region / Rural / Urban / Total Dodoma / 100 / 80 / 180 Arusha / 118 / 121 / 239 Kilimanjaro / 124 / 154 / 278 Tanga / 132 / 167 / 299 Morogoro / 88 / 120 / 208 Coast / 79 / 88 / 167 Dar es Salaam / 0 / 1127 / 1127 Lindi / 84 / 50 / 134 Mtwara / 114 / 44 / 158 Ruvuma / 69 / 49 / 118 Iringa / 124 / 128 / 252 Mbeya / 174 / 153 / 327 Singida / 82 / 41 / 123 Tabora / 99 / 72 / 171 Rukwa / 59 / 56 / 115 Kigoma / 83 / 35 / 118 Shinyanga / 153 / 54 / 207 Kagera / 193 / 24 / 217 Mwanza / 163 / 192 / 355 Mara / 97 / 63 / 160 Mainland Tanzania / 2135 / 2818 / 4953 Zanzibar / 127 / 104 / 231

Mode of data collection

Face-to-face [f2f]

Research instrument

Development of Survey Instrument.

The first draft of the household survey was developed in English in July, 1993. Training of enumerators, based on this draft, began on August 2, 1993. The month of August was devoted to training the enumerators and pre-testing the questionnaire. The first pre-test of the questionnaire took place in mid-August. The household questionnaire was almost completely precoded to eliminate coding errors and time delays. A category labeled "other: specify" was added to several questions. For those questions for which answers were not mutually exclusive, we precoded them with letters, rather than numbers, to allow for unambiguously coding of multiple answers. To minimize nonsampling errors, the questionnaire was in a form that reduced to a minimum the number of decisions required of interviewers while in the field. In anticipation of pages becoming detached from the questionnaire, every page contained a space for the household number and the last digit of the cluster code. Despite the fact that questions were written exactly as they were supposed to be asked by the interviewer, interviewers were granted some flexibility to give the interview greater semblance to a conversation, rather than an inquisition.

Pre-Test of Questionnaire.

The "pre-pre-test" of the questionnaire (August 16, 1993) was done only to discern whether the questions were understood, how long the administration of the survey required, whether all responses had been anticipated, which sections needed to be stressed during the training, etc. In this pre-pre-test, each questionnaire required an average of 4 hours to complete, far longer than the planned 1.5 hour maximum. The survey was consequently shortened and streamlined.

The true pre-test was conducted in two different types of clusters: Ubungo ward in DSM (urban) and Kibaha in the Coast Region (rural) over a period of two days. We chose these clusters because they are representative of two distinct groups, so a broader spectrum of answers and problems with the instrument could be anticipated. In the pre-test each questionnaire required an average of 2.5 hours. After a couple weeks of interviewing, the enumerators became more familiar with the instrument, resulting in their spending an average of 1.5 to 2 hours per questionnaire.

During the pre-test, each supervisor was asked to comment on each interview. The supervisor was asked to pay special attention to questions that seemed to make the respondent uncomfortable, that the respondent had difficulty understanding, or that the respondent seemed to dislike. The supervisor also evaluated which sections seemed to go slowly, had the most difficult questions, or provided insufficient opportunity for a complete response.

Revision of questionnaire.

Given the results of the two pre-tests, several areas for improvement in the questionnaire were identified. Perhaps most importantly, the willingness-to-pay amounts were adjusted. The sample distributions of the maximum willingness-to-pay questions were analyzed, and, based on that analysis, we decided to change some of the values. For example, in the child spacing question, the "pay Tsh 1,000" responses unexpectedly accounted for a large share of the bids. Thus, we provided the option of paying more by introducing "pay Tsh 50,000" and "pay Tsh 25,000" as answer choices. For the other contigent valuation sections--health and education--the first pre-test determined that there was also a large lumping of responses at the high end of the scale. We adjusted the ranges accordingly, although there remains some lumping at the high end in the final data.

We also changed the order of the sections. Based on the pre-test and judgment of the field workers, we decided to first ask the questions in the individual section, then the contigent valuation questions, then the household questions. Because the respondents enjoyed the contigent valuation questions so much, this decision helped increase interest in the questionnaire and re-energized the respondent before proceeding with the household questions--the last part of the questionnaire. The final survey instrument, incorporating all of the changes dictated by the pre-tests and other expert advice, was completed on September 12, 1993.

Translation.

Translation of the survey instrument was a joint effort of the enumerators and supervisors. Given the specific characteristics of the Kswahili language, this was a much better approach than asking one translator to translate from English to Kswahili, and another one to translate from Kswahili to English. The "group" translation, involving those who would ask the questions, was intended to avoid different interpretations of the same question and achieve uniformity. In this way the enumerators were able to better convey the message/objective of each question.

The majority of the interviews were conducted in swahili. In very few cases, because no one in the selected household could speak swahili, the need arose to use interpreters.

Our initial plan called for the field work to start no later than August 29. However, unforeseen circumstances, including both financial and logistical problems, delayed the first field trip. Both the money and the materials were available by September 6, and five of the six teams left for Tanga region on that day. Initially we had planned to have the sixth team based full-time in Dar es Salaam; however, tighter time constraints imposed by the above and subsequent delays eventually made it necessary to send the sixth team into the field as well, as detailed below.

Description of questionnaires

The main objective of the survey was to obtain data on the use of, and spending on, the social sectors. The primary emphasis was on education and health--the areas in which the major gaps in availability of data were identified. The survey was divided into five major components, each of which was further subdivided, as described below:

I. Individual Questionnaire A. Household Roster; B. Information on

This study explores the relationship between human development and market institutions and tests the performance of three alternative economic perspectives that each assign a different role to governments. Based on a sample of 34 OECD countries plus Russia across a time frame spanning 1990 to 2018, the results demonstrate that economic freedom and small size of government do not significantly affect human development as measured by the Human Development Index. Hence, we find no support for the free-market ideal. Conversely, it is found that human development is positively related to governmental interventions that aim to reduce externalities (public expenditure on education and environmental regulation). These results support the perfect-market perspective. With respect to the welfare-state perspective, the findings are mixed. On the one hand, we found that (some) labor market regulations (particularly hiring and firing regulations, hours regulations and mandated cost of worker dismissal) have a negative impact upon human development. On the other hand, human development is shown to be positively affected by governmental intervention seeking to reduce gender stratification in the labor market.

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

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.

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 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.

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

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.

Regression models predicting country-level prevalence of low ECDI scores.

Prevalence of children with low ECDI scores.

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.