(1) The Human Development Index (HDI) is compiled by the United Nations Development Programme (UNDP) to measure a country's comprehensive development in the areas of health, education, and economy according to the UNDP's calculation formula.(2) Explanation: (1) The HDI value ranges from 0 to 1, with higher values being better. (2) Due to our country's non-membership in the United Nations and its special international situation, the index is calculated by our department according to the UNDP formula using our country's data. The calculation of the comprehensive index for each year is mainly based on the data of various indicators adopted by the UNDP. (3) In order to have the same baseline for international comparison, the comprehensive index and rankings are not retroactively adjusted after being published.(3) Notes: (1) The old indicators included life expectancy at birth, adult literacy rate, gross enrollment ratio, and average annual income per person calculated by purchasing power parity. (2) The indicators were updated to include life expectancy at birth, mean years of schooling, expected years of schooling, and nominal gross national income (GNI) calculated by purchasing power parity. Starting in 2011, the GNI per capita was adjusted from nominal value to real value to exclude the impact of price changes. Additionally, the HDI calculation method has changed from arithmetic mean to geometric mean. (3) The calculation method for indicators in the education domain changed from geometric mean to simple average due to retrospective adjustments in the 2014 Human Development Report for the years 2005, 2008, and 2010-2012. Since 2016, the education domain has adopted data compiled by the Ministry of Education according to definitions from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organization for Economic Co-operation and Development (OECD).

The human development index is one indicator of a country's progress. Many things can be done by knowing the human development index. HDI explains how the population can access development outcomes in terms of income, health, education, and so on. The HDI was introduced by the United Nations Development Program (UNDP) in 1990 and is published regularly in the annual Human Development Report (HDR).

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Context

UN Human Development Index

Administrative unitsRepresents the administrative units used for GDP per capita (PPP) and HDI data products. National administrative units have id 1-999, sub-national ones 1001-admin_areas_GDP_HDI.ncGDP_per_capita_PPP_1990_2015The GDP per capita (PPP) dataset represents average gross domestic production per capita in a given administrative area unit. GDP is given in 2011 international US dollars. Gap-filled sub-national data were used, supplemented by national data where necessary. Datagaps were filled by using national temporal pattern. Dataset has global extent at 5 arc-min resolution for the 26-year period of 1990-2015. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.GDP_PPP_1990_2015_5arcminThis global dataset represents the gross domestic production (GDP) of each grid cell. GDP is given in 2011 international US dollars. The data is derived from GDP per capita (PPP) which is multiplied by gridded population data HYDE...

Human Development Reports (HDRs) have been released most years since 1990 and have explored different themes through the human development approach. They have had an extensive influence on development debate worldwide (https://hdr.undp.org/about-hdro).

In 1990 the first Human Development Report introduced a new approach for advancing human wellbeing. Human development – or the human development approach - is about expanding the richness of human life, rather than simply the richness of the economy in which human beings live. It is an approach that is focused on people and their opportunities and choices.

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).

UNDP first published the Human Development Report in 1990 in collaboration with economist Mehboob Haque, who is credited as the promoter of the HDI Index. The most important aspects of the HDI Index are longevity, healthy living, educational attainment, and quality of life as well as other important factors such as political independence, human rights, and self-respect. UNDP's Human Development Report is a combination of three principles. That is.1) Life expectancy at birth.2) Level of education. (Rate of adult education, rate of primary, secondary, higher education)3) The standard of living. (GDP per capita based on USD)The HDI index is averaged based on the maximum and minimum values of these three elements. According to the report, India was ranked 126th in the HDI Index in 2006. In 2008, Maxine Olson, UNDP Representative in India, and Motek Singh Ahluwalia, Deputy Chairman of the Planning Commission, published the Human Development Report in Delhi, in which India was ranked 128th (Value 0.619). Compared to 2006, India has slipped two places.

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This dataset was created by RobbieS

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New Human Development Index

Background and Aims: Despite known Indigenous health and socioeconomic disadvantage in countries with a Very High Human Development Index, data on the incidence of stroke in these populations are sparse. With oversight from an Indigenous Advisory Board, we will undertake a systematic review of the incidence of stroke in Indigenous populations of developed countries or regions, with comparisons between Indigenous and non-Indigenous populations of the same region, though not between different Indigenous populations.Methods: Using PubMed, OVID-EMBASE, and Global Health databases, we will examine population-based incidence studies of stroke in Indigenous adult populations of developed countries published 1990-current, without language restriction. Non-peer-reviewed sources, studies including <10 Indigenous People, or with insufficient data to determine incidence, will be excluded. Two reviewers will independently validate the search strategies, screen titles and abstracts, and record reasons for rejection. Relevant articles will undergo full-text screening, with standard data extracted for all studies included. Quality assessment will include Sudlow and Warlow's criteria for population-based stroke incidence studies, the Newcastle-Ottawa Scale for risk of bias, and the CONSIDER checklist for Indigenous research.Results: Primary outcomes include crude, age-specific and/or age-standardized incidence of stroke. Secondary outcomes include overall stroke rates, incidence rate ratio and case-fatality. Results will be synthesized in figures and tables, describing data sources, populations, methodology, and findings. Within-population meta-analysis will be performed if, and where, methodologically sound and comparable studies allow this.Conclusion: We will undertake the first systematic review assessing disparities in stroke incidence in Indigenous populations of developed countries. Data outputs will be disseminated to relevant Indigenous stakeholders to inform public health and policy research.

Dataset

This dataset was created by Than Thien

Released under Apache 2.0

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Dataset

This dataset was created by Konrad Banachewicz

Released under Attribution 4.0 International (CC BY 4.0)

Contents

UNDP first published the Human Development Report in 1990 in collaboration with economist Mehboob Haque, who is credited as the promoter of the HDI Index. The most important aspects of the HDI Index are longevity, healthy living, educational attainment, and quality of life as well as other important factors such as political independence, human rights, and self-respect. UNDP's Human Development Report is a combination of three principles. That is. 1) Life expectancy at birth. 2) Level of education. (Rate of adult education, rate of primary, secondary, higher education) 3) The standard of living. (GDP per capita based on USD) The HDI index is averaged based on the maximum and minimum values ​​of these three elements. According to the report, India was ranked 126th in the HDI Index in 2006. In 2008, Maxine Olson, UNDP Representative in India, and Motek Singh Ahluwalia, Deputy Chairman of the Planning Commission, published the Human Development Report in Delhi, in which India was ranked 128th (Value 0.619). Compared to 2006, India has slipped two places.

The Gender Inequality Index (GII), released by the UN Development Programme (UNDP), is an inequality index for 159 countries from 1995 to 2015.

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This dataset was created by Defrino Gionaldo

Released under Attribution 4.0 International (CC BY 4.0)

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In 2022, the E-Government Development Index (EGDI) composite score of India was ****. In the same year, India ranked *** out of 193 countries. India slipped down from 100th place in the year 2020. The United Nations Department of Economic and Social Affairs has been publishing this survey report since 2001 biennially which includes all member states of the United Nations.

What is EGDI?

The widespread outreach of new communication technologies and the internet is compelling governments all over the world to build digital infrastructure and provide online access to public services. The EGDI is a composite indicator that consists of three indices namely the online service index (OSI), telecommunication infrastructure index (TII), and human capital index (HCI). The assessment is a relative measure of the e-governance performance of countries, rather than an absolute measure. Higher-income countries usually have a higher EGDI value as compared to lower-income countries.

India and e-governance 

According to the United Nations,despite being in the lower-income group, India is one of the countries with a fairly high level of human capital development (HCI) and online services provision (OSI). However, it is held back in terms of lower levels of infrastructure development (TII). The Indian government’s Digital campaign and its consequent products such as the UMANG e-governance platform, Accessible India campaign, AgriMarket app, MyGov platform, and many more are aiming to bridge the digital divide amongst the Indian population.

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

Over recent years, online government services have become increasingly common. In 2024, Denmark was ranked first with a near-perfect E-Government Development Index (EGDI) rating of ******. The EGDI assesses e-government development at a national level based on three components: the online service index, the telecommunication infrastructure index, and the human capital index. E-government development and the persisting digital divide According to the UN, e-government is a pivotal factor in advancing the implementation of the Sustainable Development Goals. Public services should be accessible to all, and e-government has to harness existing and new technologies to ensure that. There is a risk of a new digital divide, as low-income countries with insufficient infrastructure are lagging, leaving already vulnerable people even more at risk of not being able to gain any advantage from new technologies. Despite some investments and developmental gains, many countries are still unable to benefit from ICTs because of poor connectivity, high cost of access and lack of necessary skills. These factors have a detrimental effect on the further development of e-government in low EGDI-ranked regions such as Africa as the pace of technological progress intensifies. E-government services Transactional services are among the most common features offered by e-government websites worldwide. In 2018, it was found that *** countries enabled their citizens to submit income taxes via national websites. The majority of countries allow citizens to access downloadable forms, receive updates or access archived information about a wide range of sectors such as education, employment, environment, health, and social protection.

ObjectivesFollowing its publication in 2008, the Global Nutritional Index (GNI) which captures the triple burden of malnutrition, has been updated to assess the overall nutritional status and nutritional trends of countries, regions and the world, including both under-nutrition and over-nutrition.MethodsThe GNI was modeled on the Human Development Index, using geometric means of three normalized indicators: protein-energy malnutrition (PEM, measured by Disability-Adjusted Life Years (DALYs) from PEM), micronutrient deficiency (MID, measured by DALYs from MID), and penalizing obesity (percent female obesity). GNI (range 0–1) was calculated from 1990–2015 for 186 countries, in seven World Bank income and WHO region groupings.ResultsWorld GNI increased from 0.433 to 0.473 as decreased deficits overcompensated for the rise in obesity. GNI for African low- and middle-income countries (LMIC) (median 0.301 to 0.392) and South-East Asian LMIC (0.456 to 0.564) improved significantly (P

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the data tables are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes found here.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

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