1 Overview World Administrative Boundaries are available from various sources (UN, WHO, Global Administrative Areas [GADM], Natural Earth, World Bank). We would like to have the most accurate one with a reasonable size for an interactive world map in a Data Exploration Application, called CLIMINET. We provide a complete Geospatial Data that covers at least all 249 countries in the international ISO 3166-1 standard. We aim to maintain a reasonable data size, with countries' boundaries as accurate as possible, to ensure FLUIDITY in data visualization applications. The data are optimized for efficient performance and smooth interactions in interactive world maps for the best possible user experience. 2. Data Overview Number of Spatial Features: 275 countries/territories Data Sources: Compiled from multiple sources to ensure completeness and precision (WHO, Global Administrative Areas [GADM]) CRS Options: WGS84 [EPSG:4326] World Robinson (1963) [ESRI:54030] World Winkel-Tripel (Winkel III) - (1921) [ESRI:54042] Data Level: Level 0 (Countries) File Format: GeoJSON File Size: WGS84 [EPSG:4326]: 18.86 MB World Robinson (1963) [ESRI:54030]: 30.91 MB World Winkel-Tripel (Winkel III) - (1921) [ESRI:54042]: 30.90 MB 3. Data Revision Date The data were last updated on 2024-12-19. For further information on data structure and implementation, refer to the metadata files.

This data shows Burkina Faso 13 regions and 45 districts boundary. The datasets are curated from the The Humanitarian Data Exchange (HDX). The Humanitarian Data Exchange (HDX) is an open platform for sharing data, launched in July 2014. The goal of HDX is to make humanitarian data easy to find and use for analysis. Our growing collection of datasets has been accessed by users in over 200 countries and territories.A team within the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) manages HDX. OCHA is part of the United Nations Secretariat, responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. To learn more about the data, please visit https://data.humdata.org/dataset/burkina-faso-administrative-boundaries

Abstract

The Global Data Regulation Diagnostic provides a comprehensive assessment of the quality of the data governance environment. Diagnostic results show that countries have put in greater effort in adopting enabler regulatory practices than in safeguard regulatory practices. However, for public intent data, enablers for private intent data, safeguards for personal and nonpersonal data, cybersecurity and cybercrime, as well as cross-border data flows. Across all these dimensions, no income group demonstrates advanced regulatory frameworks across all dimensions, indicating significant room for the regulatory development of both enablers and safeguards remains at an intermediate stage: 47 percent of enabler good practices and 41 percent of good safeguard practices are adopted across countries. Under the enabler and safeguard pillars, the diagnostic covers dimensions of e-commerce/e-transactions, enablers further improvement on data governance environment.

The Global Data Regulation Diagnostic is the first comprehensive assessment of laws and regulations on data governance. It covers enabler and safeguard regulatory practices in 80 countries providing indicators to assess and compare their performance. This Global Data Regulation Diagnostic develops objective and standardized indicators to measure the regulatory environment for the data economy across countries. The indicators aim to serve as a diagnostic tool so countries can assess and compare their performance vis-á-vis other countries. Understanding the gap with global regulatory good practices is a necessary first step for governments when identifying and prioritizing reforms.

Geographic coverage

80 countries

Analysis unit

Country

Kind of data

Observation data/ratings [obs]

Sampling procedure

The diagnostic is based on a detailed assessment of domestic laws, regulations, and administrative requirements in 80 countries selected to ensure a balanced coverage across income groups, regions, and different levels of digital technology development. Data are further verified through a detailed desk research of legal texts, reflecting the regulatory status of each country as of June 1, 2020.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The questionnaire comprises 37 questions designed to determine if a country has adopted good regulatory practice on data governance. The responses are then scored and assigned a normative interpretation. Related questions fall into seven clusters so that when the scores are averaged, each cluster provides an overall sense of how it performs in its corresponding regulatory and legal dimensions. These seven dimensions are: (1) E-commerce/e-transaction; (2) Enablers for public intent data; (3) Enablers for private intent data; (4) Safeguards for personal data; (5) Safeguards for nonpersonal data; (6) Cybersecurity and cybercrime; (7) Cross-border data transfers.

Response rate

100%

World Bank-approved administrative boundaries (Admin 0) (and polygons) including international boundaries, disputed areas, coastlines, lakes and a guide to help with their usage. Last updated: Mar 19, 2020More details on https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries

The adoption of the shared prosperity goal by the World Bank in 2013 and Sustainable Development Goal 10, on inequality, by the United Nations in 2015 should strengthen the focus of development interventions and cooperation on the income growth of the bottom 40 percent of the income distribution. This paper contributes to the incipient literature on within-country allocations of development institutions and assesses the geographic targeting of World Bank projects to the bottom 40 percent. Bivariate correlations between the allocation of project funding approved over 2005–14 and the geographical distribution of the bottom 40 as measured by survey income or consumption data are complemented by regressions with population and other potential factors affecting the within-country allocations as controls. The correlation analysis shows that, of the 58 countries in the sample, 41 exhibit a positive correlation between the shares of the bottom 40 and World Bank funding, and, in almost half of these, the correlation is above 0.5. Slightly more than a quarter of the countries, mostly in Sub-Saharan Africa, exhibit a negative correlation. The regression analysis shows that, once one controls for population, the correlation between the bottom 40 and World Bank funding switches sign and becomes significant and negative on average. This is entirely driven by Sub-Saharan Africa and not observed in the other regions. Hence, the significant and positive correlation in the estimations without controlling for population suggests that World Bank project funding is concentrated in administrative areas in which more people live (including the bottom 40) rather than in poorer administrative areas. Furthermore, capital cities receive disproportionally high shares of World Bank funding on average.

The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains historical snapshots of the Statement of Loans including the latest available snapshots. The World Bank complies with all sanctions applicable to World Bank transactions.

Description of columns

The rest of the description can be found in this link

her

The datasets are curated from the Tanzania National Bureau of Statistics (NBS) 2012 Population and Housing Census (PHC) of Tanzania which was preceded by the preparatory geographic work, which involved field visiting of all regions, districts, wards/shehia, villages/mitaa, localities and sub-villages in the country, primarily to create and delineate Enumeration Area boundaries (EAs) so as to produce maps required for census operations. The most important principle followed in delineating an EA was that under no circumstance should an EA overlap the existing administrative boundaries of regions, districts, wards/shehia or villages/mitaa. Adherence to this principle was necessary since the census results were to be presented at the level of these administrative units. The National Bureau of Statistics (NBS) intends to provide a geo-database with spatial and non-spatial information at five levels of geography, to facilitate presentation of data from censuses and other surveys. These levels are regional (level one), district (level two), ward/shehia (level three), villages/mitaa (level four) and enumeration areas (level five). Levels one and two have been put onto the NBS website in June, 2013 for use by various stakeholders, and the web-page will be updated to include other levels of shapefiles when they are ready for use. To learn more, please visit website https://sensa.nbs.go.tz/

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

The sample for the Global Education Policy Dashboard in SLE was based in part on a previous sample of 260 schools which were part of an early EGRA study. Details from the sampling for that study are quoted below. An additional booster sample of 40 schools was chosen to be representative of smaller schools of less than 30 learners.

EGRA Details:

"The sampling frame began with the 2019 Annual School Census (ASC) list of primary schools as provided by UNICEF/MBSSE where the sample of 260 schools for this study were obtained from an initial list of 7,154 primary schools. Only schools that meet a pre-defined selection criteria were eligible for sampling.

To achieve the recommended sample size of 10 learners per grade, schools that had an enrolment of at least 30 learners in Grade 2 in 2019 were considered. To achieve a high level of confidence in the findings and generate enough data for analysis, the selection criteria only considered schools that: • had an enrolment of at least 30 learners in grade 1; and • had an active grade 4 in 2019 (enrolment not zero)

The sample was taken from a population of 4,597 primary schools that met the eligibility criteria above, representing 64.3% of all the 7,154 primary schools in Sierra Leone (as per the 2019 school census). Schools with higher numbers of learners were purposefully selected to ensure the sample size could be met in each site.

As a result, a sample of 260 schools were drawn using proportional to size allocation with simple random sampling without replacement in each stratum. In the population, there were 16 districts and five school ownership categories (community, government, mission/religious, private and others). A total of 63 strata were made by forming combinations of the 16 districts and school ownership categories. In each stratum, a sample size was computed proportional to the total population and samples were drawn randomly without replacement. Drawing from other EGRA/EGMA studies conducted by Montrose in the past, a backup sample of up to 78 schools (30% of the sample population) with which enumerator teams can replace sample schools was also be drawn.

In the distribution of sampled schools by ownership, majority of the sampled schools are owned by mission/religious group (62.7%, n=163) followed by the government owned schools at 18.5% (n=48). Additionally, in school distribution by district, majority of the sampled schools (54%) were found in Bo, Kambia, Kenema, Kono, Port Loko and Kailahun districts. Refer to annex 9. for details on the population and sample distribution by district."

Because of the restriction that at least 30 learners were available in Grade 2, we chose to add an additional 40 schools to the sample from among smaller schools, with between 3 and 30 grade 2 students. The objective of this supplement was to make the sample more nationally representative, as the restriction reduced the sampling frame for the EGRA/EGMA sample by over 1,500 schools from 7,154 to 4,597.

The 40 schools were chosen in a manner consistent with the original set of EGRA/EGMA schools. The 16 districts formed the strata. In each stratum, the number of schools selected were proportional to the total population of the stratum, and within stratum schools were chosen with probability proportional to size.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey

All columns except Population and Density limited to: Metropolitan France, Britain, Mainland.http://data.worldbank.org). The columns NUTS1, NUTS2, and NUTS3 refer to the numbers of official administrative regions. Alternative partitions (Alt.) refer to the 11 historical regions of Portugal going back to the Administrative Code of 1936 [27], to the 19 regions of Ivory Coast, and to the 13 regions of Saudi Arabia. Column L1 shows the number of communities found by the community detection algorithm, L2 the number of sub-communitites after iterative application to subnetworks. Density (Dens.) is given in population per square kilometer, population data (Pop.), in millions, is taken from the World Bank, 2011 (

This layer contains the information about the first-level administrative boundaries (Districts) in Lesotho. SOURCE: WFP Country Office, 2014

Context

This dataset was uploaded to support the Data Science For Good Kiva crowdfunding challenge. In particular, in uploading this dataset, I intend to assist with mapping subnational locations in the Kiva dataset to more accurate geocodes.

Content

This dataset contains poverty data at the administrative unit level 1, based on national poverty line(s). Administrative unit level 1 refers to the highest subnational unit level (examples include ‘state’, ‘governorate’, ‘province’). This dataset also provides data and methodology for distinguishing between poverty rates in urban and rural regions.

This dataset includes one main .csv file: Subnational-PovertyData.csv, which includes a set of poverty indicators at the national and subnational level between the years 1996-2013. Many countries are missing data for multiple years, and no country has data for the years 1997-1999.

It also includes three metadata .csv files: 1. Subnational-PovertyCountry.csv, which describes the country codes and subregions. 2.Subnational-PovertySeries.csv, which describes the three series indicators for national, urban, and rural poverty headcount ratios. This metadata file also including limitations, statistical methodologies, and development relevance for these metrics. 3. Subnational-Povertyfootnote.csv, which describes the years and sources for all of the country-series combinations.

Acknowledgements

This dataset is provided openly by the World Bank. Individual sources for the different data series are available in Subnational-Povertyfootnote.csv.

This dataset is classified as Public under the Access to Information Classification Policy. Users inside and outside the World Bank can access this dataset. It is licensed under CC-BY 4.0.

Metadata

Type: Time Series Topics: Economic Growth Poverty Economy Coverage: IBRD Languages Supported: English Number of Economies: 60 Geographical Coverage: World Access Options: Download, Query Tool Temporal Coverage: 1996 - 2013 Last Updated: April 27, 2015

Abstract

The purpose of the Tajikistan LSS surveys has been to provide quantitative data at the individual, household and community level that will facilitate purposeful policy design on issues of welfare and living standards of the population of the Republic of Tajikistan. Since 2007, the studies have been done in collaboration with World Bank and UNICEF and implementation by Tajik National Committee for Statistics. The 2007 LSS survey is based on the 2003 LSS and 2005 MICS survey with additional questions and modules

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

A detailed description of the sampling methodology is available in appendix to the document "Basic Information Document".

The Tajikistan LSS sample was designed to allow reliable estimation of poverty and most variables for a variety of other living standard indicators at the various domains of interest based on a representative probability sample on the level of: • Tajikistan as a whole
• Total urban and total rural areas • The five main administrative regions (oblasts) of the country: Dushanbe, Rayons of Republican Subordination (RRS), Sogd, Khatlon, and Gorno-Badakhshan Autonomous Oblast (GBAO)

The last census was conducted in 2000 and covered all five main administrative regions (oblasts) of the country (Dushanbe, RRS, Sogd, Khatlon, and GBAO). Each oblast was further subdivided into smaller areas called census section, instructor's sector and enumeration sector (ES). Each ES is either totally urban or rural. The list of ESs has census information on the population of each ES, and the ES lists were grouped by oblast.

In 2005, UNICEF implemented a Multiple Indicator Cluster Survey (MIC-05) in Tajikistan during which an electronic database of the ES information was created. Information in this database included: oblast, rayon, jamoat, settlement type, city/village, ES code, and population. Information from this database was used in the sample design of the TLSS07.

The total number of clusters for the Tajikistan LSS 2007 was established as 270 and total number of households per cluster was established as 18, resulting in a sample size of 4,860. The sample size was determined by considering: • The reliability of the survey estimates on both regional and national level • Quality of the data collected for the survey • Cost in time for the data collection • An oversample in 7 rayons in Khatlon

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Entry and Cleaning

The data entry program was designed using CSPro, a data entry package developed by the US Census Bureau. This software allows programs to be developed to perform three types of data checks: (a) range checks; (b) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (c) inter-record checks to determine inconsistencies between the different modules of the questionnaire.

The data from the First Round were key entered at the Goskomstat headquarters in Dushanbe starting 4 October 2007 through 25 November 2007. The Second Round and Sughd data were key entered from 26 November 2007 through 12 December 2007. All of the data were double entered with both the First Round, Second Round and Sughd re-collection double entry being completed by 22 January 2008.

Data appraisal

The data cleaning process began in February 2008 and was completed at the end of May 2008.

How to Use the Data:

There are three separate data bases with the data from the TLSS07. The data from each data collection is maintained separately. The data sets have similar names in each of the three separate data collections. First Round data sets have names in the form of "r1mnp" where "n" is the number of the module, and "p" is the part of the module (if any). Data from the Subjective Poverty module would be stored as "r1m9" and data from the Migration module, Part C Family Members Living Away from the Household would be stored as "r1m2c". Second Round data set names have a similar form "r2mnp". Data sets from the Sughd collection replace the "m" of the First Round with "sm", such as sm12a1.

The variable names have a similar format. Each variable name includes the module in which the variable is found and the question number. For example, question 10 in Module 4 Health, Part B Utilization of Outpatient Health Care is "m4b_q10". The variable names in all three of the data collections have the same format.

In addition to the individual roster files for each data base, there is also one roster file for all three data bases, rosterall. This roster file contains the information on all of the households and household members who are included in the data. There is a variable (source) indicating if the household/member is: (a) in Round 1 only; (b) in Round 2 only; (c) in Round 1 and Round 2; or (d) in the Sughd data. It is important to pay attention to this variable as the recall periods for the Subjective Poverty and Food Security Module (9A) is the last 4 weeks in the First Round, but changed to the last 2 weeks in the Second Round and the Sughd collection. In addition, the order of the question in the Expenditure On Food In The Last 7 Days, Module 10, changed

Abstract

Living Standards Measurement Study surveys have been developed by the World Bank to collect the information necessary to measure living standards and evaluate government interventions in the areas of poverty alleviation and social services. The Azerbaijan Survey of Living Conditions (ASLC) applies many of the features of LSMS surveys to provide data for the World Bank Poverty Assessment.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Design

The methodology that was chosen reflects the purpose of the survey. To balance a desire for a large, representative sample with the expense of a detailed survey instrument, a sample size of 2,016 households was selected. Three separate populations were covered: households in Baku, households outside of Baku and households of Displaced Persons. Within each of those populations, the sample was chosen in such a manner that each household had an equal probability of being selected. At the same time, the logistics of locating the households and conducting the interviews within a specific time frame required that the households be grouped into "work loads" of 12 households each. The size of the workload was determined by the number of interviews that could be carried out in one day by one team of three interviewers and a supervisor.

The Azerbaijan Survey of Living Conditions sample design included 408 households in the eleven raions that make up the city of Baku, 1200 households in the population outside of Baku, and 408 households among the registered Internally Displaced Persons residing throughout the country. This results in an oversampling of the Internally Displaced Persons population and an undersampling of the urban population of Baku. In order to use all data to provide nationally representative estimates, weighting factors must be applied to the data to account for the difference between the population and sample distributions.

Outside of Baku

The most recent data on population came from the 1989 census, the most recent data on number of households was reported in 1994 by the National Statistical Committee. The country is divided into towns, villages of the town type, and villages. Every household is located in one of those three types of population points. A list prepared by the National Statistical Committee contains just over 4,250 of these population points. To choose the sample outside of Baku, Baku was excluded from this list as were all the population points located in raions of the country currently occupied (Agdam, Xankendi, Xodjali, Xodjvendi, Susha, Kubatli, Zangelan, Kelbadjar, Lachin, Fizuli and Djebrali). The remainder of the country included 3453 population points. Information on the number of households was not available for all population points, specifically, "villages of the town type" and cities did not have this information. Average household size was calculated for those points that had both population and the number of households and this number was used to impute the number of households for those population points where it was missing. Average household size was 4.25 which is smaller than expected but reflects the fact that numerator is a 1989 statistic and the denominator is from 1994. First stage of sampling: Using the list of actual and estimated number of households for each population point, 100 workloads were spread across the population points in the following manner: 1. the sampling interval, i, was calculated to be the total number of households outside of Baku divided by 100, 2. the random start, s, was calculated by taking the integer portion of [random number * i + 1], 3. the population point containing the sth household, the (s+i)th household, the (s+2i)th household, etc. were then selected. 4. in the event that more than one interval landed on the same population point, multiple workloads of 12 households were surveyed in that population point. In this manner 100 workloads were distributed in 91 population points. Second stage of sampling: In order to select the households within the selected population points, household lists maintained by the administrative office of each Selsoviet were used. Selsoviets are administrative units that cover from one to ten population points. In the population points covered by a single group of 12 households, 16 dwellings were selected--12 to be interviewed and 4 to be used as replacements if necessary. The sampling interval used was the total number of households on the list divided by 16. Each population point had been assigned a randomly generated number with which to calculate a starting point. In population points with more that one group of 12 households, 16 households were selected for each workload and the sampling interval was number of households divided by 16 multiplied by the number of workloads. It is possible that a second household with separate finances could occupy a dwelling that was only listed once in the Selsoviet’s list. If an interviewer discovered more than one family living in a single dwelling, separate questionnaires were to be filled out for both, and a household randomly selected from among the households not yet interviewed on the list for that population point was taken off the list. This replacement of households, opposed to adding households, was adopted because the schedule did not allow time for more than 12 interviews per workload.

Baku

In February of 1995, SORGU was commissioned to do a random sampling survey in Baku. At that time a list was compiled of 2000 households in Baku. The 2000 households were distributed across the 11 raions of Baku according to each raion’s proportion of the total population. In each raion, the passport office lists were consulted to select the required number of addresses. In each office, the depth of each drawer full of cards was measured, the total length was divided by the number of households to be selected from that raion and cards were then pulled out at those intervals. From each card a specific address in Baku was noted. There is one passport for each dwelling in that raion regardless of the number of separate household/family units occupied the dwelling. The passport lists are, in principle, continuously updated with information from the housing maintenance offices. However, dwellings that are used for business, unoccupied, abandoned or rented to foreigners may remain listed. Furthermore, it is not clear how new privately built housing units would be listed.The 408 households and 92 replacements for this survey were selected by choosing a random number between 1 and 4, starting with that number and then selecting every fifth address from the existing list.

Internally Displaced Population

The National Statistical Committee prepared a listing of population and number of households of internally displaced persons by raion in July 1995. From that list, 34 workloads of 12 households each were selected from 26 raions and 11 Baku Administrative Regions using with a sampling interval and a random start similar to the method used outside of Baku. Ten workloads were selected in Baku and 24 were selected in 17 raions. As before, some raions received more than one workload. In each raion, the administrative offices for the Ministry of Refugees was consulted to locate the internally displaced persons. Each office should have a list of internally displaced persons by households. An additional level of sampling took place to choose three places and four interviews will be conducted in each place. These places were buildings, towns, or tent camps depending on how the households were listed.

Sampling as Implemented

In the course of the field work, it was discovered that population lists are not maintained in major urban areas. In Kuba, Xachmas, Devichi, Qaxi, Sheki, Ali Bairamli, Gojai and Agdash, supervisors had to improvise. In some cases passport registration lists were used, as was done in Baku. In other cases electric users lists, gas office books and butter/meat coupon distribution lists were used in order to capture a sample that was as representative as possible. During field work, one population point, Xandar, was not accessible due to security concerns and its proximity to the occupied region. A second population point, Sofukent, was not accessible because of the weather. In both cases, it was not practicable to replace the population points with two other population points randomly selected from the national list. Instead, field teams were instructed to visit the nearest population point of approximately the same size to the chosen population point. The only major disruption to fieldwork occurred in Naxicevan where interviewers were shot at by terrorists, fortunately none was hurt.

Mode of data collection

Face-to-face [f2f]

Research instrument

DEVELOPMENT OF QUESTIONNAIRES

A questionnaire based on the Living Standards Measurement Study surveys was adapted for use in Azerbaijan. Significant reductions in the number of questions reflected the need to conduct the survey in a short period of time and the more limited scope of a poverty assessment as compared to a full-blown government policy analysis. Questionnaire development was done using the Russian language version. The finalized versions were translated into Azeri by SORGU personnel. A special version of the questionnaire with both Russian and English was prepared for use by data analysts.

DESCRIPTION OF QUESTIONNAIRES

The survey includes questionnaires at both the household and population point (community) levels. Population point is an administrative designation that can be a village, a "village of the town type" or a

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communites

Kind of data

Sample survey data [ssd]

Sampling procedure

A detailed description of the sampling methodology is available in appendix to the document "Basic Information Document".

The TLSS sample was designed to allow reliable estimation of poverty and most variables for a variety of other living standard indicators at the various domains of interest based on a representative probability sample on the level of: • Tajikistan as a whole
• Total urban and total rural areas • The five main administrative regions (oblasts) of the country: Dushanbe, Rayons of Republican Subordination (RRS), Sogd, Khatlon, and Gorno-Badakhshan Autonomous Oblast (GBAO)

The last census was conducted in 2000 and covered all five main administrative regions (oblasts) of the country (Dushanbe, RRS, Sogd, Khatlon, and GBAO). Each oblast was further subdivided into smaller areas called census section, instructor's sector and enumeration sector (ES). Each ES is either totally urban or rural. The list of ESs has census information on the population of each ES, and the ES lists were grouped by oblast.

In 2005, UNICEF implemented a Multiple Indicator Cluster Survey (MICS05) in Tajikistan during which an electronic database of the ES information was created. Information in this database included: oblast, rayon, jamoat, settlement type, city/village, ES code, and population. Information from this database was used in the sample design of the TLSS07.

The total number of clusters for the TLSS07 was established as 270 and total number of households per cluster was established as 18, resulting in a sample size of 4,860. The sample size was determined by taking into account: • The reliability of the survey estimates on both regional and national level • Quality of the data collected for the survey • Cost in time for the data collection • An oversample in 7 rayons in Khatlon

The final cluster allocation is as follows:

Region: Urban / Rural / Total Dushanbe 50 / 0 / 50 RRP 9 / 45 / 54 Sogd 18 / 38 / 56 Khatlon 12 / 59 / 71 GBAO 6 / 33 / 39 Total 95 / 175 / 270

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect information for the TLSS07: a household questionnaire, a female questionnaire for recording information about women of child bearing age, and a community questionnaire. These questionnaires were based on the TLSS questionnaires used in 2003, but had some changes. Questions were added to existing modules and new modules were added to collect information to be used for MICS analyses. These included HIV/AIDS awareness, and Immunizations and Anthropometric Measurements for children 0 to 5 years old. Other new modules on Migration, Financial Services, Subjective Poverty and Food Security, and Subjective Beliefs were also added. The Labor Market Module was changed substantially from 2003 to better look at the informal labor market. The food expenditures module included additional food products. The HIV/AIDS questions were removed from the female questionnaire and were applied to all household members 12 to 49 years old.

The Second Round Household Questionnaire was shorter and was used primarily to collect additional information that was not possible to collect in the First Round. Because the First Round questionnaire was very long, it was decided to collect some information in a second round of visits to the households. The Household Questionnaire was the main instrument used during the Second Round. The female questionnaire was only used if females were added to the household after the First Round and the community questionnaire was not repeated. In the Second Round Household Questionnaire, the time reference period for the Food Security module was reduced from 4 weeks to 2 weeks. This was done because in the households visited at the beginning of the Second Round, a 4 week period would have included the last portion of the Ramadan period.

Cleaning operations

Data Entry and Cleaning

The data entry program was designed using CSPro, a data entry package developed by the US Census Bureau. This software allows programs to be developed to perform three types of data checks: (a) range checks; (b) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (c) inter-record checks to determine inconsistencies between the different modules of the questionnaire.

The data from the First Round were key entered at the Goskomstat headquarters in Dushanbe starting 4 October 2007 through 25 November 2007. The Second Round and Sughd data were key entered from 26 November 2007 through 12 December 2007. All of the data were double entered with both the First Round, Second Round and Sughd re-collection double entry being completed by 22 January 2008.

The data cleaning process began in February 2008 and was completed at the end of May 2008.

Abstract

In December 2017, the World Bank, in collaboration with Somali statistical authorities conducted the second wave of the Somali High Frequency Survey to monitor welfare and perceptions of citizens in all accessible areas of 17 regions within Somalia’s pre-war borders including Somaliland which self-declared independence in 1991. The survey interviewed 4,011 urban households, 1,106 rural households, 468 households in Internally Displaced People (IDP) settlements and 507 nomadic households. The sample was drawn randomly based on a multi-level clustered design. This dataset contains information on economic conditions, education, employment, access to services, security, perceptions and details before displacement for displaced households. It also includes comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption methodology as detailed in Pape and Mistiaen (2014).

Geographic coverage

The following pre-war regions: Awdal, Bakool, Banadir, Bari, Bay, Galgaduug, Gedo, Hiran, Lower Juba, Mudug, Nugaal, Sanaag, Middle and lower Shabelle, Sool, Togdheer and Woqooyi Galbeed (Somaliland self-declared independence in 1991).

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Wave 2 of the SHFS employed a multi-stage stratified random sample, ensuring a sample representative of all subpopulations of interest. Strata were defined along two dimensions - administrative location (pre-war regions and emerging states) and population type (urban areas, rural settlements, IDP settlements, and nomadic population). Households were clustered into enumeration areas (EAs), with 12 interviews was expected for each selected EA. Primary sampling units (PSUs) were generated using a variety of techniques depending on the population type. The primary sampling unit (PSU) in urban as well as rural strata was the enumeration area (EA). For IDP strata, primary sampling units were IDP settlements as defined by UNCHR’s Shelter Cluster. Across all strata, PSUs were selected using a systematic random sampling approach with selection probability proportional to size (PPS). In IDP strata, PPS sampling is applied at the IDP settlement level. In second- and final-stage sample selection, a microlisting approach was used, such that EAs were divided into 12 smaller enumeration blocks, which were selected with equal probability. Every block was selected as 12 interviews per EA were required. A similar second-stage sampling strategy was employed for IDP strata. Each IDP settlement was segmented manually into enumeration blocks. Finally, one household per block was interviewed in all selected blocks within the enumeration area.The household was selected randomly with equal probability in two stages, following the micro-listing protocol. The strategy for sampling nomadic households relied on lists of water points. The list of water points was divided up by stratum at the federated member state level and they served as primary sampling units. Water points were selected in the first stage with equal probability, with 12 interviews to be conducted at each selected water point. The selection of nomadic households to interview relied on a listing process at each water point whose aim was to compile an exhaustive list of all nomadic households at the water point. For more details, see accompanying documents, available under the related materials tab.

Sampling deviation

EAs were replaced if security rendered field work unfeasible. Replacements were approved by the project manager. Replacement of households were approved by the supervisor after a total of three unsuccessful visits of the household.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The household questionnaire is in English. It includes the following modules: - Introduction - Module A: Administrative Information - Module B: Interview Information and Filters - Module C: Household Roster - Module D: Household Characteristics - Module E: Food Consumption - Module F: Non-Food Consumption - Module G: Livestock - Module H: Durable Goods - Module I: Perceptions and Social Services - Module J: Displacement - Module K: Fishing - Module L: Catastrophic Events and Disasters - Module M: Enumerator Conclusions - Appendix A - Enabling Conditions - Appendix B - Validation Conditions and Messages - Appendix C - Instructions - Appendix D - Options - Appendix E - Variables - Appendix F - Option Filters

The household questionnaire is provided under the Related Materials tab.

Abstract

The Sierra Leone Integrated Household Survey (SLIHS) is Sierra Leone’s Living Standard Measurement Survey (LSMS) or household income and expenditure survey (HIES), which is conducted regularly in order to collect useful socio-economic data to support government planning processes. The 2018 SLIHS data collection covered the period January-December 2018, and it was the third round, which followed the 2011 and 2003 rounds. SLIHS is a multi topic household survey that collects detailed household incomes and expenditures, which are the ingredient for the monetary poverty assessment and collects information on education, health, employment, housing, and household assets.

The 2018 SLIHS was designed specifically to provide poverty indicators required for a successful updating of the poverty profile of the country as well as the household expenditure pattern, which serves as a basis for policy making and implementing both the national development plan (NDP) or the poverty reduction strategy paper (PRSP) and sectoral plans such as in education, health and agriculture sectors. The 2018 SLIHS also serves as a baseline for the monitoring of key International benchmarks as contained in the sustainable development goals (SDGs) and African Union Commission (AUC) Agenda 2063 in health, education, environment, income, employment, and gender issues.

The 2018 SLIHS therefore provides for the analysis of household welfare and poverty characteristics; and the results are comparable to previous rounds, at national and sub-national levels. The poverty lines usually computed from the data are: - The food poverty line represents the minimum amount of money required to afford a food bundle that provides the minimum required caloric intake. - The total poverty line is the sum of the food (extreme) poverty line, plus an additional allowance for non-food items, and represents the minimum amount of money required to afford a set of basic food and nonfood needs.

Based on these lines and the level of consumption in the household, three definitions of being poor are used: - A household is classified as absolute poor if its total (food and non-food) consumption is less than the total or absolute poverty line. - A household is classified as food poor if its food consumption is less than the food poverty line. - A household is classified as extremely poor if its total (food and non-food) consumption is less than the food poverty line.

The following three poverty measures are used to aggregate poverty across households: - Incidence of poverty (headcount index). The percent of the household population living below the poverty line. - Depth of poverty (poverty gap). How far, on average, the population is from the poverty line. In other words, depth of poverty captures the mean percent consumption shortfall of the population relative to the poverty line. - Severity of poverty (squared poverty gap). Combines the distance separating the poor from the poverty line and the inequality among the poor. Conceptually, poverty severity gives greater weight to those who are farther below the poverty line.

The objectives of the survey are as follows: - To provide fresh poverty profile and determine new official poverty lines using the new World Bank poverty line of USD 1.90 per day. - To provide poverty and other indicators to serve as tools for the monitoring and evaluation of the Medium-term national development plan. - To measure households’ consumption and expenditure at a greater level of disaggregation. - To provide data for the compilation of National Accounts and computation of the Consumer Price Index (CPI). - To measure the impact of Ebola on the socio-economic characteristics of the population.

Geographic coverage

• National coverage
• Districts: Kailahun, Kenema, Kono, Bombali , Kambia , Koinadugu , Port Loko , Tonkolili , Bo, Bonthe, Moyamba , Pujehun , Western Area Rural, and Western Area Urban
• Rural and urban localities

Analysis unit

• Individuals
• Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018 SLIHS sample was selected from the 2015 Population and Housing Census (PHC) frame, which had 12, 856 Enumeration Areas (EAs) and 1, 248, 218 households. The sample was drawn from a domain which had 4 Regions, 14 Districts, 149 chiefdoms and 1322 sections in the old administrative setting.

The sampling procedure used to select the sample is similar to the procedure used in 2011 SLIHS. A two-stage stratification strategy was used to select the sample by first dividing the frame by the 14 Districts and then divided each district by rural and urban localities. Probability Proportional to Size (PPS) was used to select the 684 EAs, which were used as clusters for the survey. This meant that district and rural/urban locality were used as domain for selection as well as for analysis.

Sample Size: The Sierra Leone Integrated Household Survey (SLIHS) is a nationwide survey conducted in 684 clusters/Enumeration Areas (EAs). The sample covers all 14 Administrative District (Old) as well as the 16 Administrative Districts in the new dispensation. Given the wide variation in living conditions in the urban areas, the sample is tilted towards those areas although the bulk (63 percent) of the population lives in the rural areas.

The 2018 SLIHS collaborated with the multi-indicator cluster survey round six (MICS-6). MICS-6 collected data from 506 2018 SLIHS EAs out of 684 selected cluster for the survey. The 2018 SLIHS made use of basic household roster information from this MICS-6 (name, sex, age, relationship to household head); and we did receive this list of interviewed households from the MICS-6 Team. The collaboration was meant to produce economic and social indicators in a single dataset.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instruments were developed using the 2011 questionnaires as reference materials, updated to cover new areas include agriculture and household expenditure items and categories. Compared with the 2011 instrument, the 2018 SLIHS survey instrument was made up of 5 books of questionnaires: - Book 1: Individual Characteristics - Book 2: Household Characteristics - Book 3: Agriculture - Book 4A: Household Consumption Items (first 10 days) - Book 4B: Household Consumption Items (last 10 days)

A pre-test was conducted in order to test the suitability of the questionnaires including the structure and the formulation of the questions.

More details on the questionnaire are provided as external resources

Cleaning operations

2018 SLIHS data entry was done alongside with data collection in the field. It was a hybrid arrangement wherein completed paper questionnaires were entered in the field unlike the usual practice of centralizing data entry at the head office and carried out at the end of the survey. Data Entry Clerks (DECs) were trained and each of the 19 DECs was given a laptop computer to each to enter the data for a team in the field. The data entry mask or programme was finalized early which was installed in all laptops. DECs were responsible for syncing the data to the server which monitored by Stats SL and the World Bank.

Response rate

The response rate for the SLIHS 2018 is 100 %. All 684 clusters were covered and in each of these clusters 10 households were interviewed.

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools were sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

In order to visit two schools per day, we clustered at the sector level choosing two schools per cluster. With a sample of 200 schools, this means that we had to allocate 100 PSUs. We combined this clustering with stratification by district and by the urban rural status of the schools. The number of PSUs allocated to each stratum is proportionate to the number of schools in each stratum (i.e. the district X urban/rural status combination).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey questions.

• Survey of Public Officials: The Survey of Public Officials collects information about the capacity and orientation of the bureaucracy, as well as political factors affecting education outcomes. This survey is a streamlined and education-focused version of the civil-servant surveys that the Bureaucracy Lab (a joint initiative of the Governance Global Practice and the Development Impact Evaluation unit of the World Bank) has implemented in several countries. The survey includes questions about technical and leadership skills, work environment, stakeholder engagement, impartial decision-making, and attitudes and behaviors. The survey takes 30-45 minutes per public official and is used to interview Ministry of Education officials working at the central, regional, and district levels in each country.

Cleaning operations

Data quality control was performed in R and Stata Code to calculate all indicators can be found on github here: https://github.com/worldbank/GEPD/blob/master/Countries/Rwanda/2019/School/01_data/03_school_data_cleaner.R

Sampling error estimates

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level.

The Fiscal Monitor surveys and analyzes the latest public finance developments, it updates fiscal implications of the crisis and medium-term fiscal projections, and assesses policies to put public finances on a sustainable footing.

Country-specific data and projections for key fiscal variables are based on the April 2020 World Economic Outlook database, unless indicated otherwise, and compiled by the IMF staff. Historical data and projections are based on information gathered by IMF country desk officers in the context of their missions and through their ongoing analysis of the evolving situation in each country; they are updated on a continual basis as more information becomes available. Structural breaks in data may be adjusted to produce smooth series through splicing and other techniques. IMF staff estimates serve as proxies when complete information is unavailable. As a result, Fiscal Monitor data can differ from official data in other sources, including the IMF's International Financial Statistics.

The country classification in the Fiscal Monitor divides the world into three major groups: 35 advanced economies, 40 emerging market and middle-income economies, and 40 low-income developing countries. The seven largest advanced economies as measured by GDP (Canada, France, Germany, Italy, Japan, United Kingdom, United States) constitute the subgroup of major advanced economies, often referred to as the Group of Seven (G7). The members of the euro area are also distinguished as a subgroup. Composite data shown in the tables for the euro area cover the current members for all years, even though the membership has increased over time. Data for most European Union member countries have been revised following the adoption of the new European System of National and Regional Accounts (ESA 2010). The low-income developing countries (LIDCs) are countries that have per capita income levels below a certain threshold (currently set at $2,700 in 2016 as measured by the World Bank's Atlas method), structural features consistent with limited development and structural transformation, and external financial linkages insufficiently close to be widely seen as emerging market economies. Zimbabwe is included in the group. Emerging market and middle-income economies include those not classified as advanced economies or low-income developing countries. See Table A, "Economy Groupings," for more details.

Most fiscal data refer to the general government for advanced economies, while for emerging markets and developing economies, data often refer to the central government or budgetary central government only (for specific details, see Tables B-D). All fiscal data refer to the calendar years, except in the cases of Bangladesh, Egypt, Ethiopia, Haiti, Hong Kong Special Administrative Region, India, the Islamic Republic of Iran, Myanmar, Nepal, Pakistan, Singapore, and Thailand, for which they refer to the fiscal year.

Composite data for country groups are weighted averages of individual-country data, unless otherwise specified. Data are weighted by annual nominal GDP converted to U.S. dollars at average market exchange rates as a share of the group GDP.

In many countries, fiscal data follow the IMF's Government Finance Statistics Manual 2014. The overall fiscal balance refers to net lending (+) and borrowing ("") of the general government. In some cases, however, the overall balance refers to total revenue and grants minus total expenditure and net lending.

The fiscal gross and net debt data reported in the Fiscal Monitor are drawn from official data sources and IMF staff estimates. While attempts are made to align gross and net debt data with the definitions in the IMF's Government Finance Statistics Manual, as a result of data limitations or specific country circumstances, these data can sometimes deviate from the formal definitions.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Administrative Boundaries are derived from the land area grid to show the outlines of pixels (cells) that contain administrative Units in GRUMPv1 on a per-country/territory basis. They are derived from the pixels as polygons and thus have rectilinear boundaries at a large scale. The polygons that outline the countries and territories are not official representations; rather they represent the area covered by the statistical data as provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

Abstract

The coronavirus disease 2019 (COVID-19) pandemic and its effects on households create an urgent need for timely data and evidence to help monitor and mitigate the social and economic impacts of the crisis on the Somali people, especially the poor and most vulnerable. To monitor the socioeconomic impacts of the COVID-19 pandemic and inform policy responses and interventions, the World Bank as part of a global initiative designed and conducted a nationally representative COVID-19 Somali High-Frequency Phone Survey (SHFPS) of households. The survey covers important and relevant topics, including knowledge of COVID-19 and adoption of preventative behavior, economic activity and income sources, access to basic goods and services, exposure to shocks and coping mechanisms, and access to social assistance.

Geographic coverage

National. Jubaland, South West, HirShabelle, Galmudug, Puntland, and Somaliland (self-declared independence in 1991), and Banadir.

Analysis unit

• Households

Universe

Households with access to phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample allocation for the COVID-19 SHFPS has been developed to provide representative and reliable estimates nationally, and at the level of Jubaland, South West, HirShabelle, Galmudug, Puntland, Somaliland, Banadir Regional Administration and by population type (i.e. urban, rural, nomads, and IDPs populations). The sampling procedure had two steps. The sample was stratified according to the 18 pre-war regions—which are the country’s first-level administrative divisions—and population types. This resulted in 57 strata, of which 7 are IDP, 17 are nomadic, 16 are exclusively urban strata, 15 exclusively rural, and 2 are combined urban-rural strata. The sample size in some strata was too small, thus urban and rural areas were merged into one single strata; this was the case for Sool and Sanaag.

Round 1 of the COVID-19 SHFPS was implemented between June and July 2020. The survey interviewed 2,811 households (1,735 urban households, 611 rural households, 435 nomadic households, and 30 IDP households in settlements). The sample of 2,811 households was contacted using a random digit dialing protocol. The sampling frame was the SHFPS Round 1 data - the same households from Round 1 are tracked over time, allowing for the monitoring of the well-being of households in near-real time and enabling an evidence-based response to the COVID-19 crisis.

Round 2 of the COVID-19 SHFPS was implemented in January 2021. A total of 1,756 households were surveyed (738 urban households, 647 rural households, 309 nomadic households, and 62 IDP households in settlements). Of the 1,756 households, 91 percent were successfully re-contacted from Round 1, with the remainder reached via random digit dialing. Administration of the questionnaire took on average 30 minutes.

Sampling deviation

The target sample for Round 1 was 3,000 households. The realized sample consists of 2,811 households. Reaching rural and nomadic-lifestyle respondents proved to be difficult in a phone survey setting due to lifestyle considerations and relatively lower phone penetration compared to urban settings. To overcome this challenge, the following were performed: - Lowering the sample size of the rural stratum - Reducing the number of interviews in the oversampled urban strata of Kismayo (Jubaland – Lower Juba/Urban) and Baidoa (South West State – Bay/Urban) - Utilizing snowball sampling methodology (i.e. referrals) to increase the sample for hard-to-reach population types, namely the nomadic households.

In Round 2, initially, a sample size of 1,800 households was targeted. However, due to implementation challenges in reaching specific population groups via phone, the sample size was slightly reduced. At the end of the data collection, 1,756 households had been interviewed.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire of the COVID-19 Somali High-Frequency Phone Survey (SHFPS) of households consists of the following sections:

• Interview information (R1, R2)
• Household roster (R1, R2)
• Knowledge regarding the spread of COVID-19 (R1, R2)
• Behavior and social distancing (R1, R2)
• Concerns related to the COVID-19 pandemic (R1, R2)
• COVID-19 vaccine (R2)
• Access to basic goods and services (R1, R2)
• Employment (R1, R2)
• Income loss (R1, R2)
• Remittances (R1, R2)
• Mortality (R2)
• Shocks and coping mechanisms (R1, R2)
• Food insecurity (R1, R2)
• Social assistance and safety nets (R1, R2)
• Interaction with internally displaced persons (R2)

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Only households that consented to being interviewed were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset.

Response rate

The response rate is defined as the percentage of reached eligible households willing to participate in the survey. It is calculated as the number of interviewed households over the number of reached eligible households, thus excluding unreached households (i.e. invalid numbers or failure to contact the household) and households that were reached but were not eligible to participate in the survey (as determined by the minimum age requirement of the main respondent and sampling criteria).

The response rate for Round 1 was nearly 80 percent. In Round 2, 91 percent of the 1,756 households surveyed were successfully re-contacted from Round 1, with the remainder reached via random digit dialing.