The average for 2021 based on 47 countries was 487417 sq. km. The highest value was in Russia: 16376870 sq. km and the lowest value was in Monaco: 2 sq. km. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

This dataset provides values for WORLD reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Content

This dataset contains current estimates (live population clock), historical data, and projected figures of world countries and dependent territories. Data based on the latest United Nations Population Division estimates.

Attribute Information

• Country/Other - Name of countries and dependent territories.
• Population (2020) - Population in the year 2020
• Yearly Change - Percentage Yearly Change in Population
• Net Change - Net Change in Population
• Density (P/Km²)- Population density (population per square km)
• Land Area (Km²) - Land area of countries / dependent territories.
• Migrants (net) - Total number of migrants
• Fert. Rate - Fertility rate
• Med. Age - Median age of the population
• Urban Pop %- Percentage of urban population
• World Share - Population share

Source

Link : https://www.worldometers.info/world-population/population-by-country/

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A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Africa Arable land (% of land area) Dataset

  Overview

This dataset contains arable land (% of land area) data for African countries from the World Bank.

  Data Details

Indicator Code: AG.LND.ARBL.ZS Description: Arable land (% of land area) Geographic Coverage: 54 African countries Time Period: 1961-2022 Data Points: 3,182 observations Coverage: 90.66% of possible country-year combinations

  File Formats





  Main Dataset (ag_lnd_arbl_zs_africa.csv)… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Africa-Arable-land-percentage-of-land-area.

Abstract

The World Bank Group is interested in gauging the views of clients and partners who are either involved in development in Peru or who observe activities related to social and economic development. The following survey will give the World Bank Group's team that works in Peru, greater insight into how the Bank's work is perceived. This is one tool the World Bank Group uses to assess the views of its stakeholders, and to develop more effective strategies that support development in Peru. A local independent firm was hired to oversee the logistics of this survey.

This survey was designed to achieve the following objectives: - Assist the World Bank Group in gaining a better understanding of how stakeholders in Peru perceive the Bank Group; - Obtain systematic feedback from stakeholders in Peru regarding: · Their views regarding the general environment in Peru; · Their overall attitudes toward the World Bank Group in Peru; · Overall impressions of the World Bank Group's effectiveness and results, knowledge work and activities, and communication and information sharing in Peru; · Perceptions of the World Bank Group's future role in Peru. - Use data to help inform Peru country team's strategy.

Geographic coverage

Metropolitan Lima Area, Outside of Metropolitan Lima Area

Analysis unit

Stakeholders in Peru

Universe

Stakeholders in Peru

Kind of data

Sample survey data [ssd]

Sampling procedure

In February-April 2014, 465 stakeholders of the World Bank Group in Peru were invited to provide their opinions on the WBG's work in the country by participating in a country opinion survey. Participants were drawn from the office of the President; the office of the Prime Minister; office of a minister; office of a parliamentarian; ministries, ministerial departments, or implementation agencies; consultants/contractors working on WBG-supported projects/programs; project management units (PMUs) overseeing implementation of a project; local government officials; bilateral and multilateral agencies; private sector organizations; private foundations; the financial sector/private banks; NGOs; community based organizations; the media; independent government institutions; trade unions; faith-based groups; academia/research institutes/think tanks; judiciary branch; and other organizations.

Mode of data collection

Other [oth]

Research instrument

The Questionnaire consists of following sections:

A. General Issues Facing Peru: Respondents were asked to indicate whether Peru is headed in the right direction, what they thought were the top three most important development priorities in the country, which areas would contribute most to reducing poverty and generating economic growth in Peru, and how "shared prosperity" would be best achieved.

B. Overall Attitudes toward the World Bank Group (WBG): Respondents were asked to rate their familiarity with the WBG and other regional development banks, their effectiveness in Peru, WBG staff preparedness to help Peru solve its development challenges, WBG's local presence, WBG's capacity building in Peru, their agreement with various statements regarding the WBG's work, and the extent to which the WBG is an effective development partner. Respondents were asked to indicate the WBG's greatest values and weaknesses, the most effective instruments in helping reduce poverty in Peru, in which sectoral areas the WBG should focus most of its resources (financial and knowledge services), and to what reasons respondents attributed failed or slow reform efforts. Respondents were also asked to respond to a few questions about capacity building and whether they believe the World Bank Group should have more or less local presence.

C. World Bank Group's Effectiveness and Results: Respondents were asked to rate the extent to which the WBG's work helps achieve development results in Peru, the extent to which the WBG meets Peru's needs for knowledge services and financial instruments, the importance for the WBG to be involved in thirty one development areas, and the WBG's level of effectiveness across these areas, such as education, public sector governance/reform, water and sanitation, and transport.

D. The World Bank Group's Knowledge Work and Activities: Respondents were asked to indicate how frequently they consult WBG's knowledge work and activities and to rate the effectiveness and quality of the WBG's knowledge work and activities, including how significant of a contribution it makes to development results and its technical quality. Respondents were also asked about the WBG reports, including which of them are the most useful, whether they raised substantive new information, and whether they provided them with useful information in terms of work they do.

E. Working with the World Bank Group: Respondents were asked to rate WBG's technical assistance/advisory work's contribution to solving development challenges and their level of agreement with a series of statements regarding working with the WBG, such as the WBG's "Safeguard Policy" requirements being reasonable, and disbursing funds promptly.

F. The Future Role of the World Bank Group in Peru: Respondents were asked to indicate what the WBG should do to make itself of greater value in Peru, and which services the Bank should offer more of in the country. They were asked whether WBG has moved to the right direction, and the future role international development cooperation should play in Peru.

G. Communication and Information Sharing: Respondents were asked to indicate how they get information about economic and social development issues, how they prefer to receive information from the WBG, and their usage and evaluation of the WBG's websites. Respondents were also asked about their awareness of the WBG's Access to Information policy, were asked to rate WBG's responsiveness to information requests, value of its social media channels, and levels of easiness to find information they needed.

H. Background Information: Respondents were asked to indicate their current position, specialization, whether they professionally collaborate with the WBG, their exposure to the WBG in Peru, which WBG agencies they work with, whether IFC and the Bank work well together, and their geographic location.

Response rate

A total of 197 stakeholders participated in the survey (42% response rate).

This data base contains gridded (one degree by one degree) information on the world-wide distribution of the population for 1990 and country-specific information on the percentage of the country's population present in each grid cell (Li, 1996a). Secondly, the data base contains the percentage of a country's total area in a grid cell and the country's percentage of the grid cell that is terrestrial (Li, 1996b). Li (1996b) also developed an indicator signifying how many countries are represented in a grid cell and if a grid cell is part of the sea; this indicator is only relevant for the land, countries, and sea-partitioning information of the grid cell. Thirdly, the data base includes the latitude and longitude coordinates of each grid cell; a grid code number, which is a translation of the latitude/longitude value and is used in the Global Emission Inventory Activity (GEIA) data bases; the country or region's name; and the United Nations three-digit country code that represents that name. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ftp/db1016/

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Global Government Expenditure on R&D in All Fields by Country, 2023 Discover more data with ReportLinker!

Palau PW: Imports: cif: Countries or Area Not Specified data was reported at 0.002 USD mn in Aug 2018. This records a decrease from the previous number of 0.003 USD mn for Jul 2018. Palau PW: Imports: cif: Countries or Area Not Specified data is updated monthly, averaging 0.003 USD mn from Jan 2007 (Median) to Aug 2018, with 140 observations. The data reached an all-time high of 0.118 USD mn in Oct 2010 and a record low of 0.000 USD mn in Jan 2013. Palau PW: Imports: cif: Countries or Area Not Specified data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Palau – Table PW.IMF.DOT: Imports: cif: by Country: Monthly.

In 2019, Japan received the highest travel and tourism competitiveness index (TTCI) score in the Asia Pacific region, of which the score was ***. Japan also ranked fourth globally for its travel and tourism competitiveness index. Comparatively, for the same year India received a TTCI score of ***.

Comoros KM: Imports: cif: Countries or Area Not Specified data was reported at 0.240 USD mn in 2024. This records an increase from the previous number of 0.165 USD mn for 2023. Comoros KM: Imports: cif: Countries or Area Not Specified data is updated yearly, averaging 0.341 USD mn from Dec 1970 (Median) to 2024, with 10 observations. The data reached an all-time high of 3.283 USD mn in 1997 and a record low of 0.161 USD mn in 2022. Comoros KM: Imports: cif: Countries or Area Not Specified data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Comoros – Table KM.IMF.DOT: Imports: cif: by Country: Annual.

Abstract

The World Bank is interested in gauging the views of clients and partners who are either involved in development in Sierra Leone or who observe activities related to social and economic development. The World Bank Country Assessment Survey is meant to give the World Bank's team that works in Sierra Leone, greater insight into how the Bank's work is perceived. This is one tool the World Bank uses to assess the views of its critical stakeholders. With this understanding, the World Bank hopes to develop more effective strategies, outreach and programs that support development in Sierra Leone. The World Bank commissioned an independent firm to oversee the logistics of this effort in Sierra Leone.

The survey was designed to achieve the following objectives: - Assist the World Bank in gaining a better understanding of how stakeholders in Sierra Leone perceive the Bank; - Obtain systematic feedback from stakeholders in Sierra Leone regarding: · Their views regarding the general environment in Sierra Leone; · Their overall attitudes toward the World Bank in Sierra Leone; · Overall impressions of the World Bank's effectiveness and results, knowledge work and activities, and communication and information sharing in Sierra Leone; · Perceptions of the World Bank's future role in Sierra Leone. - Use data to help inform Sierra Leone team's strategy.

Geographic coverage

National

Analysis unit

Stakeholder

Universe

Stakeholders of the World Bank in Sierra Leone

Kind of data

Sample survey data [ssd]

Sampling procedure

In March-April 2013, 600 stakeholders of the World Bank in Sierra Leone were invited to provide their opinions on the Bank's assistance to the country by participating in a country survey. Participants in the survey were drawn from among the office of the President; the office of the Prime Minister; the office of a Minister; the office of a Parliamentarian; employees of a ministry, ministerial department, or implementation agency; consultants/ contractors working on World Bank-supported projects/programs; project management units (PMUs) overseeing implementation of a project; local government officials or staff; bilateral and multilateral agencies; private sector organizations; private foundations; the financial sector/private banks; NGOs; community-based organizations; the media; independent government institutions; trade unions; faith-based groups; academia/research institutes/think tanks; judiciary branches; and other organizations.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The Questionnaire consists of 8 Sections:

A. General Issues Facing Sierra Leone: Respondents were asked to indicate whether Sierra Leone is headed in the right direction, what they thought were the top three most important development priorities in the country, and which areas would contribute most to reducing poverty and generating economic growth in Sierra Leone.

B. Overall Attitudes toward the World Bank: Respondents were asked to rate their familiarity with the World Bank, the Bank's effectiveness in Sierra Leone, Bank staff preparedness to help Sierra Leone solve its development challenges, their agreement with various statements regarding the Bank's work, and the extent to which the Bank is an effective development partner. Respondents were asked to indicate the sectoral areas on which it would be most productive for the Bank to focus its resources, the Bank's greatest values and weaknesses in its work, the most effective instruments in helping to reduce poverty in Sierra Leone, with which stakeholder groups the Bank should collaborate more, and to what reasons respondents attributed failed or slow reform efforts.

C. World Bank Effectiveness and Results: Respondents were asked to rate the extent to which the Bank's work helps achieve development results in Sierra Leone, the extent to which the Bank meets Sierra Leone's needs for knowledge services and financial instruments, and the Bank's level of effectiveness across forty-two development areas, such as education, energy, agricultural development, job creation/employment, infrastructure, and others.

D. The World Bank's Knowledge: Respondents were asked to indicate how frequently they consult Bank knowledge work/activities, the areas on which the Bank should focus its research efforts, and to rate the effectiveness and quality of the Bank's knowledge work/activities, including how significant of a contribution it makes to development results and its technical quality.

E. Working with the World Bank: Respondents were asked to rate their level of agreement with a series of statements regarding working with the Bank, such as the World Bank's "Safeguard Policy" requirements being reasonable, the Bank imposing reasonable conditions on its lending, disbursing funds promptly, increasing Sierra Leone's institutional capacity, and providing effective implementation support. Respondents also were asked that to what extent they believed the Bank was adequately staffed in Sierra Leone.

F. The Future Role of the World Bank in Sierra Leone: Respondents were asked to rate how significant a role the Bank should play in Sierra Leone's development in the near future and to indicate what the Bank should do to make itself of greater value. They were also asked about the effectiveness of the donors in their work to see through development results on the ground and the effectiveness of the Bank in helping forge regional economic integration.

G. Communication and Information Sharing: Respondents were asked to indicate how they get information about economic and social development issues, how they prefer to receive information from the Bank, and their usage and evaluation of the Bank's websites. Respondents were asked about their awareness of the Bank's Access to Information policy, past information requests from the Bank, and their level of agreement that they use more data from the World Bank as a result of the Bank's Open Data policy. Respondents were also asked about their level of agreement that they know how to find information from the Bank and that the Bank is responsive to information requests.

H. Background Information: Respondents were asked to indicate their current position, specialization, whether they professionally collaborate with the World Bank, their exposure to the Bank in Sierra Leone, and their geographic location.

Response rate

A total of 340 stakeholders participated in the survey (57% response rate).

Worldwide geospatial database consisting of country and geographic name information on a global scale. The data is designed for the production of cartographic documents and maps, including their dissemination via public electronic networks, for the Secretariat of the United Nations, as set forth in the Administrative Instruction of the Secretary-General of the United Nations concerning. Source: UNmap The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted line represents approximately the Line of Control in Jammu and Kashmir agreed upon by India and Pakistan. The final status of Jammu and Kashmir has not yet been agreed upon by the parties. Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined. Final status of the Abyei area is not yet determined. A dispute exists between the Governments of Argentina and the United Kingdom of Great Britain and Northern Ireland concerning sovereignty over the Falkland Islands (Malvinas).

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Adults (15 years and older) with an account at a financial institution or mobile-money-service provider (%)





  Dataset Description

This dataset provides country-level data for the indicator "8.10.2 Adults (15 years and older) with an account at a financial institution or mobile-money-service provider (%)" across African nations, sourced from the World… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/financial-access-for-african-countries.

The average for 2021 based on 165 countries was 79.81 index points. The highest value was in Bermuda: 212.7 index points and the lowest value was in Syria: 33.25 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

This dataset provides values for PRODUCER PRICES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The average for 2024 based on 177 countries was 27291 U.S. dollars. The highest value was in Singapore: 132570 U.S. dollars and the lowest value was in Burundi: 836 U.S. dollars. The indicator is available from 1990 to 2024. Below is a chart for all countries where data are available.