Abstract

In an environment where the Bank must demonstrate its impact and value, it is critical that the institution collects and tracks empirical data on how its work is perceived by clients, partners and other stakeholders in our client countries.

The Country Opinion Survey Program was scaled up in order to: - Annually assess perceptions of the World Bank among key stakeholders in a representative sample of client countries; - Track these opinions over time, representative of: regions, stakeholders, country lending levels, country income/size levels, etc. - Inform strategy and decision making: apply findings to challenges to ensure real time response at several levels: corporate, regional, country - Obtain systematic feedback from stakeholders regarding: • The general environment in their country; • Value of the World Bank in their country; • World Bank's presence (work, relationships, etc.); • World Bank's future role in their country. - Create a feedback loop that allows data to be shared with stakeholders.

Geographic coverage

The data from the 29 country surveys were combined in this review. Although individual countries are not specified, each country was designated as part of a particular region: Africa (AFR), East Asia (EAP), Europe/Central Asia (ECA), Latin America (LAC), Middle East/North Africa (MNA), and South Asia (SAR).

Analysis unit

Client Country

Kind of data

Sample survey data [ssd]

Sampling procedure

In FY 2012 (July 2011 to July 1, 2012), 15,029 stakeholders of the World Bank in 29 different countries were invited to provide their opinions on the Bank's assistance to the country by participating in a country survey. Participants in these surveys were drawn from among senior government officials (from the office of the Prime Minister, President, Minister, Parliamentarian; i.e., elected officials), staff of ministries (employees of ministries, ministerial departments, or implementation agencies, and government officials; i.e., non-elected government officials, and those attached to agencies implementing Bank-supported projects), 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 agency staff, private sector organizations, private foundations; the financial sector/private banks; non-government organizations (NGOs, including CBOs), the media, independent government institutions (e.g., regulatory agencies, central banks), trade unions, faith-based groups, members of academia or research institutes, and members of the judiciary.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The Questionnaire consists of the following sections:

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

B. Overall Attitudes toward the World Bank: Respondents were asked to rate their familiarity with the World Bank, the Bank's effectiveness in the country, the extent to which the Bank meets the country's needs for knowledge services and financial instruments, and the extent to which the Bank should seek or does seek to influence the global development agenda. Respondents were also asked to rate their agreement with various statements regarding the Bank's work and the extent to which the Bank is an effective development partner. Furthermore, 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 greatest weaknesses in its work, the most and least effective instruments in helping to reduce poverty in the country, with which 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 sustainable development results in the country, and the Bank's level of effectiveness across thirty-five development areas, such as economic growth, public sector governance, basic infrastructure, social protection, and others.

D. The World Bank's Knowledge: Respondents were asked to indicate the areas on which the Bank should focus its research efforts, and to rate the effectiveness and quality of the Bank's knowledge/research, including how significant of a contribution it makes to development results, its technical quality, and the Bank's effectiveness at providing linkage to non-Bank expertise.

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, and increasing the country's institutional capacity.

F. The Future Role of the World Bank in the country: Respondents were asked to rate how significant a role the Bank should play in the country's development in the near future, and to indicate what the Bank should do to make itself of greater value in the country.

G. Communication and Information Sharing: Respondents were asked to indicate where they get information about economic and social development issues, how they prefer to receive information from the Bank, their access to the Internet, 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 to indicate 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 the country, and their geographic location.

Response rate

A total of 7,142 stakeholders (48% response rate) participated and are part of this review.

Overview

This dataset provides a comprehensive collection of time series data sourced from the World Bank Open Data Platform, covering a wide range of global indicators from 1960 to the most recently published year. It includes economic, social, environmental, and demographic metrics, making it an ideal resource for researchers, data scientists, and policymakers interested in global development trends, economic forecasting, or socio-economic analysis.

A tutorial on how to combined the dataset topics together into one large dataset can be found here

Why this Dataset?

My motivation for this project was to curate a high-quality collection of datasets for World Bank indicators organized by topics and structured in time-series, making them more accessible for data science projects. Since the World Bank’s Kaggle datasets have not been updated since 2019 https://www.kaggle.com/organizations/theworldbank, I saw an opportunity to provide more current data for the data analysis community.

Dataset Collection Contents

This collection brings together more than 800 World Bank indicators organized into 18 topic‑specific CSV files. Each file is structured as a country‑year panel: every row represents a unique combination of year (1960‑present) and ISO‑3 country code, while the columns hold the topic’s indicators.

The collection includes datasets with a variety of indicators, such as: - Economic Metrics: GDP growth (%), GDP per capita, consumer price inflation, merchandise trade, gross capital formation, and more.
- Social Metrics: School enrollment (primary, secondary, tertiary), infant mortality rate, maternal mortality rate, poverty headcount, and more.
- Environmental Metrics: Forest area, renewable energy consumption, food production indices, and more.
- Demographic Metrics: Urban population, life expectancy, net migration, and more.

Usage

This dataset is ideal for a variety of applications, including: - Economic forecasting and trend analysis (e.g., GDP growth, inflation).
- Socio-economic studies (e.g., education, health, poverty).
- Environmental impact analysis (e.g., renewable energy adoption).
- Demographic research (e.g., population trends, migration).

Topic datasets can be merged with each other using year and country code. This tutorial with notebook code can help you get started quickly.

Collection Methodology

The data is collected via a custom software application that discovers and groups high-quality indicators with rules-based logic & artificial intelligence, generates metadata, and performs ETL for the data from the World Bank API. The result is a clean, up‑to‑date collection of World Bank indicators in time-series format that is ready for analysis—no manual downloads or data wrangling required.

Modifications

The original World Bank data has been aggregated and transformed for ease of use. Missing values have been preserved as provided by the World Bank, and no significant transformations have been applied beyond formatting and aggregation into a single file.

Source & Attribution

The World Bank: World Development Indicators

This dataset is publicly available and sourced from the World Bank Open Data Platform and is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. When using this data, please attribute the World Bank as follows: "Data sourced from the World Bank, licensed under CC BY 4.0." For more details on the World Bank’s terms of use, visit: https://www.worldbank.org/en/about/legal/terms-of-use-for-datasets.

License

This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Feel free to use this data in Kaggle notebooks, academic research, or policy analysis. If you create a derived dataset or analysis, I encourage you to share it with the Kaggle community.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Uruguay in 2006, 2010 and 2017. The Enterprise Survey is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The samples for 2006, 2010 and 2017 Uruguay Enterprise Surveys were selected using stratified random sampling, following the methodology explained in the Sampling Note.

Three levels of stratification were used in Honduras ES: industry, establishment size, and region.

In 2006 ES, industry stratification was designed in the following way: In small economies the population was stratified into 3 manufacturing industries, one services industry - retail-, and one residual sector as defined in the sampling manual. Each industry had a target of 120 interviews.

In 2010 ES, industry stratification was designed in the way that follows: the universe was stratified into 3 manufacturing industries, 1 service industry -retail -, and 1 residual sector as defined in the sampling manual. All sectors had a target of 120 interviews. Regional stratification was defined in two regions (city and the surrounding business area): Montevideo and Canelones.

In 2017 ES, industry stratification was designed as follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15-37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72). For the Uruguay ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees). Regional stratification was done across two regions: Montevideo and Canelones.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires - Manufacturing amd Services were used to collect the survey data.

The Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Chad in 2009 and 2018. The Enterprise Survey is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The samples for 2009 and 2018 Chad Enterprise Surveys were selected using stratified random sampling, following the methodology explained in the Sampling Note.

Two levels of stratification were used in the Chad 2009 ES sample: firm sector and firm size. The Industry stratification was designed as follows: the universe was stratified into manufacturing and services industries. The initial sample design had a target of 75 interviews in manufacturing and 75 interviews in services.

In 2018 Chad ES, three levels of stratification were used: industry, establishment size, and region. The industry stratification was designed in the way that follows: the universe was stratified as into manufacturing and services industries- Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72). Regional stratification did not take place for the Chad ES.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires - Manufacturing amd Services were used to collect the survey data.

The Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Colombia CO: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -2.590 % in 2021. Colombia CO: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -2.590 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of -2.590 % in 2021 and a record low of -2.590 % in 2021. Colombia CO: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Poverty and Inequality. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Lebanon LB: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 10.700 % in 2011. Lebanon LB: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 10.700 % from Dec 2011 (Median) to 2011, with 1 observations. The data reached an all-time high of 10.700 % in 2011 and a record low of 10.700 % in 2011. Lebanon LB: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lebanon – Table LB.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Abstract

The survey was conducted in Italy between November 2018 and October 2019 as joint project of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) and the World Bank Group (WBG).

The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector. As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

Italy ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for 2019 Italy ES was selected using stratified random sampling, following the methodology explained in the Sampling Note.

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into four manufacturing industries and two services industries- Food and Beverages (ISIC Rev. 3.1 code 15), Fabricated Metal Products (ISIC code 28), Machinery & Equipment ( ISIC code 29), Other Manufacturing (ISIC codes 16-27, 30-37), Retail (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

For the Italy ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for the Italy ES was done across the five NUTS1 regions: Northwest, Northeast, Center, South and Islands.

Note: Refer to Sampling Structure section in "The Italy 2019 Enterprise Surveys Data Set" document for further details on sampling.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The structure of the data base reflects the fact that 2 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don't know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

The number of interviews per contacted establishments was 17.0%.

Abstract

Aceh Reintegration and Livelihood Survey (ARLS) was funded by the World Bank and designed by a team of researchers from Columbia, Harvard and Stanford Universities. The survey was implemented in Aceh by the research firm A.C. Nielsen from July - September 2008.

The far-reaching goal of this research was to assess prospects for peace and reintegration among both civilian and former-combatant populations in Aceh. The immediate goals of the ARLS were twofold. One was to collect individual-level data for an impact evaluation of the World Bank’s BRA-KDP project, a post-conflict community-drive development program. The second was to collect livelihood and reintegration data on a representative sample of ex-combatants, and a control group of civilian males. Surveys were conducted in a representative sample of 754 villages throughout Aceh. In sampled villages, four over-lapping questionnaires were implemented. 1,075 former combatants, 756 village heads, and 3,046 civilians were interviewed.

Geographic coverage

Aceh, Indonesia

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

• Long Household Questionnaire (LHS): Conducted in a representative sample of villages from the 67 rural subdistricts that received BRA-KDP, as well as in a representative sample of villages in 67 matched subdistricts (described in Section 2). Five households were randomly sampled in sampled villages, and main respondents were selected randomly from all males and females between the ages of 18-65 who had lived in the household for at least one month. The LHS is a representative sample of men and women from BRA-KDP treatment and control subdistricts. It is not representative of other subdistricts and is not representative at the district level.

• Short Household Questionnaire (SHS): Implemented in a representative sample of all subdistricts not included in the LHS. The main goal of this survey is to provide, in conjunction with the LHS, an Aceh-wide representative control group of adult males between the ages of 18-65 for the ex-combatant survey. Male respondents were sampled from two randomly sampled households in selected rural villages and eight randomly sampled households in urban villages. A representative sample of male respondents can be achieved by combining the SHS and the male sub-population of the LHS.

• Ex-TNA Questionnaire: An Aceh-wide representative sample of ex-combatants. Eligible respondents included anyone who fought with GAM-TNA, or was in the GAM-TNA command structure, for at least one month since 1998. A full list of ex-TNA was enumerated in each of the 754 villages and ex-TNA were sampled with a 6 in 10 probability.

• Village Head Questionnaire (VHS): A survey of village-head characteristics, as well as village-level characteristics in all sampled villages.

• Household Rosters: In the LHS, SHS and Ex-TNA surveys, data was collected on every member of the respondent's 1998 and 2008 households. The roster includes demographic, welfare, recruitment and conflict data on all members in the household at those times.

Belgium BE: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 8.100 % in 2021. This records an increase from the previous number of 7.900 % for 2020. Belgium BE: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 9.100 % from Dec 1985 (Median) to 2021, with 25 observations. The data reached an all-time high of 10.500 % in 2015 and a record low of 6.000 % in 1985. Belgium BE: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belgium – Table BE.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

This dataset contains the names and basic information about companies that were contacted in the process of a research which aimed to identify open data companies in emerging markets, specifically in Latin America, Southeast Asia, Africa, as well as India and Russia. The data was collected by several vendors and consultants and was not validated independently by the World Bank staff. Data was collected over a short period of 4-6 weeks in the spring of 2014 and represents data-centric companies that vendors/consultants were able to identify in that timeframe.

CCKP provides open access to a comprehensive suite of climate and climate change resources derived from the latest generation of climate data archives. Products are based on a consistent and transparent approach with a systematic way of pre-processing the raw observed and model-based projection data to enable inter-comparable use across a broad range of applications. Climate products consist of basic climate variables as well as a large collection (70+) of more specialized, application-orientated variables and indices across different scenarios. Precomputed data can be extracted per specified variables, select timeframes, climate projection scenarios, across ensembles or individual models, etc. CCKP adheres to data distributions standards defined under the Coupled Model Intercomparison Project (CMIP) and its contributions to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports and latest scientific methodologies identified by the World Meteorological Organization and climate science community. Climate products are available for the following collections. Downscaled CMIP6 global 0.25-degree – 1950-2100; ERA5 global 0.25-degree – 1950-2022; CRU global 0.50-degree – 1901-2022; Population global 0.25-degree – 1995-2100 (GPW v4).

These lidar data were collected between January 21st and January 27th, 2010, in response to the January 12th magnitude 7.0 Haiti earthquake. The data collection was performed by the Center for Imaging Science at Rochester Institute of Technology (RIT) and Kucera International under sub-contract to ImageCat, Inc., and funded by the Global Facility for Disaster Recovery and Recovery (GFDRR) hosted at the World Bank. All data are available in the public domain. More information about these data can be found at the RIT Information Products Laboratory for Emergency Response (IPLER) 2010 Haiti Earthquake page.

Abstract

The client survey is designed to collect, measure and assess the views of World Bank clients about the quality of the Bank's assistance, in terms of the importance of the Bank's various activities to them, and the Bank's effectiveness in those areas. The survey uses a mail-in questionnaire, covering the Bank's overall contribution and support to development, Bank officials' interactions with clients, various aspects of project design and implementation, and non-lending services. The questionnaire may be custom-tailored to gather needed country-specific information. Optional sections may be added to ask about the role of the Bank's country office, how well the Bank works with others, and the Bank's role in donor coordination and mobilization of resources. Interviews are also conducted with a small number of key clients and partners to provide qualitative inputs to complement the quantitative surveys.

Universe

(1) Senior government officials; (2) Implementing agency and ministerial staff (National) ; (3) private sector representative; (4) civil society representatives; and (5) representatives of bilateral and multilateral donors active in the country (6) Implementing agency and ministerial staff (Local)

Kind of data

Sample survey data [ssd]

Sampling procedure

Multi-stage stratified random sample 104 Individuals

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data collected is computed and analyzed with SPSS. Descriptive analysis (mean and standard deviation) is used to measure importance and the effectiveness the Bank, and to measure the distribution of the respondents. Data cleaning is done during the data entry stage and/or during analyzes of the data.

Abstract

The survey was conducted in Ukraine between March and December 2019. The survey was part of a joint project of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) and the World Bank Group (WBG). The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms’ experiences and enterprises’ perception of the environment in which they operate.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

For the Ukraine ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for 2019 Ukraine ES was selected using stratified random sampling, following the methodology explained in the Sampling Note.

Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Ukraine 2019 Enterprise Surveys Data Set" report, Appendix C.

Industry stratification was designed in the way that follows: the universe was stratified into six manufacturing industries: Food (ISIC Rev. 4.0 10,11), Garments (ISIC 14), Non-Metallic Mineral Products (ISIC 23), Fabricated Metal Products (ISIC 25), Machinery & Equipment (ISIC 28), Other Manufacturing (ISIC codes 12, 13, 15-22, 24, 26, 27, 29-33); and two services industries: Retail (ISIC 47) and Other Services (ISIC 41, 42, 43, 45, 46, 49, 50, 51, 52, 53, 55, 56, 58, 61, 62 and 79).

For the Ukraine ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees). Regional stratification for the Ukraine ES was done across eight regions: West, Sumska, Zaporizka; Vinnytska, Zhytomyrska; Dnipropetrovska, Kharkivska; Kirovohradska, Poltavska; Cherkaska, Chernihivska; Khersonska, Mykolaivska, Odeska; and Kyiv.

Note: See Sections II and III of “The Ukraine 2019 Enterprise Surveys Data Set” report for additional details on the sampling procedure.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires - Manufacturing amd Services were used to collect the survey data.

The Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

The number of interviews per contacted establishments was 8.9%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units.

The share of rejections per contact was 38.8%.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales. 2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Ethiopia Socioeconomic Survey (ESS) 2018-2019 and Ethiopia COVID-19 High Frequency Phone Survey of Households (HFPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Content

The dataset contains monthly prices for 70 сommodities. Columns description is available in a separate attached file.

Acknowledgements

Data is collected from the official website of The World Bank: Commodity Markets (https://www.worldbank.org/en/research/commodity-markets#1).

Inspiration

Data can be used for time series modelling or time series clustering methods as well as for conducting exploratory data analysis for research papers or any other scientific activity.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Bolivia is 1000.

Mode of data collection

Mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Abstract

The Country Opinion Survey in Thailand assists the World Bank Group (WBG) in gaining a better understanding of how stakeholders in Thailand perceive the WBG. It provides the WBG with systematic feedback from national and local governments, multilateral/bilateral agencies, media, academia, the private sector, and civil society in Thailand on 1) their views regarding the general environment in Thailand; 2) their overall attitudes toward the WBG in Thailand; 3) overall impressions of the WBG’s effectiveness and results, knowledge work and activities, and communication and information sharing in Thailand; and 4) their perceptions of the WBG’s future role in Thailand.

Geographic coverage

• Bangkok and vicinity
• Northern Thailand
• Southern Thailand
• Northeastern Thailand
• Central Thailand

Analysis unit

Stakeholder

Universe

Opinion leaders from national and local governments, multilateral/bilateral agencies, media, academia, the private sector, and civil society.

Kind of data

Sample survey data [ssd]

Sampling procedure

From May to June 2019, 390 stakeholders of the WBG in Thailand 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, Prime Minister; office of a minister; office of a parliamentarian; ministries/ministerial departments/implementation agencies; Project Management Units (PMUs) overseeing implementation of WBG projects; consultants/ contractors working on WBG-supported projects/programs; local governments; bilateral and multilateral agencies; private sector organizations; the financial sector/private banks; private foundations; NGOs and community-based organizations; the media; independent government institutions; trade unions; faith-based groups; youth groups; academia/research institutes/think tanks; judiciary branch; and other organizations.

Mode of data collection

Other [oth]

Research instrument

The questionnaire used to collect the survey data consisted of the following 8 sections:

A. General Issues Facing Thailand B. Overall Attitudes toward the World Bank Group C. World Bank Group's Effectiveness and Results D. The World Bank Group's Knowledge Work and Activities (i.e., analysis studies, research, data, reports, conferences) E. Working with the World Bank Group F. The Future Role of the World Bank Group in Thailand G. Communication and Information Sharing H. Background Information

The questionnaire was prepared in English and Thailand

Response rate

52%

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world’s most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of almost 145,000 people in 139 economies, representing 97 percent of the world’s population. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender..

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Abstract

The Country Opinion Survey in Zambia assists the World Bank Group (WBG) in gaining a better understanding of how stakeholders in Zambia perceive the WBG. It provides the WBG with systematic feedback from national and local governments, multilateral/bilateral agencies, media, academia, the private sector, and civil society in Zambia on 1) their views regarding the general environment in Zambia; 2) their overall attitudes toward the WBG in Zambia; 3) overall impressions of the WBG’s effectiveness and results, knowledge work and activities, and communication and information sharing in Zambia; and 4) their perceptions of the WBG’s future role in Zambia.

Geographic coverage

• Central Province
• Copperbelt Province
• Eastern Province
• Luapula Province
• Lusaka Province
• Muchinga Province
• North- Western Province
• Northern Province
• Southern Province
• Western Province

Analysis unit

Stakeholder

Universe

Opinion leaders from national and local governments, multilateral/bilateral agencies, media, academia, the private sector, and civil society.

Kind of data

Sample survey data [ssd]

Sampling procedure

From May to June 2021, 1,331 stakeholders of the WBG in Zambia 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, Vice President; office of a minister; office of a parliamentarian; employees of ministries/ministerial departments/implementation agencies; Project Management Units (PMUs) overseeing implementation of WBG projects; consultants/contractors working on WBG-supported projects/programs; local governments; independent government institutions; the judicial system; state-owned enterprises; bilateral and multilateral agencies; private sector organizations; the financial sector/private banks; private foundations; NGOs and community based organizations; trade unions; faith-based groups; youth groups; academia/research institutes/think tanks; the media; and other organizations.

Mode of data collection

Other [oth]

Research instrument

The questionnaire used to collect the survey data consisted of the following 8 sections: A. Overall Context B. Overall Attitudes toward the World Bank Group C. World Bank Group’s Work and Engagement on the Ground D. World Bank Group’s Support in Development Areas E. World Bank Group’s Knowledge Work and Activities F. The Future Role of the World Bank Group in Zambia G. Communication and Information Sharing H. Background Information

The questionnaire was prepared in English.

Response rate

Response rate was 43%.