Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. The Worldwide Governance Indicators (WGI) report on six broad dimensions of governance for more than 200 economies since 1996: (I) Voice and Accountability; (II) Political Stability and Absence of Violence; (III) Government Effectiveness; (IV) Regulatory Quality; (V) Rule of Law; and (VI) Control of Corruption. The WGI are composite governance indicators based on over 30 underlying data sources. These data sources are rescaled and combined to create the six aggregate indicators using a statistical methodology known as an unobserved components model. A key feature of the methodology is that it generates margins of error for each governance estimate. These margins of error need to be taken into account when making comparisons across countries and over time. The WGI aggregate indicators and underlying source data are available at http://www.govindicators.org.

For further details, please refer to http://info.worldbank.org/governance/wgi/Home/Documents

This dataset include data pulled from the World Bank 2009, the World Values Survey wave 6, Transparency International from 2009. The data were used to measure perceptions of expertise from individuals in nations that are recipients of development aid as measured by the World Bank.

The World Bank is a treasure trove of information. :- https://data.worldbank.org/

Generally the Gross Domestic Product of a country = the total output of the country = measure of development/total affluence of the country is measured by indicators such as household spending, government spending, level of investments etc.

Please see Bank of England explanation of GDP here :- http://edu.bankofengland.co.uk/knowledgebank/what-is-gdp/

I have argued that GDP could instead be measured better by primary indicators that lead to these what I call "secondary indicators".

Primary indicators are such as :- level of education. I hypothesize that a higher level of education leads to higher household income and hence higher household spending. So does knowing education levels of a country allow us to predict the GDP of the country?

I have used the list of primary indicators below to do a regression of the GDP per person :- (1) Women making informed choices regarding healthcare - The null hypotheses (H0)----> is the higher the level of women's education - the higher the level of national education and lesser infant mortality rates(which might be a stretch) and hence higher household income --> higher household spending ---> higher GDP. (2) Rural Population % - The null hypotheses (H0) is -----> higher rural population ----> lower per capita household income----> lower level of household spending----> lower GDP. (3) Ratio of Population having education ----> similar to above. You get the point hopefully by now... if not read a introductory macroeconomics textbook or course like this :- https://www.edx.org/course/introduction-economics-macroeconomics-snux-snu044-088-2x-0 (4) Legal Rights Strength Index-----> This actually comes from Islam. In Islam - the affluence of a country is related to truthfulness, rule of law being abided in the country etc.. For those who can understand Urdu/Hindi - please watch this video :- https://www.youtube.com/watch?v=XLjicUv0KYs (5) Credit to Private Sector -----> easier it is to open a business, work on ideas-----> higher should be the output of the country (6) Births attended by Skilled Staff ------> less infant mortality ----> indicates higher level of education and health care in the country ------> can indicate higher government spending among other factors ------>and should translate to higher level of GDP. (6) ATMMachines Ratio per 1000 people ---------> Higher level -----> shows finance is easily available -----> institutions are developed -----> maybe even indicates better public infrastructure-----> should indicate higher personal and government funding. (7) Agricultural Machines per hectare of land ------> higher automation -----> better access to finance for rural areas ------> should lead to higher GDP. (8) Literacy Rate Adults -----> the higher level of education in adults ----> higher private spending -----> should lead to higher GDP. (9) Accounts Ratio Financial Institutions -----> how many people have bank accounts who are male and over 15 ------> shows level of private spending-----> level of finance and infrastructure and hence government funding maybe -----> higher GDP.

Abstract

The Country Opinion Survey in Guyana assists the World Bank Group (WBG) in gaining a better understanding of how stakeholders in Guyana 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 Guyana on 1) their views regarding the general environment in Guyana; 2) their overall attitudes toward the WBG in Guyana; 3) overall impressions of the WBG’s effectiveness and results, knowledge work and activities, and communication and information sharing in Guyana; and 4) their perceptions of the WBG’s future role in Guyana.

Geographic coverage

• Region 1
• Region 2
• Region 3
• Region 4 (other than Georgetown)
• Georgetown
• Region 5
• Region 6
• Region 7
• Region 8
• Region 9
• Region 10

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 April to June 2021, 301 stakeholders of the WBG in Guyana 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; 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; indigenous peoples’ groups; academia/research institutes/think tanks; the media; and other organizations.

Mode of data collection

Other [oth]

Research instrument

The questionnaire is in English and provided as an external resource.

Response rate

37%

Bhutan BT: CPIA: Equity of Public Resource Use Rating: 1=Low To 6=High data was reported at 4.500 NA in 2023. This stayed constant from the previous number of 4.500 NA for 2022. Bhutan BT: CPIA: Equity of Public Resource Use Rating: 1=Low To 6=High data is updated yearly, averaging 4.500 NA from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 4.500 NA in 2023 and a record low of 4.000 NA in 2010. Bhutan BT: CPIA: Equity of Public Resource Use Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bhutan – Table BT.World Bank.WDI: Governance: Policy and Institutions. Equity of public resource use assesses the extent to which the pattern of public expenditures and revenue collection affects the poor and is consistent with national poverty reduction priorities.;World Bank Group, CPIA database (http://www.worldbank.org/ida).;Unweighted average;

CPIA quality of public administration rating (1=low to 6=high) in World was reported at 2.8117 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - CPIA quality of public administration rating (1=low to 6=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Georgia GE: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 36.500 % in 2016. This records an increase from the previous number of 36.400 % for 2015. Georgia GE: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 38.200 % from Dec 1996 (Median) to 2016, with 21 observations. The data reached an all-time high of 41.300 % in 1998 and a record low of 36.300 % in 2004. Georgia GE: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Georgia – Table GE.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Jordan JO: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 33.700 % in 2010. This records an increase from the previous number of 32.600 % for 2008. Jordan JO: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 36.100 % from Dec 1986 (Median) to 2010, with 7 observations. The data reached an all-time high of 43.400 % in 1992 and a record low of 32.600 % in 2008. Jordan JO: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Japan JP: Income Share Held by Lowest 20% data was reported at 7.400 % in 2008. Japan JP: Income Share Held by Lowest 20% data is updated yearly, averaging 7.400 % from Dec 2008 (Median) to 2008, with 1 observations. Japan JP: Income Share Held by Lowest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

The World Bank Group is interested in gauging the views of clients and partners who are either involved in development in Serbia or who observe activities related to social and economic development. The following survey will give the World Bank Group's team that works in Serbia, 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 Serbia. A local independent firm has been 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 Serbia perceive the World Bank Group; - Obtain systematic feedback from stakeholders in Serbia regarding: - Their views regarding the general environment in Serbia; - Their overall attitudes toward the World Bank Group in Serbia; - Overall impressions of the World Bank Group's effectiveness and results, project/program related issues, knowledge work and activities, and communication and information sharing in Serbia; and - Perceptions of the World Bank Group's future role in Serbia. - Use data to help inform Serbia country team's strategy.

Geographic coverage

Belgrade, Vojvodina, Central Serbia, South Serbia.

Analysis unit

Stakeholder

Universe

Stakeholders of the World Bank in Serbia.

Kind of data

Sample survey data [ssd]

Sampling procedure

From November 2013 to January 2014, stakeholders of the World Bank Group in Serbia were invited to provide their opinions on the WBG's assistance to the country by participating in a country survey.

Participants in the survey were drawn from among the office of the President or 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 Group-supported projects/programs; project management units (PMUs) overseeing implementation of a project; local government officials or staff; bilateral agencies; multilateral agencies; private sector organizations; private foundations; the financial sector/private banks; NGOs; community-based organizations (CBOs); the media; independent government institutions; trade unions; faith-based groups; academia/research institutes/think tanks; and the judiciary branch.

Mode of data collection

Other [oth]

Research instrument

The Questionnaire consists of 8 sections:

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

B. Overall Attitudes toward the World Bank Group: Respondents were asked to rate their familiarity with the WBG, its effectiveness in Serbia, WBG staff preparedness, the effectiveness of its activities, to what extent it should provide capacity building support to certain groups, the importance and effectiveness of the WBG's current capacity building work, their agreement with various statements regarding the WBG's work, and the extent to which it is an effective development partner. Respondents were also asked to indicate the sectoral areas on which it would be most productive for the WBG to focus its resources, the WBG's greatest values and greatest weaknesses, its most effective instruments, with which stakeholder groups the WBG should collaborate more, if the WBG should have more or less of a local presence in Serbia, and to what they attributed slowed or failed reform efforts.

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 Serbia, the extent to which the WBG meets Serbia's needs for knowledge services and financial instruments, the extent to which the WBG's internal evaluation mechanisms hold it accountable for achieving results, and the importance of the WBG's involvement and the WBG's level of effectiveness across twenty-seven development areas. Respondents were also asked to indicate if WBG decisions regarding its Serbia program were made primarily in country or at Headquarters.

D. The World Bank Group's Knowledge Work and Activities: Respondents were asked to indicate how frequently they consult WBG knowledge work and to rate the quality of the WBG's knowledge work and 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 the extent to which various aspects of the WBG's delivery work contributes to solving Serbia's development challenges and their level of agreement with a series of statements regarding working with the WBG. Respondents were also asked to indicate if the WBG operates with too much risk.

F. The Future Role of the World Bank Group in Serbia: Respondents were asked to indicate what the WBG should do to make itself of greater value in Serbia and which of its services the WBG should offer more of in Serbia.

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, their Internet access, and their usage and evaluation of the WBG's websites. Respondents were asked about their awareness of the WBG's Access to Information policy, past information requests from the WBG, and their level of agreement that they use more data from the World Bank Group as a result of the WBG's Open Data policy. Respondents were also asked to evaluate the WBG's information accessibility and responsiveness to information requests.

H. Background Information: Respondents were asked to indicate their current position, specialization, whether they currently collaborate or have ever collaborated with the WBG in Serbia, what their position was when they did work with the WBG, with which WBG agencies they work, their exposure to the WBG in Serbia, and their geographic location.

Questionnaires were in English and Serbian.

Response rate

Paper questionnaires were sent to 34 potential respondents via courier or post. Of those, 6 were completed and returned via courier or post (18% response rate). Online questionnaires were sent to 608 potential respondents via email. Of those, 247 were completed (41% response rate).

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

This Data consists of some world statistics published by the World Bank since 1961

Variables:

1) Agriculture and Rural development - 42 indicators published on this website. https://data.worldbank.org/topic/agriculture-and-rural-development

2) Access to electricity (% of the population) - Access to electricity is the percentage of the population with access to electricity. Electrification data are collected from industry, national surveys, and international sources.

3) CPIA gender equality rating (1=low to 6=high) - Gender equality assesses the extent to which the country has installed institutions and programs to enforce laws and policies that promote equal access for men and women in education, health, the economy, and protection under law.

4) Mineral rents (% of GDP) - Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.

5) GDP per capita (current US$) - GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars.

6) Literacy rate, adult total (% of people ages 15 and above)- Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.

7) Net migration - Net migration is the net total of migrants during the period, that is, the total number of immigrants less the annual number of emigrants, including both citizens and noncitizens. Data are five-year estimates.

8) Birth rate, crude (per 1,000 people) - Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.

9) Death rate, crude (per 1,000 people) - Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.

10) Mortality rate, infant (per 1,000 live births) - Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.

11) Population, total - Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.

Acknowledgements

These datasets are publicly available for anyone to use under the following terms provided by the Dataset Source https://www.worldbank.org/en/about/legal/terms-of-use-for-datasets

Banner photo by https://population.un.org/wpp/Maps/

Inspiration

Subsaharan Africa and east Asia record high population total, actually Subsaharan Africa population bypassed Europe and central Asia population by 2010, has this been influenced by crop and food production, large arable land, high crude birth rates(influx), low mortality rates(exits from the population) or Net migration.

CONTENT

This dataset contains data on key indicators of world's top 6 Economies (by GDP) which includes USA, China, Japan, Germany, United Kingdom, India between the time interval of 30 years from 1990 to 2020. Data scraped from World Bank Data website and processed using Python Pandas library. This dataset could be used to do Time Series Analysis and Forecasting.

Code notebook:

https://deepnote.com/workspace/charan-chandrasekaran-9b7f-9e1375d3-f150-44ca-a9fb-feb08a1e8585/project/Data-extraction-from-World-bank-data-on-Top-6-Economies-2cdf8112-d412-4044-a58e-5e464804e9b6

INDICATORS

1. GDP (current US$)
2. GDP, PPP (current international $)
3. GDP per capita (current US$)
4. GDP growth (annual %)
5. Imports of goods and services (% of GDP)
6. Exports of goods and services (% of GDP)
7. Central government debt, total (% of GDP)
8. Total reserves (includes gold, current US$)
9. Unemployment, total (% of total labor force) (modelled ILO estimate)
10. Inflation, consumer prices (annual %)
11. Personal remittances, received (% of GDP)
12. Population, total
13. Population growth (annual %)
14. Life expectancy at birth, total (years)
15. Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)

SOURCE

The World Bank : https://data.worldbank.org/country

The database (version August 2022) is built upon the released Global Subnational Atlas of Poverty (GSAP) (World Bank, 2021). In this database, we assemble a new panel dataset that provides (headcount) poverty rates using the daily poverty lines of US $1.90, $3.20, and $5.50 (based on the revised 2011 Purchasing Power Parity (PPP) dollars). This database is generated using household income and consumption surveys from the World Bank’s Global Monitoring Database (GMD), which underlie country official poverty statistics, and offers the most detailed subnational poverty data on a global scale to date. The Global Subnational Atlas of Poverty (GSAP) is produced by the World Bank’s Poverty and Equity Global Practice, coordinated by the Data for Goals (D4G) team, and supported by the six regional statistics teams in the Poverty and Equity Global Practice, and Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) at the World Bank. The Global Monitoring Database (GMD) is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity. The household survey data are typically collected by national statistical offices in each country, and then compiled, processed, and harmonized. The process is coordinated by the Data for Goals (D4G) team and supported by the six regional statistics teams in the Poverty and Equity Global Practice. Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) also contributed historical data from before 1990, and recent survey data from Luxemburg Income Studies (LIS). Selected variables have been harmonized to the extent possible such that levels and trends in poverty and other key sociodemographic attributes can be reasonably compared across and within countries over time. The GMD’s harmonized microdata are currently used in Poverty and Inequality Platform (PIP), World Bank’s Multidimensional Poverty Measures (WB MPM), the Global Database of Shared Prosperity (GDSP), and Poverty and Shared Prosperity Reports. Reference: World Bank. (2021). World Bank estimates based on data from the Global Subnational Atlas of Poverty, Global Monitoring Database. World Bank: Washington. https://datacatalog.worldbank.org/search/dataset/0042041

Switzerland Gini Coefficient (GINI Index): World Bank Estimate data was reported at 32.300 % in 2015. This records a decrease from the previous number of 32.500 % for 2014. Switzerland Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 32.550 % from Dec 2006 (Median) to 2015, with 10 observations. The data reached an all-time high of 34.300 % in 2007 and a record low of 31.600 % in 2012. Switzerland Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high) in World was reported at 2.7792 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Abstract

The World Bank has launched a quick-deploying high-frequency phone-monitoring survey of households to generate near real-time insights on the socio-economic impact of COVID-19 on households which hence to be used to support evidence-based response to the crisis. At a moment when all conventional modes of data collection have had to be suspended, a phone-based rapid data collection/tracking tool can generate large payoffs by helping identify affected populations across the vast archipelago as the contagion spreads, identify with a high degree of granularity the mechanisms of socio-economic impact, identify gaps in public policy response as the Government responds, generating insight that could be useful in scaling up or redirecting resources as necessary as the affected population copes and eventually regains economic footing.

Analysis unit

Household-level; Individual-level: household primary breadwinners, respondent, student, primary caregivers, and under-5 years old kids

Sampling procedure

The sampling frame of the Indonesia high-frequency phone-based monitoring of socio-economic impacts of COVID-19 on households was the list of households enumerated in three recent World Bank surveys, namely Urban Survey (US), Rural Poverty Survey (RPS), and Digital Economy Household Survey (DEHS). The US was conducted in 2018 with 3,527 sampled households living in the urban areas of 10 cities and 2 districts in 6 provinces. The RPS was conducted in 2019 with the sample size of 2,404 households living in rural areas of 12 districts in 6 provinces. The DEHS was conducted in 2020 with 3,107 sampled households, of which 2,079 households lived in urban areas and 1,028 households lived in rural areas in 26 districts and 31 cities within 27 provinces. Overall, the sampled households drawn from the three surveys across 40 districts and 35 cities in 27 provinces (out of 34 provinces). For the final sampling frame, six survey areas of the DEHS which were overlapped with the survey areas in the UPS were dropped from the sampling frame. This was done in order to avoid potential bias later on when calculating the weights (detailed below). The UPS was chosen to be kept since it had much larger samples (2,016 households) than that of the DEHS (265 households). Three stages of sampling strategies were applied. For the first stage, districts (as primary sampling unit (PSU)) were selected based on probability proportional to size (PPS) systematic sampling in each stratum, with the probability of selection was proportional to the estimated number of households based on the National Household Survey of Socio-economic (SUSENAS) 2019 data. Prior to the selection, districts were sorted by provincial code.

In the second stage, villages (as secondary sampling unit (SSU)) were selected systematically in each district, with probability of selection was proportional to the estimated number of households based on the Village Potential Census (PODES) 2018 data. Prior to the selection, villages were sorted by sub-district code. In the third stage, the number of households was selected systematically in each selected village. Prior to the selection, all households were sorted by implicit stratification, that is gender and education level of the head of households. If the primary selected households could not be contacted or refused to participate in the survey, these households were replaced by households from the same area where the non-response households were located and with the same gender and level of education of households’ head, in order to maintain the same distribution and representativeness of sampled households as in the initial design.

In the Round 8 survey where we focused on early nutrition knowledge and early child development, we introduced an additional respondent who is the primary caregiver of under 5 years old in the household. We prioritized the mother as the target of caregiver respondents. In households with multiple caregivers, one is randomly selected. Furthermore, only the under 5 children who were taken care of by the selected respondent will be listed in the early child development module.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire in English is provided for download under the Documentation section.

Response rate

The HiFy survey was initially designed as a 5-round panel survey. By end of the fifth round, it is expected that the survey can maintain around 3,000 panel households. Based on the experience of phone-based, panel survey conducted previously in other study in Indonesia, the response rates were expected to be around 60 percent to 80 percent. However, learned from other similar surveys globally, response rates of phone-based survey, moreover phone-based panel survey, are generally below 50 percent. Meanwhile, in the case of the HiFy, information on some of households’ phone numbers was from about 2 years prior the survey with a potential risk that the targeted respondents might not be contactable through that provided numbers (already inactive or the targeted respondents had changed their phone numbers). With these considerations, the estimated response rate of the first survey was set at 60 percent, while the response rates of the following rounds were expected to be 80 percent. Having these assumptions and target, the first round of the survey was expected to target 5,100 households, with 8,500 households in the lists. The actual sample of households in the first round was 4,338 households or 85 percent of the 5,100 target households. However, the response rates in the following rounds are higher than expected, making the sampled households successfully interviewed in Round 2 were 4,119 (95% of Round 1 samples), and in Rounds 3, 4, 5, 6, 7, and 8 were 4,067 (94%), 3,953 (91%), 3,686 (85%), 3,471 (80%), 3,435 (79%), 3,383 (78%) respectively. The number of balanced panel households up to Rounds 3, 4, 5, 6, 7, and 8 are 3,981 (92%), 3,794 (87%), 3,601 (83%), 3,320 (77%), 3,116 (72%), and 2,856 (66%) respectively.

CPIA efficiency of revenue mobilization rating (1=low to 6=high) in World was reported at 3.2468 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - CPIA efficiency of revenue mobilization rating (1=low to 6=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National

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

All formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency, which in the case of Jamaica was the Companies Office of Jamaica. The universe table is the total number of eligible establishments, and the table is partitioned by the stratification groups (industry classification, establishment size, and subnational region) in a country.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Jamaica 2024 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance.6 These topics include general firm characteristics, infrastructure, sales and supplies, trade, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

Response rate

Overall survey response rate was 27.0%.

Abstract

The World Bank is interested in gauging the views of clients and partners who are either involved in development in Croatia 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 Croatia, 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 Croatia. The World Bank commissioned an independent firm to oversee the logistics of this effort in Croatia.

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

Geographic coverage

National

Analysis unit

Stakeholder

Universe

Stakeholders of the World Bank in Croatia

Kind of data

Sample survey data [ssd]

Sampling procedure

In May-July 2012, 694 stakeholders of the World Bank in Croatia 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; international partners; bilateral and multilateral agencies; private sector organizations; private foundations; the financial sector/private banks; NGOs; community-based organizations (CBOs); the media; independent government institutions; trade unions; academia/research institutes/think tanks; and the judiciary branch.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The Questionnaire consists of 8 Sections:

A. General Issues facing Croatia: Respondents were asked to indicate whether Croatia is headed in the right direction, what they thought were the top three most important development priorities, which areas would contribute most to achieving EU integration and EU income convergence for Croatia, and which areas would contribute most to generating economic growth in Croatia.

B. Overall Attitudes toward the World Bank: Respondents were asked to rate their familiarity with the World Bank, the Bank's effectiveness in Croatia, Bank staff preparedness, the extent to which the Bank should seek to influence the global development agenda, their agreement with various statements regarding the Bank's work, and the extent to which the Bank is an effective development partner. Respondents were also 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 Croatia achieve EU integration and EU income convergence, 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 development results in Croatia, the extent to which the Bank meets Croatia's need for knowledge services and financial instruments, and the Bank's level of effectiveness across thirty-six development areas, such as EU integration and EU income convergence, economic growth, job creation, and others.

D. The World Bank's Knowledge: Respondents were asked to indicate how frequently they consult Bank knowledge/research, 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, the Bank's effectiveness at providing linkage to non-Bank expertise, and the extent to which Croatia received value for money from the Bank's fee-for-service products.

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 Croatia's institutional capacity, and providing effective implementation support.

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

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, 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 Croatia, and their geographic location.

Response rate

A total of 198 stakeholders participated in the country survey (29% response rate).