Abstract

Key components of the WFSO database cover the prevalence of severe food insecurity, including estimates for countries lacking official data, population sizes of the severely food insecure, required safety net financing, and corresponding estimates expressed on the Integrated Phase Classification (IPC) scale. Data is presented in a user-friendly format.

WFSO data primarily relies on hunger and malnutrition data from the State of Food Security and Nutrition in the World (SOFI) report, led by the Food and agriculture Organization (FAO) in collaboration with multiple UN agencies. WFSO complements SOFI data by providing estimates for unreported countries. Historical estimates are produced with a machine learning model leveraging World Development Indicators (WDI) for global coverage. This model has been extended to express outputs on the IPC scale by converting estimates using a nonlinear beta regression estimated on a normalized range, and distributionally adjusted using a smooth threshold transformation.

Financing needs for safety nets are calculated similarly to past approaches by the International Development Association (IDA) to assess food insecurity response needs (IDA (2020) and IDA (2021)). Preliminary estimates and projections rely on the same model and incorporate International Monetary Fund (IMF)'s World Economic Outlook (WEO) growth and inflation forecasts. WEO data reflects the IMF's expert analysis from various sources, including government agencies, central banks, and international organizations.

Minor gaps in WDI data inflation data are replaced with unofficial WEO estimates. Minor inflation data gaps not covered by both, are replaced with unofficial inflation estimates from the World Bank's Real Time Food Prices (RTFP) data.

The WFSO is updated three times a year, coinciding with IMF's WEO and SOFI releases. It provides food security projections that align with economic forecasts, aiding policymakers in integrating food security into economic planning.

The WFSO database serves various purposes, aiding World Bank economists and researchers in economic analysis, policy recommendations, and the assessment of global financing needs to address food insecurity.

Additionally, the WFSO enhances transparency in global food security data by tracking regional and global figures and breaking them down by individual countries. Historical estimates support research and long-term trend assessments, especially in the context of relating outlooks to past food security crises.

Geographic coverage

World

Geographic coverage notes

191 countries and territories mutually included by the World Bank's WDI and IMF's WEO databases. The country coverage is based on mutual inclusion in both the World Bank World Development Indicators database and the International Monetary Fund’s World Economic Outlook database. Some countries and territories may not be covered. Every attempt is made to provide comprehensive coverage. To produce complete historical predictions, missing data in the WDI are completed with unofficial data from the WEO and the World Bank's RTFP data when inflation data is not available in either database. Final gaps in the WDI and WEO are interpolated using a Kernel-based pattern-matching algorithm. See background documentation for equations.

Analysis unit

Country

Kind of data

Process-produced data [pro]

Dataset is generated using this notebook, which * begins with the original CETS (Catalog of Economic Time Series)[1] containing 1504 indicators and updates the list according to additions, deletions and code changes released by World Bank on 1st July 2020[2]. This step produces a list of 1273 indicators, each uniquely identified by an indicator code. Indicators come coded according to the convention TT.GGG.SSSS.EE, where TT is the 2-character code for the topic, GGG the 3-character code for the general subject, SSSS the 4-character code for the specific subject and EE the 2-character code for the extension. As an example, SH.IMM.HEPB is the indicator code for HepB3 immunization, a social health (SH) indicator. * downloads the data for 1270 of the 1273 indicators; one .csv file per indicator. The 3 indicators missing (SI.POV.2DAY, SI.POV.GAP2 and TX.VAL.MRCH.R6.CD), when searched [3], are reported as 'no longer available'. * removes 434 of the 1270 indicators. Indicators are identified for removal based on the proportion of missing values. The 434 identified for removal do not have data for >20% of the 187 countries of interest [4]. We are left with 1270 - 434 = 836 indicators. * renames some countries so that country names agree with those of [4]. * extracts the most recent value (out of multiple years where data is available) for each indicator for each country. Which year's data gets taken for which country -- this info is logged in f'{OUT}/wbank01july_year.csv'. * fills any missing value with the continental median of the respective indicator. * collates the 836 indicators for 187 countries into a single 187x838 pandas dataframe. The 2 additional columns are Country/Region and continent.

[1] http://databank.worldbank.org/data/download/site-content/WDI_CETS.xls

[2] http://databank.worldbank.org/data/download/WDIrevisions.xls

[3] https://data.worldbank.org/indicator/

[4] https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/

The Global Welfare Dataset (GLOW) is a cross-national panel dataset that aims at facilitating comparative social policy research on the Global North and Global South. The database includes 381 variables on 61 countries from years between 1989 and 2015. The database has four main categories of data: welfare, development, economy and politics.The data is the result of an original data compilation assembled by using information from several international and domestic sources. Missing data was supplemented by domestic sources where available. We sourced data primarily from these international databases:Atlas of Social Protection Indicators of Resilience and Equity – ASPIRE (World Bank)Government Finance Statistics (International Monetary Fund)Social Expenditure Database – SOCX (Organisation for Economic Co-operation and Development)Social Protection Statistics – ESPROSS (Eurostat)Social Security Inquiry (International Labour Organization)Social Security Programs Throughout the World (Social Security Administration)Statistics on Income and Living Conditions – EU-SILC (European Union)World Development Indicators (World Bank)However, much of the welfare data from these sources are not compatible between all country cases. We conducted an extensive review of the compatibility of the data and computed compatible figures where possible. Since the heart of this database is the provision of social assistance across a global sample, we applied the ASPIRE methodology in order to build comparable indicators across European and Emerging Market economies. Specifically, we constructed indicators of average per capita transfers and coverage rates for social assistance programs for all the country cases not included in the World Bank’s ASPIRE dataset (Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, and United Kingdom.)For details, please see:https://glow.ku.edu.tr/about

United States US: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Latin America & The Caribbean data was reported at 17.755 % in 2016. This records an increase from the previous number of 17.642 % for 2015. United States US: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Latin America & The Caribbean data is updated yearly, averaging 14.701 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 23.170 % in 1960 and a record low of 10.495 % in 1986. United States US: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Latin America & The Caribbean data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Imports. Merchandise imports from low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Latin America and the Caribbean region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

[This study utilizes a balanced panel dataset covering 39 Sub-Saharan African countries over the period 2000 to 2022. The dataset includes key indicators across three thematic areas: climate finance, institutional quality, and development outcomes. Climate finance variables comprise mitigation-related finance (LNMRF) and climate-related finance (LNCRF), both measured in natural logarithmic form to normalize distribution and reflect elasticity. Institutional quality (INSTQI) is a composite index derived from six governance dimensions, standardized for cross-country comparability. Development outcomes are measured using three dependent variables: the Climate Resilience Index (CRI), the Inclusive Development Index (IDI), and a newly constructed Climate-Resilient Inclusive Development Index (CRIDI), which combines both resilience and inclusion metrics. Control variables include foreign direct investment (FDI, % of GDP), trade openness (log of trade as % of GDP), GDP growth (annual %), and population growth (annual %), sourced primarily from the World Bank’s World Development Indicators, OECD Climate Finance Database, and relevant institutional governance databases (e.g., Worldwide Governance Indicators). All variables are cleaned, transformed (e.g., log-linearized where appropriate), and harmonized to ensure temporal and spatial consistency. Missing data were treated using multiple imputation and interpolation techniques to preserve the panel structure. The final dataset is suitable for econometric estimation using dynamic panel models such as System GMM and EGLS (Period SUR), which correct for potential endogeneity, heteroskedasticity, and autocorrelation., This study utilizes a balanced panel dataset covering 39 Sub-Saharan African countries over the period 2000 to 2022. The dataset includes key indicators across three thematic areas: climate finance, institutional quality, and development outcomes. Climate finance variables comprise mitigation-related finance (LNMRF) and climate-related finance (LNCRF), both measured in natural logarithmic form to normalize distribution and reflect elasticity. Institutional quality (INSTQI) is a composite index derived from six governance dimensions, standardized for cross-country comparability. Development outcomes are measured using three dependent variables: the Climate Resilience Index (CRI), the Inclusive Development Index (IDI), and a newly constructed Climate-Resilient Inclusive Development Index (CRIDI), which combines both resilience and inclusion metrics. Control variables include foreign direct investment (FDI, % of GDP), trade openness (log of trade as % of GDP), GDP growth (annual %), and population growth (annual %), sourced primarily from the World Bank’s World Development Indicators, OECD Climate Finance Database, and relevant institutional governance databases (e.g., Worldwide Governance Indicators). All variables are cleaned, transformed (e.g., log-linearized where appropriate), and harmonized to ensure temporal and spatial consistency. Missing data were treated using multiple imputation and interpolation techniques to preserve the panel structure. The final dataset is suitable for econometric estimation using dynamic panel models such as System GMM and EGLS (Period SUR), which correct for potential endogeneity, heteroskedasticity, and autocorrelation.]

Abstract

The SABER Service Delivery survey tool was developed in 2016 in the Global Engagement and Knowledge Unit of the Education Global Practice (GP) at the World Bank, as an initiative to uncover bottlenecks that inhibit student learning in low and middle income countries and to better understand the quality of education service delivery in a country as well as gaps in policy implementation. The SABER SD survey collects strategic information on school inputs and processes that influence learning outcomes. The data collected aims to uncover the extent to which policies translate into implementation and practice. As a global initiative, SABER SD provides data for the new global lead indicator on learning, which makes it easier to monitor the Sustainable Development Goal of achieving universal primary education.

SABER SD was created using knowledge and expertise from two major initiatives at the World Bank: SABER (Systems Approach for Better Education Results) and the SDI (Service Delivery Indicators) tools. The SABER program conducts research and knowledge from leading expertise in various themes of education. Using diagnostic tools and detailed policy information, the SABER program collects and analyzes comparative data and knowledge on education systems around the world and highlights the policies and institutions that matter most to promote learning for all children and youth. The SDI program is a large-scale survey of education and health facilities across Africa. The new SABER SD tool builds on and contributes to the growing SABER evidence base by capturing policy implementation measures identified as important in the frameworks of the core SABER domains of School Autonomy and Accountability, Student Assessment, Teachers, Finance, Education Management Information Systems, and Education Resilience.

The SABER SD instrument collects data at the school level and asks questions related to the roles of all levels of government (including local and regional). The tool provides comprehensive data on teacher effort and ability; principal leadership; school governance, management, and finances; community participation; and student performance in math and language and includes a classroom observation module.

Geographic coverage

The SABER SD survey in Lao PDR was nationally representative. Schools from all 18 provinces in Lao PDR were included in the sample.

Analysis unit

The unit of analysis varies for each of the modules. They are as follows: Module 1, the unit of analysis is the school. Module 2, the unit of analysis is the teacher. Module 3, the unit of analysis is the school/principal. Module 4, the unit of analysis is the classroom/school/teacher. Module 5, the unit of analysis is the student. Module 6, the unit of analysis is the teacher.

For modules where the unit of analysis is not the school (i.e., teachers and/or students), it is possible to create an average for the school based on groupings by the unique identifier – the school code.

Universe

The target was to have a nationally representative sample. All primary schools in Lao PDR were included in the original sample. The final pool of primary schools from which the sample was drawn included those with a grade 4 population of students.

Kind of data

Sample survey data [ssd]

Sampling procedure

The SABER Service Delivery survey was implemented in primary schools across Lao PDR, with detailed information being collected from Grade 4. According to official records from the Ministry of Education and Sports (MOES) education management information system for the school year 2015-2016, 8,864 primary schools exist across the country. The sample was created using probability proportional to size (PPS) according to the size of students enrolled in Grade 4. The target population of the survey was Grade 4 students, so all schools with at least one student enrolled in Grade 4 were considered in the sample.

Schools were stratified for sampling along four dimensions to ensure representation. For each of these, stratification was done on a discrete variable. The four sampling strata used for this survey with a target sample size of 200 schools across Lao PDR are the following: Urban/Rural, Public/Private, Single grade/Multi-grade, Priority/Non-Priority.

Multiple sampling scenarios were created according to the number of schools within a stratum. The final sample option was selected based on the standard errors of the sample as a whole and the errors within a subgroup. Please see the sampling appendix in the final report for more information (Appendix A).

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

After a first round of cleaning and editing carried out by IRL, the raw data was sent to the World Bank team by the survey firm. The World Bank team ran data checks on the raw data files, with comments and questions sent back to the survey firm on inconsistencies and/or missing data. The survey firm then responded to the questions, if any data are missing, the field team collects the data again or corrects the incorrect information. This happened in a few cases where the principals of the schools were contacted again to confirm and verify certain answers from the school.

Once the data was finalized, the weights were attached back to the dataset. The weighting procedure was done by the Development Economics Vice Presidency (DEC) team at the World Bank. Finally, with weights attached, the final datasets for each module (1 through 6) were produced.

For this data, many modules were also merged together to run analysis across different school components. There is one final data file which has merged modules 1, 2, 3, 5, and 6.

Response rate

100% response rate from all 200 schools in sample. No reserve schools were activated.

Ethiopia ET: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Within Region data was reported at 30.231 % in 2016. This records an increase from the previous number of 28.119 % for 2015. Ethiopia ET: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Within Region data is updated yearly, averaging 1.627 % from Dec 1960 (Median) to 2016, with 55 observations. The data reached an all-time high of 33.146 % in 2013 and a record low of 0.176 % in 1996. Ethiopia ET: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Within Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank: Exports. Merchandise exports to low- and middle-income economies within region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in the same World Bank region as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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. 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 by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in the majority of economies was 1,000 individuals.

Mode of data collection

Landline and cellular telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

This folder contains data used in chapter 4 of the thesis. Various data sources are used. Data on trade openness, services sector employment, education, and financial development are sourced from the World Development Indicators Database of the World Bank. The data on digital infrastructure captures Internet access, fixed telephone subscriptions (per 100 people), and mobile cellular subscriptions (per 100 people). Data on institutional quality and inflation comes World Governance Indicators and while data on International Monetary Fund (IMF) database respectively. For missing observations, besides the institutional quality variable, we impute these missing observations using their growth trend. However, for the variable of institutional quality, data points for the years 1997, 1999, and 2001 are not available. We use the averages of the two periods before and after to impute them. The final sample contains data on 45 Sub-Saharan African countries for the period 1996–2017. The analysis was implemented in stata. We use Fixed-Effects Method for the baseline estimates. Subsequently, we address endogeneity by employing the Fixed Effect IV (FEIV) method and the Lewbel (2012) Fixed Effect IV (FE-IV LB) approach.

This panel dataset presents information on the impact of democracy and political stability on economic growth in 15 MENA countries for the period 1983-2022. The data are collected from five different sources; the World Bank Development Indicators (WDI), the World Bank Governance Indicators (WGI), the Penn World Table (PWT), Polity5 from the Integrated Network for Societal Conflict Research (INSCR), and the Varieties of Democracy (V-Dem). The dataset includes ten variables related to economic growth, democracy, and political stability. Data analysis was performed using statistical methods such as R in order to ensure data reliability through imputing missing data; hence, enabling future researchers to explore the impact of political factors on growth in various contexts. The data are presented in two sheets, before and after the imputation for missing values. The potential reuse of this dataset lies in the ability to examine the impact of different political factors on economic growth in the region.

CO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data was reported at 14.530 % in 2023. This records a decrease from the previous number of 15.087 % for 2022. CO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data is updated yearly, averaging 0.847 % from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 19.356 % in 2021 and a record low of 0.024 % in 1961. CO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region 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: Exports. Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.;World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.;Weighted average;

CO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: East Asia & Pacific data was reported at 5.857 % in 2023. This records an increase from the previous number of 4.622 % for 2022. CO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: East Asia & Pacific data is updated yearly, averaging 0.276 % from Dec 1960 (Median) to 2023, with 59 observations. The data reached an all-time high of 12.014 % in 2019 and a record low of 0.005 % in 1971. CO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: East Asia & Pacific 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: Exports. Merchandise exports to low- and middle-income economies in East Asia and Pacific are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.;World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.;Weighted average;

Context

The World Happiness Ranking focuses on the social, urban, and natural environment. Specifically, the ranking relies on self-reports from residents of how they weigh the quality of life they are currently experiencing which englobes three main points: current life evaluation, expected future life evaluation, positive and negative affect (emotion). Half of the underlying data comes from multiple Gallup world polls which asked people to give their assessment of the previously mentioned points, and the other half of the data is comprised of six variables that could be used to try to explain the individuals’ perception in their answers.

Content

The data sources’ datasets were obtained in two different formats. The World Happiness Ranking Dataset is a Comma-separated Values (CSV) file with multiple columns (for the different variables and the score) and a row for each of the analyzed countries.

The rankings of national happiness are based on a Cantril ladder survey. Nationally representative samples of respondents are asked to think of a ladder, with the best possible life for them being a 10, and the worst possible life being a 0. They are then asked to rate their own current lives on that 0 to 10 scale. The report correlates the results with various life factors.

1. GDP per capita is in terms of Purchasing Power Parity (PPP) adjusted to constant 2011 international dollars, taken from the World Development Indicators (WDI) released by the World Bank on November 28, 2019. See Statistical Appendix 1 for more details. GDP data for 2019 are not yet available, so we extend the GDP time series from 2018 to 2019 using country-specific forecasts of real GDP growth from the OECD Economic Outlook No. 106 (Edition November 2019) and the World Bank’s Global Economic Prospects (Last Updated: 06/04/2019), after adjustment for population growth. The equation uses the natural log of GDP per capita, as this form fits the data significantly better than GDP per capita.
2. The time series of healthy life expectancy at birth are constructed based on data from the World Health Organization (WHO) Global Health Observatory data repository, with data available for 2005, 2010, 2015, and 2016. To match this report’s sample period, interpolation and extrapolation are used. See Statistical Appendix 1 for more details.
3. Social support is the national average of the binary responses (0=no, 1=yes) to the Gallup World Poll (GWP) question, “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”
4. Freedom to make life choices is the national average of binary responses to the GWP question, “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
5. Generosity is the residual of regressing the national average of GWP responses to the question, “Have you donated money to a charity in the past month?” on GDP per capita.
6. Perceptions of corruption are the average of binary answers to two GWP questions: “Is corruption widespread throughout the government or not?” and “Is corruption widespread within businesses or not?” Where data for government corruption are missing, the perception of business corruption is used as the overall corruption-perception measure.
7. Positive affect is defined as the average of previous-day affect measures for happiness, laughter, and enjoyment for GWP waves 3-7 (years 2008 to 2012, and some in 2013). It is defined as the average of laughter and enjoyment for other waves where the happiness question was not asked. The general form for the affect questions is: Did you experience the following feelings during a lot of the day yesterday? See Statistical Appendix 1 for more details.
8. Negative affect is defined as the average of previous-day affect measures for worry, sadness, and anger in all years.

Acknowledgements

The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by data from the Gallup World Poll, and supported by the Ernesto Illy Foundation, illycaffè, Davines Group, Blue Chip Foundation, the William, Jeff, and Jennifer Gross Family Foundation, and Unilever’s largest ice cream brand Wall’s.

Inspiration

Find the relationship between the ladder score and the other pieces of data.

This study utilizes an unbalanced panel dataset covering 46 Sub-Saharan African countries over the period 2000–2023. The dependent variable is the Sustainable Development Index (SDI), constructed as a composite measure across the three sustainability pillars: economic (GDP per capita), social (DPT immunization coverage), and environmental (CO₂ emissions per capita). The key explanatory variable is energy poverty (ENPV), proxied by access to electricity and modern energy services. Human capital (HCI), measured using indicators such as life expectancy and school enrolment, is included as a mediating variable, while institutional quality (INSTQ), derived from the World Governance Indicators, serves as a complementary factor influencing development outcomes. Control variables include inflation, trade openness, population growth, and foreign direct investment (FDI). Data were sourced primarily from the World Development Indicators (World Bank), Worldwide Governance Indicators, and UNDP Human Development datasets, ensuring consistency and cross-country comparability.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in 148 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples.The Global Findex indicators will be collected again in 2014 and 2017.

Surveys were conducted face-to-face. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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 selected sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. 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 by means of the Kish grid.

The sample size in Mozambique was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

United States US: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data was reported at 39.822 % in 2016. This records a decrease from the previous number of 39.995 % for 2015. United States US: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data is updated yearly, averaging 27.222 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 40.980 % in 2014 and a record low of 20.569 % in 1989. United States US: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Exports. Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Abstract

The survey was conducted in Ghana between December 2012 and July 2014 as part of the Africa Enterprise Survey 2013 roll-out, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Data from 720 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an 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 private 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. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Ghana was selected using stratified random sampling. Three levels of stratification were used in this country: firm sector, firm size, and geographic region.

Industry stratification was designed in the way that follows: the universe was stratified into four manufacturing industries (food, textiles and garments, chemicals and plastics, other manufacturing) and two service sectors (retail and other services).

Size stratification was defined following the standardized definition for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees).

Regional stratification for the Ghana ES was defined in four regions: Accra, North (Kumasi and Tamale), Takoradi, and Tema.

For the Ghana ES, several sample frames were used. The first was supplied by the World Bank and consists of enterprises interviewed in Ghana 2007. The World Bank required that attempts should be made to re-interview establishments responding to the Ghana 2007 survey where they were within the selected geographical regions and met eligibility criteria. Due to the fact that the previous round of surveys seemed to have utilized different stratification criteria (or no stratification at all) and due to the prevalence of small firms and firms located in the capital city in the 2007 sample the following convention was used. The presence of panel firms was limited to a maximum of 50% of the achieved interviews in each cell. That sample is referred to as the Panel.

The second frame was constructed using different lists acquired from relevant institutions in Ghana. The main lists used were obtained from the Ghana Statistical Service (GSS). These include: 1) The 2012 Firm Registry. The registry lacked information on firm employee size. 2) The list of firms paying VAT. The VAT dataset included a variable on firms; turnover. The VAT dataset and Firm Registry were merged by using the firms' identification number (TIN). VAT information was not available for all firms in the Firm Registry. 3) The list of Large Tax Payers. The Large Tax Payers file also lacked information on firm employee size.

Since firm size was missing from all lists mentioned above, after having discussed with GSS and with the local contractor the following methods were used to predict firm size. - All firms who were in the Firm Registry but not in the VAT dataset were considered to be micro firms and therefore not use in the current survey. - Firms who were in the Firm Registry and in the VAT dataset were considered to be small firms. - Firms in the Large Tax Payers dataset were considered medium or large firms. The original design was divided into two size groups: small firms and medium and large firms.

During fieldwork the GSS lists proved to be very inaccurate and not sufficient to reach the target sample design, As such they were complemented with additional lists of firms from the Ghana Chamber of Commerce and Industry and Business Associations. The list from the Ghana Chamber of Commerce lacked information on firm employee size or firm turnover. Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 1.3% (26 out of 1,990 establishments).

Finally, a block enumeration was also undertaken in order to build an additional list. The block enumeration allowed to physically creating a list of establishments from which to sample from. A total of 41 blocks were enumerated in the four locations included in the project out of the total 804 blocks identified. The enumeration was conducted without major problems in the time planned. The list of enumerated firms contained 958 records eligible for main Enterprise Survey.

Note: Unlike the standard ES, the universe for the Ghana ES is characterized by the presence of 5 size categories. The category medium&large was added as stratum in order to sample from the GSS large payers list, while the category "unknow size" was included in order to sample the firms in the Chamber of Commerce and Industry list.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

There is a skip pattern in the Service Module Questionnaire for questions that apply only to retail firms.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

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 "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.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve

CO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Europe & Central Asia data was reported at 1.810 % in 2023. This records a decrease from the previous number of 4.137 % for 2022. CO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Europe & Central Asia data is updated yearly, averaging 0.113 % from Dec 1968 (Median) to 2023, with 49 observations. The data reached an all-time high of 4.137 % in 2022 and a record low of 0.000 % in 1979. CO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Europe & Central Asia 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: Exports. Merchandise exports to low- and middle-income economies in Europe and Central Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Europe and Central Asia region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.;World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.;Weighted average;

Costa Rica CR: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data was reported at 20.081 % in 2023. This records a decrease from the previous number of 20.289 % for 2022. Costa Rica CR: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data is updated yearly, averaging 0.638 % from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 21.742 % in 2021 and a record low of 0.079 % in 1981. Costa Rica CR: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Imports. Merchandise imports from low- and middle-income economies outside region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.;World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.;Weighted average;

United States US: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data was reported at 50.851 % in 2016. This records an increase from the previous number of 50.455 % for 2015. United States US: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data is updated yearly, averaging 30.077 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 51.333 % in 2010 and a record low of 17.932 % in 1972. United States US: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Imports. Merchandise imports from low- and middle-income economies outside region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;