Description:

*The World Development Indicators (WDI) is a premier compilation of cross-country comparable data about development. It provides a broad range of economic, social, environmental, and governance indicators to support analysis and decision-making for development policies. The dataset includes indicators from different countries, spanning multiple decades, enabling researchers and policymakers to understand trends and progress in development goals such as poverty reduction, education, healthcare, and infrastructure.*

*The dataset is a collection of multiple CSV files providing information on global indicators, countries, and time-series data. It is structured as follows:*

1. series:
Contains metadata for various indicators, including their descriptions, definitions, and other relevant information. This file acts as a reference for understanding what each indicator represents.

2. country_series:
Establishes relationships between countries and specific indicators. It provides additional metadata, such as contextual descriptions of indicator usage for particular countries.

3. countries:
Includes detailed information about countries, such as country codes, region classifications, income levels, and other geographical or socio-economic attributes.

4. footnotes:
Provides supplementary notes and additional context for specific data points in the main dataset. These notes clarify exceptions, limitations, or other special considerations for particular entries.

5. main_data:
The core dataset containing the actual indicator values for countries across different years. This file forms the backbone of the dataset and is used for analysis.

6. series_time:
Contains time-related metadata for indicators, such as their start and end years or periods of data availability.

*This dataset is ideal for analyzing global development trends, comparing country-level statistics, and studying the relationships between different socio-economic indicators over time.*

Columns and Examples:

Series Code:

Description: Unique code identifying the data series.

Example: AG.LND.AGRI.K2 (Agricultural land, sq. km).

Topic:

Description: Category under which the indicator is classified.

Example: Environment: Land use.

Indicator Name:

Description: Full name describing what the indicator measures.

Example: Agricultural land (sq. km).

Short Definition:

Description: A brief explanation of the indicator (if available).

Example: Not applicable for all indicators.

Long Definition:

Description: Detailed explanation of the indicator’s meaning and methodology.

Example: "Agricultural land refers to the share of land area that is arable, under permanent crops, or under permanent pastures."

Unit of Measure:

Description: Unit in which the data is expressed.

Example: Square kilometers.

Periodicity:

Description: How frequently the data is collected or reported.

Example: Annual.

Base Period:

Description: The reference period used for comparison, if applicable.

Example: Often not specified.

Other Notes:

Description: Additional context or remarks about the data.

Example: "Data for former states are included in successor states."

Aggregation Method:

Description: Method used to combine data for groups (e.g., regions).

Example: Weighted average.

Limitations and Exceptions:

Description: Constraints or exceptions in the data.

Example: "Data may not be directly comparable across countries due to different definitions."

Notes from Original Source:

Description: Remarks provided by the data source.

Example: Not specified for all indicators.

General Comments:

Description: Broad remarks about the dataset or indicator.

Example: Not available in all cases.

Source:

Description: Organization providing the data.

Example: Food and Agriculture Organization.

Statistical Concept and Methodology:

Description: Explanation of how the data was generated.

Example: "Agricultural land is calculated based on land area classified as arable."

Development Relevance:

Description: Importance of the indicator for development.

Example: "Agricultural land availability impacts food security and rural livelihoods."

Related Source Links:

Description: URLs to related information sources (if any).

Example: Not specified.

Other Web Links:

Description: Additional web resources.

Example: Not specified.

Related Indicators:

Description: Indicators conceptually related...

International Data & Economic Analysis (IDEA) is USAID's comprehensive source of economic and social data and analysis. IDEA brings together over 12,000 data series from over 125 sources into one location for easy access by USAID and its partners through the USAID public website. The data are broken down by countries, years and the following sectors: Economy, Country Ratings and Rankings, Trade, Development Assistance, Education, Health, Population, and Natural Resources. IDEA regularly updates the database as new data become available. Examples of IDEA sources include the Demographic and Health Surveys, STATcompiler; UN Food and Agriculture Organization, Food Price Index; IMF, Direction of Trade Statistics; Millennium Challenge Corporation; and World Bank, World Development Indicators. The database can be queried by navigating to the site displayed in the Home Page field below.

The International Macroeconomic Data Set provides data from 1969 through 2030 for real (adjusted for inflation) gross domestic product (GDP), population, real exchange rates, and other variables for the 190 countries and 34 regions that are most important for U.S. agricultural trade. The data presented here are a key component of the USDA Baseline projections process, and can be used as a benchmark for analyzing the impacts of U.S. and global macroeconomic shocks.

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

In July 2024, the merchandise exports index worldwide, excluding the U.S., stood at 204.8. This is compared to an index value of 143 for the United States in the same month. The index was highest in emerging economies, reaching an index score of 353. Moreover, the merchandise imports index was also highest in emerging economies. The merchandise exports index is the U.S. dollar value of goods sold to the rest of the world, deflated by the U.S. Consumer Price Index (CPI).

Analysis of ‘Country Socioeconomic Status Scores: 1880-2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sdorius/globses on 14 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the world’s people live in a country with a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS: UNID: ISO numeric country code (used by the United Nations) WBID: ISO alpha country code (used by the World Bank) SES: Socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174) country: Short country name year: Survey year SES: Socioeconomic status score (1-99) for each of 174 countries gdppc: GDP per capita: Single time-series (imputed) yrseduc: Completed years of education in the adult (15+) population popshare: Total population shares

DATA SOURCES: The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita: 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. Maddison population data in 000s; GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls. 2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm 3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/ Total Population 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.
2. United Nations Population Division. 2009.

--- Original source retains full ownership of the source dataset ---

GLOBAL ISSUES News Classification Dataset

Challenges

The dataset is highly correlated and imbalance in nature. All the classes are equally important so without removing correlated features make a stable classification model is a challenge. Because the dataset is increasing auto on weekly basis using dynamic scrapper

GLOBAL ISSUES is a collection of more than 10k news articles. News articles have been gathered from benchmark news publishers around the world. The dataset is provided by the academic community for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity.

The GLOBAL ISSUES news topic classification dataset is constructed by Abdul Aleem (abdul.raheem.aleem.gcu@gmail.com.edu) and Sohail Asghar. It is constructed to use as a text classification benchmark in the following paper: Abdul, Aleem, Sohail Asghar. GLOBAL ISSUES: A first public new benchmark dataset from benchmark news publishers on GLOBAL ISSUES around the world (submitted in journal) 2021 This dataset is generating real-time so this type of dataset is not available on popular benchmark websites like Kaggle, UCI, and other free dataset repositories that help out researchers to find the latest trends, impacts on society regarding GLOBAL ISSUES and did classification, clustering, and other text mining task.

DESCRIPTION

The GLOBAL ISSUES news classification dataset is constructed by choosing 13 largest classes from the original corpus. The total number of samples is 16000 and will increase weekly basis.

Content

This dataset has 13 classes, The classes of this data set are flowing: 1 global energy politics, 2 international security 3 nuclear security
4 human rights 5 Palestine conflicts 6 middle east crisis 7 climate change 8 Kashmir Issue 9 COVID 19 10 global economy 11 world population trends 12 nuclear politics in South Asia 13 international trade

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Brazilian and Indian share prices became the highest performing of the major developed and emerging economies as of June 2023, with index values of 235.25 and 230.91 respectively in that month. Conversely, the lowest-performing were China and the Germany, both with index values of 86.98 and 113.04 respectively at this time. The index value is calculated with 2015 values as the baseline (i.e. 2015 = 100).

This dataset is about book subjects. It has 1 row and is filtered where the books is Study guide with worked examples for use with international economics. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

In July 2024, global industrial production, excluding the United States, increased by 1.5 percent compared to the same time in the previous year, based on three month moving averages. This is compared to an increase of 0.2 percent in advanced economies (excluding the United States) for the same time period. The global industrial production collapsed after the outbreak of COVID-19, but increased steadily in the months after, peaking at 23 percent in June 2021. Industrial growth rate tracks the output production in the industrial sector.

United States US: Women Business and the Law Index Score: scale 1-100 data was reported at 91.250 NA in 2023. This stayed constant from the previous number of 91.250 NA for 2022. United States US: Women Business and the Law Index Score: scale 1-100 data is updated yearly, averaging 83.750 NA from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 91.250 NA in 2023 and a record low of 66.875 NA in 1974. United States US: Women Business and the Law Index Score: scale 1-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Governance: Policy and Institutions. The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score.;World Bank: Women, Business and the Law. https://wbl.worldbank.org/;;1. For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases. 2. The 2024 Women, Business and the Law (WBL) report has introduced two distinct datasets, labeled as 1.0 and 2.0. The WBL data in the Gender database is based on the dataset 1.0. This dataset maintains consistency with the indicators used in previous WBL reports from 2020 to 2023. In contrast, the WBL 2.0 dataset includes new areas of childcare and safety. For those interested in exploring the WBL 2.0 dataset, it is available on the WBL website at https://wbl.worldbank.org.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

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

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1004.

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 multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

The World Development Indicators from the World Bank contain over a thousand annual indicators of economic development from hundreds of countries around the world.

Here's a list of the available indicators along with a list of the available countries.

For example, this data includes the life expectancy at birth from many countries around the world:

The dataset hosted here is a slightly transformed verion of the raw files available here to facilitate analytics.

The average for 2023 based on 153 countries was 94.91 percent. The highest value was in Luxembourg: 386.03 percent and the lowest value was in Sudan: 6.77 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

This dataset represents the polygons of the Exclusive Economic Zones (EEZ) of the world, in a high resolution: the coastline is based on GSHHG (Global Self-consistent, Hierarchical, High-resolution Geography Database) The data set of the Exclusive Economic Zones can be used in many applications. In biogeography for example, it is possible to create for instance species distribution lists per country.

Sweden SE: Women Business and the Law Index Score: scale 1-100 data was reported at 100.000 NA in 2023. This stayed constant from the previous number of 100.000 NA for 2022. Sweden SE: Women Business and the Law Index Score: scale 1-100 data is updated yearly, averaging 87.500 NA from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 100.000 NA in 2023 and a record low of 71.250 NA in 1973. Sweden SE: Women Business and the Law Index Score: scale 1-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank.WDI: Governance: Policy and Institutions. The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score.;World Bank: Women, Business and the Law. https://wbl.worldbank.org/;;1. For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases. 2. The 2024 Women, Business and the Law (WBL) report has introduced two distinct datasets, labeled as 1.0 and 2.0. The WBL data in the Gender database is based on the dataset 1.0. This dataset maintains consistency with the indicators used in previous WBL reports from 2020 to 2023. In contrast, the WBL 2.0 dataset includes new areas of childcare and safety. For those interested in exploring the WBL 2.0 dataset, it is available on the WBL website at https://wbl.worldbank.org.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

Sample includes only Saudi nationals, Arab expatriates, and non-Arabs who were able to participate in the surveyin Arabic or English.

Analysis unit

Individuals

Universe

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

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1009.

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 multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Weather Conditions and Economic Analysis

Investigating the Relationship between Weather and Economic Activity

By [source]

About this dataset

This dataset consists of detailed information about the weather conditions in different cities from one of the official weather websites. It includes several variables including temperature, humidity, pressure, wind speed and direction, precipitation levels, cloud cover etc. which can be used to analyze the correlation between economic activities in these cities and their weather conditions. For example, this data can help us understand how certain types of business like tourism, retail or leisure activities are affected by changes in temperature and humidity levels. Additionally, it allows us to identify which specific kind of weather has more economic impact in a certain region and thus create accurate forecasts which could further improve commercial performances. All in all, this dataset is an invaluable source of information for people interested in understanding the relation between climate dynamics and economic outcomes

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How to use the dataset

• City Name: This column provides the name of the cities covered in this dataset.
• Weather Condition: This column lists the weather conditions associated with each city, such as sunny, cloudy, windy, etc.
• Temperature (C): This column provides the temperature (in Celsius) of each city as provided by official weather sources.
• Population: This column lists the population size (in millions) of each city covered in this dataset.
• GDP Per Capita: This column presents GDP per capita (measured in US Dollars) for each city included in our dataset 6 Economic Activity Index: This index measures economic activity levels for a particular state or region and can be used to analyze how different weather conditions affect economic activities such as tourism, retail, and leisure activities

How to use this dataset?

This dataset can be used to explore relationships between different factors that might influence economic activity levels at a regional level—namely population size and wealth as well as weather condition—or across countries over time and certain seasons or months to identify trends in regional differences between regions regarding their respective economics activities levels due to varying climates or meteorological events . Some specific analysis that could be done includes:

• Use City Name & Weather Condition columns together to calculate correlations between types of weather patterns/conditions seen throughout different locales; temperatures could also potentially be included for more comprehensive data exploration/analysis on climate dynamics - research on how “cold” vs “warm” periods affect local economies overall would also benefit from including these two columns together;

• Analyze Population & Economic Activity Index together - use these variables together to see if any correlation exists between populations sizes within a given region versus their respective economic performance level; other related variables such as GDP Per Capita could also potentially provide valuable insight into how economic activity varies depending on population density;

• Using all 6 columns together would enable even more comprehensive analysis e..g comparing temperatures & storm information versus expected tourist visits data or analyzing effects/correlations between strong winds & droughts versus changes seen within agricultural outputs . With careful combination of all 6 columns you could easily create some interesting models & computations for understanding broad implications which climate dynamics have upon global economics ; conversely you may explore individual cities too!

Research Ideas

• Use this dataset to analyze the correlation between weather conditions and consumer sentiment by comparing customer purchasing decisions in different cities under different weather conditions.
• Use this dataset to identify the optimal temperature for selling certain products, so that retailers can optimize their prices accordingly.
• Use this dataset to study how changes in weather influencers the types of transportation used by the population of a certain city, and help suggest improvements to public systems for better customer experience in changing climate situations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

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