This Dataset comes from the R Package wbstats. The World Bank[https://www.worldbank.org/] is a tremendous source of global socio-economic data; spanning several decades and dozens of topics, it has the potential to shed light on numerous global issues. To help provide access to this rich source of information, The World Bank themselves, provide a well structured RESTful API. While this API is very useful for integration into web services and other high-level applications, it becomes quickly overwhelming for researchers who have neither the time nor the expertise to develop software to interface with the API. This leaves the researcher to rely on manual bulk downloads of spreadsheets of the data they are interested in. This too is can quickly become overwhelming, as the work is manual, time consuming, and not easily reproducible. The goal of the wbstats R-package is to provide a bridge between these alternatives and allow researchers to focus on their research questions and not the question of accessing the data. The wbstats R-package allows researchers to quickly search and download the data of their particular interest in a programmatic and reproducible fashion; this facilitates a seamless integration into their workflow and allows analysis to be quickly rerun on different areas of interest and with realtime access to the latest available data.

World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates. Copied from https://databank.worldbank.org/source/world-development-indicators.

Highlighted features of the wbstats R-package: * Uses version 2 of the World Bank API that provides access to more indicators and metadata than the previous API version * Access to all annual, quarterly, and monthly data available in the API * Support for searching and downloading data in multiple languages * Returns data in either wide (default) or long format * Support for Most Recent Value queries * Support for grep style searching for data descriptions and names * Ability to download data not only by country, but by aggregates as well, such as High Income or South Asia

More information can be found at https://www.rdocumentation.org/packages/wbstats/versions/1.0.4

Note for Version 1. Version 1 published January 2023. Its primary focus is on the featured indicator of climate change. Other versions planned will cover other featured indicators such as economy, education, energy, environment, debt, gender, health, infrastructure, poverty, science and technology.

“gdp etc.dta” is downloaded from World Development Indicator. https://databank.worldbank.org/source/world-development-indicators This file summarizes several country-level control variables such as GDP per cpita, GDP growth, and population, all in the log-transformed values.

The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

For further details, please refer to https://datatopics.worldbank.org/world-development-indicators/

The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates. You can create your own queries; generate tables, charts, and maps; and easily save, embed, and share them. (From the World Bank DataBank website). It is one of the databases in the World Bank DataBank.

We provide the data used for this research in both Excel (one file with one matrix per sheet, 'Allmatrices.xlsx'), and CSV (one file per matrix).

Patent applications (Patent_applications.csv) Patent applications from residents and no residents per million inhabitants. Data obtained from the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

High-tech exports (High-tech_exports.csv) The proportion of exports of high-level technology manufactures from total exports by technology intensity, obtained from the Trade Structure by Partner, Product or Service-Category database (Lall, 2000; UNCTAD, 2019)

Expenditure on education (Expenditure_on_education.csv) Per capita government expenditure on education, total (2010 US$). The data was obtained from the government expenditure on education (total % of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Scientific publications (Scientific_publications.csv) Scientific and technical journal articles per million inhabitants. The data were obtained from the scientific and technical journal articles and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Expenditure on R&D (Expenditure_on_R&D.csv) Expenditure on research and development. Data obtained from the research and development expenditure (% of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Two centuries of GDP (GDP_two_centuries.csv) GDP per capita that accounts for inflation. Data obtained from the Maddison Project Database, version 2018 (Inklaar et al. 2018), and available from the Open Numbers community (open-numbers.github.io).

Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing “Maddison”: new income comparisons and the shape of long-run economic development (GD-174; GGDC Research Memorandum). https://www.rug.nl/research/portal/files/53088705/gd174.pdf

Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985‐98. Oxford Development Studies, 28(3), 337–369. https://doi.org/10.1080/713688318

Unctad. 2019. “Trade Structure by Partner, Product or Service-Category.” 2019. https://unctadstat.unctad.org/EN/.

World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

This data set measures squared kilometers and GDP per capita PPP (constant 2017 international $) per year from 2005 - 2022 per country. This data set has each country twice as the column series name says what the next cells related to that year's values are about. For easier analyses, this column should be pivoted.

This dataset has been slightly altered. I have changed the ".." to be blank instead as ".." represented null values.

Source: The World Bank Database (https://databank.worldbank.org/source/world-development-indicators#)

Contains all fetchable Development Indicators for India from 1960-2020 as curated from World Bank .

The data for all the variables has been collected from (https://databank.worldbank.org/source/world-development-indicators) except English education. For English education, data has been collected from (https://www.statista.com/)

Dataset contains 233 country and their development indicators between years 1985 and 2016.

For each country, GDP per capita growth (annual %), GDP per capita (current US$), GDP growth (annual %), GINI index (World Bank estimate) Unemployment, male (% of male labor force) (modeled ILO estimate) Unemployment, female (% of female labor force) (modeled ILO estimate) Unemployment, total (% of total labor force) (modeled ILO estimate)

Data Source World Bank. (2019), “World Development Indicators Online database.” https://databank.worldbank.org/source/world-development-indicators".

The dataset comprises the Quad (Australia, India, Japan, and the United States) member countries’ military expenditure (ME) and related economic indicators, 1991-2020. lnME is logarithms of the Quad member countries’ ME. lnSpillover1 is the product of the Quad member countries’ ME divided by its own ME. lnSpillover2 is logarithms of the sum of the Quad member countries’ ME minus its own ME. lnGDP is the Quad member countries’ GDP. And lnChineseME is logarithms of Chinese ME. lnME_fd is the first difference value of lnME. lnSpillover1_fd is the first difference value of lnSpillover1. lnSpillover2_fd is the first difference value of lnSpillover2. lnGDP_fd is the first difference value of ln lnGDP. And lnChineseME_fd is the first difference value of lnChineseME. IV_1_1 is the 2 periods lagged lnSpillover1_fd. IV_1_2 is logarithms of the first difference value of the product of the Quad member countries’ GDP divided by its own GDP. IV_2_1 is the 2 periods lagged lnSpillover2_fd. IV_2_2 is logarithms of the first difference value of the sum of the Quad member countries’ GDP minus its own GDP. Data on the Quad member countries’ ME (in current US dollars) from 1991–2020 were obtained from Stockholm International Peace Research Institute (2022), and data on their GDP (in current US dollars) during the same period were obtained from World Bank (2022). Further, Chinese ME (in current US dollars) from 1991–2020 were obtained from Stockholm International Peace Research Institute (2022). The data were converted to constant US dollars using the US GDP deflator taken from World Bank (2022). Data source Stockholm International Peace Research Institute. 2022. “SIPRI Military Expenditure Database.” https://www.sipri.org/databases/milex. World Bank. 2022. “World Development Indicators.” https://databank.worldbank.org/source/world-development-indicators.

This is a metadata only record. The datasets used in this thesis are open and available via https://databank.worldbank.org/source/world-development-indicators We use panel dataset for 115 countries for the time span 1990-2016. The countries are categorized into four groups as per gross national income (GNI) measured using World Bank Atlas (2018) method [the 9 of low ($1005 or less), 32 of lower-middle ($1006-$3955), 35 of upper-middle ($3956-$12,235), and 39 of high ($12,236 or more) income panels]. The data on different variable of interests are collected from World Development Indicators (CD-ROM, 2018). We use real estimation adjusting inflation. The collected datasets of dependent variables are carbon dioxide (CO2) measured in metric tons per capita, methane (CH4) in Kt. of CO2 equivalent, and the particulate matter (PM2.5) in microgram per cubic meter. The independent variables of the collected datasets are gross domestic product (GDP) per capita (constant 2010 US$), energy consumption (EC) in kg of oil equivalent per capita, trade openness (TO) measured as the share of total trade volume in GDP, urbanization (UR) in terms of the share of urban population in total population and TR is the total transport services in percentage of total commercial service of exports and imports, financial development (FD) measured in domestic credit to private sector, foreign direct investment (FDI) is measured by the net inflows of FDI as a percentage of GDP, the human development index (HDI) measured by the UNDP as a proxy for human capital formation. Moreover, we measure the agricultural sector by its output share of GDP (constant 2010 US$) and the manufacturing sector by its output share of GDP (constant 2010 US$).

World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. This database contains the most current and accurate global development data available and includes national, regional, and global estimates.

This replication folder describes the Stata v17 “do file” (code file) for statistical analysis for "Food inflation and child undernutrition in low and middle income countries " by Derek Headey & Marie Ruel. This do file can be used to replicate the analysis in the study mentioned above, published in Nature Communications. The study uses a combination of Demographic Health Survey (DHS) data for child, maternal, household level variables and national level indicators on real food price changes drawn from FAOSTAT, as well as conflict and climate variables. In summary, this is a large multi-country DHS dataset merged with FAO food and total consumer price indices (CPIs) and various other national level control variables. These are DHS surveys from 2000 onwards only.

The authors cannot publicly share the DHS data but can share it upon request, provided we can obtain approval from the DHS implementers. To make a request to access the data for this paper, please email Derek Headey at d.headey@cgiar.org. Alternatively researchers can access the raw DHS data from: https://dhsprogram.com/data/available-datasets.cfm and the country level indicators from the Food and Agriculture Organisation Consumer Prices data portal (https://www.fao.org/faostat/en/#data/CP) as well as The World Bank World Development Indicators (https://databank.worldbank.org/source/world-development-indicators) for obtaining data on various control variables.

We use historical data for the land-based passenger (in passenger-kilometers (km)) across 38 countries and freight transport (in tonne-km) for 43 countries between 1990 and 2018 from the Transport Outlook of the International Transport Forum (ITF) transport database, to investigate the key drivers of transport energy demand source: ITF. (2019). ITF Transport Outlook 2019. ITF Transport Outlook 2019. https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en

We collect the historical socioeconomic variables from the World Bank’s global open data bank source: World Bank. (2020). Data Bank: World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

For this scenario analysis, we rely on the shared socioeconomic pathways (SSPs) from the IIASA database (Riahi et al., 2017). source: Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009 Available Online: https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about

The lack of data disaggregated by country and end-use sector in countries of interest was a significant drawback in the data collection process. We make a crucial assumption in this modeling exercise that historical demand profiles in developing countries track the global average per capita transport trends. Therefore, the resulting estimates are indicative and must be interpreted within this analysis's scope given the future is unknown and highly uncertain.

World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. This database contains the most current and accurate global development data available and includes national, regional, and global estimates.

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]

The Sustainable Development Goals (SDGs) are a set of 17 global goals adopted by all United Nations Member States in 2015 as part of the 2030 Agenda for Sustainable Development. They serve as a universal call to action to end poverty, protect the planet, and ensure that all people enjoy peace and prosperity. For further details, please refer to https://databank.worldbank.org/source/sustainable-development-goals-(sdgs) This collection includes only a subset of indicators from the source dataset.

The Identification for Development (ID4D) Global Dataset, compiled by the World Bank Group’s Identification for Development (ID4D) Initiative, presents a collection of indicators that are of relevance for the estimation of adult and child ID coverage and for understanding foundational ID systems' digital capabilities. The indicators have been compiled from multiple sources, including a specialized ID module included in the Global Findex survey and officially recognized international sources such as UNICEF. Although there is no single, globally recognized measure of having a ‘proof of legal identity’ that would cover children and adults at all ages or, of the digital capabilities of foundational ID systems, the combination of these indicators can help better understand where and what gaps in remain in accessing identification and, in turn, in accessing the services and transactions for which an official proof of identity is often required.

Newly in 2022, adult ID ownership data is primarily based on survey data questions collected in partnership with the Global Findex Survey, while coverage for children is based on birth registration rates compiled by UNICEF. These data series are accessible directly from the World Bank's Databank: https://databank.worldbank.org/source/identification-for-development-(id4d)-data. Prior editions of the data from 2017 and 2018 are available for download here. Updates were released on a yearly basis until 2018; beginning in 2021-2022, the dataset will be released every three years to align with the Findex survey.

Latest poverty and inequality indicators compiled from officially recognized international sources. Poverty indicators include the poverty headcount ratio, poverty gap, and number of poor at both international and national poverty lines. Inequality indicators include the Gini index and income or consumption distributions. The database includes national, regional and global estimates. This database is maintained by the Global Poverty Working Group (GPWG), a team of poverty measurement experts from the Poverty Global Practice, the Development Research Group, and the Development Data Group.

The database is part of the Poverty and Inequality Platform, https://pip.worldbank.org/home

Context

WorlWorld Development Indicators sheet is collected to understand various countries and their development score.

Content

I have collected this data from the world data bank by selecting specific countries.

World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available and includes national, regional, and g and global estimates. [Note: Even though Global Development Finance (GDF) is no longer listed in the WDI database name, all external debt and financial flows data continue to be included in WDI. The GDF publication has been renamed International Debt Statistics (IDS) and has its own separate database, as well.

Acknowledgements

Thanks to the World Data Bank for the dataset.

Inspiration

You can work on finding which country is having the highest innovation score or social development score.