Data Stories
A collection of short analytical pieces on selected topics concerning Africa's economic development and global linkages, combining data and visualizations in interactive notebook formats.
The Africa Monitor is a joint effort of the research center for International Development and the Kiel Institute's Africa Initiative, to build an interactive platform that tracks leading economic indicators for 55 African economies, and provides economic analysis as well as analytical tools to monitor economic developments in Africa. The monitor consists of several building blocks accessible through this website:
A collection of short analytical pieces on selected topics concerning Africa's economic development and global linkages, combining data and visualizations in interactive notebook formats.
Web-Application to interactively visualize monitor data and build custom charts.
Portal to access monitor data interactively and download it in various standard formats.
Provides an overview of the indicators and African countries available through the monitors database, and the sources from which data were collected. The database itself covers all 195 countries, but data for non-African countries is only accessible through the APIs.
High-speed access to the entire database through dedicated R and Python packages.
Data stories are short analytical pieces combining data visualizations and analysis to expound a topic concerning economic development in Africa.
This data story provides an overview of the African's middle class. It includes different measures of the middle class share and population for a set of 46 African countries from 2009 to 2020. Additionaly, it gives some projections of what will be the size of the middle class in Africa under different scenarios.
This datastory provides an overview of Africa's external positions and flows with the rest of the world, as recorded in the current account and international investment position. It includes detailed analysis of trade flows, international reserves, and external debt.
While the majority of Sub-Saharan African firms operate in resource constrained environments, analyzing innovation strategy among formally registered Nigerian firms shows that cooperation is a frequently used form of sourcing knowledge for innovative activities.
This data story uses the EORA Global Supply Chain Database to map the structure of world production in value-added terms, with a specific emphasis on Africa's role as a provider and consumer of value-added, and as an emerging participant in Global Value Chains.
This data story provides a detailed analysis of logistic performance and the business environment across African economies. A particular focus is laid on the development of public infrastructure and its role in enhancing logistics and economic growth.
am_sources() and am_series(), as well as the am_countries table provide information about the available data, analogous to the 'Data Catalog'
tab. Country and series codes from these tables are needed to query data using the am_data() function. An additional am_entities table records country membership in various regional entities.
The API is fully documented on the R help pages, calling help(africamonitor) after installation. A small demonstration is provided below.install.packages('africamonitor')
am_pivot_longer() and am_pivot_wider() to reshape data from the API.am_data() is wide = TRUE.am_expand_date() can be used to generate additional identifiers from the date for series at higher frequency. This function can also be invoked directly in the API call with option expand.date = TRUE.am.sources() and am.series(), as well as the am.countries() table provide information about the available data, analogous to the 'Data Catalog'
tab. Country and series codes from these tables are needed to query data using the am.data() function. An additional am.entities() table records country membership in various regional entities.
The API is fully documented on the Python help pages, calling help(am) after installation. A small demonstration is provided below.pip install africamonitor
am.pivot_longer() and am.pivot_wider() to reshape data from the API.am.data() is wide = True.am.expand_date() can be used to generate additional identifiers from the date for series at higher frequency. This function can also be invoked directly in the API call with option expand_date = True.The Africa Monitor provides research briefs (data stories), custom visualizations, and access to public macroeconomic data on Africa, allowing you to track macroeconomic developments across the continent. Data is collected from several public sources (mostly from the IMF and the World Bank) and updated once a month. The data is serviced to the monitor from a database hosted at the Kiel Institute for the World Economy. API packages for R and Python allow free high-speed access to the entire database.
This website was built using the shiny web-application framework in R, complemented with some custom HTML and CSS elements.
Author:
Sebastian Krantz.
Any issues with this website, the database, or the R and Python API packages, can be reported on GitHub (preferred), or by sending an e-mail to sebastian.krantz@ifw-kiel.de
pip install africamonitor.
Furthermore, the database was expanded to include data for all countries in the world (193 UN Members, Western Sahara and Taiwan) where available. The API packages for R and Python by default only query data for Africa,
but now include an additional countries_wld dataset with ISO3 codes and descriptions of all 195 countires. Users can use these codes to query data for other countries using the API packages. am_pivot_wider() and am_pivot_longer().
See NEWS.