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Research Datasets

In addition to providing a searchable database of more than 1 million aid activities from the 1940s to present, AidData has assembled a set of datasets specifically for researchers. Three of these datasets are derived from AidData's core database: a 'Research Release' of all project-level records as of November 2011, a dataset of aggregate financial transfers between donors and recipients (generated from the November 2011 Research Release), and a dataset of aggregate financial transfers between individual financing agencies and recipients (generated from the November 2011 Research Release).  There are also links to other useful datasets (e.g. NGO aid, military aid, Chinese aid, IMF conditionality compliance) that are not derived from AidData's core database, but may be of interest to researchers.

AidData Research Release 2.1
Source: AidData (see release notes)
Contents: AidData's Research Release 2.1 is a snapshot of the AidData's entire project-level database from February 2013. This database includes over 1 million aid activities funded by more than 80 donors from the 1940s to present. AidData Research Releases are particularly useful for researchers because they are static versions of the data that allow for replication and comparison. AidData’s goal is to publish updated research releases on an annual basis (or more frequently if appropriate). For detailed information about this data set, see the AidData User’s Guide.
Data (.zip files, with readme): .
     AidData 2.1 Full [all fields for all data] (CSV, TXT, SQL insert and create, copy of PostgreSql) .
     AidData 2.1 "Thin" [only 6 fields] (CSV, TXT) .
     AidData 2.1 Since 1996 [short enough to open in Excel] (CSV, TXT) .
     AidData 2.1 non-OECD (CSV, XLSX, TXT) .
     AidData 2.1 Aggregate: Donor, Recipient, Year (CSV, TXT) .
     AidData 2.1 Aggregate: Donor, Recipient, Year, Purpose Name (CSV, TXT) .
Official Citation: Tierney, Michael J., Daniel L. Nielson, Darren G. Hawkins, J. Timmons Roberts, Michael G. Findley, Ryan M. Powers, Bradley Parks, Sven E. Wilson, and Robert L. Hicks. 2011. More Dollars than Sense: Refining Our Knowledge of Development Finance Using AidData. World Development 39 (11): 1891-1906.

AidData Sector Coding Scheme
As described in the AidData User's guide, the AidData sector coding scheme consists of two components:
Purpose Codes are assigned 1:1 to project records and capture the main purpose of a project, in the same style as OECD CRS purpose codes, upon which AidData purpose codes are based.
Activity Codes are assigned n:1 to project records and capture each activity within the project.
Both coding schemes are included in the spreadsheet below. Also available is a simple visualization which may help in introducing the AidData purpose codes.
Downloads
     AidData sector codes, spreadsheet (.xlsx)
     AidData sector codes, visual (.jpg, .pdf, .svg)

GDP Deflators & Exchange Rates
Source: OECD and AidData
These files contain the deflators and exchange rates used to convert all transactions in the AidData web portal to constant USD-2009. Information about sources and how to use these exchange rates and deflators is included in the PDF below.
Data
     Documentation (.pdf)
     Exchange Rates (.xlsx)
     GDP Deflators (.xlsx)

World Bank Mapping for Results
Contents: This dataset contains nearly 2,500 active World Bank projects in over 30,000 locations across 144countries. The dataset was created by AidData in partnership with the World Bank Institute and is current to September 2011. 
Data: Available Here
Official Citation: Strandow, Daniel, Michael Findley, Daniel Nielson, and Josh Powell. 2011. The UCDP-AidData codebook on Geo-referencing Foreign Aid. Version 1.1. Uppsala Conflict Data Program. Uppsala, Sweden: Uppsala University.

African Development Bank Geocoded Projects 
Contents: This dataset contains all African Development Bank activities approved in 2009-2010. In total, there are 183 projects worth over $15 billion in total funding, working in nearly 2,000 locations across 43 African countries. The work was featured in the  AfDB’s 2011 Annual Development Effectiveness Review. 
Data: Available here
Official Citation: Strandow, Daniel, Michael Findley, Daniel Nielson, and Josh Powell. 2011. The UCDP-AidData codebook on Geo-referencing Foreign Aid. Version 1.1. Uppsala Conflict Data Program. Uppsala, Sweden: Uppsala University.

Malawi Aid Management Platform Geocoded Projects
Contents: This dataset contains geocoded aid locations from 30 donor agencies in the Malawi Ministry of Finance’s Aid Management Platform. The data include US$5.3 billion in commitments, representing approximately 80% of all external funding reported to the Malawi Ministry of Finance since 2000.
Data: Available here
Official Citation: Peratsakis, Christian, Joshua Powell, Michael Findley, and Catherine Weaver. 2012. Geocoded Activity-Level Data from the Government of Malawi's Aid Management Platform. Washington D.C. AidData and the Robert S. Strauss Center for International Security and Law.

Chinese Official Finance to Africa from 2000-2011, Version 1.0
Contents: AidData's first application of its Media-Based Data Collection methodology, this detailed project-level database covers official Chinese development finance flows (ODA and OOF) to Africa from 2000 to 2011. The static version of the dataset from April 2013 includes more than 1700 pledged, initiated, and completed projects, collectively worth over $75 billion.
Data: Available here
Official Citation:  Austin Strange, Bradley C. Parks, Michael J. Tierney, Andreas Fuchs, Axel Dreher, and Vijaya Ramachandran. 2013. "China’s Development Finance to Africa: A Media-Based Approach to Data Collection." CGD Working Paper 323. Washington DC: Center for Global Development.

PLAID 1.9 with Environmental Impact Codes
PLAID 1.9 was a precursor dataset to the modern AidData 2.0 research release and AidData web portal. Available here is the PLAID 1.9 dataset with environment codes used in Greening Aid? Researchers should note:
     Donor names have been harmonized, but may not match current AidData donor names.
     Recipient names not harmonized and some core contributions to multilateral institutions are included, with the  multilateral institution listed as recipient.
     Sector code coverage is not as extensive as AidData 2.0.
     Not all projects have unique AidData IDs.
     "Constant USD" is USD-2000, unlike AidData 2.0's USD-2009
See the Cover Sheet (xlsx, 12KB) for more information and details on the Environmental Impact Code field and possible values. AidData is also merging these Environmental Impact Codes with the AidData web portal. It will be announced on the First Tranche when this variable is available for download from the web portal. In the meantime, researchers can access the Environmental Impact Codes either at the project-level or as aggregate flows. These data are available here: 
Project-Level:
     Full form (CSV , SQL) (136 cols x 962005 obs)
     Short form (includes ID, donor, recipient, year, commitment, purpose, env_code) (CSV) (7 cols x 962005 obs)
Aggregate:
     By donor, recipient, year and environment code (CSV) (156611 obs)
     By donor, recipient, year, purpose and environment code (CSV) (498723 rows)
Official Citation: Hicks, Robert L., Bradley C. Parks, J. Timmons Roberts, and Michael J. Tierney. 2008. Greening Aid? Understanding the Environmental Impact of Development Assistance. Oxford, UK: Oxford University Press.

Climate Aid in Malawi
Source: Climate Change and African Political Stability (CCAPS) program
Data: Available here
Contents: The first-ever dataset tracking all climate aid activities in a single country. In the pilot study, CCAPS and AidData researchers applied their new "climate coding" methodology to all official development aid projects in Malawi's Aid Management Platform, assessing each project activity for its relevance to climate change adaptation. The resulting database reveals how much of Malawi's current aid portfolio represents funding allocated specifically for climate-oriented development. It also reveals how much of Malawi's aid is not explicitly climate-related, comprised of development projects that could have varied degrees of positive or negative impacts on climate change adaptation. This new Malawi Climate-coded and Geocoded Aid Dataset is alsomapped on the CCAPS aid dashboard.
Codebook: Available here.
Official Citation: Peratsakis, Christian, Justin Baker, and Catherine Weaver. 2012. Tracking Climate Adaptation Aid: CCAPS Climate Codebook. Austin, TX: Robert S. Strauss Center for International Security and Law.

GFDRR Disaster Aid Tracking (DAT) Dashboard
Source: GFRDRR Disaster Aid Tracking (DAT) Dashboard
Data: Available here
Contents: The Disaster Aid Tracking (DAT) dashboard is a collaborative effort between the Global Facility for Disaster  Reduction and Recovery (GFDRR) and AidData. GFDRR is a partnership of 41 countries and 8 international organizations committed to helping developing countries reduce their vulnerability to natural hazards and adapt to climate change. The DAT dashboard tracks the allocation of development finance addressing natural hazards (both risk management and risk reduction). 

Global Humanitarian Aid Flows, 2000-2012
Source: Financial Tracking Service
Data: Available here.
Contents: The Financial Tracking Service (FTS) is a global, real-time database which records all reported international humanitarian aid since 2000. FTS is managed by the United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA).

Financing Global Health 2011: Continued Growth as MDG Deadline Approaches (DAH Funding 1990-2008)
Source: Institute for Health Metrics and Evaluation
Data: Available here
Contents:  A comprehensive view of trends in public and private financing of health assistance with preliminary estimates for health financing in the most recent years. The 2011 dataset updates previous efforts by including: preliminary estimates for 2010 and 2011 using models and preliminary financial statements obtained directly from channels of assistance; improved estimates of DAH flowing through US-based non-governmental organizations by collecting additional health expenditure data; more accurate DAH estimates by incorporating more detailed World Bank data regarding projects’ intended purposes .