Frequently Asked Questions

Please note: All statistics and figures represented in this section draw upon  from the AidData’s Chinese Official Finance to Africa Dataset (Version 1.2). We refer to this dataset (found here) as the 1.2 Research Release. We do not include pledges or cancelled/suspended projects in our reporting of aggregate amounts of Chinese official financing. We advise all users to do the same unless they have principled reasons for including pledges or cancelled/suspended projects in their analysis.

Table of Contents
1. Citations and Licensing
2. General: TUFF Methodology and Scope of Data
3. Caveats
4. Statistics - What is China doing in Africa?
5. Geocoded Data/Geospatial Dashboard
6. Miscellaneous

Citations and Licensing

What is the citation for your research releases? 

To cite the Chinese Global Official Finance Research Releaseversion 1.0, please choose from one of the following citations: 

For academic purposes, please cite AidData’s Chinese Global Official Finance Dataset in the following manner:

Dreher, Axel, Andreas Fuchs, Bradley Parks, Austin M. Strange, and Michael J. Tierney. 2017. Aid, China, and Growth: Evidence from a New Global Development Finance Dataset. AidData Working Paper #TBD. Williamsburg, VA: AidData. 

For non-academic purposes, please cite AidData’s Chinese Global Official Finance Dataset in the following manner:

AidData. 2017. Chinese Global Official Finance Dataset, Version 1.0. Retrieved from 

To cite the Chinese Official Finance in Africa Research Releases (versions 1.0, 1.1, or 1.2), please use the following citation:

Strange, Austin M., Axel Dreher, Andreas Fuchs, Bradley Parks, and Michael J. Tierney. 2017. Tracking Underreported Financial Flows: China’s Development Finance and the Aid–Conflict Nexus Revisited. Journal of Conflict Resolution 61 (5): 935-963.

To cite the 1.1.1 Chinese Official Finance in Africa Research Release, which features data from the 1.1 Chinese Official Finance in Africa Research Release that has been georeferenced using AidData’s Geocoding Methodology, please use the following citation:

Strange, Austin M., Axel Dreher, Andreas Fuchs, Bradley Parks, and Michael J. Tierney. 2017. Tracking Underreported Financial Flows: China’s Development Finance and the Aid–Conflict Nexus Revisited. Journal of Conflict Resolution 61 (5): 935-963.
Updated in: Dreher, Axel, Andreas Fuchs, Roland Hodler, Bradley C. Parks, Paul A. Raschky, and Michael J. Tierney. 2016. Aid on Demand: African Leaders and the Geography of China’s Foreign Assistance. AidData Working Paper #3. Williamsburg, VA: AidData.

Please note: both works count as the official citation for the 1.1.1 research release.

Are there terms of use for the Geospatial Dashboard? Can I re-publish visualizations generated using this tool?

This dashboard is designed to facilitate exploration and use of Chinese development finance data by a wide variety of stakeholders. Please note that analysis or visualization of the data published by independent users does not necessarily reflect the views of AidData, and care must be exercised in using and interpreting the data on this website.

Visualizations generated from this dashboard are a public good - they are open to re-publishing. We simply ask that you appropriately attribute the visualization with the following citation:

Strange, Austin M., Axel Dreher, Andreas Fuchs, Bradley Parks, and Michael J. Tierney. 2017. Tracking Underreported Financial Flows: China’s Development Finance and the Aid–Conflict Nexus Revisited. Journal of Conflict Resolution 61 (5): 935-963.

Updated in: Dreher, Axel, Andreas Fuchs, Roland Hodler, Bradley C. Parks, Paul A. Raschky, and Michael J. Tierney. 2016. Aid on Demand: African Leaders and the Geography of China’s Foreign Assistance. AidData Working Paper #3. Williamsburg, VA: AidData.

Please note: both works count as the official citation.

General: TUFF Methodology and Scope of Data

What publications have AidData released that feature data from the Chinese development finance database?

Our team of researchers has released the following publications:

What is the geographical scope of your data on Chinese official financing?

The 1.2 research release tracks Chinese official financing to all countries in Africa, as well as regional projects in Africa or sub-Saharan Africa.

What is the time series of your data?

The 1.2 research release tracks Chinese official financing from 2000-2013. The 1.1 version covers 2000-2012.

Please note that 1.2 research release not only adds an extra year to its predecessor, but also contains historical revisions to the project records spanning 2000-2012. As such, the 1.2 version is the latest and most updated research release currently published on our website.

What is the unit of analysis?

The unit of analysis is the “project”. Broadly defined, a “project” is a discrete transfer of goods, services or cash.

What are important definitions to keep in mind regarding Chinese official finance?

O(fficial)D(evelopment)A(ssistance)-Like: ODA-like flows meet the definition for Official Development Assistance as set forth by the Organization for Economic Cooperation and Development’s (OECD) Development Assistance Committee (DAC). The DAC definition of ODA is as follows: “grants or loans to developing countries and territories and to multilateral agencies which are: (a) undertaken by the official sector; (b) with promotion of economic development and welfare as the main objective; (c) at concessional financial terms (if a loan, having a grant element of at least 25 per cent). Includes financial flows and technical co-operation.”

O(ther)O(fficial)F(lows)-Like: OOF-like flows pertain to official financing that does not meet the OECD-DAC’s definition for ODA. These flows constitute export financing and other activities that promote the donor’s commercial interests; projects that are representational in nature (designed to promote the donor's culture or deepen institutional ties between the donor and recipient); or developmental loans that are not concessional enough to be considered ODA-like.

Vague (Official Finance): The Vague (Official Finance) classification is assigned to any flow that constitutes official financing, but for which we do not have enough information to confidently classify as ODA-like or OOF-like. For example, if we do not have information on the concessionality rates of a loan, we classify it as Vague (OF) to prevent misclassification. This category has been created by AidData to be transparent about uncertainty in our data collection efforts.

How many variables do you track?

We track 56 variables for each project record in the dataset. The most important variables include:

  • project_title - title of the project, as created by our researchers

  • year - year in which the project was agreed upon between the donor and recipient (i.e. year committed)

  • recipient - the recipient country of the financing

  • status - the current state of the project. Options include: pipeline: pledge; pipeline: commitment; implementation; completion; suspended; and cancelled. Please see Appendix E of the methodology for a more detailed explanation of project status.

  • intent - the donor’s intent for the project.

  • flow_type - the modality by which the project is conducted. Options include: grants; technical assistance; interest-free loans; in-kind contributions of goods and services; debt relief; export credits; grants with a representational or commercial purpose; commercial loans. Please see Appendix D of the methodology for a more detailed explanation of flow types.

  • flow_class - a categorization of the flow according to the level of concessionality, as established by the OECD-DAC. Options include: ODA-like, OOF-like, Vague (OF). Please see Appendix D of the methodology for a more detailed explanation of flow class.

What improvements have you made to your data and methodology since the 1.0 Research Release? What about since the 1.1 research release?

Since the initial launch of our project to track the official finance activities of non-traditional, emerging donors, our methods and data quality have improved in the following ways:

  1. Diversification of source types from which we gather project-level information.

  2. Refining the way we classify Chinese Official Financing to improve conform to Beijing’s stated aid policies.

  3. Introducing automated quality checks to improve data standardization.

  4. Developing data quality scores to improve data transparency.

For a more in-depth explanation of each improvement, please see a forthcoming post on The First Tranche, AidData’s blog.

What do you mean when you say that your data allows you to “follow the money”?

We believe that if you want to know what providers of official finance are really doing, you have to “follow the money”. You must follow projects through their entire life cycle.

To this end, our team created the status variable. By tracing the progress of projects over time and triangulating a wide variety of sources (all of which are posted on the individual project pages on, we categorize each record in our database as either a pledge, an official commitment, a project in implementation, a completed project, or a suspended/cancelled project.

Please see Appendix E of our methodology for an in-depth explanation of the status variable, as well as our Caveats section for an explanation of how we differentiate between ‘pipeline: commitments’ and ‘pipeline: pledges’.

What are the advantages and disadvantages to using the TUFF methodology to track Chinese official financing, along with that of other non-traditional, emerging donors?


  • It allows users to gauge commitments from countries that do not publicize their official funding at the project-level, specifically non-DAC donors such as China, Cuba, Iran, Russia, Saudi Arabia, and Venezuela

  • It is transparent - we seek feedback and suggestions from our users - and objective.

  • Created flow categorizations enable comparison with OECD-DAC measures of development finance.

  • It allows for a more comprehensive use of bilingual sources; we plan to expand our linguistic coverage given the availability of data within documents written in other languages.

  • The triangulation of media reports, official government documents, and academic articles minimizes the negative impact of sensitive or flawed information.  

  • It is universally accessible and can be improved by anyone with valuable information.

  • Created flow categorizations (ODA-like and OOF-like) enable comparisons with the OECD-DAC’s measures of development finance, and hence, with OECD-DAC donors.


  • Media reports do not always make clear who is financing a project, nor do they accurately classify what is Chinese aid versus what it is not. There is a higher likelihood of missing project cancellations and suspensions through an exclusive reliance on media sources. Moreover, an analysis of media-based data collection by AidData staff reveals that there is an urban bias in media reporting. Many of these biases have prompted our researchers to diversify the type of sources from which we collect and curate data.  

  • There is always potential for human error on behalf of the coder.

  • Our methods are never a substitute for accurate and comprehensive numbers from official agencies.

What is AidData’s methodology for measuring the “health” of project records?

The purpose of our ‘Health of Record’ methodology is to rate the completeness and verifiability of each project record. The source triangulation and data completeness scores are used internally to prioritize data  quality checks. They can also be utilized by external users to sort and filter project records according to two measures of data quality. Please see our Health of Record webpage, as well as this blog post on The First Tranche regarding the health of record score, for more detailed information regarding this scoring system.


To what year do you deflate financial amounts?

In the 1.2 research release, we deflate financial values to both USD-2009 and USD-2011. The latter is the deflation year and currency used for AidData’s other data products, such as our donor, geocoded, and research release datasets. On the, financial values are deflated to USD-2014.   

Do you track disbursements of Chinese official financing?

We do not track disbursements on a large-scale. However, by using the status variable, users may classify projects that have been implemented or completed as disbursements of Chinese official financing; during the quality assurance phase, our team strives to update the status variable for each project record to accurately reflect the most up-to-date status of the project.

What is a pipeline: pledge? Do you include them in your analysis?

A pledge is a “verbal, informal agreement” between the development partner and partner country. We do not include pledges in our reporting of aggregate financial amounts of Chinese official financing, because there is no concrete evidence that these pledges have progressed. We advise all users to do the same in excluding pledges for their analysis.

What is the difference between a pipeline: pledge and pipeline: commitment?

  • OECD definition of a pledge: Announced contribution, promise of contribution of aid.

  • OECD definition of a commitment: A firm obligation, expressed in writing and backed by the necessary funds, undertaken by an official donor to provide specified assistance to a recipient country or a multilateral organisation.

Please see Appendix E of the methodology for a more detailed explanation of the difference between pledges and commitments, including potential terms used to classify projects into one of these two status categories.

Do you include project cancellation and suspension data in your 1.2 Research Release?

No, we do not include project cancellation and suspension data in the 1.2 research release. However, in our attempts to “follow the money,” we do feature projects in our database that may have been cancelled or suspended - these records are generally “deactivated” when our researchers discover that the projects have been cancelled or suspended.

Which forms of official financing are excluded from or not comprehensively covered in your database?

We do not systematically capture flows that are not considered official financing. Examples include forms of investment such as Joint Ventures and Foreign Direct Investment. We also do not systematically capture official investment. Though we may capture some of these types of flows, our repository of such projects is neither systematic nor comprehensive due to the sheer volume of such flows from China to Africa.

Do you include military aid in your 1.2 research release?

No, we do not.

How do you prevent double-counting of projects or financial amounts?

We use a web-based data platform with filtering and keyword search functions to identify and eliminate duplicate projects. Our researchers undergo a rigorous process of de-duplication by filtering projects according to their recipient and sector values and identifying duplicate records along those criteria. During this process, they may encounter records that share many attributes, which may suggest that they are duplicate records. Any uncertainty is resolved by a trained AidData arbitrator.

Statistics - What is China doing in Africa?

How much official financing did China allocate to Africa from 2000-2013?

Chinese official finance (ODA+OOF+OF Vague) includes 2,312 projects to 50 African countries over the 2000-2013 period that have at least reached commitment stage, totaling US$94.31 billion.

Of the $94.31 billion in official finance commitments that China allocated to Africa between 2000 and 2013, 33% (31.48 billion USD) fell in the ODA category, 38% (36 billion USD) fell in the OOF category, and 28% (26 billion USD) fell in the Vague Official Finance category.

However, 43% of official projects are missing a financial amount.

How does China’s official financing to Africa compare to the DAC’s between 2000-2013?

  • China top 5 sectoral priorities (cumulative ODA from 2000-2013, to Africa): 21.8 billion USD to Transport and Storage, 18.81 billion USD to Energy Generation and Supply, 15.28 billion to Other Multisector, 5.53 billion to Industry, Mining and Construction, and 5.39 billion to Communications.

  • DAC top 5 sectoral priorities (cumulative ODA from 2000-2013, to Africa): 86.03 billion USD to Action Relating to Debt, 52.2 billion USD to Emergency Response, 50.39 billion USD to Population Policies/Programmes and Reproductive Health, 43.3 billion USD to Transport and Storage, and 41.06 billion USD to General Budget Support.

Who are China’s top recipients of official financing in Africa between 2000-2013?

  • In terms of projects, Zimbabwe (151 projects) receives the most official finance. The recipients with the next highest numbers of projects are Angola (123), Ghana (111), Tanzania (106), and Ethiopia (100).

  • Monetarily, DRC ($7.62 billion) receives the most Chinese official finance. Sudan ($7.56 billion), Ethiopia ($7.51 billion), Angola ($7.46 billion), and Nigeria ($7.32 billion) receive the next highest amounts.

What are China’s sectoral priorities in Africa between 2000-2013?

  • Top Sectors for ODA by number of projects (Share of Number of Projects/ Share of Amount):

    • Health: 26.4% (424)/ 3.9% ($1.25 billion)

    • Government and Civil Society: 12.3% (198)/ 4.8% ($1.54 billion)

    • Unallocated/Unspecified: 11.45% (184) /7% ($2.206 billion)

    • Education: 9.83% (158)/ 1.2% ($378.33 million)

  • Top sectors for ODA by amount:

    • Transport and Storage: 5.3% (85)/ 20.57% ($6.47 billion)

    • Action Related to Debt Relief: 3.9% (62)/ 13.58% ($4.362 billion)

    • Energy Generation and Supply: 2.3% (37) /13.18% ($4.35 billion)

    • Other Multisector: 2.8% (45)/ 7.76% ($2.44 billion)

    • Unallocated/Unspecified: 11.45% (184) /7% ($2.206 billion)

  • Top Sectors for OOF by number of projects:

    • Education: 15.31% (15)

    • Government/Civil Society: 14.29% (14)

    • Other Social Infrastructure and Services: 10.20% (10)

    • Transportation/Storage: 10.20% (10)

  • Top Sectors for OOF by amount:

    • Other Multisector: 31.57% ($10.59 billion)

    • Transport and Storage: 18.26% ($6.12 billion)

    • Energy Generation and Supply: 17.37% ($5.28 billion)

    • Industry, Mining, Construction: 7.88% ($2.64 billion)

    • Agriculture, Forestry and Fishing: 5.9% ($2.00 billion)

Geocoded Data/Geospatial Dashboard

Given that China is a non-transparent donor, how did you find sub-national locations for its official financing activities?

All location information was collected from the source documentation used to create project records. Media reports, government document, and academic articles can provide very detailed information on the sub-national location of projects. AidData’s geocoding methodology, which has also been adopted as a global reporting standard by the International Aid Transparency Initiative, has been
applied to more than 1,900 Chinese development projects in our database. Our geocoding team triangulated the available information about each project and applied this methodology, which we also use to code projects from the World Bank, the African Development Bank, the Asian Development Bank, and other development finance institutions around the world.

How confident are you in the accuracy of the location information you are providing?

To provide information about the spatial granularity of each project location, we attribute a precision code to each location as detailed in the geocoding methodology. Ranging from 1 to 8, these codes display information on how close to a particular lat/long a project occurs. Here is a distribution of precision codes in the 1.1.1 dataset:
Precision 1 (coordinates correspond to an exact location): 1387 (39%)
Precision 2 (coordinates correspond to a district or a location that is known to be < 25 km from project location): 134 (4%)
Precision 3 (the location is, or is analogous to, a second-order administrative division (ADM2), such as a municipality or commune): 261 (7%)
Precision 4 (the location is, or is analogous to, a first-order administrative division (ADM1), such as a province, state, or governorate): 389 (11%)
Precision 5 (the location can only be related to estimated coordinates, such as when a location lies between populated places; along rivers, roads and borders; more than 25 km away from a specific location; or when sources refer to parts of a country greater than ADM1 such as a National Park which spans across several provinces): 63 (1%)
Precision 6 (the location can only be related to an independent political entity. This includes country-wide projects as well as aid intended for larger areas that cannot be geo-referenced to a more precise level): 694 (20%)
Precision 8 (the location is estimated to be the national capital): 625 (18%)

What is your process for vetting user-contributed content?

Submitted comments or suggested projects that AidData receives via  trigger a stage-two search on the project, which is performed by a Senior Research Assistant or AidData staff member (see our TUFF methodology for full details). All comments that are verified by stage two searches are approved without being edited.

All suggested projects that are verified by stage two searches will be edited and expanded before being published. Because many project fields on have very specific definitions, external users are not allowed to populate those fields. AidData staff will also perform a duplicate check on all suggested projects before publication.

How can I contribute data to the map?

The dashboard provides several different options for users to contribute new information to our dataset. Please find a list of options below:

  • Comment on project locations: A user with information on a particular project can click on the location on the map or the project ID in the “Results” section. The comment box provided can be used to provide quantitative data (correcting or verifying project information) or qualitative data (general impressions about the impact of a project).

  • Navigating to a project page: The dashboard will also allow users to navigate through to a project page. This will allow the user to view complete project details and challenge or confirm each individual project field.

  • Suggesting a project: If a user knows of a Chinese-funded official finance project not included on the dashboard, they can also suggest a new project by right clicking on the map. Suggested projects will prompt one of our research team to perform online searches to verify the existence of a project. Projects that meet the quality standards of the rest of our data will be approved as “checked” records. Projects that do not will be approved as “suspicious” records.

  • Contributing multimedia content: Users can also upload photographic evidence of a project or YouTube videos containing project details. Again, these records will be vetted by AidData staff before being approved.

Do you have plans to update this map with new data/baselayers?

Yes. Following the geocoding of the 1.2 research release, and the publication of a 1.2.1 dataset, we will update the spatial layers on the dashboard. We are also open to demand-driven updates for new spatial layers.

What caveats should users keep in mind when drawing findings from the dashboard?

This dashboard is primarily a tool for visualization and user feedback, not analysis. The visualization features will give a user the opportunity to explore possible trends related to the nature, impact and distribution of Chinese aid and development finance projects. However, it does not substitute for a thorough spatial econometric analysis needed to derive to substantive conclusions (for such an analysis, please see the AidData’s ‘Aid on Demand’ working paper)

Generally, the spatial base layers provided are an aggregate across our time-series. If a user is looking at a single year of Chinese development finance data, there could be a mismatch between what the base layers are displaying and the year. This could lead to users to draw spurious correlations. In addition, when the map renders national either precision code 6 or 8, it will place them in the center of the country. It is important to recognize that these projects are not actually located at that set of latitude and longitude coordinates.


How do you account for detection bias? Are you under-representing the extent of China’s involvement in non-English speaking countries that lack press freedom?

This year, our staff performed some preliminary analysis on potential biases in media coverage of Chinese financing to Africa. We found that the level of press freedom was not a significant predictor of media coverage. However, we did find that projects in urban areas received a disproportionate amount of media coverage compared to projects in rural areas.

In a new AidData working paper entitled ‘Apples and Dragon Fruits’, we also control for detection bias by including a control variable that measures whether the country of interest is predominantly English-speaking.

How many unique sources of information were used to assemble this database?

In the 1.2 research release, we have collected project-level information from over 6000 distinct information sources in order to assemble the dataset. On average, each project entry is informed by 3 sources. Although 27% of project records are based on information from a single resource, they represent only 6% China’s total financial commitments to Africa.

What is unofficial finance? Why do you have a separate category called this and what goes into it?

Official financing is the sum of ODA-like, OOF-like, Vague Official Financing and Official Investment.

Unofficial financing is where state may (or may not) have some level of involvement, but its level of involvement is not clear. Examples include: Joint Venture with a state-owned enterprise, NGO aid, and corporate aid.

We do not currently track unofficial financing from China in a comprehensive manner. These flows are often identified inadvertently during the process of identifying the known university of official financing activities. In the interest of full transparency, we record rather than discard these unofficial financing activities; however, we do not use the data in any of our research, analysis, or data visualization activities.