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Aiddata

What data do decision makers really use, and why?

Sharon Felzer's picture
Also available in: العربية | Français | Español

When it comes to revolutions, the data revolution has certainly been less bloody than, say, those in the 18th and 19th centuries. Equally transformative? A question for historians.

AidData, a research and innovation lab located at the College of William & Mary in the US, set out in 2017, to identify what data decision makers in low and middle-income countries use, whose data they use, why they use it, and which data are most helpful.

What can the World Bank learn from AidData’s study, and do data from our own Country Opinion Survey Program, align with AidData’s findings?

Decoding data use: 3500 leaders in 126 low- and middle-income countries.

In 2017 nearly 3500 leaders responded to AidData’s Listening To Leaders Survey (LTL) to help uncover how, when, and why this audience uses information from a range of sources.

This rich data is featured in the report “Decoding Data Use: How do Leaders Source data and Use It To Accelerate Development” and can help any institution target important audiences. For example, what are CSOs and NGOs using most frequently, and for what purpose? How about government respondents? Development partners? The private sector? Does it differ region to region?

Here are some of the key findings:

 

  • Policymakers consult information from the World Bank more than other foreign/international organizations.
  • If you want opinion leaders in client countries to be aware of the Bank’s data and knowledge, bring it to their attention. If you expect them to find it through an internet search, you might be disappointed.
  • Opinion leaders are most likely to regard the knowledge and information helpful if it helps them better understand challenging policy issues and will help them develop implementation strategies in response.
  • Make sure the knowledge and information reflects the local context (be inclusive).
  • Stay focused on policy recommendations to ensure value.

Now let’s see how AidData’s findings compare with the Bank’s Country Opinion Survey Data.

First thing’s first: Accessing data

The AidData survey findings demonstrate that in the world of information and knowledge, decision makers around the world are accessing the Bank’s data.

Where does Chinese development finance go?

Tariq Khokhar's picture

This post looks at the recently updated “Global Chinese Official Finance Dataset” from research group AidData. The post is also available here as an R Notebook which means you see the code behind the charts and analysis.

Credit: A city park in Tianjin, China. Photo: Yang Aijun / World Bank
Credit: A city park in Tianjin, China. Photo: Yang Aijun / World Bank

China has provided foreign assistance to countries around the world since the 1950s. Since it’s not part of the DAC group of donors who report their activities in a standard manner, there isn’t an official dataset which breaks down where Chinese foreign assistance goes, and what it’s used for.

A team of researchers at AidData, in the College of William and Mary have just updated their “Chinese Global Official Finance” dataset. This is an unofficial compilation of over 4,000 Chinese-financed projects in 138 countries, from 2000 to 2014, based on a triangulation of public data from government systems, public records and media reports. The team have coded these projects with over 50 variables which help to group and characterize them.

Activity-level data on an increasingly important donor

This dataset is interesting for two reasons. First, China and other emerging donors are making an impact on the development finance landscape. As the Bank has reported in the past (see International Debt Statistics 2016), bilateral creditors are a more important source of finance than they were just five years ago. And the majority of these increases are coming from emerging donors with China playing a prominent role.

Second, this dataset’s activity-level data gives us a look at trends and allocations in Chinese bilateral finance which can inform further analysis and research. Organizations like the World Bank collect data on financial flows directly from government sources for our operational purposes, but we’re unable to make these detailed data publicly available. We compile these data into aggregate financial flow statistics presented from the “debtor perspective”, but they’re not disaggregated by individual counterparties or at an activity-level. So there can be value added from sources such as AidData’s China dataset.

A detailed view, but only part of the picture of all financial flows

However, this dataset has limitations. It only presents estimates of “official bilateral credits”. These are flows between two governments, and are just one part of the total financial flows coming from China. By contrast, the World Bank is able to integrate the granular data it collects from countries into the full set of financial flows to and from its borrowing countries. This situates official bilateral credit among the broader spectrum of providers of long-term financing (such as bondholders, financial intermediaries, and other private sector entities), sources of short-term debt (including movements in bank deposits), and equity investments (foreign direct and portfolio investments). This data integration leads to better quality statistics.

In short, AidData’s China dataset provides more detail on one type of financial flow, but is likely to be less reliable for a number of low-income countries. With these caveats in mind, I’ve done a quick exploration of the dataset to produce some summary statistics and give you an idea of what’s inside. 

Looking at foreign assistance by type of flow

First, let’s see what the trends in different types of foreign assistance look like. AidData researchers code the projects they’ve identified into three types of “flow”:

  1. Official Development Assistance (ODA), which contains a grant element of 25% or more and is primarily intended for development.
  2. Other Official Flows (OOF), where the grant element is under 25% and the the financing more commercial in nature.
  3. Vague Official Finance, where there isn’t enough information to assign it to either category.

Here are the total financial values of the projects in AidData’s dataset, grouped by flow type and year:

It looks like more Chinese finance is classed as OOF ($216bn in the period above) than ODA ($81bn), and that 2009 is a bit of an outlier. With this dataset, we next can figure out which countries are the top recipients of ODA and OOF, and also which sectors are most financed.