28 May 2018 ~ 0 Comments

Mapping the International Aid Community

A few years ago (2013, God I’m old), I was talking to you on how to give a “2.0” flavor to international aid: at CID we created an Aid Explorer to better coordinate the provision of humanitarian work. I’m happy to report that “Aid Explorer” has been adopted — directly or indirectly —  by multiple international organizations, for instance USAID and the European Union. The World Bank’s Independent Evaluation Group contacted me to make an updated version, focused on estimating the World Bank’s position in the global health arena. The result is a paper, “Mapping the international health aid community using web data“, recently published in EPJ Data Science, and the product of a great collaboration with Katsumasa Hamaguchi, Maria Elena Pinglo, and Antonio Giuffrida.

The idea is to collect all the webpages of a hundred international aid organizations, looking for specific keywords and for hyperlinks to the other organizations — differently from the old Aid Explorer in which we relied on the index from Google. The aim is to create different networks of co-occurrences connecting:

  • Aid organizations co-mentioned in the same page;
  • Aid organizations mentioned or linked by another;
  • Issues co-mentioned in the same page;
  • Countries co-mentioned in the same page.

We then analyze these structures to learn something about the community as a whole.

One thing I didn’t expect was that organizations cluster by type. The “type” here is the force behind the organization — private philanthropy, UN system, bilateral (a single country’s aid extension of the foreign ministry), multilateral (international co-operations acting globally), etc. In the picture above (click on the image to enlarge), we encode the agency type in the node color. Organizations are overwhelmingly co-mentioned with organizations of the same type, which is curious because bilaterals often have nothing in common with each other besides the fact they are bilaterals: they work on different issues, with different developed and developing partners.

We can make a similar map connecting issues if they are co-mentioned in a web page. The map is useful as validation because it connects some “synonyms”, for instance “TB” and “Tubercolosis”. However, you can do much more with it. For instance, you can use color to show where an organization is most often cited. Below (click on the image to enlarge) you see the issue map for the World Bank, with the red nodes showing the issues strongly co-mentioned with the World Bank. Basically, the node color is the edge weight in a organization-issue bipartite network, where the organization is the World Bank. To give you an idea, the tiny “Infant Survival” node on the right saw the World Bank mentioned in 9% of the pages in which it was discussed. The World Bank was mentioned in 3.8% of web pages discussing AIDS.

This can lead to interesting discussions. While the World Bank does indeed discuss a lot about some of the red issues above — for instance about “Health Market” and “Health Reform” — its doesn’t say much about “Infant Survival”, relatively speaking at least. It’s intriguing that other organizations mention this particular issue so often in conjunction with the World Bank.

This difference between the global speech about issues and the one specific to another organization allows us to calculate two measures we call “Alignment” and “Impact”. By analyzing how similar the issue co-occurrence network of an organization is with the global one — a simple correlation of edge weights — we can estimate how “Aligned” it is with the global community. On the other hand, an “Impactful” organization is one that, were it to disappear, would dramatically change the global issue network: issues would not be co-mentioned that much.

In the plot above, we have Alignment and Impact on the x and y axis, respectively. The horizontal and vertical lines cutting through the plot above show the median of each measure. The top-right quadrant are organization both impactful and aligned: the organizations that have probably been setting the discourse of the international aid community. No wonder the World Health Organization is there. On the top left we have interesting mavericks, the ones which are not aligned to the community at large, and yet have an impact on it. They are trying to shape the international aid community into something different than what it is now.

A final fun — if a bit loose — analysis regards the potential for an organization to spread a message through the international aid network. What would be the reach of a message if it originated from a specific organization? We can use the Susceptible-Infected model from epidemiology. A message is a “virus” and it is more likely to infect an agency if more than a x% of the agency’s incoming links come from other infected agencies.

This depends on the issue, as shown above. In the figures we see the fraction of “infected” agencies (on the y-axis) given an original “patient zero” organization which starts spreading the message. To the left we see the result of the simulation aggregating all issues. The World Bank reaches saturation faster than UNICEF, and USAID is only heard by a tiny fraction of the network. However, if we only consider web pages talking about “Nurses” (right), then USAID is on par with the top international aid organizations — and UNICEF beats the World Bank. This happens because the discourse on the topic is relatively speaking more concentrated in USAID than average.

As with the Aid Explorer, this is a small step forward improving the provision of international aid. We do not have an interactive website this time, but you can download both the data and the code to create your own maps. Ideally, what we did only for international aid keywords can be extended for all other topics of interest in the humanitarian community: economic development, justice, or disaster relief.

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15 April 2013 ~ 0 Comments

Aid 2.0

After the era of large multinational empires (British, Spanish, Portuguese  French), the number of sovereign states exploded. The international community realized that many states were being left behind in their development efforts. A new problem, international development, was created and nobody really had a clue about how to solve it. Eventually, the solution started by international organizations such as the UN or the World Bank culminated on the Millennium Development Goals (MDGs): a set of general objectives that humanity decided to achieve. The MDGs are obviously very noble. Nobody can argue against eradicating hunger or promoting gender equality. The real problem is that the logic that produced them is quite flawed. Some thousands of people met around 2000 and decided that those eight points were the most important global issues. That was probably even true, but what about particular countries, where none of the eight MDGs is crucial, but a ninth is? More importantly: why the hell am I talking about this?

I am talking about this because, not surprisingly, network science can provide a useful perspective on this topic. And it did, in a paper that I co-authored with Ricardo Hausmann and César Hidalgo, at the Center for International Development in Boston. In the paper we explain that the logic behind MDGs is a classical top-down, or strictly hierarchical, one: there are few centers where all information is collected and these centers direct all efforts towards the most important problems. This implies that (see the above picture):

  1. The information generated at the bottom level passes through several steps to get to the top, in a perverted telephone game where some information is lost and some noise is introduced;
  2. If some organization at the bottom level wants to coordinate with somebody else at the same level, it has to pass through several levels even before starting, instead of just creating a direct link.

In this world, if all funds for health are allocated to fighting HIV and child mortality, countries that do not have these problems but face, say, a cholera or a malaria epidemic are doomed to be left behind.

What it is really necessary is a mechanism with which aid organizations can self-organize, by focusing on the issues they are related to and on the places where they are really needed, without broad and inefficient programs. In this world, a small world, everybody can establish a weak link to connect to anybody else, instead of relying on a cumbersome hierarchy. In an editorial in the Financial Times, Ricardo Hausmann used the Encyclopedia Britannica as a metaphor for representing the top-down approach of the MDGs, against the Wikipedia of a self-organized and distributed system.

The question now is: is it really possible to enable the self-organization of international aid? Or: how do we know what country is related to what development issue, and which organization has an expertise on it? Well, it is not an easy question to answer, but in our paper we try to address it. In the paper we describe a system, based on web crawling (i.e. systematically downloading web pages), that capture the number of times each aid organization mentions an issue or a country in its public documents. That is no different from what Google does with the entire web: creating a global knowledge index that is at your fingertips.

Using this strategy, we can create network maps, like the one above (click to see a higher resolution version), to understand what is the current structure of aid development. We are also able to match aid organizations, developing countries and development issues according to how closely they are related to each other. The possible combinations are still quite high, so to actually use our results it is necessary to create a nice visualization tool. And that’s another thing we did: the Aid Explorer (developed and designed by yours truly).

In the Aid Explorer you can confront organizations, countries and issues and see if they are coordinating as they should. For example, you can check what are the issues related to Nordic Fund. Apparently, Microenterprise is a top priority. So, you can check how Nordic Fund relates to countries, according to how they are related to Microenterprise. That’s a good positive correlation! It means that indeed the Nordic Fund really relates most to the countries that are very related to Microenterprise. If we would have found a negative correlation that would have been bad, because it would have meant that Nordic Fund relates with the wrong countries. A general picture over all issues (or over all countries) of Nordic Fund can also be generated. Summing up these general pictures, we can generate rankings of organizations, countries and issues: the more high relevance and high correlation we observe together, the better.

Hopefully, this is the first step toward an ever more powerful Aid Explorer, that can help organizations to get the maximum bang for their buck and countries to get more visibility for their peculiar issues, without being overlooked by the international community because they are not acting in line with the MDG agenda.

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