What does the networked power of global indices really mean for the GIN Project? For the purposes of our research, networked power has a much broader meaning than simple linkages between indices. These connections matter, but more generally, we are most interested in the indices’ power to influence the structure of other indices, as well as their power to change actions of real-life actors. Different conceptual measures of how an index influences the network can be found both within and from outside the immediate network of deconstructed indices. The directionality and overall connections within the network may constitute an index’s power; its ability to generate attention and to affect actors outside of the network space may also lend to its influence. These concepts of power are discussed in more detail below, with some examples from our preliminary network.
Within the Network
At the base, the networked power of global indices comes from the density of connections that an index has to other indices. That is, the more connections that an index has, the more relative power it holds in the networked ecosystem of global indices. This is related to the network concept of degree. In the collapsed network of our first batch of deconstructed indices, the Worldwide Governance Indicators - Rule of Law had the highest degree (the most connections with other indices, regardless of direction) at a total of 19 connected paths with other indices.
However, it is not simply the number of connections that matter in our network - the directionality of a connection is even more important. In terms of our network, in-degree comes from the number of connections other indices borrow from a certain index, and out-degree comes from the number of connections an index borrows from other indices. The Global Competitiveness Index had the highest in-degree, with 17 other indices that borrowed from it. This could be an example of what we are looking for in a “powerful” index. If there is a change in the quantitative value or the methodology of such a highly connected index with a high in-degree, all the other indices that are connected – dependent – on that index will be affected, and a ripple effect will occur in the network. Meanwhile, if an index has high out-degree, it would be susceptible to changes in indices that it borrows from. The Worldwide Governance Indicators - Voice and Accountability had the highest out-degree with 18 connected paths. This means that it borrows from (and is prone to being affected by changes in) 18 other indexes.
This leads us to believe that there may also be indices with “hidden” power. When a lesser connected, lesser known index is connected to a powerful index, its influence over the entire network of indices has the potential to be amplified, indirectly, through this connection to the powerful index. For instance, the Country Credit Ratings has only one connection with another index, but because that connection is with the (highly influential) Global Competitiveness Index borrowing from the Country Credit Ratings, its power in the overall network is very high, with an eigenvector centrality value of 0.9703.
Outside the Network
Although looking at the power of global indices within their network gives us an explicit understanding of what networked power is in the structural sense, it is not necessarily the most tangible or practical way of conceptualizing power. In reality, the network structure that we are interested in at the GIN project is more or less irrelevant to the unquestioning user of an index. In this sense, looking outside the network can give a different perspective in conceptualizing power. We look at external network power mainly in terms of the attention given to an index, the external network of users it generates, and the power to influence action of such users.
In this external network, higher levels of attention toward an index are generated by the number of times an index is utilized and referenced in mediums such as journal articles, newspaper articles, or online posts. This is a self-propagating process, where additional users pick up and reference more visible indices with more ease, with the more frequently referenced indices becoming established proxies for the social phenomena they represent. Through repeated use, the information contained in indices grow power to influence academic analyses, news stories, and public opinion. A simple search of articles within the time range of 2013 to 2018 found that the Global Entrepreneurship Monitor (17000 hits on Google Scholar and 445 hits on Factiva), the Corruption Perceptions Index (14500 hits on Google Scholar and 1318 hits on Factiva), and the Global Competitiveness Index (11800 hits on Google Scholar and 1458 hits on Factiva) were the indices that were most heavily referenced in academic studies and frequently cited in the media.
Going a step further in this external network of users, another way to define the networked power of an index is in how much leverage it holds in actually shaping the behavior of its users. National governments, international organizations, and advocacy groups produce indices that rate and rank countries to advance their political and organizational mission and exerting influence (Cooley and Snyder, 2015). In some cases, these indices become a mechanism of international governance in which rankings bring both financial consequences and political naming and shaming. In countermeasures/response to these scorecards, countries may choose to alter their policies, tailoring policies to improve scores and rankings. This type of action-altering influence can be seen as another form of the power of global indices.
We are still in an exploratory phase of networked power. With our ongoing analysis, we hope to refine our understanding of power in both the internal and external networks of global indices. Diving deeper into our database of deconstructed indices and identifying indices with varying types of power will be our next step.
Cooley, A., & Snyder, J. (2015). Rank Has Its Privileges. Foreign Affairs, 94(6), 101.
Hanneman, R., & Riddle, M. (2005). Introduction to Social Network Methods. Retrieved from http://faculty.ucr.edu/~hanneman/nettext/