In the last decade it has become increasingly popular for academic journals to publish their impact factors on the cover.
Essentially impact factors just represent what is common wisdom; that some publications matter more than others, and impact factors attempt to quantify this.
Today we are publishing the Onalytica Influence Index for economics blogs. The top 100 can be seen below. (Methodology after the list)
We use our standard methodology for measuring influence: All references and citations between the blogs are fed into an Input/Output model (How appropriate to use the work of the 1973 Economics Nobel Prize laureate to calculate the impact factors for economic blogs).
The table contains metrics for each blog: InfluenceIndex, Popularity and Over-Influence.
InfluenceIndex is the impact factor, normalised to the value of the most influential blog.
Popularity represents how popular or well-known the blog is among other economics blogs.
Over–Influence seeks to capture how influential a blog is compared to how popular it is. There is a fairly linear (r2=~0.93) relationship between how popular or well-known a blog is and its influence. However some blogs carry more influence than their popularity leads us to believe; this is what we call over-influence. The blog Fight Entropy for example has an over-influence value of 1.9, meaning it carries 90% more influence that we would expect from how well-known it is. Conversely, if a blog has an over-influence of 0.8 it only carries 80% of the influence we would expect from how well-known it is.
If a blog is over-influential there is only one possible explanation: Those who reference or cite the blog have themselves more than average influence.
The list shows, that the combination of a premier newspaper and Nobel Prize winner is hard to beat.
At Onalytica we have been in the business of measuring influence since 2004. Our clients use the influence we calculate in two ways. First, they use it to understand who are influential in the debate and who is gaining and loosing influence. However, the second usage is the most important: By applying the influence weights of each stakeholder when analysing their views and statements we can transform the online noise into excellent predictions about the present and the future.
To learn more about our work on Influence click on the influence tag on the blog.
Thanks to Oliver Brown for helping with data and suggestions.
The Conscience of a Liberal (Paul Krugman)
Tim Duy's Fed Watch
The Money Illusion
Library of Economics and Liberty
The Big Picture
Worthwhile Canadian Initiative
Next New Deal
Stumbling and Mumbling
The Baseline Scenario
Dani Rodrik's weblog
A Fistful Of Euros
Conf. of a Supply-Side Liberal
The Undercover Economist
Adam Smith Institute
Organizations and Markets
Liberty Street Economics
The Becker-Posner Blog
Division of Labour
Daniel W. Drezner
David Smith's EconomicsUK.com
Peter Gordon's Blog
Increasing Marginal Utility
The Capital Spectator
China Financial Markets
Club For Growth
EclectEcon Economics Studies
A Fine Theorem
Ludwig von Mises Institute
Aplia Econ Blog
Duncan’s Economic Blog
Forest Policy - Forest Practice
All Road Leads to China
Don't worry, I'm an Economist!
Truth on the Market
Federal Reserve Bank of Chicago
The RePEc Blog
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