Google Scholar and Economics Nobelists

[Stephen J Bensman]

[Let us try this again.  Hopefully it will not come through as the former mess.]

We are engaged in research here at Louisiana State University on Google Scholar and economics Nobelists.  A preliminary working paper on this research has been posted on arXiv at the following URL:

arXiv:1411.0928 [pdf]
Power-law distributions, the h-index, and Google Scholar (GS) citations: a test of their relationship with economics Nobelists
Stephen J. Bensman, Alice Daugherty, Lawrence J. Smolinsky, Daniel S. Sage, J. Sylvan Katz

An interesting phenomenon has emerged concerning Eugene Fama, who is one of the ten most popular economics Nobelists and probably has had the most important impact on society.  He won the prize for developing the efficient market theory, laying the foundation of index funds, which dominate modern investing.  Most retirement plans use these, and I myself have become economically secure by investing according to his principles.  He has the most Google cites but the lowest R^2, which we hypothesize as a measure of how completely new findings have been integrated into the knowledge corpus of a given discipline.  As the table below shows, Fama is also the only one who is on the borderline of the lognormal  and the Gaussian domain.  Inspection of his histogram below shows he is  extremely deficient in low-cited publications and excessive in high-cited publications particularly at the right tip of the horizontal asymptote  This deficiency at the low end causes the trendline to rotate counter clockwise, reducing the slope and the concomitant Lotka exponent.  This indicates that his works are more broadly read.  It also disproves that the R^2 is broadly applicable as a measure of knowledge integration.  It also seems to indicate that Smolinsky, Lercher, and McDaniel (2015) are right:  you cannot have a power-law distribution if you are deficient on the low end of the horizontal asymptote, where the low cited works concentrate, and the Yule-Simon model does a piss-poor job in estimating the upper end of the horizontal asymptote.  This finding should be emphasized.  One can compare Fama's histogram to that of Krugman, who is also one of the ten most popular economics Nobelists but more academic without the huge, practical impact on society.  Krugman comes nowhere near the lognormal and the Gaussian domain.   It thus seems that Fama is more  broadly read, whereas Krugman's influence comes mainly from his works on the extreme right end of the asymptote, which contain the works for which he was awarded the prize.  It does show that the power-law distribution  is a wrong but useful scientific model, with which one has to be very careful.  This may raise more questions than it answers-a low exponent leading to the Gaussian domain!?-but I thought that I'd throw these thoughts before you.

Respectfully,
Stephen J Bensman, Ph.D.
LSU Libraries
Lousiana State University
Baton Rouge, LA 70803 USA
E-mail: notsjb at lsu.edu<mailto:notsjb at lsu.edu>

Smolinsky, L., Lercher, A. and McDaniel, A. (2015), Testing theories of preferential attachment in random networks of citations. Journal of the Association for Information Science and Technology. doi: 10.1002/asi.23312

Tests for Whether the H-Index Tuncated Belong to the Lotkaian or Gaussian Domains

Prize Winner

Index of Dispersion Test for Contagion*

Shapiro- Wilks Test for Normality

Index of Dispersion

Type Distribution

P Value

Lognormal Hypothesis

Name

Year

October 2013 Download

Robert William Fogel

1993

400.4

Contagious

P = 0.005

Rejected

C. W. J. Granger

2003

7604.7

Contagious

P < 0.001

Rejected

Thomas J. Sargent

2011

694.6

Contagious

P < 0.001

Rejected

Lloyd S. Shapley

2012

1529.1

Contagious

P =0.002

Rejected

Eugene F. Fama

2013

4682.1

Contagious

P = 0.001

Rejected

March 2015  Download

Robert William Fogel

1993

426.9

Contagious

P < 0.001

Rejected

C. W. J. Granger

2003

9217.4

Contagious

P < 0.001

Rejected

Thomas J. Sargent

2011

581.8

Contagious

P < 0.001

Rejected

Lloyd S. Shapley

2012

1854.2

Contagious

P = 0.001

Rejected

Eugene F. Fama

2013

5613.7

Contagious

P = 0.009

Borderline

* The index of dispersion is the variance divided by the mean. If it is significantly above one, then the distribution is contagious.


** If P is significantly below either 0.01 or 0.05, then the null hypothesis of the lognormal distribution is rejected.



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