Viral Economics: An Epidemiological Model of Knowledge Diffusion in Economics

We model the diffusion of economic knowledge using an epidemiological model of susceptible, exposed, infected/inspired, and recovered populations (SEIR). Treating peer-reviewed journal publications as evidence of a scholar’s infection/inspiration with energy for an idea or theme, we estimate the coefficients of a four-equation simultaneous system for each of 759 sub-fields of economics. Results show that some sub-fields of economics (or viruses, here) grow endogenously much faster than others, and just over half have basic reproduction properties sufficient to ensure survival without the annual addition of new protege scholars.

[1]  Casper Goffman,et al.  And What is Your Erdös Number , 1969 .

[2]  Carlos Castillo-Chavez,et al.  Population modeling of the emergence and development of scientific fields , 2008, Scientometrics.

[3]  P. Geroski.  Models of technology diffusion , 2000 .

[4]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  J. Eaton,et al.  Measuring Technology Diffusion and the International Sources of Growth , 1996 .

[6]  Daniel K. N. Johnson.  Six Degrees of GM Bacon: Network Analysis of Biotechnology Inventors , 2006 .

[7]  F. Lichtenberg,et al.  The Comparative Advantage of Educated Workers in Implementing New Technology , 1987 .

[8]  David Kaiser,et al.  Spreading the Tools of Theory , 2004 .

[9]  James Gleick.  The Information: A History, A Theory, A Flood (Gleick, J.; 2011) [Book Review] , 2011, IEEE Transactions on Information Theory.

[10]  Jerrold W. Grossman,et al.  Famous trails to Paul Erdős , 1999 .

[11]  Manuel Trajtenberg,et al.  Patents, Citations, and Innovations: A Window on the Knowledge Economy , 2002 .

[12]  Wilbur John Coleman,et al.  Cross-Country Technology Diffusion: The Case of Computers , 2001 .

[13]  M. Newman,et al.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Daniel K. N. Johnson.  Not Far From the Madding Crowd: The Role of Proximity in Biotechnology Innovation , 2009 .

[15]  Teresa A. Sullivan,et al.  Sociology of Science , 1975 .

[16]  Z. Griliches.  HYBRID CORN: AN EXPLORATION IN THE ECONOMIC OF TECHNOLOGICAL CHANGE , 1957 .

[17]  C. Heath,et al.  Made to Stick: Why Some Ideas Survive and Others Die , 2007 .

[18]  M. Gladwell.  The Tipping Point , 2000 .

[19]  P. Krugman.  Geography and Trade , 1992 .

[20]  N. Mankiw.  The Growth of Nations , 1995 .

[21]  Barbara Wejnert.  Integrating models of diffusion of innovations: a Conceptual Framework. , 2002 .

[22]  Albert N. Tabah,et al.  Literature Dynamics: Studies on Growth, Diffusion, and Epidemics , 1999 .

[23]  Toshihiko Mukoyama.  Diffusion and Innovation of New Technologies under Skill Heterogeneity , 2004 .

[24]  Mark E. J. Newman,et al.  Structure and Dynamics of Networks , 2009 .

[25]  L. Bettencourt,et al.  The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models , 2005, physics/0502067.

[26]  M. Trajtenberg.  A Penny for Your Quotes : Patent Citations and the Value of Innovations , 1990 .

[27]  S. Goyal,et al.  Economics: An Emerging Small World , 2004, Journal of Political Economy.

[28]  Ray Bert,et al.  \IThe Information: A History, A Theory, A Flood\N By James Gleick. New York City: Pantheon Books, 2011 , 2011 .

[29]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[30]  T. Kuhn,et al.  The Structure of Scientific Revolutions. , 1964 .

[31]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[32]  Daniel K. N. Johnson,et al.  Does Distance Matter Less Now? The Changing Role of Geography in Biotechnology Innovation , 2012 .