These data are based on public disclosures of standards-related intellectual property at thirteen Standard Setting Organizations. The data are described in several of my papers, and there is additional documentation available at the link. The data include information on SSO, company, disclosure date, patent and/or application numbers, and licensing terms (when available). I am currently working with Rudi Bekkers on a major upgrade of these data.
Code, data and documentation for Marc Rysman, Tim Simcoe and Yanfei Wang, "Differentiation Strategies in the Adoption of Environmental Standards: LEED from 2000-2014" (Working Paper)
Link to a zip file containing Stata code, IETF publication data and documentation for "Standard Setting Committees: Consensus Governance for Shared technology Platforms" (forthcoming AER). The files in this directory can be used to generate all Tables and Figures in the working paper. Please cite me if you use these data or programs.
Link to a zip file containing data (csv and Stata format) and code for the paper "Status, Quality and Attention: What's in a (Missing) Name?", Management Science, Feb 2011, 274--290, co-authored with David Waguespack.
The Stata code, data and documentation for Mehta Rysman and Simcoe (Identifying the Age Profile of Patent Citations: New Estimates of Knowledge Diffusion) are available via the data archive for the Journal of Applied Econometrics. The link takes you to the main page for the archive, To find our materials, you will need to search on any of the author last names.
Wooldridge (JOE 1999) shows that the fixed effects Poisson estimator produces consistent estimates of the parameters in an unobserved components multiplicative panel data model under very general conditions. In fact, all that is required is an assumption about the conditional mean of the dependent variable. This is quite useful for two reasons. First, it implies that fixed effects Poisson estimation is appropriate for any non-negative dependent variable—not just count data that follow a Poisson distribution. Second, the estimator is robust to arbitrary patterns of serial correlation. In spite of these obvious attractions, the fixed-effect Poisson estimator does not appear to be widely used in practice. This is partly because statistical software does not generally allow computation of the appropriate (robust) standard errors for inference. This ado file runs Stata’s pre-packaged fixed effects Poisson estimator and then computes the robust standard errors suggested by Wooldridge (1999).
A Stata r-class routine for calculating the index of agglomeration (or disperion) proposed in Rysman and Greenstein (2003). This statistic is closely related to the Ellison and Glaeser (1998) “dartboard index” can be used to test for dispersion.
NOTE: This Feb 2017 version supersedes the old MTAD code, which computes incorrect p-values!