Economists studying innovation and technology have used patent data for decades. Patent data are exceptionally rich and - when properly understood - can generate powerful empirical insights.They are also difficult to analyze jointly with economic data because their underlying classification systems do not easily map into each other. 

With funding from the NSF SciSIP Program, several collaborators and I formulated a text-mining approach we call Algorithmic Links with Probabilities (ALP) to construct probablistic concordances between patent, trade and industrial classification systems. We applied similar methods to construct ALP concordances for trademarks as well.

The complete and current set of ALP concordances are available for download below. 

Published and Working Papers

Getting Patents & Economic Data to Speak to Each Other: An ‘Algorithmic Links with Probabilities’ Approach for Joint Analyses of Patenting & Economic Activity

Travis J. Lybbert and Nikolas J. Zolas. 2014. Research Policy 43: 530-542. Article | Slides

An ‘Algorithmic Links with Probabilities’ Concordance for Trademarks with an Application Towards Bilateral IP Flows

Nikolas Zolas, Travis J. Lybbert, and Prantik Bhattacharyya. 2016. The World Economy. Article | Slides

An ‘Algorithmic Links with Probabilities’ Crosswalk for USPC and CPC Patent Classifications with an Application Towards Industrial Technology Composition

Nathan Goldschlag, Travis J. Lybbert and Nikolas J. Zolas, Working Paper

Downloadable ALP Concordance Files

Each of the zipped files below include a readme file that explains the format and use of the concordances. For details on their construction, see the papers linked above. 

Intellectual Property Classification	Economic Classifications Included
IPC (5.9 MB)	ISIC	NAICS	SITC	HS
USPC (2.0 MB)	ISIC	NAICS	SITC	HS
CPC (5.7 MB)	ISIC	NAICS	SITC	HS
NICE (538.0 KB)	ISIC	NAICS	SITC	HS

IPC=International Patent Classification

USPC=US Patent Classification

CPC=Common Patent Classification

NICE=Trademark classification system