The index of patent systems strength 1998-2015 study is part of a wider ongoing research project that studies the effect of patent and IPR enforcement strength on international business activity and behaviour. As part of the research project, we carried out a thorough literature search and reviewed all available academic and practitioner indices that attempted to measure IPR strength. We identified a number of indices that can individually approximate for different enforcement related aspects of a national patent system.

However there was no index providing an overall numerical score that could capture the enforcement related strength of a country’s patent system. We developed a composite index that approximates for the overall enforcement related aspects of patent systems strength using existing secondary data.  The methodology used to construct the new index is described briefly below. A detailed discussion of the complete methodology can be found in the full article published at the Journal of World Business.

Conceptual framework and data collection

Following OECD recommended techniques we developed a conceptual framework that identifies different enforcement related aspects of a patent system and connects them to the three types of transaction costs that firms are expected to face when engaging in patent enforcement activities in a country. These are monitoring costs, property rights protection costs and servicing costs. We then selected the most suitable secondary datasets that can enable the quantification of the conceptual framework. To strengthen the reliability and validity of the new composite index, and to allow it to be annually updated and recalculated, we decided a priori that the selection of secondary data should first satisfy six criteria.

These were that the data should:

  1. Have a close conceptual relevance to the theoretical framework in order to serve as meaningful proxies of the patent system and its associated transaction costs
  2. Have an early date of initial publication to increase the longitudinally of the index
  3. Be collected consistently over time to enhance the reliability of the index
  4. Be reported frequently to strengthen the discriminatory power of the index
  5. Cover a wide range of countries to bolster the applicability of the index; and
  6. Be readily available to facilitate future replication and regular updating of the index by any researcher.

    The secondary data sources where the ten variables comprising the composite index originate from are:

  7. The Global Competitiveness Report (GCR) of the World Economic Forum
  8. The World Competitiveness Yearbook (WCY) of the International Institute of Management Development (IMD)
  9. The International Country Risk Guide (ICRG) published by the Political Risk Services (PRS) Group
  10. The Corruption Perceptions Index (CPI) published by Transparency International
  11. Data on piracy rates reported by the Business Software Alliance (BSA); and
  12. The USTR Special 301 Report. *


To construct the index we first normalised the ten secondary data variables using a standardisation technique (z-scores) and transformed them into a single scale with a mean of zero (0) and a standard deviation of one (1). Following the conceptual framework, we categorised and aggregated the data according to the transaction cost construct they are used to proxy.

We then applied two multivariate analysis tests that are commonly reported in the index scale construction literature, namely Cronbach’s coefficient alpha and factor analysis (OECD, 2008). The reliability analysis tests revealed strong internal consistency for each of the three transaction cost constructs. We then used factor analysis to inform the application of a weighting scheme to aggregate the variables into a single numerical value for each construct.

According to the OECD (2008, p. 89), a weighting scheme derived from factor analysis ‘‘intervenes only to correct for overlapping information between two or more correlated indicators, and is not a measure of the theoretical importance of the associated indicator. If no correlation between indicators is found, then weights cannot be estimated with this method’’. In other words, the weighting applied to construct the index does not differentiate between the importance of each factor but instead represents the highest possible variation in the indicators. Thus, ‘‘the composite (index/construct) no longer depends upon the dimensionality of the dataset but rather is based on the ‘‘statistical’’ dimensions of the data’’ (OECD, 2008, p. 89). The same process (use of reliability and factor analysis) was then followed to construct the overall composite index of international patent systems strength.

The variable based on the data from the USTR Special 301 Report was not included for the calculation of the US scores. This is because the data are compiled by the United States Trade Representative and there is no information provided regarding the US patent system.