Table 3 Summary table of ex-post studies

Abbott et al. [21]

To assess the impact of stronger intellectual property protection in Jordan on the access to medicines

Mean and frequency comparison.

Outcome: lag years in launching new medicines.

Comparison groups: difference in years of lag in launching new innovative medicines in Jordan before and after the US-Jordan FTA.

Country: Jordan;

Medicines: 46 essential medicines.

Sample: a sample of 29 of 46 essential medicines;

Range: 1999 and 2004, pooled cross-section

Alawi & Alabbadi [22]

To analyze the effect of data exclusivity on the pharmaceutical sector in Jordan before and after the implementation of data exclusivity.

Trend analysis

Outcome variables: prices, sale values, sale volume and sales

Comparison groups: generic medicines, only originator medicines, originator to generic medicines, and generic to originator.

Country: Jordan;

Medicines: all pharmaceutical products in Jordan.

Sample: a sample of 140 products representing 36.8% of total sales value in 2010.

Range: 2004–2010.

Borrell [23]

To estimate the impact of patents on pricing of HIV/AIDS medicines in low and middle income countries in the late 1990’s.

Quasi-experimental study is used to study how the outcome variable differs for treatment groups and comparison groups that are not randomly assigned.

Treatment group: all the country medicine pairs for which any ARV medicine is under a patent regime

Comparison group: all the country-medicine pairs for which the medicine is not under a patent regime.

Outcome variable: price

Country: Developing and least developed countries.

Medicines: HIV/AIDS’ ARV medicines.

Sample: 21 developing and least developed countries with two groups of developing and low income countries, and 15 ARVs.

Range: January 1995 to June 2000.

Duggan, Garthwaite & Goyal [24]

To estimate the effects of the 2005 implementation of a product patent system in India on pharmaceutical prices, quantities sold, and market structure.

OLS regressions

Outcome variables: prices, sales volume

difference specification and event study framework, where OLS regressions with patent dummy that takes value 1 in post patent regime and 0 in pre-patent regime are estimated, to investigate whether there is any statistically difference in log prices

Country: India.

Medicines: All single molecule medicines

Sample: approximately 5100 Indian stockists.

Range: 2003q1 to 2012q2.

Jung & Kwon [25]

To estimate the effect of stronger IPR on medicine access in low and middle income countries

Pooled cross-country multilevel techniques with subgroup analyses to identify factors both at country level and individual level that affect access to medicine and financial burden of purchasing medicines.

Country: all developing and least developed countries.

Medicines: all medicines.

Sample: 35 countries, 660 to 38424households and 585 to 38,618 individuals.

Range: 2002–2003.

Kyle & Qian [26]

To examines how TRIPS affects new medicine launches, prices and sales using data from 59 countries of varying levels of development.

Difference-in-difference estimation framework

Outcome variables: speed of launch or new medicines, price, sales volume

Country: 59 countries of varying degrees of development.

Sample: 716 medicine-country pairs linked with patents;

Range: 2000–2013 for prices and units sold and 1990–2013 for launch of new medicines.

Berndt & Cockburn [27]

To study the trade-off between stronger patent laws and early access to new medicines.

Survival analysis

Outcome variable: sales volume, lag time of new medicine launch in India as compared to Germany and the U.S. due to Indian patent policies.

Country: India, Germany and USA;

Medicines: new innovative medicines.

Sample: 184 new molecular entities approved by the US FDA.

Range: 2000 to 2009.

Shaffer & Brenner [28]

To estimate the effect of IPR provisions in the Central American Free Trade Agreement on access to low price generic medicines in Guatemala.

Price comparison

Outcome variables: Price

Intervention group: Medicines purchased by both private and public sector in Guatemala of those that received data protection due to IPR provisions in the CAFTA

Comparison group: Brand or generic equivalents that have no data protection.

Country: Guatemala.

Medicines: all medicines available through various public-sector health programs.

Sample: 730 medicines on the Open Contract list.

Range: 2005–2007.