|Abbott et al. ||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.
Medicines: 46 essential medicines.
|Sample: a sample of 29 of 46 essential medicines;|
Range: 1999 and 2004, pooled cross-section
|Alawi & Alabbadi ||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.
Medicines: all pharmaceutical products in Jordan.
|Sample: a sample of 140 products representing 36.8% of total sales value in 2010.|
|Borrell ||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 ||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
Medicines: All single molecule medicines
|Sample: approximately 5100 Indian stockists.|
Range: 2003q1 to 2012q2.
|Jung & Kwon ||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.|
|Kyle & Qian ||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 ||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 ||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.
Medicines: all medicines available through various public-sector health programs.
|Sample: 730 medicines on the Open Contract list.|