1. Improving the quality of data in the Global Price Reporting Mechanism for a reliable market intelligence project International Multi-stakeholder Consultation on National AIDS programmes Nairobi,19th April 2012 Luis SAGAON TEYSSIER Ph.D. Econometrics I

2. Historical Background 2001 : UNAIDS meeting on need for international data collection of ARV drug prices based on observed transactions 2005: Establishment of Global Drug Price Reporting Mechanism (GPRM) 2009: RFP issued by UNITAID for Global Data Exchange 2011 February: MOUs signed with WHO/AMDS; ANRS; FIND Enhancement of GPRM Collect data from partners Data cleaning (algorithms) Data analysis (start with ARVs) Diagnostics Input to restructuring database Identify new data sources, additional variables Develop new interface Develop standard reports, queries Extension to TB and Malaria commodities Joint responsibilities

3. Global Price Reporting Mechanism (AMDS/WHO)

4. Data set on ARVs (2003-2011) n= 36,347 transactions 2011 data are not complete GLOBAL FUND33.9% PEPFAR (SCMS)20.7% UNICEF15.6% UNITAID11.7% World Bank(IDA)11.6% Mission Pharma3.2% CHAI1.5% JSI0.6% MSH0.5% WHO0.4% WHOCPS0.3% The main problems are the presence of atypical unit prices & non-comparability of unit prices due to International Commerce Terms (INCOTERMS)

5. 5 The effect of outliers on analyses

6. 6 X Y Not controlling for the presence of outliers may induce to erroneous inference. Are prices atypical because of a problem in the sources reporting mechanisms, or/and because the heterogeneity introduced by different Incoterms? Prices are determined by several factors: need of a multivariate framework The effect of outliers on analyses

7. 7 2003-20092010-2011* Variables used: drug-specific dummy variables, destination country, quantity bougth, Incoterms, GNIpc., innovator/generic Outliers detection based on robust regression *Provisory.

8. 8 2003-20092010-2011* *Provisory. Price differences induced by incoterms: the effect of outliers on the estimation -40% to 30%4.6% to 25.1% -6% to 61%2.5% to 20%

9. Descriptive statistics of unit prices Final dataset: 32,427 transactions of ARVs; Proportion of outliers: 10.8% Mean unit prices: not removing outliers: 0.347 US$; removing outliers: 0.239 US$ Unit prices with outliers 31.12% > prices controlling for outliers Mean unit prices without outliers: ExWorks 0.239 US$; observed 0.258 US$ Once outliers controlled: ExWorks prices 7.8% < than observed prices

10. Some examples

11. Conclusions Statistical procedures need to be implemented for a reliable GPRM Quality (good or bad) of data may have important consequences in procurement policies Understanding the market tendencies on the basis of quality data would contribute to better identification of problems in the market (globally, at the level country, etc.)