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Luis SAGAON TEYSSIER Ph.D . Econometrics

I. 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. Historical Background.

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Luis SAGAON TEYSSIER Ph.D . Econometrics

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

  2. Historical Background • Enhancement of GPRM • Collect data from partners • Data cleaning (algorithms) • Data analysis (start with ARVs) • Diagnostics • Input to restructuring database Joint responsibilities • Identify new data sources, additional variables • Develop new interface • Develop standard reports, queries • Extension to TB and Malaria commodities 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

  3. Global Price Reporting Mechanism (AMDS/WHO)

  4. Data set on ARVs (2003-2011)n= 36,347 transactions2011 data are not complete The main problems are the presence of atypical unit prices & non-comparability of unit prices due to International Commerce Terms (INCOTERMS)

  5. The effect of outliers on analyses

  6. The effect of outliers on analyses Y Not controlling for the presence of outliersmayinduce to erroneousinference. Are pricesatypicalbecause of a problem in the sources’ reportingmechanisms, or/and because the heterogeneityintroduced by different Incoterms? Prices are determined by severalfactors: need of a multivariateframework X

  7. Outliers detection based on robust regression Variables used: drug-specific dummy variables, destination country, quantity bougth, Incoterms, GNIpc., innovator/generic 2003-2009 2010-2011* *Provisory.

  8. Price differences induced by incoterms: the effect of outliers on the estimation 2003-2009 2010-2011* -40% to 30% 4.6% to 25.1% -6% to 61% 2.5% to 20% *Provisory.

  9. Descriptive statistics of unit prices Final dataset: 32,427 transactions of ARVs; Proportion of outliers: 10.8% Mean unit prices: not removingoutliers: 0.347 US$; removingoutliers: 0.239 US$ Unit priceswithoutliers 31.12% > pricescontrolling for outliers Mean unit priceswithoutoutliers: ExWorks0.239 US$; observed0.258 US$ Once outlierscontrolled: ExWorksprices 7.8% < thanobservedprices

  10. Someexamples

  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.)

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