improving the quality of data in the global price reporting mechanism for a reliable market...
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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 TEYSSIERPh.D. Econometrics
I
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
Public database
Working database
Staging database
0 - Load
2 - Validate
4 - Publish
IntranetServer
Administrator
Extranet Upload Server
Reports
DownloadExtranet
WebServer
Public/Private
Analysts
Adjustvalidationrules
Publish reports
.....Sources…..
3 - Normalize
1 - Standardize
Global Price Reporting Mechanism (AMDS/WHO)
Data set on ARVs (2003-2011)n= 36,347 transactions
2011 data are not complete
GLOBAL FUND 33.9%
PEPFAR (SCMS) 20.7%
UNICEF 15.6%
UNITAID 11.7%
World Bank(IDA) 11.6%
Mission Pharma 3.2%
CHAI 1.5%
JSI 0.6%
MSH 0.5%
WHO 0.4%
WHOCPS 0.3%
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
Effect of vertical outliers
Effect of bad leverage points
Effect of good leverage points
Theoretical linear regression X
YNot 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
2003-2009 2010-2011*
Variables used: drug-specific dummy variables, destination country, quantity bougth, Incoterms, GNIpc., innovator/generic
Outliers detection based on robust regression
*Provisory.
8
2003-2009 2010-2011*
*Provisory.
Reference: ExWork Estimate P-value Estimate P-valueFCA 0.014 0.225 0.046*** 0.000FOB 0.065*** 0.000 0.085*** 0.000CFR 0.068 0.413 0.092** 0.046CIF 0.094*** 0.000 0.146*** 0.000CPT 0.060** 0.014 0.042*** 0.002CIP 0.118*** 0.000 0.127*** 0.000DAF 0.042 0.886 0.239 0.123DES -0.406*** 0.008 0.062 0.472DAP 0.105*** 0.001 0.251*** 0.000DDU 0.150*** 0.000 0.185*** 0.000DDP 0.300*** 0.000 0.215*** 0.000
Not accounting for outliers
Accounting for outliers
OTHER CONTROLS
Not accounting for outliers
Accounting for outliers
Reference: ExWork Estimate P-value Estimate P-value
FCA -0.060*** 0.000 0.025*** 0.000
FOB 0.066* 0.076 0.013 0.235
CFR 0.149 0.291 0.087** 0.034
CIF 0.098*** 0.000 0.066*** 0.000
CPT 0.442*** 0.000 0.028 0.102
CIP 0.183*** 0.000 0.052*** 0.000
DAP 0.189*** 0.000 0.200*** 0.000
DDU 0.148*** 0.000 0.132*** 0.000
DDP 0.611*** 0.000 0.073*** 0.000
OTHER CONTROLS
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%
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
Groups Prices N Min Max Mean SD
Observed unit price .0036 24.83 .24 .43
Estimated ExWorks price .0036 20.01 .22 .38
Observed unit price .00000030 217.52 1.30 6.25
Estimated ExWorks price .00000032 199.82 1.19 5.67
Observed unit price .0051 14.00 .44 1.00
Estimated ExWorks price .0053 14.00 .40 .93
Observed unit price .0046 44.56 1.53 3.84
Estimated ExWorks price .0047 39.25 1.40 3.48
Bad leverage points
909
Non-outliers 29415
Vertical outliers
3011
Good leverage points
3012
Some examples
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.)