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.)