1. Review of data variability as it affects aggregation of PPPs
Frederic Vogel
3rd Regional Coordinating Agencies meeting
October 28-30, 2015 Washington, DC

2. Purpose of Presentation
Aggregation methods include 148 countries
FOC review suggests “Elimination of weak links through countries with different price and expenditure structures” which includes
Bilateral aggregation of basic heading PPPs using Laspeyres/Paasche to get Fisher PPPs for 148 countries
Binary PPPs used to compute direct PPP of each country with the US and all 144 indirect links to compute aggregated PPPs
Are there weak links—especially indirect PPPs across regions?
Are weak links result of different price and expenditure structures or the result of poor quality data?

3. Overview Review sources of variability at each step Conclusions
Basic Heading Linking Factors
Global Basic Heading PPPs—after linking
Aggregation—L/P ratios
Aggregation—GEKS direct and indirect PPPs
Conclusions
Recommendations

4. Table 1. Variability of Basic Heading Linking factors
Matrix of linking factors
154 basic heading between region PPPs for Africa, Asia, Eurostat-OECD, Latin America, Western Asia– with Eurostat-OECD = 1.00
Within region, computed 95 % confidence interval of basic heading linking factors
Compared confidence interval with maximum and minimum linking factors within region
Identified basic headings very extreme with maximum and minimum values

5. Table 1. Variability of Basic Heading Linking factors
Region
95 % Confidence interval of linking factors (geomean)
Maximum linking factor
Minimum linking factor
Max/Min ratio
Africa
(5.23)
28.01*
.70#
39.81
Asia
(5.88)
22.60**
1.67##
13.50
Eurostat/OECD --
1.00
Latin America
(1.57)
4.65***
.249###
18.65
Western Asia
(.206)
2.00****
.031####
64.16
*Electricity & gas **Gas & domestic services ***Jams & Honey, & Electricity **** Eggs & Pork
#Transport by RR ##Bicycles ###Compensation of employees #### Outpatient services

6. Tables 2 and 3 Variability of global basic heading PPPs
For each country calculated the ratio—maximum PPP/minimum PPP
Within each region calculated the 95% confidence interval of the max/min ratios
Examined the countries in each region with largest max/min ratios and their basic headings with the largest and smallest PPPs

7. Table 2. Variability of Global Basic Heading PPPs by region
Geo Mean
Max/min ratios*
95% confidence interval max/min ratios
Largest Max/min ratio in region
Country in region with largest Max/min ratio
Smallest Max/min ratio in region
Country in region with smallest Max/min ratio
Africa 36
2-70 93
Equatorial Guinea 13
Botswana
Asia 50
1-126
141
Indonesia 14
Hong Kong
CIS
1-208
258
Tajikistan
Russia
Eurostat OECD 9
1-27 53
Albania 1 US
Latin America** 35
1-161
275
Venezuela
Uruguay
Caribbean 27
1-69
101
Grenada
Virgin Islands
Western Asia 38
1-96
124
Kuwait 15
United
Arab Emirates
*Geo mean of country max/min ratios **Does not include Cuba with Max/min ratio =583

8. Table 3. Countries with greatest variability in BH PPPs, max/min ratio, and basic headings with largest and smallest PPPs
Country
Max/min ratio
Basic Headings with largest PPPs
Basic Headings with smallest PPPs
Equatorial Guinea 93
Eggs
Out Patient svcs
Indonesia
141
Wine
Medical svcs
Lao
113
Education
Mynmar
118
Other fuels
Nepal
107
Motor Cars
Vietnam
112
Tajikistan
258
Transport by air
Compensation of employees
Albania 53
Venezuela
275
Frozen & preserved fruit
Fuels/lubricants
Grenada
101
Tools & Equipment
Kuwait
124
Other services
Electricity

9. Table 4—L/P ratios from aggregation
Matrix of 148x148 binary Fisher indexes, Laspeyres and Paasche aggregations and L/P ratios.
For each country, started with the L/P ratio with each other country and calculated the geomean of the respective ratios and the standard deviation
Identified country pair with the largest L/P ratio

10. Table 4. Countries with largest L/P ratios
Country
Geomean of L/p ratios
+/-
2 Stdev
Max L/p
Country pair with max L/P ratio
Burundi
1.51
.46
3.03
Venezuela
Comoros
1.57
.44
3.45
Bahrain
Gambia
1.45
.38
3.01
Madagascar
1.32
.35
Rwanda
1.73
3.69
Uganda
1.42
.36
3.05
Cambodia
1.39
2.76
Japan
.34
2.63
1.91
.42
1.70
Data—Matrix of 148x148 binary Fishers, Laspeyres and Paasche PPPs, and L/P Ratios across all countries (Venezuela expenditures example: housing 72% of motor cars, 90% transport by road, 30% catering services

11. Table 5. Variability of direct and indirect PPPs from the GEKS method
Matrix of 148 x 148 countries. For each country have column of direct PPP to the US and 146 indirect PPPs through each other country
For each country computed the ratio of the largest indirect PPP to the smallest indirect PPP—max/min ratio
For each region computed geo mean and standard deviation of the country max/min ratios
Identified country in each region with largest Max/min ratio
For that country, identified countries with largest and smallest indirect PPPs

12. Table 5. Variability in direct and indirect bilateral PPPs—148 countries—measured by ratio of maximum PPP to minimum PPP for each country
Region
Geomean of max/min PPPs in region
+/- 2 Stdevs of country max/min
values
Country in each region with largest Max/min ratio*
Country with largest indirect PPP with col (c)
Country with smallest indirect PPP with col (c)
(a)
(b)
(c)
(d)
(e)
Africa
1.61
.20
Burundi
1.87
Lithuania
Liberia
Asia
1.43
.22
Nepal
1.68
Luxembourg
Sierra Leone
Eurostat/OECD
1.32
.16
Albania
1.48
Kuwait
Latin America
1.37
Nicaragua
1.51
Venezuela
Western Asia
1.45
.18
Yemen
1.54
Mauritania
The ratio of the largest indirect PPP to the smallest PPP for Burundi was The indirect PPP through Lithuania was the largest indirect PPP for Burundi and Liberia the smallest PPP for Burundi

13. Conclusions
Significant basic heading outliers for linking factors and global PPPs
Electricity, gas serious problem in three regions
Significant L/P outliers in aggregation to Fisher PPPs
Venezuela, Bahrain, Comoros, Rwanda
More robust results for GEKS method—direct-indirect PPPs
Need close look at Liberia
Consider there is more of a data quality problem than “weak links”

14. Recommendations Basic Heading regional linking factors
Review electricity and gas & other BHs with largest values
Global Basic Heading PPPs
Do same analysis of BH PPPs within region before linking
Aggregation—L/P ratios
Do same analysis within regions
GEKS—Variability in direct-indirect PPPs—within regions: Remaining questions
Are global PPPs more variable than within region PPPs before linking?
Need to consider criteria identifying “weak links” but only after reviewing data quality in more detail.