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STATISTICS DIRECTORATE
National Accounts
Consistency Between PPP Benchmarks and National Price and Volume Indices
Paper Prepared for the 27th General Conference of
The International Association for Research in Income and Wealth
Stockholm, Sweden. August 18 – 24, 2002
OECD MEETING OF NATIONAL ACCOUNTS EXPERTS
Château de la Muette, Paris
8-11 October 2002
Beginning at 9:30 a.m. on the first day
Session Number: Plenary session 5
Session Title: New Developments in International Price Comparisons, Production and Consumption
Paper Number:
2
Session Organizer:
Bart van Ark, University of Groningen, Groningen, The Netherlands
Discussant: Györgi Szilágyi, Statistical Office Hungary, Budapest, Hungary
Paper Prepared for the 27th General Conference of
The International Association for Research in Income and Wealth
Stockholm, Sweden. August 18 – 24, 2002
Consistency Between PPP Benchmarks
and National Price and Volume Indices
Esben Dalgaard
and
Henrik Sejerbo Sørensen
For additional information please contact:
Esben Dalgaard ([email protected])
Henrik Sejerbo Sørensen ([email protected])
Statistics Denmark
Sejroegade 11
DK-2100 Copenhagen
This paper is placed on the following websites:
www.iariw.org
www.econ.nyu.edu/iariw
2
The views expressed in this paper are those of the authors. They do not necessarily represent the views of
Statistics Denmark.
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ABSTRACT
The paper deals with the relationship between successive benchmark calculations of purchasing power
parities (PPPs) and national volume and price indices in the national accounts. It defines consistency
requirements between these two sets of data and analyses the extent to which those requirements are met
in practice by the data published by international organisations. It features a plausibility check on the PPP
figures for consumption of housing services and shows the figures currently published to be insufficiently
reliable. The paper finally makes four suggestions for improving the consistency of PPP and national
accounts data.
1. INTRODUCTION
The consistency of temporal and spatial price and volume indices is an important topic which apparently
was not addressed until several benchmark calculations of purchasing power parities became available and
hence the problem had to be faced in practice, see Heston, Summers and Aten (2001). The first systematic
study appears to be Eurostat (1983) which provides an analysis of the two PPP benchmarks for the EU in
1975 and 1980. The theoretical literature on the topic is surprisingly small, perhaps because of its intrinsic
difficulty and lack of any breakthroughs. The literature on index numbers typically deals either with
indices in time or indices in space, and draws analogies between the two sets of index number problems,
but rarely addresses the two index number problems simultaneously. This is not because of the topic's lack
of importance to users of statistics, though. On the contrary, in statistical practice the consistency issue is
becoming increasingly important, since almost all international comparisons of per capita GDP and its
components are now done by means of PPP data rather than data converted to a common unit using
official exchange rates.
It is clear that if the relative developments of real per capita GDP of country A and country B between
year t and year t+n implied by the PPP figures are not broadly consistent with the real GDP and population
growth rates published in the national accounts of the two countries, economic statisticians have some
explaining to do vis-à-vis the users of statistics. The same reasoning applies, albeit with smaller potential
consequences for the credibility of macroeconomic statistics, to the components of GDP.
The present paper looks at the issue from an empirical point of view with the aim of clarifying whether the
international and national price and volume indices currently published by international organisations and
national statistical offices are reasonably consistent or not. By way of introduction it also explains the
approaches used in the compilation of national and international price and volume indices and identifies a
number of reasons why one cannot expect these two sets of indices to yield identical results in terms of
relative price and volume developments between countries. Complete consistency in the sense of
simultaneous transitivity in time and space can only be achieved by using a common, fixed price vector in
aggregation, an approach that would seriously distort many of the comparisons both in time and space.
The question is whether the current international and national price and volume indices, while not
4
satisfying complete transitivity, are nevertheless sufficiently close to being transitive to be used alongside
each other at least at the level of GDP and the main uses of GDP.
Besides the theoretical explanations for lack of transitivity, which one may term formula-related
intransitivity, there are of course a lot of practical measurement problems that may lead to the index
numbers being intransitive, even though the underlying data, if correctly measured, should give transitive
results. An example of the latter is where average national prices are derived from price observations in
the capital by applying certain geographical coefficients.
2. TRANSITIVITY AND CONSISTENCY
2.1
Transitivity across space
Consider a multilateral comparison between m countries involving several periods. A number n of
products occurs in at least one country in at least one of the periods covered. Let pit i = 1,…m stand for
the price vector in country i in period t. Let the quantities of the n products be denoted x1 , …, x n , let
subscript i denote country i and let the superscript t denote the time period concerned i.e. xit1 is the
 
m
 
m
quantity of the first product in period t in country i. Let x t = xit i 1 and p t = pit i 1 be the vectors of
quantities and prices respectively in all of the countries in period t. Define a set of aggregator functions
(index number formulas) f i t  x, p  giving the real values or volume indices for a national accounts
aggregate in country i in a multilateral comparison concerning period t. The arguments of the aggregator
function are the quantities and prices in all the m countries in period t. We may then define transitivity
across space in a multilateral comparison of m countries in a given period as:






 
 
fit xt , p t
f i t x t , p t f kt x t , p t

f jt x t , p t
f kt x t , p t f jt x t , p t


(1)
for all aggregates and all countries i, j and k.
(1) says that comparing countries i and j directly gives the same result as comparing them indirectly via a
third country.
Transitivity across space is generally considered an indispensable property in multilateral comparisons.
The method currently used in the PPP programme, the EKS method, has this property as does the main
alternative the Geary-Khamis method which was used in earlier phases of the PPP programme. Actually,
EKS imposes transitivity on a data set which originally is not transitive. Provided all n goods are available
in all the countries in all the periods, the Geary-Khamis method automatically yields transitive results at
all levels of aggregation, since it is based on aggregating the n products by means of a common price
vector of average international prices.
2.2
Transitivity across time
5
 
Let xi  xit
q
t 1
 
and pi  pit
q
t 1
be the vectors of quantities and prices in all q periods in country i. Let
g i xi , pi  be the aggregator function function used for temporal comparisons of volumes in the national
accounts of country i, and let superscript t denote the value for period t. We may then define transitivity
across time as:
g it  y xi , pi  g it  y xi , pi  g it  x xi , pi 

g it xi , pi  g it  x xi , pi  g it xi , pi 
(2)
(2) says that comparing two periods directly gives the same result as comparing them indirectly via a third
period.
Transitivity across time is satisfied by the national accounts volume and (implied) price indices, no matter
whether the volume indices are fixed-base Laspeyres, chained Laspeyres or chained Fisher.
2.3
Simultaneous transitivity across space and time
We may define simultaneous transitivity across space and time of the multilateral and temporal
comparisons as (1) transitivity across space, (2) transitivity across time plus the further requirement




 f i t 1 x t 1 , p t 1

 f t 1 x t 1 , p t 1
 j
 f it x t , p t 


 f t xt , pt 
 j





 g it 1 xi , pi  
 t

 g x , p  
i
i
i



t 1
 g j x j , p j  


 g t x , p  
 j j j 
(3)
(3) says that the ratio of the relative positions of two countries in two PPP benchmarks is the same as the
ratio of their respective volume indices in the intervening period.
2.4
Consistency requirements for purchasing power parities
Neither the EKS nor the GK-method taken together with national accounts data satisfies simultaneous
transitivity across space and time. Would it at all be possible to imagine a method for temporal and spatial
comparisons which would have such a property that many users would undoubtedly find to be "nice"? It
would seem that the only (direct) way one could achieve simultaneous transitivity across space and time
would be to aggregate the quantities in all countries and in all periods by means of a common, fixed price
vector. One may think of such a price vector as a sort of base-year GK price vector of average
international prices used not only for making the multilateral comparison in the year to which the prices
refer but also for subsequent multilateral comparisons, as well as the price vector used for deriving
national fixed-base Laspeyres volume indices.
This observation demonstrates that simultaneous transitivity across time and space, even though users
might like it, is not a realistic or reasonable requirement. Imposing a common, fixed international price
vector on all spatial and temporal comparisons would violate the equi-characteristicity principle in
multilateral comparisons for the base year and, in addition, the characteristicity principle in later
multilateral comparisons as well as in deriving real growth rates at the national level. It should be recalled
that the GK-method was abandoned in the PPP programme in favour of EKS precisely because of its
6
failure to meet the equi-characteristicity criterion. By the same reasoning, extending the application of a
given price vector beyond its reference period would only exacerbate the problem.
Consequently, it is not reasonable to say that PPP benchmarks and national price and volume data are
"inconsistent" when they fail to satisfy simultaneous transitivity across space and time. Rather, the two
sets of data must be regarded as being consistent to the extent that the violations of simultaneous
transitivity can be explained in terms of the different index number formulas employed in order to serve
different purposes. The two sets of data are only inconsistent to the extent that the implied relative real
growth rates differ by more than can be explained by differences in concepts and more particularly the
index number formulas.
If there is a residual that cannot be explained in terms of conceptual differences, then it is clear that there
are measurement errors either in one or more of the PPP benchmarks or the national accounts. It is only
such measurement errors that pose a genuine problem of consistency.
The situation is much like the relationship between the national accounts deflator for household
consumption expenditure and the consumer price index. The former is an implicit Paasche (or Fisher)
price index derived from a volume index which is either a fixed-base or chained Laspeyres volume index
or a chained Fisher volume index. The latter is a fixed-base Laspeyres price index.
Besides the difference in index number formulas there are a number of conceptual differences between the
national accounts deflator and the consumer price index e.g. regarding housing, insurance premiums and
lotteries. However, once correction is made for these conceptual differences, one would normally expect
the national accounts deflators and the CPI to show broadly the same price development, especially at the
level of detailed COICOP consumption groups. Of course, the two indices should not give identical price
developments due to the difference in index number formula and the broader coverage of goods and
services in the national accounts. Even so, experience shows that major differences at the level of detailed
consumption groups are likely to be the result of errors of compilation either in the national accounts or
the CPI.
In a similar manner, if there are major differences between the evolution of relative per capita GDP
implied by successive PPP benchmarks and by the national accounts, chances are that this is due to errors
in either data set and most likely in one or more of the PPP benchmarks. In some cases, major differences
may be explained by changes in relative prices e.g. the effect on net exports of changing oil prices, but in
comparisons between countries with a similar economic structure it seems unlikely that major differences
can be explained primarily in that way.
It is clear that changes in relative prices between two successive benchmark years imply that the relative
positions of two countries in the two benchmarks will differ from what is implied by the national accounts.
The growth rates of the two countries are measured at constant prices - and hence the relative prices - of
either the preceding year or a fixed base year, whereas the PPP benchmarks by nature reflect the current
prices of the benchmark years. The following simple example, which is taken from OECD (2001),
demonstrates the impact of relative price changes between two benchmark years.
Assume that two countries, A and B, are compared, and that for simplicity they have the same GDP and
price level (and exchange rate = 1) in year t. GDP expenditure consists of expenditure on two products
"goods" and "services", and PPPs between the countries are 1 in both products and consequently also for
GDP in year t. The only difference between the countries is that expenditure on goods has a dominating
share of GDP in country A whereas services are more important in country B.
Product
Country A
GDP Price Quantity
Country B
GDP
GDP Price Quantity GDP
7
Period index
t
t+1
Goods
Services
Total
index
t+1
t+1
Period index
t
t+1
index
t+1
t+1
80
20
1.0
2.0
1.0
1.0
80
40
20
80
1.0
2.0
1.0
1.0
20
160
100
1.2
1.0
120
100
1.8
1.0
180
Purchasing power parities (price level in country B/ price level in country A)
Product
PPP
PPP
t
t+1
Goods
Services
1.0
1.0
1.0
1.0
Total
1.0
1.0
In year t+1, prices of goods remain the same in both countries as in year t, but prices of services double.
There is no growth of GDP (volume growth) in either of the countries. Due to the different expenditure
structures, the implicit price index of GDP goes up by 20 per cent in country A whereas the change is 80
per cent in country B.
PPPs and GDP in real terms for period t+1 are different depending on whether t+1 is a benchmark year or
whether PPPs are updated from t at the GDP level.
In the benchmark estimation, t+1, the PPPs for goods and services are 1 (as in t) and therefore so are the
PPPs for GDP. Due to the higher nominal GDP in country B in t+1, this results in 50 per cent higher GDP
in real terms in B. Thus, the greater importance of services in B results in higher GDP compared to A
although their GDP was at the same level in t and the growth of GDP from t to t+1 was the same.
When extrapolating PPPs at the GDP level, the GDP volumes for t+1 are derived from t by applying GDP
growth rates (or equivalently, updating PPPs by using changes in the implicit price index of GDP). Thus,
the countries' real GDP in relation to each other remains unchanged. This means that in effect the PPP for
GDP rises to 1.5 in country B.
What perhaps is less obvious is the fact that the relative positions of two countries in two benchmarks will
differ from that implied by the real growth rates in the national accounts, even if the relative prices stay
constant in both countries. This occurs if the relative prices in the two countries differ. The reason is that
the growth rates in the national accounts of the respective countries are calculated based on the relative
prices of the two countries, whereas the PPP benchmarks are calculated by the EKS method which gives
weight to the relative price structures of all the participating countries. This effect is illustrated in the
following example which has the same basic structure as the above example taken from the OECD paper.
There are two countries A and B which have the same quantities of the two products in both periods. They
have different relative prices. Goods cost the same (price = 1) in both countries but services are twice as
expensive in country A (price = 2) as in country B (price = 1). One explanation for this could be that
country B has a much larger population than country A, and thus with equal quantities of goods and
services a much lower living standard, and hence lower relative prices of services that are mainly nontradable. Prices in both countries remain constant between the two periods. The quantity of goods stays
constant in both periods in both countries, whereas the quantity of services doubles between periods t and
t+1 in both countries. The Fisher quantity index (and hence the EKS index) between the two countries is
equal to 1 in both periods. However, when extrapolating period t GDP using the national volume indices
8
we get the following (period t+1 / period t) indices: country A 1.67; country B 1.50. It is thus seen that
simultaneous transitivity gets violated, even though the prices stay constant, merely as a result of
differences in relative prices between the two countries.
Product
Country A
Price Quantity
Country B
GDP
Price Quantity
GDP
period t
Goods
Services
1.0
2.0
50
50
50
100
1.0
1.0
50
50
150
Total
50
50
100
period t+1
Goods
Services
1.0
2.0
50
100
50
200
1.0
1.0
50
100
250
Total
50
100
150
Purchasing power parities (price level in country B/ price level in country A)
Product
Goods
Services
Total
PPP
PPP
t
t+1
1.0
0.5
1.0
0.5
0.667
0.6
However, by comparing the two examples it is clear that the impact of relative price changes is much
stronger than that of differences in relative price structure. This is because the relative price changes
between the two PPP benchmarks affect the levels of the PPPs directly, whereas the different relative price
structures only affect them indirectly via the extrapolation of the levels from the first benchmark to the
second.
In seeking to find out how much of the apparent inconsistency of PPP benchmarks and national accounts
price and volume indices can be explained by differences in index number formulas it is therefore
appropriate to concentrate on relative price changes.
3.
REASONS FOR LACK OF CONSISTENCY IN PRACTICE
As seen in section 2, PPP benchmarks and national accounts price and volume indices cannot be expected
to satisfy simultaneous transitivity because of the difference in index number formulas. However, in
practice this factor may be overshadowed by other explanations that pose genuine problems of consistency
in published statistics. The most important of these are:
1) Measurement errors in one or more of the PPP benchmarks
2) Measurement errors in the national accounts
3) Revisions to national accounts data
9
First, it is clear that if there are measurement errors or lack of representativity in the price data used for
one or more of the PPP benchmarks, then there is a genuine problem of inconsistency which affects the
credibility of the data. Looking at PPP benchmarks in conjunction with national accounts data at a detailed
level of final household consumption, as is done e.g. in OECD (2001), it is apparent that there are rather
big intransitivities and that these must be attributed mainly to the PPP data. It seems highly improbable
that the national accounts deflators, which are checked against the CPI, should be so widely off the mark.
In this connection it should be borne in mind that both the national accounts deflators and the CPI at the
individual basic headings level are based on a far larger number of goods and services and price
observations than those underlying the PPPs. Many of the measurement errors at the basic heading level
may thus be stochastic in the sense that the representative products chosen at the basic heading level in the
PPP programme may not be sufficiently representative, but that the lack of representativity causes
measurement errors in both directions concerning the price evolution between successive benchmarks.
Regarding the second factor namely measurement errors in the national accounts, it should be recalled that
since both the PPP benchmarks and the extrapolations of older benchmarks are derived using national
accounts data at current prices as the starting point, the general uncertainty regarding national accounts
figures at current prices is irrelevant. It is only the uncertainty regarding the deflators which is relevant in
this connection.
In practice, the higher the level of aggregation considered, the more probable the PPP data tend to look. At
the level of GDP the most widely held view is that the per capita levels of the individual PPP benchmarks
are plausible, see OECD (1997), section 6. The problems at the level of GDP in most cases only become
apparent when the comparison with national accounts growth rates reveals apparent inconsistencies in the
form of lacking transitivity.
The good question of course is whether this relative robustness of per capita GDP can be interpreted as the
result of the law of large numbers, and thus something that can be relied upon, or whether it is more likely
to be a coincidence involving relatively few observations and thus something that can easily be
overthrown by the stochastic measurement errors as well as systematic bias.
This question is rather similar to the one about the reliability of unit value indices in foreign trade
statistics. It is well known that in many cases unit value indices of exports and imports at detailed level are
highly volatile and unreliable as indicators of price developments. However, at more aggregate levels the
indices are normally regarded as being relatively reliable indicators of the evolution of export and import
prices, at least if they are adjusted for any bias resulting from goods become lighter over time. This case is
a classic example of the law of large numbers in applied statistics.
A third factor which in practice may be very important in explaining apparent inconsistencies between
published PPP benchmarks and national accounts volume indices (growth rates) is revisions to national
accounts data. The national accounts figures used for the PPP benchmarks are normally provisional data of
year t-1 or t-2. National accounts typically become "final" only in year t+3 or t+4, so there may be
significant revisions even at the level of GDP after the data have been taken for use in the PPP
benchmarks.
As far as current revisions are concerned, the changes to GDP per capita occurring between year's t+1 and
t+3 or t+4 are usually relatively modest. However, in addition to these current revisions there may be
comprehensive revisions which in some cases may lead to sizeable changes to the level of GDP. As we
shall see in section 4, this has been the case for the EU in the 1990s not least as a result of the
harmonisation efforts necessitated by the use of GNI for fiscal purposes. Revisions to national accounts
data subsequent to their use in PPP benchmarks may thus lead to major apparent inconsistencies that are
not genuine.
10
One way around this problem is to recalculate the PPP benchmarks using the revised national accounts.
This is what is done e.g. in OECD (2001). However, it is not done systematically in connection with the
publication of PPP benchmarks, and users are therefore sometimes left puzzled by major unexplained
shifts in relative levels of GDP per capita.
4.
EMPIRICAL ANALYSIS FOR THE EU COUNTRIES
In this section we shall analyse the consistency issue by looking at data for the 15 EU countries over the
period 1990-1998 which is covered by annual PPP figures based on the old European system of national
accounts ESA79. To our knowledge this data set is the biggest and conceptually most consistent set of
PPP data available. From the reference year 1999 onwards, the annual PPP exercise in the EU has moved
from ESA79 to ESA95 (SNA93) as conceptual basis, a fact which implies discontinuities between 1998
and 1999.
The limitation of the analysis to the EU countries does not imply any loss of generality. All the problems
regarding consistency over time and space are present in that data set which has the advantage of being
more coherent than those with broader coverage. In all probability, the consistency problems are bigger in
the broader international comparisons than in the EU context.
As far as the EU countries are concerned price surveys are conducted as rolling surveys covering parts of
GDP. The periodicity is normally three years. Between the surveys, PPPs are extrapolated by means of the
CPI or national accounts deflators. Between 1990 and 1998 all expenditure components of GDP have been
covered by new price surveys. The comparison of the two years 1990 and 1998 is therefore a genuine
benchmark comparison which does not include any figures that have only been extrapolated between 1990
and 1998.
An empirical analysis of the consistency issue has recently been undertaken by OECD (2001) for the
whole OECD area. The approach taken by the OECD paper is to use the purchasing power parities at the
level of basic headings used for the 1990, 1996 and 1998 benchmarks and to redo the calculations based
on the revised set of national accounts according to the 1993 SNA now available in the OECD databanks.
As previously explained, in that way the OECD paper gets around the problem of revisions to national
accounts data. However, since this new, unofficial PPP data set constructed by the OECD involves a
number of conversions between the new 1993 SNA and the old 1968 SNA concepts and classifications,
there is a slight uncertainty as to how much of the apparent inconsistency between PPP benchmarks and
national accounts growth rates may be explained by the conversions. The apparent inconsistencies
revealed by the OECD paper must raise serious concerns in a number of cases. For the EU countries the
result of comparing the 1998 benchmark for per capita GDP with the 1990 benchmark extrapolated to
1998 by means of the growth rates in the national accounts are reproduced in table 1.
It is seen that the differences in some cases are very significant. The most extreme example is the France Greece comparison where the differences are in opposite direction. If one believes the new 1998
benchmark, GDP per head in France is 48 per cent higher than in Greece. If on the other hand one believes
that the 1990 benchmark extrapolated by means of national accounts GDP growth rates gives the correct
measurement, per capita GDP in France is 75 per cent higher than in Greece. This is a span of no less than
27 percentage points which of course is problematical to say the least.
11
Even the comparison of two of the largest economies in the EU in 1998 i.e. Germany and France is
apparently somewhat uncertain. According to the new 1998 benchmark GDP per head is 7 per cent higher
in Germany than in France. On the other hand if one takes the 1990 benchmark extrapolated by means of
national accounts growth rates, one arrives at the conclusion that GDP per head in France is 4 per cent
higher than in Germany. This gives a span of 11 percentage points which can hardly be characterized as a
convincing result for two economies with so relatively similar an economic structure.
Table 1
Per capita GDP: 1990 benchmark and 1998 benchmark versus
from 1990 benchmark (EU 15 = 100)
Country
1990
benchmark
1998
benchmark
Extrapolation
from 1990
benchmark
Difference
106
107
109
104
109
105
60
78
105
153
106
65
80
114
101
109
109
118
102
99
106
67
107
106
177
114
72
80
103
102
108
109
114
103
107
103
61
109
104
186
112
70
84
109
104
0,9%
-0,1%
4,0%
-0,8%
-7,7%
3,1%
9,9%
-1,5%
1,6%
-4,9%
2,3%
1,5%
-5,6%
-5,1%
-2,2%
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
United Kingdom
extrapolation
Source: OECD (2001), table 1.
In order to see whether the apparent inconsistencies found by the OECD may to some extent be due to
conceptual problems in the transition to SNA 1993 data we shall take a look at the same type of
comparison as in the OECD paper but using the Eurostat data set mentioned at the start of this section
which is conceptually consistent over the period 1990 to 1998. Since it is known that the national accounts
of a number of EU countries have undergone substantial revisions over the period from 1990 till now, we
shall make a correction for the revisions to GDP in 1990 occurring after the 1990 PPP benchmark in order
to eliminate the effect of such national accounts revisions. Likewise we shall make a correction for
revisions to national accounts data for 1998 occurring after the 1998 PPP benchmark. Our analysis will be
confined to GDP. A similar exercise can of course be done for all the expenditure components of GDP.
In the following section we shall then take the analysis one step further by looking at how big a share of
the remaining discrepancies can be explained by changes in relative prices between 1990 and 1998.
Table 2
Country
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Per capita GDP: 1990 benchmark and 1998 benchmark versus extrapolation from
1990 benchmark adjusted for NA revisions (EU 15 = 100)
1990
benchmark
1998
benchmark
Extrapolation
from 1990
benchmark
Difference
105
105
107
105
111
116
47
68
102
123
100
109
112
121
101
99
106
68
112
104
176
114
110
108
118
106
107
104
48
107
103
130
108
-0.1%
4.0%
2.2%
-4.3%
-7.6%
1.5%
40.4%
5.1%
1.0%
35.7%
5.3%
12
Portugal
Spain
Sweden
United Kingdom
56(53)
75
109
101
72
79
102
102
58
80
105
105
23.1%
-1.5%
-3.1%
-2.4%
Source: Eurostat (1994) and Eurostat (2000) for the 1990 and 1998 PPP benchmarks respectively. Eurostat's New
Cronos database for ESA79 national accounts data. 1990 data for Germany does not include ex-GDR.
If one looks at the raw data i.e. the PPP benchmarks for 1990 and 1998 published by Eurostat in 1994 and
2000 respectively together with the official ESA79 GDP figures for the period 1990-1998 published in
Eurostat's New Cronos databank, one gets the following picture. It should be added, though, that for some
of the countries there are no ESA79 GDP figures available for 1998 and in four cases even for 1997. In
those cases the official ESA95 GDP growth rates have been used to extrapolate the ESA79 time series.
There appears to be a typing error in the 1990 index for Portugal in the Eurostat publication for 1990.
Starting from the basic data and the published PPP for GDP one arrives at an index of 53 for Portugal in
1990 instead of the published figure of 56. In the extrapolation it is assumed that the correct figure for
Portugal in the 1990 benchmark is 53
First of all it is seen that the PPP benchmarks originally published by Eurostat in a number of cases differ
considerably from the new data set constructed by OECD. The 1990 index for Greece for example is 60 in
the OECD data set but only 47 in the data published by Eurostat. Such important deviations are largely
explained by comprehensive revisions to national accounts data taking place after the publication of PPP
benchmarks. If one compares the GDP data according to ESA79 used for the 1990 and 1998 benchmark
calculations with the ESA79 data now available for those years in Eurostat's New Cronos databank, one
gets the picture presented in table 3.
Table 3
Country
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
United
Kingdom
GDP (ESA79) in billions of national currency in 1990 accor-ding to PPP publication
and according to New Cronos 2002
GDP 1990 GDP 1990 in
in PPP New Cronos
publication
databank
1789.4
6429.3
810.45
524.95
6484.1
2404.5
10455
25.672
1306833
291.5
508.31
8507.4
50074
1361.5
549.18
1813.48
6554.375
798.035
515.43
6499.213
2426
13143.05
27.524875
1310659
359.01801
514.55
9838.053
51537.71
1359.879
544.74
Difference
1%
2%
-2%
-2%
0%
1%
26%
7%
0%
23%
1%
16%
3%
0%
-1%
GDP GDP 1998 in
1998 in PPP New Cronos
publication
databank
2585.049
9022.0
1168.306
676.244
8485.710
3721.625
35910.654
63.349
2057731
665.736
761.809
19692.907
86132.218
1894.341
836.635
2630.028
9083.232
1131.684
667.520
8413.625
3761.712
35667.129
59802.992
211187.249
659.461
747.368
19305.734
84887.915
1806.675
825.527
Difference
2%
1%
-3%
-1%
-1%
1%
-1%
-6%
3%
-1%
-2%
-2%
-1%
-5%
-1%
It is seen that there are sizeable revisions to the 1990 data for four countries namely Greece, Ireland,
Luxembourg and Portugal. These revisions are mainly the result of better coverage of economic activity as
a consequence of statistical improvements required under the GNP directive. In addition, even for
countries which tend to have small revisions at the national level there are significant revisions in table 3.
Some of these are not genuine data revisions to nationally published figures but rather the result of
clarifications of the relationship between the national interpretations of the 1968 SNA and the old
European system ESA79. Nonetheless, they affect the outcome of comparing the PPP benchmarks of
ESA79 based national accounts data with extrapolations from old benchmarks.
13
Table 3 shows that revisions to national accounts data after the figures have been used in PPP benchmarks
are so big that extrapolated results which do not capture these revisions will yield a distorted picture of the
size of the consistency problems. In table 4 the extrapolation of the Eurostat 1990 benchmark to 1998 in
table 2 as well as the 1998 benchmark are adjusted for the revisions to the underlying national accounts
data that have taken place in the meantime. The correction is done at the level of GDP and is thus only an
aggregate or summary adjustment for the data revisions in the national accounts.
A proper adjustment whereby not only the GDP level in national currency is adjusted but also all the
weights in the calculation of purchasing power parities involves recalculating all the PPP data and is
outside the scope of this paper. It is in fact what has been done in the OECD (2001) paper but in that case
on the basis of a new set of national accounts data namely the revised SNA93 / ESA95 national accounts.
Table 4
Country
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
United Kingdom
Per capita GDP: 1990 benchmark and 1998 benchmark versus extrapolation from
1990 benchmark adjusted for data revisions (EU 15 = 100)
1990
1998
benchmark
benchmark
adjusted for NA adjusted for NA
revisions
revisions
106
106
105
103
110
116
58
72
101
150
101
61
77
108
100
Extrapolation from
1990 benchmark,
1998 adjusted for
NA revisions
Difference
110
108
115
102
106
104
60
113
102
158
108
67
82
104
102
1.6%
3.7%
1.9%
-2.8%
-7.6%
3.2%
12.0%
-6.3%
4.8%
12.2%
3.7%
6.4%
-4.4%
-5.8%
-1.5%
112
113
117
100
98
108
67
106
107
177
112
71
78
98
101
Source: Eurostat (1994) and Eurostat (2000) for the 1990 and 1998 PPP benchmarks respectively. Eurostat's New
Cronos database for ESA79 national accounts data. 1990 data for Germany does not include ex-GDR.
Looking at the differences found between benchmarks and extrapolated figures in the OECD data set and
the Eurostat data set used here as shown in tables 1 and 4 respectively, it is seen that the problematic cases
in the OECD study (Greece, France, Spain and Sweden) are also found in the present calculation based on
the Eurostat ESA79 data. In addition, the comparison in this paper comes out with much worse results for
Luxembourg, Portugal and Ireland. As seen in table 3 those three countries have experienced drastic
revisions to their national accounts. These revisions may have changed the expenditure structure in a way
that has a significant impact on the purchasing power parity for GDP. Such an impact will be captured by
the detailed recalculations in the OECD (2001) paper but not by the global GDP revision adjustment in
this paper.
It is remarkable that the differences between the 1998 benchmark and the 1990 figures extrapolated to
1998 for Germany and France are very similar in tables 1 and 4. The problematic comparison of those two
large economies thus appears to be a robust result. In the following section we shall analyse whether this
comparison looks more consistent when one takes account of relative price movements between 1990 and
1998.
14
5.
IDENTIFICATION OF THE IMPACT OF CHANGES IN RELATIVE PRICES
In order to identify the impact on the PPP results of relative price changes between the two benchmark
years 1990 and 1998, we proceed as follows. First, we calculate national deflators from the national
accounts figures based on ESA79 in the New Cronos databank. The level of detail basically corresponds to
the publication level of the PPP figures except that exports and imports are available instead of only the
balance of exports and imports as in the PPP data set.
15
Table 5
Relative price adjustment factors EU15 (1998/1990)
Classification
Price factors
Private final consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Food, beverages, tobacco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Food
Bread and cereals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Meat
Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Milk, cheese and eggs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oils and fats
Fruits, vegetables, potatoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Other food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Beverages
Non-alcoholic beverages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alcoholic beverages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tobacco
Clothing and footwear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Clothing including repairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Footwear including repairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Gross rents, fuel and power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Gross rents
Fuel and power
Household equipment and operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Furniture, floor coverings and repairs . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Household textiles and repairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Household appliances and repairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Other household goods and services . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Medical and health care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Transport and communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Personal transport equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Operation of transport equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Purchased transport services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Communication
Recreation, education and culture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Recreational equipment and repairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Recreational and cultural services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Books, newspapers, magazines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Education
Miscellaneous goods and services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Restaurants, cafés and hotels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Other goods and services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Net purchases abroad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Collective consumption of P.N.P.I. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Government final consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Gross fixed capital formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Construction
Residential buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Non-residential buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Civil engineering works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Machinery and equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Transport equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Non-electrical machinery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Electrical machinery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Change in stocks
Balance of exports and imports
Imports
Exports
Gross Domestic Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1,018
0,970
0,934
0,934
0,917
0,936
0,926
1,010
0,914
0,946
0,978
0,917
0,986
1,260
0,928
0,918
0,948
1,165
1,238
0,895
0,965
0,970
0,959
0,865
0,989
1,006
1,010
0,984
1,049
1,070
0,862
0,955
0,795
1,037
1,031
1,162
1,088
1,139
1,045
1,000
1,227
0,992
0,928
0,946
0,956
0,964
0,972
0,890
0,928
0,894
0,857
1,015
(1,000)
For each heading deflators are then weighted together with the real values in the PPP data set as weights to
form an EU15 price index. This is done for GDP and all the components. Relative (1998/1990) price
factors are then calculated as the ratio between the EU15 component price index (1998/1990) and the
EU15 GDP price index (1998/1990). This is done for all expenditure components except exports and
imports. For the latter components the relative price factors are defined as the ratio between the national
exports and imports price indices and the national GDP deflator. This is because the bulk of exports and
imports for the EU countries consists of intra-EU trade.
16
The relative price adjustment factors from 1990 to 1998 other than the country-specific ones for exports
are shown in table 5. It is seen that there are major shifts in relative prices even over the relatively short
time span of eight years studied here.
For each country all the expenditure components in euro are multiplied by the above-mentioned relative
price factors and the effect on the level of GDP in 1990 of applying the relative price structure of 1998 is
derived by summation of the components. The effect on GDP for EU15 is then calculated by adjusting the
real values of GDP in 1990 in PPS for each country by the ratio of GDP after and GDP before the
adjustment to the 1998 relative price structure and deriving the EU15 total as the sum. The results are
presented in table 6.
Table 6 shows that, in general, making the correction for relative price changes does not go a long way
towards explaining the apparent inconsistencies between the two PPP benchmarks under consideration and
the national accounts price and volume indices. In 5 cases does it help to explain the differences, but in 9
cases the correction for relative price changes actually makes matters worse and in one case the deviation
is unchanged. It does, however, reduce the apparent inconsistencies for Ireland and Luxembourg quite
considerably. On the other hand, adjusting for relative price changes gives a substantial increase in the
discrepancy for the UK.
Table 6
Per capita GDP: 1990 benchmark and 1998 benchmark versus
from 1990 benchmark
Country
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
United
Kingdom
Difference
between 1998
benchmark
and extrapolated 1990
published
benchmark
Difference between
1998 benchmark and
extrapolated 1990
benchmark. Both
benchmarks adjusted
for national accounts
revisions
Effect of
adjusting
1990 benchmark GDP
to 1998
relative
prices
Remaining difference
between 1998 benchmark
and extrapolated 1990
benchmark after
adjustment for national
accounts revisions and
changes in relative prices
-0.1%
4.0%
2.2%
-4.3%
-7.6%
1.5%
40.4%
5.1%
1.0%
35.7%
5.3%
23.1%
-1.5%
-3.1%
-2.4%
1.6%
3.7%
1.9%
-2.8%
-7.6%
3.2%
12.0%
-6.3%
4.8%
12.2%
3.7%
6.4%
-4.4%
-5.8%
-1.5%
-1,4%
-0.8%
1.3%
-1.3%
0.4%
-0,0%
1.8%
-3.1%
-0.2%
5.6%
-0.4%
-1.3%
0.6%
1,3%
3.2%
3.0%
4.5%
0.6%
-1.5%
-8.0%
3.2%
10.2%
-3.2%
5.0%
6.6%
4.1%
7.7%
-5.0%
-7.1%
-4.7%
extrapolation
Source: Eurostat (1994) and Eurostat (2000) for the 1990 and 1998 PPP benchmarks respectively. Eurostat's New
Cronos database for ESA79 national accounts data. 1990 data for Germany does not include ex-GDR.
It is remarkable that the adjustment for relative price changes does not alter the picture for France which
has a big discrepancy between the 1998 PPP benchmark and the extrapolated 1990 benchmark. The same
is true of the smaller difference for Germany. As a result, the problematic comparison of Germany and
France in the two PPP benchmarks of 1990 and 1998 is not improved by adjusting for relative price
changes.
The effect of relative price changes on the consistency of PPP benchmarks and national price and volume
indices has not been the subject of much research. However, the 1983 Eurostat study mentioned in the
introduction does in fact to some extent provide an analysis of the impact of relative price changes, even
17
though the study does not deal with the issue explicitly. This is because the Eurostat study compares the
1975 and 1980 benchmarks with extrapolations of the 1975 benchmark to 1980 at three different levels of
aggregation (GDP level, main aggregates level and basic headings level). The difference between these
three extrapolations gives a measure of the impact of relative price changes. According to Eurostat (1983)
a significant part of the apparent inconsistency between the two PPP benchmarks studied and the national
accounts could in some cases in fact be explained in terms of relative price movements over the five-year
period. The conclusion of the present paper is largely the same. Both studies find that there are large
remaining discrepancies that cannot be explained in terms of relative price changes.
Since the 1983 study, Eurostat has generally evaded the consistency issue in its publication of PPP results
by stating that there may be problems, but that the latest benchmark figures are better than earlier
benchmarks and should therefore always be regarded as the most reliable figures, see e.g. Eurostat (2000).
To the extent that the measurement errors are stochastic due to too few price observations this reasoning
seems less than convincing.
The same strategy appears to have been adopted by the other international organisations involved in the
PPP programme until the work undertaken by the OECD in recent years, see OECD (1999), OECD (2001).
6.
6.1
SPECIFIC PROBLEM AREAS IN THE PPP EXERCISE
Imputed and conventionally calculated flows
Two expenditure components pose big specific problems within the PPP exercise namely the gross rents
component of housing and the non-market services produced by general government and non-profit
institutions serving households (NPISH). These expenditure components have the common characteristic
that they are dominated by transactions for which there is no observed market price. As far as gross rents
are concerned, the part consisting of actual rents is of course observable, but the usually much bigger part
relating to imputed rents of owner-occupied dwellings is not directly observed but estimated using certain
conventions. Moreover, even for actual rents there are big problems in ensuring sufficient geographical
coverage with only relatively few observations.
As for non-market services of general government and NPISH, these are calculated by convention as the
sum of costs (i) intermediate consumption, (ii) compensation of employees, (iii) other taxes less subsidies
on production, and (iv) consumption of fixed capital. In the PPP calculations (iii) is disregarded in
practice.
Both these areas are extremely important for the overall result of the PPP exercise due to their weight in
GDP. For EU15 gross rents amount to about 10 per cent of GDP, and consumption of non-market services
of general government and NPISH accounts for about 20 per cent of GDP for the EU as a whole.
Measurement problems in these two areas will therefore show up in GDP and, moreover, probably not in a
stochastic way, as one may hope in the case of transactions with an observed market price, but more likely
in a way giving rise to systematic bias in a whole sequence of PPP benchmarks. It is therefore appropriate
to take a closer look at these two areas to see first of all if the results currently produced are in line with
other evidence such as physical indicators, and secondly if improvements could be achieved by changing
the methodology.
18
6.2
Gross rents
Let us start by looking at how the results from the PPP programme tally with the information about
relative housing standards that can be derived from the population and dwelling census. In this connection
it is necessary to recall one basic principle regarding the treatment of housing in the PPP programme,
namely that location is irrelevant. A dwelling of a given type represents the same real dwelling service if it
is located in central London as if it is placed in a small provincial town. This convention in the PPP
programme makes it easy to assess the reliability of the results by comparing with physical information on
the housing stock in the censuses.
The most important physical indicator is the number of dwellings and their average size. Ideally one would
measure the size of dwellings by the number of square (or even better cubic) metres, but unfortunately this
information is not available in the EU census. Instead, the number of rooms must be used as a proxy
variable for the size of dwellings. The real consumption of housing services per capita according to the
PPP data may thus be compared with the (inverse of the) number of occupants per room. The data are
presented in table 7 where an index of the average number of occupants is compiled - a relatively low
number of occupants per room results in a relatively high index.
The result of this comparison for the 15 EU countries is shown in figure 1. This figure shows the index for
the average number of occupants per room plotted against the index for real per capita gross rents. If there
were complete agreement between real per capita housing consumption as measured in the PPP
programme and the physical indicator of relative housing standards from the census, all the points should
lie on the 45-degrees line. It is seen from the figure that in practice the correlation is far from perfect.
Indeed, in a number of cases (Italy, France on the upper side, Ireland and Greece on the lower side) the
deviations are so large that they have major implications for the levels of real per capita GDP.
Figure 1
Comparison of number of occupants per room (horizontal axis) with real per capita
housing consumption according to PPP data (vertical axis). Index EU15=100
150
140
I
130
120
DK
F
S
110
L
100
A
90
D
P
80
UK
NL
SF
70
B
E
60
50
GR
40
40
50
60
70
80
IRL
90
100
110
120
130
140
150
Note:
Horizontal
axis:
occupants per room, inverted scale. Index EU15=100. Vertical axis: real per capita gross
rents. Index EU15=100.
If one believes the PPP data, Italy in 1991 had the second highest housing standard in the EU. If, on the
other hand, one believes the physical indicator, Italy only ranks ninth. For the majority of countries real
per capita housing consumption in the PPP data as compared to the EU average is lower than the level
indicated by the physical indicator.
19
The PPP figures are those for 1991 published by Eurostat (1994). There are no PPP data for Finland for
1991. The figures for real per capita housing consumption for that country in 1990 have been used instead.
The census data are those published by Eurostat (1996 b). Information on the number of rooms is missing
for Sweden. For that country the average number of rooms per dwelling has been set equal to the average
for the 14 other countries. The census data are from 1991 for Belgium (B), Denmark (DK), Greece (GR),
Spain (E), Ireland (IRL), Italy (I), Luxembourg (L), Netherlands (NL), Austria (A), Portugal (P), and the
UK. They are from 1990 for France (F), Finland (SF), Sweden (S) and from 1987 in the case of Germany
(i.e. before unification).
Table 7
Average number of occupants per room and real per capita housing consumption
according to PPP data 1991
Country
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
United
Kingdom
Average
number of
occupants
per room
Average
number of
occupants
per room.
Inverted
scale
(EU15= 100)
Real per
capita gross
rents in PPP
(EU15=
100)
Average
number of
occupants
per qualityadjusted
room
Average number
Real per
of occupants per capita gross
quality adjusted rents in PPP
room.
(EU13=
Inverted scale
100)
(EU13 = 100)
0,81
0,59
0,47
0,67
0,68
0,55
0,77
0,64
0,66
0,49
0,60
0,71
0,69
0,48
0,49
76,18
105,52
131,97
92,81
91,61
113,52
80,13
97,44
93,96
126,37
103,08
87,15
90,24
128,43
126,72
95,24
73,14
133,22
77,66
118,05
92,63
45,91
43,10
130,81
105,89
81,68
86,60
68,82
114,03
104,99
0,91
0,67
0,49
0,71
0,73
0,58
0,83
0,72
0,80
0,52
0,63
76,85
104,21
143,45
98,07
95,97
120,28
83,97
96,45
87,13
133,26
110,20
95,21
73,12
133,18
77,64
118,01
92,60
45,90
43,09
130,77
105,86
81,65
0,82
84,77
68,80
0,51
136,97
104,96
One may object to the comparison in figure 1 that it does not take the quality of dwellings into account
such as amenities or lack thereof. In principle, the average Italian dwelling for instance might be so much
better than the average dwelling in the EU as to justify the high value of real per capita housing
consumption suggested by the PPP data which appears to be contradicted by the census data. To test this,
the four quality-related variables in the census have been used to construct a quality-adjusted physical
indicator. The census contains information about the following types of amenities:
1)
2)
3)
4)
Piper water in dwelling
Fixed bath or shower within dwelling
Flush toilet within dwelling
Central heating
All four variables are missing for Sweden which therefore has to be left out of the quality-adjusted
comparison. For Portugal the information about central heating is missing, so that country too must be left
out. For France, the UK and Luxembourg the information on piped water is not available. It is assumed
that all dwellings in those three countries have piped water in the dwelling. For the quality-adjustment the
stock of dwellings and the stock of rooms are reduced by 20 per cent for each of the above-mentioned
amenities that is missing. So a dwelling without two of the amenities is reduced to 60 per cent of a
dwelling having all four. The result of comparing the values of real per capita housing consumption in the
PPP data with the quality-adjusted physical indicator is shown in figure 2.
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As can be seen from figure 2, making the quality adjustment does not make the PPP figures look more
plausible. Indeed, in some cases the apparent contradiction of census data is even more pronounced. The
deviations from the relative positions indicated by the physical data are so big that they will have a
sizeable impact on GDP per capita. The conclusion must be that the evidence from the census suggests
that the PPP programme has major problems in measuring real housing consumption correctly, and that
these problems are so big as to put a question mark on quite a few of the real per capita GDP values.
Figure 2
Comparison of number of occupants per quality-adjusted room (horizontal axis)
with real per capita housing consump-tion according to PPP data (vertical axis).
Index EU13=100
150
140
I
130
DK
120
F
110
L
100
D
A
90
UK
NL
80
SF
70
B
E
60
50
GR
40
40
50
60
70
80
IRL
90
100
110
120
130
140
150
Note:
Horizontal
axis:
occupants per quality-adjusted room, inverted scale. Index EU13=100.
Vertical axis: real per capita gross rents. Index EU13=100.
Let us now turn from the level of real per capita housing consumption to the evolution between the two
benchmarks 1990 and 1998. To illustrate the problems involved in the PPP data one may look at the
bilateral comparison between Italy and the UK. In the 1990 benchmark the indices of real per capita
consumption of gross rents (EU12=100) are Italy 122 and UK 118. Over the period 1990 to 1998 the
national accounts volume indices in New Cronos give (1990=100) Italy 109 and UK 106. Over the same
period the Italian population fell by 0.12 per cent, while the UK population grew by 2.89 per cent. From
extrapolating the 1990 benchmark figures to 1998 we would expect real per capita consumption of
housing in Italy in 1998 to be 9.5 per cent higher than in the UK. However, the published 1998 benchmark
figures (index EU15=100) yield Italy 141, UK 97 implying a per capita housing consumption in Italy
which is 45 per cent higher than in the UK.
The Italy/UK comparison is already dubious in the 1990 benchmark, and it is seen to become worse over
the period to 1998. Not only are the relative levels in the benchmark years contradicted by other evidence,
but so too are the implicit relative growth rates between them. If one were to believe the 1998 PPP
benchmark, Italy would have by far the highest housing standard in the world. It is thus seen that the
picture regarding extrapolations is not rosier than that regarding levels.
The conclusion emerges that the present method of calculating PPPs for the gross rents component of GDP
i.e. real housing services yields results that are not believable. As far as the comparison among the EU
countries is concerned, it would probably be better to do away with the direct collection of information on
rents altogether. It could be replaced by implicitly calculated PPPs based on the ten-yearly population and
housing censuses extrapolated to the current year by means of appropriate physical indicators such as the
numbers of dwellings completed and discarded since the last census. There is hardly any doubt that such a
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method would yield figures that are much more plausible both in terms of levels and evolutions. As far as
the evolutions are concerned, with such an indirect approach, the volume indices between benchmarks
would be close to those normally used in the national accounts in years between benchmarks based on
housing censuses.
It seems likely that a similar solution would be preferable for the whole OECD area. Only in cases where
there is no comparatively recent census information on the stock of dwellings does the current method
seem to be preferable. This is potentially the case in some of the developing countries.
All in all, the consistency between national accounts price and volume indices on the one hand and the
PPP figures on the other would no doubt be substantially improved by the move to implicitly derived PPPs
for gross rents.
6.3
Non-market services
Since non-market services in the national accounts are valued from the input side as the sum of costs, the
PPPs in regard to these services are spatial indices for compensation of employees, intermediate
consumption and consumption of fixed capital. Of these the latter two are fixed by convention as being
equal to the PPPs for household consumption and gross fixed capital formation, respectively, in the
absence of standardised price information. By far the most important cost component is compensation of
employees. For that component data are collected for compensation of employees in a number of different
occupations. As far as one can see from the Eurostat publications, there is as yet no weighting of those
occupations based on the numbers of employees and corresponding wage bill in those different
occupations based on labour market statistics. The PPPs for compensation of employees appear to be the
simple average of the PPPs for the various occupations. The measurement of real labour input into the
production of non-market services is therefore relatively imprecise. The implicit assumption is basically
that the composition in terms of groups with various qualification levels is the same among countries.
A big and cost-efficient improvement to the PPP figures could be achieved by weighting the
compensation indices according to the wage bill (or, failing that, numbers employed) in the different
occupational groups. In the absence of data on government employees classified by occupational category
(ISCO), even an approximate weighting scheme based on level of education, say, is bound to be much
better than a simple unweighted average.
7.
METHODS FOR IMPROVING CONSISTENCY
The analysis in the preceding sections suggests four ways to improve the consistency of PPP benchmark
figures and national accounts price and volume indices plus the consumer price index.
1) Carry out the consistency check before publication of new benchmarks as an extra check on
results
2) Analyse the impact on changes in relative prices before publishing new benchmarks as an extra
check
3) Change the methodology for gross rents
4) Employ weighting in the index for compensation of employees in non-market services
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The first point is obvious. If the international organisations carry out the consistency check against
national accounts and national CPI growth rates they have a chance of spotting errors that would otherwise
go unnoticed. To the extent that the lack of consistency can be attributed to errors in the PPP benchmark
which is being compiled, these errors can still be corrected before publication of the results. It amounts to
an extra plausibility check on the PPP figures.
The second point is of the same nature as the first. It really amounts to introducing an extra check on the
data before publication. In some cases relative price movements may explain large apparent
inconsistencies. A case in point is changes in oil (and other energy) prices which can have a great impact
on countries that are large net exporters or importers of energy products.
As for the third point, the PPP figures for real consumption of housing services can be greatly improved
by changing the current methodology. Instead of basing the PPPs for gross rents on price (rent)
observations of doubtful quality, it would be preferable to calculate the PPPs implicitly based on a volume
index derived from information from housing censuses. The generally ten-yearly censuses can be updated
using standard methods from the national accounts calculations of dwelling services. The change of
methodology will improve the reliability of both levels and growth rates.
The fourth point is self-explanatory. It is obviously too crude a methodology not to have an explicit
weighting scheme for an expenditure component as important as consumption of non-market services.
8.
CONCLUSION
The consistency of PPP figures with national price and volume indices in the form of volume indices,
national accounts deflators and the consumer price index is an issue of great importance to both users and
producers of statistics. Today almost all international comparisons of relative living standards rely on
national accounts data which are made internationally comparable by using purchasing power parities for
calculating real values i.e. the volume of goods and services that the flows in the national accounts
actually represent. Unexplained deviations between the evolution in relative country positions in the PPP
data and in national accounts volume indices pose major problems for the interpretation of the economic
development.
The paper explains why consistency in the strongest sense of simultaneous transitivity across space and
time is not a reasonable requirement. Imposing such a restriction on the data would violate the principle of
characteristicity both in a spatial and a temporal context. What is a reasonable requirement is that users
should be informed about how much of the apparent inconsistency is a natural and unavoidable
consequence of the different index number formulas used for different purposes, and how much is a
genuine problem which must be explained in terms of measurement errors in one or more of the statistics
being compared.
Based on analyses of a conceptually consistent data set that covers the EU countries over the period 19901998 it is concluded that formula-related factors (relative price changes) can only explain a part of the
apparent inconsistencies. Relatively large discrepancies between extrapolations from an old PPP
benchmark and a new benchmark remain even after correction for both national accounts revisions and
relative price changes. Getting these unexplained deviations down to a more acceptable level would
appear to be one of the largest challenges for the PPP programme in the future.
23
The specific analysis for the large expenditure component consisting of dwelling services has revealed
major problems in that field which appear to lead to systematic bias of such a magnitude that it even puts a
question mark on some of the real GDP figures.
The paper recommends four methodological improvements to the compilation of PPPs. None of them
should be very costly to implement, since they do not require collecting more price information than now.
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