A Guide to the Compilation of Subnational
Purchasing Power Parities (PPPs)

2021

A Guide to the Compilation of
Subnational Purchasing Power Parities (PPPs) 1
0F

July 14, 2021

1

This guide was prepared by Luigi Biggeri, University of Florence, Italy, and D.S. Prasada Rao, the University of Queensland,
Australia, with case study contributions from Bettina Aten, Bureau of Economic Analysis, United States; Sharne Bailey,
Office for National Statistics, United Kingdom; Tiziana Laureti, University of Tuscia, Italy and Federico Polidoro, Italian
National Institute of Statistics; and Đỗ Thị Ngọc, General Statistics Office, Vietnam. Nada Hamadeh, Yuri Dikhanov, Marko
Rissanen, William Vigil Oliver, Elizabeth Purdie, and Rui Costa, World Bank, provided inputs to this work. The guide wasreviewed by the International Comparison Program (ICP) Inter-Agency Coordination Group. Cover design by Will Kemp.

Contents
Preface ................................................................................................................................................... 4
1

Introduction ................................................................................................................................... 5

1.1

Purchasing power parities (PPPs) from the International Comparison Program .................... 5

1.2

Need for subnational PPPs ...................................................................................................... 7

1.3

Nature and scope of the guide on subnational PPPs .............................................................. 82

Subnational purchasing power parities ......................................................................................... 8

2.1

The concept ............................................................................................................................. 8

2.2

The scope of subnational PPPs ................................................................................................ 8

3

Uses of subnational PPPs ............................................................................................................. 12

3.1

Uses of subnational PPPs at the national level...................................................................... 12

3.2

Uses of subnational PPPs at the international level .............................................................. 14

4

Frequency of compilation of subnational PPPs ........................................................................... 15

5

Governance and administrative structure for subnational PPPs ................................................ 15

5.1
6

Benefits of integrating CPI and subnational PPP activities .................................................... 17
Compilation of subnational PPPs ................................................................................................. 18

6.1

Construction of product lists ................................................................................................. 18

6.2

Framework for price collection.............................................................................................. 20

6.3

Availability of regional level data for GDP structure ............................................................. 24

7

Price data and validation ............................................................................................................. 25

7.1

Price data ............................................................................................................................... 25

7.2

Validation of price data ......................................................................................................... 26

7.3

Treatment of outliers ............................................................................................................. 27

8

Expenditure data and weights ..................................................................................................... 27

8.1

National accounts versus household expenditure survey data ....................................... 27

8.2

Household expenditure surveys and national accounts ................................................ 27

8.3Classifications of household expenditures ............................................................................ 2829Aggregation methodology and price and real expenditure comparisons ................................... 28

9.1

Hierarchical structure for compilation of PPPs ..................................................................... 29

9.2

Aggregation of item level prices to basic heading level ........................................................ 30

9.3

Aggregation of basic heading level PPPs to PPPs for higher level aggregates ...................... 30

9.4

Index number methods for multilateral comparisons – desirable properties ...................... 31

9.5Country-product-dummy (CPD) method ............................................................................... 33

9.6

Gini-Éltető-Köves-Szulc (GEKS) method ................................................................................ 34

9.7

Specification of the reference region for subnational PPPs .................................................. 35

10

A survey of studies computing subnational PPPs and spatial consumer price index numbers .. 35

10.1

Subnational PPPs computed at national level using the ICP framework .............................. 36

10.2

Spatial adjustment factors and the Eurostat-OECD PPP Programme ................................... 37

10.3

Subnational spatial CPIs for household consumption based on CPI data ............................. 38

11

Conclusion.................................................................................................................................... 41

12

References ................................................................................................................................... 43

13

Appendices: Country Case Studies .............................................................................................. 47

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Preface
The purchasing power parities (PPPs) and real income comparisons from the International Comparison
Program (ICP) are well established and have been used by researchers, policy makers and internationalorganizations for over three decades. It is also well recognized that in many countries there can be variation in
price levels across different areas within the country (states, regions, provinces, etc.). Policy makers andresearchers interested in the nature of subnational development processes—such as assessing intra-country
inequality and the incidence of poverty and analyzing catch-up and convergence and productivity performanceof different areas within the country—may find country-level PPPs from the ICP to be of limited scope andapplicability, as these PPPs are broad economy-level measures designed to facilitate cross-country
comparisons. As such, ICP PPPs cannot inform the user when it comes to comparisons of entities which aresubnational, because they are not designed to capture such subnational price level differences. Hence, what isneeded in such instances are measures of price level differentials across regions or cities within a country –pointing toward the need for PPPs or spatial price index numbers at a subnational level.
While there have been several experimental studies by researchers and national statistical offices (NSOs)
during the last two decades, it is only recently that the role and significance of subnational PPPs and spatial
Consumer Price Indices (CPIs) has come into prominence. The World Bank’s report on the ICP to the fortyseventh Session of the United Nations Statistical Commission (UNSC) recognized the importance of subnational
PPPs: “… To meet the growing demand for official estimates of PPPs at the subnational level, selected countries
in various regions have undertaken the compilation of subnational PPPs in the interim period. The ICP Global
Office, in collaboration with the ICP regional implementing agencies, is compiling an inventory of subnational
PPP projects and exploring future expansion and harmonization of the exercises to maximize synergies andcomparability.” 2 Compilation of subnational PPPs is also included in the ICP Research Agenda. 3 As such, theWorld Bank in its submission to the forty-ninth Session of the UNSC reported: “…The first meeting of the Task
Force on Country Operational Guidelines and Procedures 4 was held on 25 October 2017 in Washington, D.C.
Participants discussed the topics assigned to the task force, including moving towards rolling price surveys, CPIICP synergies to improve spatial and temporal price consistency and subnational PPPs.” The Task Force has
subsequently been entrusted with the preparation of a guide on the compilation of subnational PPPs.
1F

2F

3F

2

Names of the organizational units and technical terms have been harmonized in this document, including direct quotes
from existing reports, papers and documents.
3

http://pubdocs.worldbank.org/en/774201507663170526/pdf/ICP-GB02-Doc-ICP-Research-Agenda.pdf

4Prasada Rao, University of Queensland; Brian Graf, International Monetary Fund; David Roberts, Independent Expert;
Dilip Kumar Sinha, India Ministry of Statistics and Program Implementation; Levan Gogoberishvili, International
Monetary Fund; Luigi Biggeri, University of Florence; Majed Skaini, United Nations Economic and Social Commission forWestern Asia; Patrick Kelly, National Statistical Office of South Africa; Yuri Dikhanov, World Bank; and William VigilOliver, World Bank.

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This guide is presented within the methodological framework of the ICP and it is designed to provide guidance
to the regional implementation agencies of the ICP and to the NSOs that may decide to embark on a programof compilation of spatial price indices at the subnational level, taking into account their large dimension and/or
the significant variation in price levels across different subareas within the country. As price comparisons withina country are anchored on price data collected as a part of the compilation of the CPI, this guide has a symbiotic
relationship with the guide on CPI-ICP integration5 (International Comparison Program, 2021). NSOs involvedin the compilation of subnational PPPs are advised to consult both guides.
This guide on subnational PPPs has several objectives. First, to communicate effectively to potential users suchas NSOs, price statisticians, policy makers, politicians, academics, and others the objectives and outcomes of aprogram for the computation of subnational PPPs and/or spatial CPIs. Second, to serve as a guide to ICP and
CPI practitioners, whatever their role, in planning, coordinating, and monitoring the implementation of a
subnational PPP program and, as such, providing basic reference materials. Third, to advise the users of
subnational spatial price indices on the use and interpretation of the resulting price level and real expenditurecomparisons. Finally, but equally importantly, the guide discusses challenges in terms of the scope andapplications, establishment of the survey framework, and methodological guidelines, and highlights some ofthe practical issues that arise in setting up such a program. A few country case studies highlighting theexperience gained from the compilation of subnational PPPs are included in the appendices.

1

Introduction

1.1

Purchasing power parities (PPPs) from the International Comparison Program

Country-specific data on economic aggregates are regularly produced and disseminated by NSOs and otherproducers of official statistics, such as central banks. However, direct cross-country comparability of national
data is limited because such data are usually expressed in their respective national currency units. The
incomparability of published data on national economic aggregates also stems from differences in price levels,
which imply differential purchasing powers of currencies. At the other end, the conversion of currencies
through market exchange rates (MERs) are not adequate to make comparisons in ‘real terms’, because they
do not reflect differences in price levels and are largely determined by the forces determining the demand for,and supply of, currencies. PPPs from the ICP provide a framework for comparing the price and volume of grossdomestic product (GDP) and its expenditure components and other economic indicators across countries.
PPPs as a concept, and by construction, are designed for making price comparisons across countries. Broadly,
PPPs are anchored on both national annual average prices for a given large basket of comparable andrepresentative goods and services and expenditure weights from the national accounts. Therefore, price dataand national accounts expenditure data are the two key inputs for the estimation of PPPs.

5

https://thedocs.worldbank.org/en/doc/2b29c1445d7fa006e5f4ca00087dbe36-0050022021/original/Guide-CPI-andICP.pdf

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A PPP for a given country represents the number of currency units required to buy a similar basket of goods
and services in the given country in relation to a reference or base country. For example, if the PPP for India is
20 INR (Indian rupees) per one United States (U.S.) dollar, this means that a given basket of goods and services
that can be purchased for one dollar in the U.S. can be purchased in India for 20 rupees. Suppose the MER
between these currencies is 60 INR per one U.S. dollar, then the price level (PL) in India is the ratio of PPP to
exchange rate (20/60) which implies that prices in India are roughly one-third of the prices in the US.
The ICP was established to provide PPP conversion factors as an alternative to the conversion of currencies
based on MERs, so as to facilitate comparisons of economic aggregates and indicators between countries inreal terms. The actual implementation of the ICP and the process of the compilation of PPPs for all theparticipating countries is quite complex. A description of the framework for the ICP is presented in Rao (2013a)
and the full set of ICP procedures are discussed in various chapters of World Bank (2013), “Measuring the RealSize of the World Economy: The Framework, Methodology, and Results of the International Comparison
Program (ICP)”.
A brief overview of the concept, principles, computation of PPPs and their uses is presented here in order tofacilitate the reader in the understanding of different parts of this guide.
PPPs from the ICP refer to the GDP and its components from the expenditure side. These include: household
consumption expenditure; government expenditure; gross capital formation; changes in inventories and net
acquisition of valuables; and balance of exports and imports. For the purpose of the ICP, it is necessary to break
down total expenditures into sub-groups in order to be able to deal with relatively homogeneous groupings ofproducts. This is necessary from the price collection point of view as well as to establish weights. The smallest
group of homogeneous products considered in the ICP is called a basic heading and is defined as the lowest
level of aggregation within the national accounts at which expenditure and expenditure share data are
available. Therefore, it is the lowest level at which elementary PPPs are calculated, based on underlying prices
for each basic heading. The current ICP classification comprises 155 basic headings.
Basic headings fall into three main categories. The first category is those basic headings covering goods andservices for which prices can be typically easily collected. The second category covers basic headings for which
prices are more difficult to obtain, such as dwelling rents, health, education, government compensation,
construction and civil engineering, and machinery and equipment. The third category is those basic headings
for which prices are either not available or relevant, or are too difficult or expensive to obtain, and therefore
PPPs for these headings are imputed.
A critical step in the ICP is the definition of the list of items or products to be priced. The choice of the products
is dictated by criteria of comparability, representativity and importance. The focus of the ICP is a cross-country
comparison of prices of goods and services comprising the GDP and its components. Given the diversity of
products which are consumed in different countries, it is typically not possible to use national CPI baskets asthey are, not least because goods and services in the national CPI baskets are not usually specified in therequired detail for inter- or intra-country price comparisons to be made. Therefore, the ICP utilizes Structured
Product Descriptions (SPDs), which contain generic descriptions of the characteristics relevant to a particularnarrow cluster of products to be priced. The key concept is comparison of “like with like”. However, strict6

comparability is difficult to achieve, as a product which is comparable may not be representative of the national
consumption habits in one or more countries. In this case there is a conflict between comparability andrepresentativity and it is important to strike a balance between these two requisites. This issue has been inpart resolved by asking each country to specify whether a product is important or less important within thebasic heading. Unimportant products should not be priced, although they may be available but consumed bya very small share of residents within a country.
As noted earlier, PPPs from the ICP are broad country-level measures. This implies that the survey frames used
for price collection relate to the entire country. However, countries may collect prices only in the capital city
and/or only in urban areas, in which case spatial adjustment factors are needed to arrive at national average
prices. In general, greater variability of prices across the country requires wider survey frames, in order toensure the representativity of the PPP for the entire country.
From a methodological point of view, computation of PPPs at various levels of aggregation, being referred toas multilateral comparisons, requires the use of index number methods which satisfy various properties suchas transitivity, base invariance, characteristicity, and additivity. A specific section towards the end of this guide
is devoted to the main methods for aggregation at the elementary or basic-heading level and for aggregation
at higher levels.
PPPs from the ICP are used in making cross-country comparisons of price levels, real incomes, and standardsof living and in assessing economic performance of nations and catch-up and convergence. PPPs are currently
used by international organizations such as the International Monetary Fund (IMF), the World Bank, theEuropean Union (EU), and the Organisation for Economic Co-operation and Development (OECD) as well as byresearchers, policy makers and national governments around the world (see Chapters 21 to 24 in World Bank,2013). The PPPs from the ICP have been used in the measurement and analysis of global inequality and poverty;
as inputs into the human development index; and in the estimation of global growth and inflation, among otheruses, and are included in many of the indicators curated through World Development Indicators (WDI). In
particular, PPPs for household consumption are a critical input into the determination of the international
poverty lines used by the World Bank. The PPPs and real expenditures from the ICP are also used in monitoring
progress towards the United Nations Sustainable Development Goals or SDGs (United Nations, 2015).

1.2

Need for subnational PPPs

As noted above, it is generally recognized that in many countries there are variations in price levels acrossdifferent areas within the country. For example, while the India-USA PPP (20 INR per U.S. dollar in the example
considered) is useful for comparing price levels and real incomes between the two countries, these broadcountry-level measures cannot inform users when it comes to comparisons of prices or price levels acrossstates, regions, provinces within a country. For example, the World Bank makes adjustments to ICP PPPs toaccount for rural-urban differences for some countries in its estimation of regional and global poverty using
international poverty lines like $1/day or $2/day. Hence, what is needed in such instances are measures of
price level differentials across regions or cities within a country – pointing toward the need for PPPs or spatial

7

CPIs at a subnational level. Given the limited resources, this goal is most feasibly achieved through using
information from the national CPIs.
The computation of subnational PPPs, through the integration of the CPI and ICP activities within the processof CPI collection of price data, as well as satisfying the mentioned needs, will also increase the awareness of
the ICP and their application for additional uses at the national and subnational levels.

1.3

Nature and scope of the guide on subnational PPPs

At the conclusion of the 2011 ICP cycle, the UNSC established the Friends of the Chair (FOC) group to evaluate
the Program and to make recommendations for the future rounds of the ICP. The ICP Technical Advisory Group
formulated a Research Agenda in response to the recommendations of the FOC and the resolutions of the
UNSC, and identified a number of topics for research, including the compilation of subnational PPPs.
This guide is designed to provide guidelines for the compilation of PPPs at the subnational level. As pricecomparisons within a country are anchored on price data collected as a part of the compilation of CPIs, thisguide has a symbiotic relationship with the guide on CPI-ICP integration, as noted earlier. NSOs involved in thecompilation of subnational PPPs are advised to consult both guides.

2

Subnational purchasing power parities

2.1

The concept

The concept of subnational PPPs, hereinafter referred to as SN-PPPs, is similar to the concept of PPPs used in
the context of international comparisons. SN-PPPs measure the amount of money that is needed in a
subnational geographical area to buy the same basket of goods and services than can be purchased with one
unit of the same currency in the reference region or area. As the currency unit is the same across all the subregions or areas within a country, SN-PPPs are in essence spatial price level comparisons within a country.
Spatial price level comparisons between countries need to take into account the different currencies in the
countries being compared. ICP PPPs relative to the market exchange rates, PPP/MER, are used for this purpose,and are published by the ICP as price level indexes (PLIs). The temporal equivalent of SN-PPP, when restricted
to consumer goods and services, is the CPI, where price levels are compared over time within a region in acountry or over time for the whole country.

2.2

The scope of subnational PPPs

The scope and coverage of subnational PPPs differ from those of the ICP PPPs. As the main purpose of the ICP
is to compile internationally comparable national accounts aggregates, PPPs from the ICP refer to broad
national accounts aggregates, such as GDP, household consumption, government expenditure, gross capitalformation, and lower-level aggregates such as expenditures on food, clothing, housing, transport, and others.

8In comparison to the scope of the ICP PPPs, which is within the confines of the national accounts frameworkand the aggregates within GDP, subnational PPPs can be somewhat open in their scope and can be tailored tothe purpose for which they are being compiled. Further, the scope of SN-PPPs is necessarily limited by thenature of the data available in different geographical regions within the country. Suppose the purpose of thesubnational PPPs is to make price level comparisons between rural and urban areas. Then, the scope of SNPPPs cannot be at the GDP level as national accounts statistics are not compiled separately for rural and urban
areas. However, it may be feasible to construct GDP level SN-PPPs at the state or provincial level if state levelaccounts and GDP estimates are available. This possibility essentially depends on the territorial organizationand capacity of a country’s national statistical system.

2.2.1 Subnational PPPs by coverage of expenditure categories
This section discusses the scope of SN-PPPs and the types of possible aggregates for a range of policy purposesat the national and subnational level.

Household consumption
An important objective of SN-PPPs is to make comparisons, in real terms, of household expenditure headings,
such as food, clothing, and transport. These SN-PPPs and real expenditures are, in turn, used in themeasurement and analysis of material well-being, inequality, and poverty across different geographical areas
within a country. Compilation of SN-PPPs for household consumption should be feasible, since almost all NSOs
collect data on consumer prices for the computation of their national CPI on a regular basis.

Expenditure on housing
Comparisons of housing costs across different geographical regions is just as important as comparisons of pricelevels and real expenditure on housing across countries. The computation of SN-PPPs for housing expenditures
is critical to evaluating the cost of living across cities and regions, in assessing and comparing poverty, in
analyzing housing markets, and in designing housing policies at the local level. However, challenges arise dueto the varying mix of owner-occupied housing versus rented dwellings and due to the presence of rental
subsidies or subsidized housing. Imputations for non-market rents and owner-occupied housing are necessary.

Health and education
Health and education are two areas that are considered important by governments and policy makers in bothdeveloped and developing countries. Providing equitable access to health and education are major aspirations
for most governments, more so in developing countries. Consequently, reliable data on price levels and real
expenditures on health and education in different geographical regions of the country is a critical input into
evidence-based policy making, designed to achieve the objectives and goals of national governments. These9data assume special significance when it comes to devising strategies to achieve the various targets set by the
SDGs. Assessing performance against the SDGs at the national level as well as at provincial or state level
requires the collection of appropriate data, and health and education form two of the most importantcomponents for such analysis.
Comparisons of price levels and expenditures on health and education are somewhat difficult and are usuallyclassified as comparison-resistant services. The ICP has devoted significant resources to devise appropriate
methods for the measurement of both. Blades (2013) discusses in detail issues concerning the measurementof health and education and the recommended procedures for price and real expenditure comparisons.

Government compensation
SN-PPPs can be compiled by using data on government compensation and salaries for government employees
in different provinces or regions. Government salaries of employees working for the federal or central
government are likely to be the same in different regions within the country while salaries of provincial or state
government employees may differ. In addition, SN-PPPs depend on differences in structure of government
occupations and differences in the composition and characteristics of the employees such as age andqualifications etc. Computation of SN-PPPs for government compensation depends on the availability of dataon both salaries and the appropriate weights at the regional level.

Machinery and equipment and construction and civil engineering
The compilation of SN-PPPs for machinery and equipment, construction, and civil engineering is particularly
important from an economic development perspective. Construction, relating to infrastructure development,
is a major consideration in policy formulation and hence it is important to assess price level differences acrossregions for residential and non-residential construction as well as for civil engineering in different regions.
However, the comparison of construction prices is just as challenging at the subnational level as it is for crosscountry comparisons. The methodology used for comparing construction prices and expenditures within the
ICP can be implemented in the case of subnational comparisons.
Collection of prices for machinery and equipment could be similar to that used for household goods andservices, that is, based on a list of products along with their detailed description. However, productspecifications under machinery and equipment are usually quite technical and difficult to describe, and someequipment goods are unique because they are designed for a specific location or purpose. Moreover, most of
the machinery and equipment are purchased by producers (private enterprises, government, and nonprofit
institutions) and it is not possible to conduct surveys to obtain information on prices from them. On the other
hand, the use of prices collected from the sellers from the different geographic areas may not be adequate
since purchasers can buy machinery and equipment outside their territorial area. Given these considerations,
it is likely that price levels for machinery and equipment are likely to be similar across different geographic
regions within a country.

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2.2.2 Different types of subnational PPPs according to geographical coverage
It is possible to conceptualize different types of SN-PPPs depending on the specification of the geographicalareas for the purpose of price comparisons. A few examples, by no means exhaustive, are described below.

Spatial adjustment factors
Spatial adjustment factors (SAFs) are scaling factors used in converting prices collected in different
geographical areas into national level prices. These factors are one way of reducing the cost of collection ofprice data for the computation of national average prices as required by the ICP. Countries participating in theEurostat-OECD PPP Program mainly collect prices of items from capital cities and then convert them to thenational level by applying spatial adjustment factors.
According to the Eurostat-OECD PPP Manual 6 there are at least two main approaches that can be followed bycountries in the computation of SAFs: (i) calculation on the basis of available CPI data; or (ii) by conducting
specific price surveys aimed at measuring subnational differences in consumer price levels. The second
approach is likely to be more resource intensive. Within the first approach, countries can take the PPP productlist and try to match it against CPI products. This method has the advantage that the prices are comparable tothose collected for PPP purposes. Alternatively, one can search CPI data for products that are comparable
across subnational areas and base the SAFs on the prices of the identified CPI products. A combination ofavailable CPI data with additional price collection or the use of additional data sources for products wherecomparable CPI data are lacking may be optimal.
4F

Capital city PPPs
In countries with several major metropolitan cities or cities which serve as capitals for different regions,comparison of prices across regional capital cities are of interest for businesses with employees located indifferent cities. Capital city PPPs can be used for making adjustments for differences in the cost of living indifferent cities. These are similar to “Post Adjustments” made by the International Civil Service Commission.
Good quality rental data are likely to be available for the purpose of comparing housing expenditures acrosscapital cities. Capital city PPPs may be feasible and more relevant for aggregates like machinery and equipment,
as purchases of machinery and equipment items are mostly recorded in capital cities.

6

Eurostat-OECD PPP manual: https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-RA-12-023

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Regional level PPPs
In most countries comparisons of price levels in different regions are important from a policy perspective.
Measuring standards of living across different regions and the measurement of inequality and poverty are highpriorities for governments and policy makers. Regional SN-PPPs may be used in the compilation of the Human
Development Index and in assessing growth performance and catch-up and convergence of different regions.
Generally, the compilation of SN-PPPs is similar to the compilation of PPPs for international comparisons,especially in countries which are large and where regions are economically and geographically diverse, such as
in China and India.

Rural-urban price differences
Measures of rural-urban price level differences are essential inputs into the measurement of inequality andpoverty. Poverty lines established at the national level need to be adjusted for rural-urban price differences
prior to the estimation of incidence of poverty. While SN-PPPs for rural and urban areas are important,compilation of PPPs for these purposes is operationally challenging and it poses serious methodologicalproblems. These range from differences in the list of goods and services sold and purchased in rural and urban
areas through to identifying what areas are “rural” and what are “urban”. Dijkstra, Hamilto, Lall and Wahba(2020) discuss issues surrounding the definition and identification of rural and urban areas.
The main conclusion from this section is that in the case of SN-PPPs it is important to carefully identify thegeographic areas or regions reflecting the purpose for which the SN-PPPs are being compiled. This, in turn,implies that it is important to frame the main objectives and the purpose for which SN-PPPs are being compiled.
Once the geographic areas are identified for the purpose of price and real expenditure comparisons, the usual
ICP procedures can be applied with necessary modifications.

3

Uses of subnational PPPs

Regular compilation of SN-PPPs by countries participating in the ICP will increase the usefulness andapplicability of international price comparisons for policy makers and researchers at the national andinternational levels. National institutions and international organizations such as the World Bank, the IMF, andthe United Nations and its many agencies, are interested in the compilation of indicators for the SDGs; the
Human Development Index; and estimates of regional and global inequality and poverty. These organizations
can extend the scope of these measures to cover sub-regions within countries.

3.1

Uses of subnational PPPs at the national level

Comparisons of prices and real expenditures at the subnational level for different expenditure aggregatesprovide policy makers with valuable input into formulating evidence-based policy making. Commercial firms

12and business enterprises as well as researchers and the general public are likely to benefit from the resultsfrom subnational price and real expenditure comparisons.
•

•

•

•

•

•

•

The primary focus of governments and policy makers is to improve the living conditions of the general
population uniformly across all the provinces and regions within their respective countries. Monitoring theefficacy of government policies in delivering equitable outcomes is critical in identifying issues and in
forging effective policies. The availability of reliable price comparisons across the country throughsubnational PPPs enables analysts and policy makers to measure the purchasing power of incomes ofhouseholds in different geographical locations thus making it possible to identify areas in need of further
attention.
A principal objective in all countries is to achieve balanced development across all the regions and to
reduce regional disparities. Availability of real per capita household expenditure and real per capita
regional GDP, where available, are useful inputs into the process of assessing economic performance interms of relative levels of income and productivity. Comparative analysis of the performance of regions
will help assess regional inequality and to examine catch-up and convergence among different regions of
the country. Moreover, policy makers are interested in the computation and comparisons among different
subareas, including urban and rural areas, of real disposal income of households, salaries, labor costs,
productivity, health, and education etc.
The SN-PPPs contain valuable information on price level differences across different regions and for
different components of expenditure. Such detailed information on differential price levels can be used for
spatial price level analysis (to verify market structures and to measure price distortions); for inequality andpoverty analyses that involve spatial comparisons for which price level differences are important; and forassessing the material well-being of people in different territorial areas and to calibrate poverty lines andother economic indicators.
In large economies with federal systems, the regional or provincial governments can benefit from regularregional comparisons of prices and real expenditures. In particular, regional governments will be able tocompare and contrast relative prices of important services such as transport, health, and education.
Sector-specific SN-PPPs will enable provincial governments to undertake comparative analysis of thesource of price differentials by comparing price levels in geographically contiguous (neighboring) provinces
as well as in provinces which are at similar levels of development.
Estimates of real government expenditure on behalf of households in education and health can help
assessment of inequities in the provision of such services across different provinces. These data also help
in the assessment of adequacy of provision of such services in rural, urban, and metropolitan areas withina province.
National and local governments are interested in calibrating regional poverty lines using information on
price level differences through SN-PPPs and to obtain reliable estimates of the number of poor in different
regions so that a proper allocation of financial and other resources can be made. Inequality and poverty
analyses are increasingly focusing on local and small areas.
To conduct analyses of the incidence and extent of poverty at the regional and national level, researchers
may need to compile estimates of poverty-specific SN-PPPs, because general PPPs may not be adequate

13

•
•

•

3.2

for spatial price comparisons that involve the poor. This means that in a program for computing SN-PPPs
it is important to consider the compilation of poverty-specific SN-PPPs.
The private firms operating in different subnational areas can apply SN-PPPs for the purpose ofcomparative analysis involving prices, sales, market share, and production costs.
Firms with employees located in different regions aim to ensure that employees are adequately
compensated for differences in living costs in different cities and regions. A major component of living
costs is the cost of housing in different metropolitan cities and urban centers. Regular publication of SNPPPs for housing will help in determining appropriate compensation for employees.
In an era of increased labor mobility, individuals considering employment opportunities in different regions
can rely on SN-PPPs to assess cost of living differences in their salary negotiations when moving from one
place to another.
Uses of subnational PPPs at the international level

Regular compilation and dissemination of SN-PPPs will help achieve the strategic objectives of many
international organizations as well as multinational companies as they formulate global strategies for theirbusiness operations. A few applications and uses of SN-PPPs at the international level are discussed below.
•••

With the general mission of a world without poverty, the World Bank regularly compiles estimates of theincidence and severity of extreme poverty in different regions of the world. The World Bank uses
international poverty lines to estimate regional and global poverty. The current international poverty line
(IPL) is set at $1.90 per day based on revised ICP 2011 results. In the actual process of estimating thenumber of poor in different countries, the international poverty line is converted to national currency units
using PPPs from the ICP. However, PPPs from ICP are based on national average prices and therefore a
directly converted IPL expressed in national currency units cannot be used in counting the number of poor
in a large country, where price levels in different regions may differ significantly from the average national
prices. For example, food prices in rural areas may be lower than the national average while urban prices
may be higher. In counting the poor, it is important to account for these regional differences. Chen andRavallion (2010) and Ferreira et al. (2016) provide details of adjustments made for spatial differences inprices in estimating global poverty. SN-PPPs would eliminate the need for using any ad hoc or guessestimates for making adjustments for within country differences in prices.
A related application is in the analysis of global and regional inequality, which currently uses PPPs from the
ICP to convert average incomes in each country into U.S. dollars. The use of PPPs from the ICP implicitly
assumes that price levels are the same across all the regions within a country which is an untenableassumption. SN-PPPs can help further fine-tune and improve inequality measures by accounting for withincountry regional price differences.
The United Nations Civil Secretariat devotes resources to measure cost of living differences in different
capital cities and major metropolitan areas around the world. If SAFs were available, the ICP PPPs could be
adjusted to account for differences in prices across different cities in the world.

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Policy makers may use SN-PPPs also for operational purposes. The European Commission uses GDP andother indicators expressed in real terms to allocate funds at the subnational areas of the EU for the
allocations of structural and cohesion funds.
Finally, the compilation of SN-PPPs on a regular basis is likely to enhance the statistical capacity of theparticipating countries. In some cases, CPI compilation itself may be improved when the NSO involved in
the compilation of SN-PPPs is also actively integrating ICP and CPI activities.

•

4

Frequency of compilation of subnational PPPs

The optimal frequency for the compilation of SN-PPPs is annual. In most countries, CPI is compiled on a monthly
basis and estimates of price movements are published on a monthly, quarterly, and annual basis. In contrast,
PPPs from the ICP have been made available in roughly six-year intervals. However, Eurostat and OECD publish
PPPs and real expenditures on an annual basis, compiled using the rolling price survey approach. 7 The
recommendation of the UNSC to increase the frequency of the ICP to be conducted every three years is
consistent with the ultimate aim to produce ICP PPPs on an annual basis. Given the nature, scope and likely
uses of subnational PPPs, it is recommended that countries compile subnational PPPs on an annual basis.
5F

5Governance and administrative structure for subnational PPPs

An efficient and well-organized governance and administrative structure is essential for successful
implementation of a program for the production and dissemination of subnational PPPs. As SN-PPPs are
essentially spatial price index numbers, the governance structure for SN-PPPs must be closely aligned withstructures already in existence for the compilation of CPI and for coordinating activities relating to PPPs fromthe ICP. The guiding principles for the establishment of governance structure for SN-PPPs are:
•
•
•
•

7

the structure must ensure the production and dissemination of timely and reliable SN-PPPs;
as SN-PPPs are measures of spatial price levels, structures for SN-PPPs should recognize therelationships with ICP approaches;
the structure must exploit the overlap and similarity in the survey framework and subsequentvalidation, editing, and computational procedures used in the ICP; andthe structure must ensure that SN-PPP activities are not disruptive to the work associated withcompilation of the CPI and at the same time exploit any possible synergies from the integration of CPI,
SN-PPP, and ICP activities.

See section 11.5 of https://www.oecd.org/sdd/prices-ppp/PPP%20manual%20revised%202012.pdf

15

Countries are advised to refer to the CPI-ICP guide 8 (International Comparison Program, 2021) and follow the
principal recommendations for the integration of CPI and ICP activities and implement them in the context of
SN-PPPs.
6F

Figure 5.1 serves as an illustrative guide to the establishment of governance and administrative structures. The
structure shown below is only an example of many possible structures and it is not meant to be prescriptive.
Figure 5.1: Governance and administrative structures for temporal and spatial price comparisonsNational Statistical Office(or equivalent)Price Comparisons
Coordinating Agency

Consumer Price Index Unit

Spatial Price Comparisons Unit

Advisory Board
Price Surveys
Data Editing
Sub-national
PPPs

International
Comparison
Program PPPs
Price Surveys
Data Editing
Submission of data to Regional
ICP

Index compilation
Dissemination

Figure 5.1 shows an indicative structure designed to integrate activities associated with the compilation of the
CPI with activities associated with spatial comparisons. All price comparison activities will be coordinated by asingle Price Comparisons Coordinating Agency which is responsible for setting procedures and operationalguidelines and for the compilation and dissemination of CPI and SN-PPPs in a timely manner. Spatial pricecomparisons are classified into those dealing with price comparisons across regions within the country (SNPPPs) and price comparisons across countries (ICP PPPs). As the compilation of subnational PPPs and8

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participation in the ICP are not regular activities associated with the CPI, it may be beneficial to establish anadvisory board to guide the activities of the NSO. The flowchart shows that CPI and SN-PPPs share a similar
survey framework and it may be possible to use the same facilities for processing the price data collected. The
grey boxes for price surveys and editing used for CPI and SN-PPPs on the left, and for ICP PPPs on the right,
indicates that all these three activities can share the same data storage and facilities and procedures for data
verification and editing. Following this general template, each country may establish its own technical andmanagement structures for compiling temporal and spatial price index numbers and to meet its obligations
with respect to its participation in the ICP.

5.1

Benefits of integrating CPI and subnational PPP activities

Given the common features of CPI and PPP compilation, there is scope for the integration of CPI and SN-PPP
activities. To achieve such an integration, NSOs and organizations involved in the CPI construction should
recognize that SN-PPP computation and any results from PPPs are a natural extension of the current CPI. There
are several advantages to CPI and SN-PPP integration. A cost-effective approach to the compilation of SN-PPPs
for household consumption is to make maximum possible use of price data collected by the NSOs for compilingthe CPI. Here the reader is referred to the CPI-ICP guide 9 (International Comparison Program, 2021) for
additional insights into such an integration.
7F

•

•

•

The NSOs are best placed to provide data inputs into the construction of subnational spatial priceindexes, which are important analytical inputs for national and local policy makers, economists,
academics, and international organizations.
The computation of SN-PPPs within the process of CPI collection of price data will increase theawareness of ICP at the country level. Providing familiarity with ICP methods and processes, withoutcompromising or disrupting regular statistical priority programs, such as the CPI, will underscore theimportance of the computation of SN-PPPs and ICP PPPs.
Through the integration of the CPI and SN-PPP activities, the ICP and the NSOs can save resources andtime. In the first phase of the implementation, significant work may be needed to prepare the pricedatabase for CPI, SN-PPPs, and ICP PPPs. Price data for the CPI are often collected in a manner thatmakes it difficult for using it in the compilation of subnational PPPs and for estimating spatial pricedifferences. The integration of price data activity will increase the synergies between CPIs, SN-PPPs
and ICP PPPs, and will facilitate their simultaneous computation under a unified framework. In thisway, the ICP PPPs could be computed frequently, possibly annually, which is a strategic objective of
the ICP.

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•

Employing a unified framework would allow the construction of SN-PPPs consistent with CPI
movements in the time domain and consistent with PPP comparisons in the spatial domain.

Therefore, it is crucial that the NSOs consider ICP PPP and SN-PPP work as an integral part of a national pricecomparison program.

6

Compilation of subnational PPPs

The major steps involved in the compilation of SN-PPPs are similar to those involved in the compilation of ICP
PPPs. Countries seeking to compile SN-PPPs are encouraged to consult Measuring the Real Size of the World
Economy: The Framework, Methodology, and Results of the International Comparison Program (ICP) (World
Bank, 2013) and Operational Guidelines and Procedures for Measuring the Real Size of the World Economy(World Bank, 2015) for detailed descriptions of the procedures involved. This section highlights some
important aspects of SN-PPP compilation.

6.1

Construction of product lists

The first step is the construction of a product list consisting of goods and services that are to be priced for the
purpose of SN-PPP compilation. A key consideration is that the product list adequately covers goods andservices purchased in different regions of the country. This process is likely to be easier in the case of SN-PPPs
compared to ICP PPPs, as regions within a country are likely to be more homogeneous than countries. It isnoted, however, that for large countries exhibiting geographic and economic diversity, preparation of the
product list may be just as complex as that for inter-country comparisons.

6.1.1 Exploit commonalities in the CPI lists from different regions and the ICP product lists
As the SN-PPPs are measures of price level differences across regions within a country, it is recommended thata starting point for the preparation of the product list is to closely examine the CPI product lists from different
regions and identify possible overlaps in such lists. If no region-specific product lists are available, then the CPI
product list for the country may be used as a starting point with any products that may be considered importantin one or more regions added to this list. This process may not be as simple as it appears, since it may bedifficult to obtain a list of products which are regularly priced as a part of the CPI. The CPI, unlike the ICP, does
not typically make use of SPDs and does not maintain lists of characteristics and details of the products priced
for the CPI. Even when such a list exists, CPI price collection may not require documentation of thespecifications of products to be priced. In establishing the product list for SN-PPPs, it is recommended that
products appearing in the ICP product list are considered and, if possible, a subset of those products areincluded in the SN-PPP product list. Such an overlap can lead to synergies between the ICP PPP and SN-PPP
production.

18

6.1.2 Comparability
Comparability of products for spatial price comparisons is, in concept, similar to pricing the same products at
different points of time within the context of CPI. Defining and pricing comparable products ensures thatdifferences in prices between areas for a product reflect actual price differences and are not influenced bydifferences in quality. Two or more products are said to be comparable if their physical and economic
characteristics are identical, or if they are sufficiently similar that consumers are generally indifferent about
the choice of the product. Usually, it is difficult to decide on the level of comparability to be achieved. The
more tightly the products are defined, the more difficult it becomes to find products meeting the specifications
in all the subnational areas. It may be necessary to define products somewhat more loosely to be able to findproducts that meet the specifications and are purchased in all the areas.

6.1.3 Representativity and Importance Indicators
The products selected must be representative of the products purchased in each area or region within the
country. Representative products are those products that are frequently purchased by resident households
and are likely to be widely available throughout the country.
Comparability and representativeness are two competing requirements. Products may be made comparable
through tight specification of the products, in which case such products, while available in the outlets, may not
be representative of consumption in the regions. In the preparation of the product list, it is therefore important
to strike a balance between comparability and representativity. On the one hand, comparability is clearly
important because it is difficult to make sense of price comparisons unless the products have similar
characteristics, including quality. On the other hand, representativity is also important because the prices of
non-representative products may differ from representative ones. Though comparability is likely to be a lessserious issue in the context of SN-PPPs than it is in the computation of ICP PPPs, the two competing
requirements need to be considered carefully and caution is required in the preparation of the product list.
Since the price data at the item level are aggregated to form an elementary level index or form a basic heading
price parity without the use of any weights, it is useful to attach an indicator for each item as to whether theitem is considered important or whether it is just available for price collection. Elementary level or basic
heading level is the lowest level below which no weights data are available. For example, the item group rice
is a basic heading in spatial price comparisons and the ICP, it is at the elementary level within the CPI. Different
types of rice like basmati, long grain, short grain, glutinous rice, and parboiled rice all belong to the rice basic
heading. The price index for this group within CPI is computed using a simple unweighted geometric averageof prices (Jevons index). If some varieties of rice are not considered important in a particular region, prices ofthat variety of rice should be given less weight in the computation of that basic heading price parity.
A strong recommendation is that indicators of importance be attached to each item in the product list priced
by a region or province used for the compilation of SN-PPPs.

19

6.1.4 Price determining characteristics and structured product descriptions (SPDs)
Preparation of a structured product description (SPD) for each product to be priced is an important step inestablishing a suitable survey framework for price collection. The SPD of each item lists a set of important pricedetermining characteristics which are designed to assist the price collector in identifying the products for which
prices are to be collected. To decide on the product specifications, the agency responsible for this process may
be able to utilize information on the collection of price data and the subsequent compilation of the CPI.
The type of information useful in this process may relate to:•••

Coverage of the CPI by territorial area (including the classification between urban and rural areas),
List(s) of products and services (“basket of goods and services”) - it is useful if products included in eachelementary aggregate or basic heading are clearly listed.
Product code and key identifiers, that is, a description of the characteristics that identify each productand service to be priced in terms of technical features, brand, model, packaging, and other aesthetics
features (such as in SPDs), are developed prior to price collection. In practice, such details for the CPI
products may not be available in a documented form. A closer integration of CPI and ICP activities mayhelp bridge this gap. Further discussion and details on this aspect can be found in the CPI-ICP guide 10(International Comparison Program, 2021).
There are two strategies to increase the number of comparable products: (i) use loose specifications of
products, or (ii) collect more information on the characteristics of the products (include physical andfunctional properties) and then achieve product comparability by adjusting prices for quality differences.
Further, there are two different ways to loosen the product specifications. First, it is possible to reduce
the number of characteristics used in comparing products and services priced in different outlets andlocation within all the subnational areas. Second, having information on the characteristics collected on
all the products and services priced in the CPI surveys (usually a large number), clusters of products andof services that have at least a minimum common (overlapping) sets of characteristics that satisfy thesame needs can be identified.
Classification of products and services by the type of expenditures, such as the Classification ofIndividual Consumption by Purpose (COICOP) or other classification, would be useful.
8F

•

•

6.2

Framework for price collection

To implement an efficient survey framework for building up an “ideal” micro-database to obtain reliable
estimates of the SN-PPPs, it is necessary to work within a theoretical and practical framework based on amultistage stratified sampling design. Following the principles underlying such a framework, it is possible totake into consideration variability of the sampled population within each stratum to obtain reliable estimates

10

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of the price index. The survey framework currently used in the compilation of CPIs may be used as a starting
point for designing price surveys for SN-PPPs.
Usually, the NSOs collect price data to compile CPIs by conducting separate surveys, each covering different
aspects: products and services; prices; outlets; and geographical location etc. Most of them are sample surveys
based on concepts of simple random sampling, stratified sampling, and also on judgmental sample schemes.
However, prices can be collected for a subset of items centrally through administrative sources or via telephone
and/or the internet when national pricing exists. More recently, prices are also obtained from scanner datafrom retail trading chains of the modern distribution system, or by ad hoc collection of data for certain
groups of products by means of electronic devices and data collected through web scraping techniques.
Once again, the survey framework adopted would necessarily depend on the purpose for which SN-PPPs arecompiled. For example, if the purpose is to provide information to national and local policy makers regarding
the incidence, nature, and extent of poverty, three additional aspects are important: (i) to consider areas as
small as possible (local areas); (ii) the need to compile SN-PPPs separately for urban and for rural areas; and(iii) the possible collection of data on prices and expenditures that facilitate the compilation of poverty specificPPPs.

6.2.1 Conventional sources of price data
The primary source for price data for the compilation of SN-PPPs would be the standard or conventionalsources of price data used in the compilation of CPIs, as well as other price indices, such as the wholesale andretail price indices. The conventional sources such as prices of goods and services from different types ofoutlets used by consumers for their purchases would be the primary source of price data for SN-PPPs. This isalso the primary source for price data for ICP purposes. The case study by Aten in Appendix A1 to this guidedescribes how price data gathered from the CPI sources can be used for inter-area price comparisons in the
United States.

6.2.2 Use of scanner data and alternative sources of data
The availability of high-frequency ‘‘scanner data’’, in addition to other sources of data, enables pricestatisticians to deal with SN-PPP compilation from a new perspective. The scanner data provide large volumesof data on consumer transactions with specific information on sales, expenditures, quantities, and on qualityof the item transacted, along with very detailed information on product characteristics (such as brand, size andtype of outlet) provided at barcode level or, more precisely, the Global Trade Item Number (GTIN) code. The
scanner data from the modern distribution chains can provide millions of prices for thousands of products.
They predominantly refer to supermarkets and hypermarkets, especially for food, beverages, and personal andhome care products. After a process of data cleaning and trimming outliers, the unit value price per item code
can be computed by dividing total turnover for that item by the total quantity sold over a given period. These
unit values, based on data from different geographical regions and over a given period of time, are similar to
the national annual average prices required by the ICP for price comparisons.
21

Many NSOs around the world are integrating scanner data with price data collected from traditional sources
using standard data collection methods in the process of compiling the CPI. Based on the experienced gained,
for example in Italy and other countries, this new source of data makes it possible to identify representativeand comparable products across subnational areas, resolving the important issue of balancing the two competing
requirements of representativity and comparability. The Italian case study by Laureti and Polidoro in Appendix
A3 of this guide, illustrates how scanner data can be blended with data from standard sources in the compilationof subnational PPPs.
Moreover, the scanner data is able to: (i) capture frequent and often large shifts in quantities purchased in
response to price changes; (ii) provide information on “quality” characteristics that may influence the price ofa product; (iii) add a time dimension to multilateral spatial price comparisons, since detailed data are usuallyavailable at the point of sale and at the time of transaction; and (iv) account for the economic importance ofeach item in its market by using data on turnover, thus providing a reliable indicator of the importance of items.
Finally, it is clear that using scanner data to carry out spatial comparisons will result in cost efficiencies sinceprice data collection can then be limited to traditional outlets thus lowering data collection costs for the
NSOs.
Several limitations of scanner data must be noted in this context.
•

•
•

•

Scanner data may not be available in many developing countries or available data may be limited to one
or two retail chains and, therefore, may not be sufficient to provide price data representative of all the
areas and all the outlets within a country.
From the view of geographical coverage, there is the advantage that scanner data can cover all the cities
across the whole country, but it may also be that rural areas are not adequately covered.
Coverage of scanner data is just for the purchases made in the outlets of the modern distribution chains,and scanner data may not be used for perishables and seasonal products such as fresh vegetables and fruit,
meat, and fresh fish, since many of these products are usually sold at price per piece (e.g. price for one
mango or price of a whole fish and not by its weight) and are not pre-packaged with GTIN codes.
The traditional CPI surveys covering other outlets and markets (as hard discount, small shops, and open
markets) must be used for the compilation of the SN-PPPs to attain complete coverage of the markets andfor the kind of grocery products sold in these outlets. It is important to evaluate the share of total
expenditure of consumers covered by scanner data and how to integrate PPPs compiled using scanner datawith PPPs obtained for other products and services.

The general recommendation is to make use of data available from all the sources as long as they meet therequirements and standards set by the NSOs. Where such data are used, the multistage stratified sampling
design should be followed to attain an effective use of the existing micro-data, following post-stratification of
the existing CPI micro-data.

6.2.3 Outlet selection
The recommendations here are similar to the guidelines for the ICP. NSOs are responsible for price collection:22

they are required to collect prices from a sample of outlets chosen to reflect consumer purchasing patterns forthe types of products and services being surveyed. Therefore, it is necessary to select the outlets that are to
be visited by price collectors using a well-defined sampling frame and then contacting the outlets selected to
explain why they are to be visited.
Households purchase products and services from a range of outlets that tend to vary from country to country
and across different subnational areas. The ICP classification of outlet by type is the following: (1) Large shops;(2) Medium and small shops; (3) Markets; (4) Street outlets; (5) Bulk and discount shops; (6) Specialized shops;(7) Private service providers; (8) Public or semi-public service providers; and (9) Other kinds of trades andoutlets. For each price observation, the NSO must identify the type of outlet the price is collected from.
The selection of outlets is of particular importance because of the effect it will have on the efficient estimationof the average prices of the products to be surveyed. Different products have different distribution profiles.
In principle, it is necessary to refer to a stratified sample by products and services, by type of outlet, and by
location within each subnational area. The selection should consider variability of prices within outlet type; ofthe location of outlets within and around each area, and on the volume of the outlet’s sales, although this lastpiece of information is often difficult to obtain.
A good starting point for the selection of outlets is the sample of outlets used for the CPI, but it is only a starting
point. The final product lists for the price surveys used for SN-PPPs may differ from the product lists for the
CPI. There will be products that are common to both lists. These will be mainly food items, such as fresh fruitand vegetables, but other products may be covered as well. Prices for such products may not be separately
collected because the prices collected for the CPI can be used instead. A challenge may be that the CPI
surveys in many countries are urban based and in such cases these data need to be supplemented with dataon prices from rural areas.

6.2.4 Seasonal products
Seasonal products are defined as those products for which both prices and quantities sold vary appreciably
throughout the year. Typically, the patterns of variation are repeated from one year to the next. By thisdefinition, certain fruits, vegetables, fish, and flowers are obviously seasonal products. Various types ofclothing are also seasonal products. So too are those goods that are sold in substantial amounts at prices well
below normal prices during seasonal sales.
The approach to be adopted for seasonal food products is to collect prices at more frequent time intervals, forexample collecting prices monthly instead of quarterly. Annual average prices are computed using quantity
weighted averages of the price data collected. Where prices are collected less frequently, it may be necessary
to use temporal adjustment factors based on CPI price movements.
Neither the selection of seasonal food products, nor their seasonality, are necessarily the same for all theregions within a country or for different metropolitan cities when it comes to large and geographically diverse
countries. It is left to the NSOs to decide which of the items specified are regarded as seasonal.

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6.2.5 Housing and rental price surveys
The approach recommended for housing is similar to the ICP recommended approach. Heston (2013) describes
the ICP practice in dealing with dwelling services. Although housing is a part of household consumption
expenditure, it is not included in the price surveys for consumer goods and services. Instead, it iscovered by special rental surveys or by extracting them from existing statistical sources, like household
budget surveys, housing censuses, and other national data sources. Data are collected on rents paid by tenants
and on the rents imputed to owner-occupiers for a set of broadly defined types of dwellings. Quantity andquality data on the housing stock are also collected.
Rents do not cover additional payments for the maintenance and repair of the dwelling, water supply, refuse
and sewage collection, electricity and gas, and heating and hot water supplied by district heating plants. Nor
do rents cover co-proprietor charges for caretaking, gardening, stairwell cleaning, maintenance of lifts andrefuse disposal chutes, and heating and lighting, etc., in multi-apartment building complexes.

6.3

Availability of regional level data for GDP structure

In order to use household expenditure share data as weights to compute the SN-PPPs, it is necessary that thenational accounts be compiled at the desired subnational geographical level and by classes of expenditure.
However, NSOs do not typically provide such detailed breakdown. Given their national coverage, household
expenditure surveys (HES) are often a reliable source of expenditure weights. It is possible that the sample sizeused for HES may not be sufficiently large to obtain accurate estimates of expenditure by commodity groups
at local level. In such cases there are two possible options. One option is to restrict the scope of subnational
PPPs to larger geographical areas for which reliable expenditure weights are available. An alternative approachis to obtain the matrix of expenditure shares by territorial areas and basic headings using small area estimation(SAE) methods, which provide imputed weights based on indirect indicators such as population.
In summary, it is necessary to have the following information on the system of expenditure weights:••••

source(s) of data used: household expenditure or budget surveys; surveys for national and regional
account compilation; retail surveys and others;
methods of estimation of expenditure weights;
level of disaggregation: availability of weights for each product and/or elementary aggregates by typeof outlets and/or by territorial area and or at higher level; andperiodicity and method of updating the system of weights.

6.3.1 Validation of expenditure share data
The agencies involved in the compilation of SN-PPPs need to examine spatial consistency of expenditure
shares. Though expenditure patterns across regions may exhibit significant differences, it is important toeliminate inconsistencies arising out of differences in interpretation and implementation. For example, someregions may include the cost of food from restaurants under food expenditure, while others may include it24under restaurants. Such discrepancies are likely to be discovered by a comparative assessment of expenditure
share data from different regions. Another example is rental data for housing. Rents should not normally
include charges for heating or for miscellaneous services relating to the dwelling such as refuse collection or
co-proprietor charges in dwellings in multi-apartment building complexes.

7

Price data and validation

7.1

Price data

The objective of the SN-PPP price surveys is to collect prices that purchasers actually pay to acquire goods andservices specified on the final product list at the time of the survey. The prices should be the actual transaction
price and should include delivery and installation costs, value added taxes (VATs) and other indirect taxes on
products and invoiced service charges and voluntary gratuities. These principles need to be established inadvance to ensure consistency in the approach used by price collectors.

7.1.1 Annual average prices
The ICP advocates the use of annual average prices for the purpose of PPP computations. The same notion isapplicable in the case of SN-PPPs. Once the price surveys are conducted, annual average prices should becomputed for each product within each type of outlet and within each stratum. Then average prices arecomputed for each product at the level of territorial or geographical area. If the survey framework utilizes aself-weighing design, a simple unweighted average of all the price quotations can be used in computing
national average prices. Otherwise, it is necessary to use weights based on quantities or expenditures.

7.1.2 Number of quotations, average prices, and standard deviation
The number of prices to be collected for a product will vary from product to product within a basic heading
and it will also differ from one basic heading to another. Normally, the number of prices collected for a productdetermines the reliability of its average price. The larger the number of price observations, the more accurate
the average price. The actual number depends on the degree to which prices of the product vary. The numberof prices to be collected for each product could be decided using random sampling techniques, but the numberof price observations to be collected per product could be decided by taking into account the type of
specification being priced, the conditions prevailing in its market, and experience gained from previous survey
rounds. It is a sound practice to collect more price quotations for products in basic headings with large
expenditure weights and/or with larger price variations than prices of items in other basic headings.
In addition to supplying average prices, it is important to have information on the number of quotations usedin the computation of the averages, as well as the standard deviations of the price quotations used in the
averages. These can serve as a measure of the reliability of price data. The current practice with the ICP is thatthe national implementing agencies provide such information to the regional implementing agencies.

25

7.2

Validation of price data

Once price data are collected from different regions (territorial areas), an important next step is to ensure thequality of price data through data editing and validation. It is essential that the prices on which the SN-PPPs
are based are rigorously checked and corrected for errors and validated. Two types of errors can affect thecollected prices: sampling errors and non-sampling errors. Sampling errors can be controlled through the useof an appropriate statistical framework for the price surveys. Non-sampling errors occur for reasons such as
pricing the wrong product (product error) or incorrectly recording the product’s price or unit of measure(pricing error).
Editing consists of checking prices for possible non-sampling errors. Verification consists of either confirmingthat the prices identified for verification are indeed correct or correcting them if they are not. Validation is an
iterative process requiring several rounds of editing and verification. Possible errors are found by identifying
prices that have a measure of divergence that is greater than a given critical value or a value that falls outside
acceptable bounds.
Validation is a two-stage process. In the first stage, prices collected are edited and verified within a region or
territorial area, referred to as intra-regional validation. The second stage involves inter-region validation where
prices collected by all the regions involved in the comparison are used in the process of editing and verification.
In each of these stages, validation has the same aim which is to identify and eliminate non-sampling errors,
product error and price error, from the survey price data.

7.2.1 Validation at the regional and national level
The intra-region validation is designed to establish that price collectors within the same region have priced
products that match the product specifications and that the prices they have reported are correct. This isachieved by searching for outliers first among the individual prices that a region has collected for each productit has chosen to survey, and then among the survey prices for these products.
The main diagnostics used to validate prices are:••extreme values among price observations are identified by means of two tests: the ratio to average
price test and the t-value test;
extreme values among average prices are also identified by two tests: the max-min ratio test and thecoefficient of variation test.

In the next stage, the inter-regional validation is designed to screen average prices from different regions. The
objective is to verify that the average prices are for comparable products and that the price collectors haveinterpreted product specifications in the same way and that they have also priced them accurately. This is doneby comparing average prices for the same product across regions and by analyzing the dispersion of the priceratios that the average prices generate between regions across products and across regions.
Two methods have been mainly used in the ICP for the purpose of validation: the Quaranta tables and the
Dikhanov tables. Both tables provide measures of price variation suitable for validating price data. Details of

26

these diagnostic tools along with illustrative examples can be found in Chapter 9 (Blades, 2013) and in Chapter
10 (Vogel, 2013) of Measuring the Real Size of the World Economy (World Bank, 2013), and in Chapter 14 ofOperational Guidelines and Procedures for Measuring the Real Size of the World Economy (World Bank 2015).

7.3

Treatment of outliers

Editing for product errors and price errors involves identifying prices that have extreme values. Prices withextreme values that are shown to be accurate observations are “outliers” and should be retained if they arepart of the population defined by the rest of the price observations. The selection of outlets can result inchoosing outlets that are themselves outliers and not representative of the purchasing patterns of the average
consumer. In this case the outlet should be replaced by one whose prices are closer to the average. Intra-region
validation of average prices by outlet type and location can help identify outlier outlets.
Correction of price observations and deletion of prices: prices that have extreme values suggest that they could
be wrong and need to be investigated if they are genuine observations. If they are genuine outliers and are notdue to product or pricing errors, such prices should be retained. However, if errors are found then those prices
are either corrected or dropped.

8

Expenditure data and weights

As already noted, there are two main stages in the calculation of SN-PPPs. The ﬁrst is the collection of pricedata and the calculation of spatial price indices, or in other words PPPs at the basic heading level. The second
stage is where basic heading price indices are further aggregated leading to PPPs at higher levels of aggregation
up to the overall SN-PPP itself. Expenditure data are needed irrespective of the index number formula used for
aggregation purposes.

8.1

National accounts versus household expenditure survey data

Expenditure share weights, for the aggregation of price differences above the elementary level or basic headinglevel, are a common data requirement for both CPI and PPP compilation. The CPI weights are usually based ondata collected through household expenditure surveys (HES). Weights used in the calculation of ICP PPPs referto the shares in the national accounts. The share of household expenditure recorded in the national accounts
are based on data from HES and on data compiled through the commodity flow approach. Therefore, common
ground exists, and an integrated approach can result in more reliable estimates of both the CPI and PPPs. An
assessment has to be made on a country-by-country basis.

8.2

Household expenditure surveys and national accounts

The principal data source for household consumption expenditures in most countries is the HES, a sample
survey of thousands of households that are asked to keep records of their expenditures on diﬀerent kinds of27

consumer goods and services over a speciﬁed period. The size of the sample obviously depends on the
resources available, but also on the extent to which it is desired to break down the survey results by region ortype of the household and kind of expenditure. Household expenditure surveys may be taken at speciﬁed
intervals of time, such as every ﬁve years, or they may be conducted every year on a continuing basis.
The use of the commodity ﬂow method within the supply and use tables of the System of National Accounts(SNA) enables data drawn from diﬀerent primary sources to be reconciled and balanced against each other.
The commodity ﬂow method may be used to improve estimates of household consumption expendituresderived by adjusting them to take account of the additional information provided by statistics on the sales,
production, imports and exports of consumer goods and services. By drawing on various sources, thehousehold expenditure data in the national accounts may provide the best estimates of aggregate household
expenditures, although the classiﬁcations used may not be detailed enough for SN-PPP purposes.
Countries that conduct continuous expenditure surveys can revise and update their expenditure weights eachyear. Even with continuous expenditure surveys, however, there is a lag between the time at which the data
are collected and the time at which the results are processed and ready for use. Thus, even when the weights
are updated annually, they still refer to some period that precedes the time reference period used for SN-PPP
compilation. In any case, it may be preferable to use expenditure weights that are averages of expenditure
shares over periods of two or three years in order to reduce ‘‘noise’’ caused by errors of estimation (theexpenditure surveys are based on samples) or erratic consumer behavior over short periods of time resulting
from events such as booms or recessions, stock market ﬂuctuations, oil shocks, or natural or other disasters
and pandemics.

8.3

Classifications of household expenditures

The individual consumption expenditure by households is broken down into expenditure categories andsubsequently into expenditure groups and so on, and finally into elementary sub-classes (basic headings for
the ICP). The classifications used for the ICP, the CPI and the HES are different. This compromises comparability
across the three statistical domains, and can make the expenditure data reconciliation difficult, and can makeit difficult to use the CPI expenditure weights for the computation of PPPs.
To address the issue, a good starting point is the COICOP Classification at five-digit, or subclass, level which canbe used as the basis to harmonize the three different classifications. The aim at the five-digit level is to draw
up a master classification with subclasses defined to meet the needs of the ICP, the CPI and the HES.

9Aggregation methodology and price and real expenditure comparisons

The methodology for the compilation of PPPs within the ICP is well established. Detailed discussion on theframework for PPP compilation and the methods for aggregation at the elementary or basic heading level andfor aggregation at higher levels are available, respectively, in chapters 1, 4 and 5 (Rao 2013a; Rao, 2013b; andDiewert 2013) in Measuring the Real Size of the World Economy (World Bank, 2013). Estimation of SN-PPPs

28

and real expenditures at the regional level is like estimation of ICP PPPs and therefore all the methods used in
the context of ICP are equally applicable in the case of subnational comparisons. The recommendations below
focus on several important aspects of the estimation of PPPs for subnational comparisons of prices and real
expenditures.

9.1

Hierarchical structure for compilation of PPPs

The compilation of PPPs for household consumption or for higher levels of aggregation can follow a pyramidlike structure that starts with item-level prices and progressively aggregates to higher levels eventuallyreaching a desired level of aggregation. In the context of SN-PPPs, it may be appropriate to consider individual
consumption expenditure by households (ICEH) and by non-profit institutions serving households (NPISHs);
government expenditure; and gross fixed capital formation. The sum of these three major aggregatesconstitutes domestic absorption – an aggregate that is a meaningful concept for subnational price and real
expenditure comparisons. Figure 9.1 shows the structure of higher-level aggregates used in the most recent
ICP cycles in 2005, 2011 and 2017.

Figure 9.1: Hierarchical Structure for Different Level Aggregates

Source: Based on a similar figure on page 17, World Bank (2013)
A complete list of basic headings belonging to different classes, groups, categories, six main aggregates andthe GDP are available in recently released reports for the ICP 2017 cycle (World Bank, 2020; and AsianDevelopment Bank, 2020). For the purpose of compiling SN-PPPs, it is sufficient to identify the part of the29

pyramid that is of relevance for the particular subnational PPP under consideration. For example, if PPPs forfood are of interest, then prices of all items and basic headings classified under food along with expenditure
weights would be considered.

9.2

Aggregation of item level prices to basic heading level

Basic heading (BH) or the elementary index level is the lowest level at which expenditure share data are
available. The initial step in the compilation of BH-level PPPs is aggregating prices of items that belong to thebasic heading level. For example, Rice is a basic heading and different varieties of rice are items that belong tothis basic heading. The country-product-dummy (CPD) method is recommended for use at this level ofaggregation. If information on importance indicators is available, then a weighted CPD (WCPD) method withweights 3:1 attached to important and unimportant items is recommended. The CPD and WCPD methods arebriefly described in Section 9.3 below. At the end of this step, PPPs are available for all the basic headings thatbelong to a particular aggregate under consideration

9.3

Aggregation of basic heading level PPPs to PPPs for higher level aggregates

The estimated PPPs for the basic headings are matched with expenditure data either from household
expenditure surveys or from national accounts. At this level, price and expenditure (and hence quantity data)
are available for index computation.
The recommendation is to use data on PPPs and expenditures for all the basic headings belonging to a certain
group. For example, if the PPP for the class Bread and Cereals is to be computed, then only BH PPPs for the fivebasic headings included in this class (Rice; Other Cereals, flour, and other cereal products; Bread; Other Bakery
Products; and Pasta Products) along with expenditures for each of these basic headings are used in PPP
computation. Since expenditure data are available, the ICP recommendation is to use the Gini-Éltető-KövesSzulc (GEKS) method for aggregating BH PPPs and expenditure data. The GEKS method is explained in Section
9.6 below.
A further recommendation for aggregation above basic heading level is to always use PPPs and expenditure
data for all the basic heading levels belonging to the group considered for aggregation. For example, suppose
SN-PPPs for the Food group are being compiled. Then the recommendation is to make use of all BH PPPs thatbelong to the Food group for computations and NOT to use PPPs from higher level aggregates. For example,
PPPs for Bread and Cereals, Non-alcoholic Beverages, and Alcoholic Beverages should not be aggregatedleading to Food PPP. Instead, all the BHs like Rice, Other cereals, Bread, and others that comprise the Food
aggregate should be used.

30

9.4

Index number methods for multilateral comparisons – desirable properties

Aggregation of price data leading to PPP estimation requires the use of index number formulae. Not all index
number methods are suitable for the purpose of making spatial comparisons. Since multilateral comparisonsinvolve comparisons between all pairs of subnational regions, only index number methods satisfying important
properties can be employed for compiling SN-PPPs. There are many desirable properties but a subset of threeproperties are considered desirable. These are transitivity, base invariance, and additivity, described briefly
below.

9.4.1 Transitivity
Suppose there are R regions involved in the comparison indexed as r = 1,2,…,R. Let PPPjk : j, k = 1, 2,..., R
represent purchasing power parity (PPP) or the spatial price index for region k relative to the base region j.
Multilateral spatial price comparisons require that PPPs for all pairs of regions need to be computed thus fillingthe following price comparison matrix PPP : PPP11
 PPP
21
PPP =  ...

 PPPR1

PPP12
PPP22
...
PPPR 2... PPP1R ... PPP2 R 
...
... 

... PPPRR 

Transitivity is an internal consistency requirement which guarantees that price comparisons between any
two regions, say A and B, is the same whether it is derived through a direct comparison of region A andregion B or through an indirect comparison which compares region A with C and region C with B. This means
that

PPPAB = PPPAC  PPPCB
Suppose prices in region C are on average 10 percent higher than in region A and if prices in region B are 20
percent higher than in region C, then transitivity implies that prices in region B must be 32 percent higher
than prices in A ( 1.10 x 1.20 = 1.32).
If the matrix of PPPs satisfies transitivity, all the elements of the matrix can be filled if the values of PPPs in thefirst row are known. That is, if PPPs for all regions expressed relative to a reference or base region 1 are known,
it is possible to derive PPP for any other pair of regions, j and k, by using
PPPjk =

PPP1k
PPP1 jFor example, when cross-country comparisons in the ICP are considered, the transitivity property of theaggregation method implies that PPPs for all the countries expressed relative to USA as the reference country(with the U.S. dollar as the reference currency) can be used to find PPPs for currencies relative to anotherreference currency. World Bank (2020) has published PPPs for the reference year 2017 in May 2020 for all the31participating countries with USA as the base country. Suppose the PPP for India with Japanese Yen as thereference currency is needed, then it can simply be obtained as:

PPPJapan , India =PPPUSA, India
PPPUSA, Japan

9.4.2 Base invariance
In simple terms, base invariance requires all the participating regions to be treated symmetrically and no region
is given special prominence. Suppose, SN-PPPs for regional capital cities in India are compared to New Delhi,
which is the capital of India. Comparisons between any other pair of capital cities is done through New Delhi
using the Fisher binary price index number formula. The resulting SN-PPP comparisons are transitive but notbase invariant as New Delhi is given special status in the comparisons. Here, New Delhi is like a star or pivot in
the price comparisons. If New Delhi is replaced by Mumbai as the star or the pivot, the resulting sety of SNPPPs will once again be transitive, but they will be different from those obtained using New Delhi as the pivot.

9.4.3 Additivity
Additivity is a useful accounting requirement, which ensures that the PPP-converted (real) expenditures fordifferent components of an aggregate add up to the real expenditure for the aggregate. Additivity can beobserved for expenditures in local currency units and for aggregates converted using market exchange rates.
An index number method satisfies additivity if the sum of real expenditures for components adds up to real
expenditure for the aggregate. While it is an attractive property, additivity comes at a cost. Additive methods
use a common set of prices (or, as they sometimes called, international prices) at which quantities are valued.
However, it has been shown that constant price comparisons that underpin additive methods violate importanteconomic theoretic properties (see Diewert (2013) for a detailed explanation). Such comparisons result in abias (the Gerschenkron effect) which could be significant in international comparisons where price andquantity structures can differ greatly. Based on these considerations, the ICP discontinued the use of the GearyKhamis method, which is an additive method, and replaced it with the GEKS method in 2005. However, withina country price structures differ much less, and the effects from additive methods are consequently muchsmaller, which explains why CPI and SNA mostly use such indexes as the Paasche and Laspeyres in their
compilation without much of an adverse effect. In this paper, Appendix A1 describes the U.S. Bureau of
Economic Analysis (BEA) methodology for SN-PPPs within the USA using a Geary-Khamis type additive index.
Based on the discussion above, it is recommended that if additivity is not required, then the GEKS -type
indices are to be used in the compilation of SN-PPPs. If additivity is required a Geary-type system can be32

used (that is, the Geary-Khamis index, or the Ikle-Dikhanov-Balk (IDB) index) 11. Those indices satisfy
transitivity and base invariance, in addition to additivity. It should also be noted that formulae commonlyused by the NSOs, such as the Laspeyres, Paasche, Fisher, and Tornqvist indices, do not satisfy transitivity.
As the GEKS-based comparisons do not satisfy additivity, users must be careful when comparing real
expenditures at the aggregate level and at the component level.
9F

9.5Country-product-dummy (CPD) 12 method
10F

The country-product-dummy (CPD) method is the recommended method for aggregating item level price data
to estimate PPPs at the basic heading level. The method was first proposed by Summers (1973) for the purposeof treating missing price data, but it has assumed prominence as a method to aggregate item level price data
(Rao, 2013b).
Suppose there are N items belonging to a particular basic heading. Let  pij : i = 1, 2,..., N and r = 1, 2,..., R
represent the price of the i-th commodity in region r. The CPD model is based on the law of one price whichstates that:pir = Pi  PPPr  uir
where Pi and PPPr are the average price of the i-th commodity and purchasing power parity for the basic
heading for the region r respectively. Taking logarithms on both sides, the model can be re-written andexpressed in terms of item and region dummy variables (see Rao, 2013a for details):
N

R

i =1

r =1

ln pir = i Di +   r Dr* + vij where i = ln Pi and  r = ln PPPr
for all i = 1, 2,..., N ; and r = 1, 2,..., R

Here Di is the dummy variable for commodity i which takes a value of 1 when item considered is i, and a valueof 0 otherwise; Dr* is the region dummy which takes a value of 1 if the price quotation is from region r, and avalue of 0 otherwise.
Parameters of the CPD model are estimated using ordinary least squares imposing one restriction, which is in
the form of PPP for the reference region equals 1. Once the parameters are estimated, then PPP for region r is
estimated as:

11

Diewert (2010) describes these indices in detail ( https://onlinelibrary.wiley.com/doi/abs/10.1111/j.14754991.2010.00398.x )12It may be more appropriate to call this region-product-dummy or RPD, but CPD is a generic term which can be used
for regions, capital cities, and provinces.

33

exp (ˆ k )ˆ
and PPPjk =exp (ˆ j )ˆ = exp (ˆ )PPPrr

for all j , k

It is worth noting that if all the items are priced in all the regions then the estimated PPP equals the Jevons
Index, that is,
1/ N

p 
PPPjk =   ik 
i =1 
 pij N

which is the simple geometric average of price relatives of all the items included in the basic heading.
The CPD method is the method recommended by the ICP Technical Advisory Group (TAG) for aggregation ofitem level price data leading to PPPs at the basic heading level. However, in the Eurostat and OECD regions aslightly different set of aggregation procedures which are similar to the GEKS method are used. Rao (2013b)
describes these methods in detail. A distinct advantage of the CPD model is that it allows for the use of weights,
such as importance weights, in the estimation of PPPs. Furthermore, the CPD model can also accommodate
additional information on characteristics of products priced when loose specifications are used in productdescriptions.

9.5.1 Weighted CPD with importance indicators
Suppose the regions provide additional information as to whether a particular item priced in their region isimportant or not. Then it is recommended that a system of weights 3:1 for important items is applied whenusing weighted least squares to estimate the CPD model. This is equivalent to running ordinary least squares
after transforming the CPD model as:
N

R

i =1

r =1

wir ln pir =  wir i Di +  wir  r Dr* + vij where i = ln Pi and  r = ln PPPr
for all i = 1, 2,..., N ; and r = 1, 2,..., R

where wir is equal to 3 if the item is important and equal to 1 if the item is not important.
However, for SN-PPPs, it may be difficult to come up with reliable importance information. Additionally, if the
SN-PPPs are based on CPI information, it can be assumed that all the price observations are important, as CPI
includes only representative items. In this case the regular unweighted CPD should be used.

9.6

Gini-Éltető-Köves-Szulc (GEKS) method

The GEKS method is the ICP TAG recommended procedure for aggregation above the basic heading level forinternational comparisons. Details of this method can be found in Diewert (2013). The data used for thispurpose are in the form of PPPs for all the basic headings that belong to a particular aggregate andexpenditure/quantity associated with the particular BH.

34

Let  pir ,eir : i = 1, 2,..., N ; and r = 1, 2,..., R represent, respectively, the PPP for basic heading i in region r andexpenditure associated with the commodity group. The implicit quantity, represented by qir , is simply given
by qir = eir pir . Then the GEKS PPP for region k relative to reference region j is computed as:

The GEKS method is the recommended procedure for compiling PPPs for all aggregates above the basic headinglevel.

9.7

Specification of the reference region for subnational PPPs

ICP PPPs are expressed relative to the U.S. dollar as a matter of convention (World Bank, 2020). Relative pricecomparisons and PPPs for a country with another reference country are not affected by the choice of thereference or numeraire country. In subnational PPP comparisons, a similar approach can be used by choosing
one of the regions or capital cities as the reference. However, it is more practical if subnational PPPs forgeographical regions (rural-urban or for provinces) are expressed with the whole country as the reference or
numeraire. This means that PPPs for all the regions are expressed such that PPP for the whole country is equal
to 1. Such a choice of numeraire, while not affecting relativities across regions, will enable easy linking of PPPs
from the ICP to sub-regional or subnational PPPs. However, this approach does not apply in the case ofcomparisons across capital cities as the collection of capital cities does not equal the whole country. In thiscase, SN-PPPs may be expressed relative to all capital cities instead of the whole country.
In view of this discussion, it is recommended that SN-PPPs within a country are expressed with the wholecountry as the numeraire.

10 A survey of studies computing subnational PPPs and spatial consumer price index
numbers
This section provides an overview of studies and experiments on the computation of subnational PPPs andspatial consumer price indexes conducted by individuals and national statistical organizations over the last two
decades. This survey is designed to provide insights into these studies, and it is by no means exhaustive. There
have been several reviews of exercises on subnational PPPs by researchers (Biggeri, Laureti and Polidoro(2017); Laureti and Rao (2018); Biggeri and Rao (2018); and Weinand and von Auer (2019)). Countries
embarking on the compilation of subnational PPPs based on the guidelines in this document may find it
instructive and useful to closely examine and follow studies surveyed in this section.
The review offered in this section is divided into three broad categories. The first set are attempts to compile
subnational PPPs with methodology comparable to that of PPPs computed within the ICP framework. The
35

second set of studies covered are the attempts made in different countries to compile spatial adjustment
factors (SAFs) which are required for the implementation of the rolling price survey approach used in theEurostat-OECD PPP Programme. The last set of studies show how subnational spatial consumer price indexes
for household consumption are compiled using CPI data and data from household expenditure surveys.

10.1 Subnational PPPs computed at national level using the ICP framework
The first and the most comprehensive experiment in this direction was carried out by the Asian Development
Bank (ADB) and by McCarthy (2010) and Dikhanov et al. (2011). Their study was aimed at the plausibility ofintegrating the ICP methodology with the Philippine CPIs by computing subnational PPPs using regional prices
and expenditure weights from the CPI. The purpose of the research study was to examine whether and to what
extent the prices collected for the CPI could be used to provide reliable estimates of price levels for a range of
products in each region (subnational area) and to verify if the results are consistent with the information
obtained from the ICP process. Experiments were conducted to compute PPPs for 17 major regions ofPhilippines. The CPD method was used at the basic heading level and GEKS formula to aggregate the detailed
PPPs to higher level aggregates for each of the 17 regions. Dikhanov et al. (2011) describe all the necessary
phases for constructing regional PPPs, underlining the main issues and solutions for overcoming them, bothconcerning data preparation and methods of index computation and aggregation. This experimental study
paved the way for further experiments in the Asia-Pacific Region. The ADB also conducted experiments for
Bangladesh and Malaysia (Capilit and Dikhanov, 2017). Preliminary results of these studies reinforce earlier
findings and confirm that it is possible to integrate the ICP with the national statistical program.

A second important pilot project was implemented by the United Nations Economic and Social Commission forWestern Asia (UN-ESCWA) for the computation of subnational PPPs for the seven emirates in the United ArabEmirates (UAE) for the year 2015 (Skaini, 2016; Skaini and Samara, 2017). The results from these studies arevaluable, because the project evaluated the feasibility of subnational PPP production, by exploring thesynergies between CPI and ICP methodologies. Because the organization and computation of the CPIs in the
UAE emirates were different, the UN-ESCWA prepared a unified product list, based on the subnational CPI and
ICP product lists. In addition, special surveys were developed or improved for categories such as construction
and housing rentals, based on the ICP methodology. The seven UAE emirates collected prices from this unified
product list, covering all the components of GDP expenditure, and the data were subsequently validated using
ICP software. The computation of subnational PPPs for 2015 was successfully concluded, providing measuresof differences in price levels of goods and services across the seven emirates. Moreover, the adoption of a
unified framework harmonizing with ICP methodology, improved the CPI infrastructure and coordination ofsubnational statistical offices.

36

10.2 Spatial adjustment factors and the Eurostat-OECD PPP Programme
The computation of the spatial adjustment factors (SAFs) is an essential element in the Eurostat-OECD PPP
Programme. SAFs are in concept like subnational PPPs.
In the Eurostat-OECD PPP Programme each country collects price data mainly from capital cities. This is one ofthe principal elements of the rolling price survey approach and is designed to reduce the burden and cost ofcollecting price data. The prices observed in the capital cities may not adequately represent price levels in theremaining areas of the countries and, therefore, may not reflect national average prices. Consequently, inorder to have an accurate measure of price levels and PPPs at the country level, Eurostat requires 13 theparticipant countries to produce SAFs once every six years.
11F

Currently, two countries produce and publish information on SAFs. The United Kingdom’s (UK) Office for
National Statistics (ONS) and the Turkish Statistical Institute (TurkStat). It is possible that NSOs in some othercountries also produce SAFs, as is the case with the Italian Statistical Institute (Istat), but do not publish theseresults. The ONS computed SAFs for the year 2016, for 167 BHs, 10 Divisions (of COICOP classification) and 5
regions, then enlarged to 12 (ONS, 2018). ONS compiled the “relative regional consumer price levels of goods
and services (RRCPLs)”, the results of its computation and detailed information on the methodology, prices
and weights data sources used are included in ONS (2018).
ONS started experiments for the computation of RRCPLs in 2001 using CPI data, supplemented with a purposedesigned regional price level survey for products and services not covered by CPI data. ONS carried out
successive experiments in 2004 and 2005 in order to improve the quality of the results obtained. ONS
subsequently published a report on UK 2010 SAFs for use in the PPP calculations (ONS, 2011). From the two
publications of ONS (2011 and 2018) it is apparent that the ONS created a small team in order to compute theSAFs; replicated the sampling methodology used by Eurostat for the European Comparison Program (ECP) bothfor the items and locations, but using sub-samples of items and locations, in order to reduce costs.
The ONS implementation in 2016 used a sub-sample of 539 PPP items spread across 168 BHs. The ONS used
CPI data for 129 items; and in 2016 engaged an external research company to conduct field data collection for
324 items spread across 21 locations in UK. For several categories of goods and services, in particular, in thegroups of health and education, motor vehicles, property rents and owner occupiers’ housing costs, special
treatment or exclusion was used focusing on representative and non-representative products. At the basic
heading level (BH), price relatives of the items included in the BH were combined with Jevons index which isan equally weighted geometric mean of price relatives. The RRCPLs at BHs were then aggregated to successive
COICOP levels by using regional expenditure weights obtained and adapted from the ONS’s Costs and Foodsurvey and applying the GEKS formula. A case study based on the ONS experience is included in Appendix A2of this guide.
The Turkish Statistical Institute (TurkStat) carried out three experiments and published “Regional Price Level
Indices” and Regional Purchasing Power Parities for the years 2008, 2012, and 2017 for 26 regions and 12 main
13

The request is in accordance with Regulation (EC) No 1445/2007 of the European Parliament and of the Council.

37

aggregates of COICOP (TurkStat, 2009, 2013 and 2018; Daskiran, 2011). The main objective was to estimate“regional price level differences” for consumer goods and services and to estimate “spatial adjustment factors”
for Turkey. TurkStat has undertaken a special study to select a basket of goods and services based on the CPI
basket items appropriate for subnational price comparisons. These have been divided into comparable itemswith strict specifications; comparable items with special cases; non-comparable items; and a specific item
group (rent). The representativity information was surveyed and based on the availability of items in eachregion. In the computation for 2017, among the 1,005 total items included in the basket, 597 items are fromthe regional purchasing power parity basket (prices were collected using special price surveys), and 408 fromthe CPI (using prices from the CPI database). Different types of surveys were conducted to collect data fordifferent categories of products: food and beverages (which was the most successful category); household
equipment and furniture; clothing and footwear products; and services. The collection of price data covered
the city centers of 81 provinces in 26 regions. Regional expenditure weights on consumption goods and services
were used to aggregate price indexes at the region level. The computation of the spatial price indices is basedon the Eurostat-OECD PPP methodology.

10.3

Subnational spatial CPIs for household consumption based on CPI data

Computation of subnational spatial price indexes for household consumption, using mainly CPI data(sometimes called subnational household consumption PPPs) have been conducted by some NSOs and many
researchers, using the national COICOP classification of products without any formal link to the structured
product descriptions (SPDs) used in the ICP specification of products.
Experimental estimates of spatial price indexes began in the early 2000’s with a formalized relationshipbetween the Bureau of Economic Analysis (BEA) and the Bureau of Labor Statistics (BLS). The estimates used
the CPI microdata collected by the BLS to calculate interarea price indexes for 38 geographic sampling areas
and about 200 item strata or basic headings (Aten 2005, 2006). The work was later expanded to include dataon rent price levels and expenditures from a new annual survey by the Census, the American Community
Survey (ACS), which enabled estimates to cover the entire U.S., at the level of 366 metropolitan areas and 50
states plus the District of Columbia (Aten and Figueroa, 2014). Since 2014, these subnational PPPs, termed
Regional Price Parities (RPPs), have become official annual statistics published by the BEA. Details of the U.S.
approach are included as a case-study in Appendix A1 of this guide.
In summary, the CPI price quotes are averaged over the year and then entered into a weighted CPD or ahedonic regression, with the resulting area coefficients, or class means, aggregated to 16 expenditure classes
using the Geary multilateral index. The subsequent stage merged the results with the more geographicallydetailed rent data from the ACS, expanding the scope of the RPPs to states and metropolitan areas. In 2020,
BEA began revising its National Income and Product Accounts to include estimates of housing expenditures forboth tenant and owner-occupied homes using the ACS. These revisions include an imputation for owneroccupied homes using a new methodology called an owner-premium approach (Aten and Heston, 2020, Aten,
Figueroa and Rassier, 2021). Final all-item RPPs incorporate both expenditures and price levels using the new
approach.

38

The Australian Bureau of Statistics (ABS) compiled and disseminated experimental indexes on the cost of livingin the eight regional capital cities of Australia using price data collected for CPIs. Spatial price indexes werecomputed using the GEKS formula (Waschka et al, 2003). ABS continued to compute and disseminate data on
the average retail prices of selected items in the eight capital cities, advising a careful interpretation of theresults. However, in 2011, the ABS discontinued the publication. Separately from the efforts of the ABS, in
1998, the Government of Western Australia Department of Primary Industries and Development started tocompute a Regional Price Index (RPI) with a common basket with Perth as the basis for comparison with eachregional location. The RPIs are indicative of the differences of cost of living at different locations. The
computation of the RPI was repeated in the following years seven times (Government of Western Australia,
2017). The 2017 RPI comprised a basket of more than 600 goods and services, which were priced in 27 centersaround Western Australia. The RPI is formally used in setting the district allowance.
At the beginning of 2000s, the Italian National Statistical Institute (Istat) conducted experiments on the use of
CPI data to calculate regional consumer price level indexes and disseminated results on two occasions: in 2008
with reference to price data from 2006; and in 2010 with reference to 2009 data. Istat used the sameprocedures that were used in the ICP following the principle of strict comparability of products which required
a lot of work because of the vague definitions of the products in CPI data. Moreover, ad-hoc surveys were
designed and carried out for the product groups “Clothing and Footwear” and “Furniture” - products groups
for which the CPI data are not adequate. The complete computations involved 1,865 item products and thecollection of 717,200 elementary quotations (prices). The subnational SPIs were compiled for all the COICOP
expenditure divisions (and published for eight divisions) and 20 Italian regional capital cities. The GEKS
approach was used for the computation of PPPs, with the exception of rents for which the spatial comparisonswere carried out using CPD model and Household Budget Survey (HBS) data which includes some detailed
information about the characteristics of the dwellings. Further research studies have been implemented by
Istat in cooperation with the University of Florence and the University of Tuscia. These research studies had
the following objectives: (i) to establish a data warehouse collecting data from various available sources of data
(to avoid the need to conduct ad hoc surveys that are costly); (ii) to verify if and how much the methods ofsubnational PPPs computation are affected by the type of data; and (iii) to examine the possibility of using
electronic devices, scanner and web data (scraped by automatic procedures and robots) for the prices of someproducts purchased by the households. These experiments have been completed and Istat is building up a
database suitable for constructing subnational consumer SPIs on a regular base (for detailed information see:
Biggeri, Laureti and Polidoro, 2017; Laureti and Polidoro, 2017; Laureti and Rao, 2018; Biggeri and Laureti,2018). A case study based on the Istat experience is included in Appendix A3 of this guide.
The General Statistical Office (GSO) of Viet Nam started a pilot research project in 2010 to compute subnational
PPPs in terms of SCOLI (spatial cost of living) index, supported by the World Bank, based on CPI data available.
The GSO produced and published SCOLI indices for the period 2010 to 2017 (GSO, 2019). The SCOLI indiceshave been compiled for six geographical regions and 63 provinces/cities. These comparisons were made,respectively, with the average price of the Red River Delta and Hanoi City, covering 11 groups of consumer
goods by purpose (COICOP) with 572 goods and services. Moreover, GSO (2019) describes the methodology
used, outlines the sources of price and weights data, and details the survey framework and on the identification39

of products overlapped among provinces/cities. This work in Viet Nam demonstrates that prices of commodity
items from CPI surveys are adequate to compute the SCOLI indices and that the use of CPI data can be used inassessing performance of poverty alleviation programs and in the computation of a human development index
at a regional level.
In France, the National Institute of Statistics and Economic Studies (INSEE) has conducted special prices surveys
in 1985, 1992, 2010 and 2015 and published analyses based on these surveys. INSEE (2016) published resultsfrom the 2015 survey comparing prices between the Paris agglomeration and the provinces and between thefive overseas départements (DOMs). In 2015 (INSEE, 2016), the collection of prices, for the same basket ofabout 400 groups of products and services, has been realized in representative agglomerations and all typesof outlets in each territory (more than 90,000 prices in Metropolitan France, and from 4,000 to 7,000 for each
DOM). The comparison covered products that are consumed significantly in different territories and themethods applied are the same as those used in the Eurostat-OECD PPP Programme. The main findings
published in INSEE (2016) are that: in 2015, prices in the overseas DOM are higher than in Metropolitan France;
and that prices in the Paris region are 9 percent higher than in the provinces. INSEE has decided to conduct
these studies every five years from now on (INSEE, 2019).
As far as the estimation of subnational consumer spatial price index numbers using CPI data and/or COICOP
classification, many researchers have compiled subnational price comparisons in various countries, such as
Brazil, China, the Czech Republic, Poland and Germany, publishing their results as research papers in academic
journals (Jansky and Kolcunova, 2017; Biggeri, Ferrari and Zhao, 2017; Menggen, Yan and Rao, 2018; Rokicki
and Hewings, 2019; Weinand and von Auer, 2019).
A recent estimation of Regional Price levels (RPIs) for the Czech Republic was conducted by Kocourek, Simanov,and Smida (2016). Their study covered 35 districts (Local Administrative Unit-1, LAU 1, formerly Nomenclatureof Territorial Units for Statistics-4, NUTS-4) and the Capital of Praha. Price comparisons were made for 12 CZCOICOP Headings using raw data from price surveys carried out by the Czech Statistical Office. Their study
analyzed 1,717,102 surveyed prices, from years 2011-2013, and split the prices representative for 4,673
varieties. For the computations of the RPIs at regional level (NUTS-2 and NUTS-3), the authors used the Jevonsindex obtaining unweighted price parities. Then, geometric Laspeyeres and Paasche indexes have been
computed and synthesized by the Tornqvist price index to obtain the RPIs. The choice of this superlative index
is justified by the fact that it can be decomposed so that the share of each price representative in the total
price level can be easily determined. However, the Tornqvist index is not a transitive index. Because, at the
district level, price data were uneven and the expenditure weights were not available, the authors estimate
these data at the district level using auxiliary variables and SAE (Small Area Estimation) methods.
Regional Price Deflators in Poland were recently estimated by Rokicki and Hewings (2019). The computationof the so-called PPP deflators has been done for the years 2000-2012, for 11 COICOP categories and for the 16
regions (NUTS-3) and for the 66 NUTS-3. The authors used price data obtained by the Polish Central Statistical
Office for over 300 consumer goods and services collected from up to 26 locations in each of the 16 NUTS-2
regions. All those 300 products were supposed to be representative and the structure of spending within themain expenditure categories was assumed to be homogeneous within the 16 regions. The estimation of the40

regional PPP deflators was based on the Eurostat-OECD methodology and the GEKS aggregation method. The
estimated regional price deflators at the NUTS-2 level and the regressors identified as significant determinantsof RPDs at the NUTS-2 level were used in a multiple imputation approach leading to estimates of regional pricelevels for the 66 NUTS-3 regions.
Finally, turning to Germany, the most complete and interesting research on this topic appeared as a Discussion
Paper of the Deutsche Bundesbank (Weinand and von Auer, 2019). The authors use a highly disaggregated andreliable micro price dataset that has been collected, through a kind of stratified sample, for the computationof the CPIs by the Statistical Offices of the Länder and the Federal Statistical Office of Germany. The German
territory is subdivided into 402 regions (295 counties and 107 cities) and the dataset from 2016 included
366,401 consumer prices for goods and services and 15,582 for rents classified into 650 categories of the
COICOP classification, denoted as basic headings (645 for goods and services and 5 for rents). The database
contains not only the prices, but also their precise specifications and the outlet types where the products arepurchased. The prices assigned to the same group cannot be considered as directly comparable, therefore theauthors introduced an alternative automated compilation process that identified pairs of only perfectly
matching products, rejecting all products that have been observed in only one of the regions. This proceduredefines for each individual product so determined (identical for characteristics and outlet type) its own vectorof regional prices. To compute the Regional Price indexes (RPIs) for the 12 divisions of COICOP classification atregional level, the authors applied a methodology that they called a multi-stage version of the CPD method.
First, they computed unweighted CPD regression of prices to the same basic headings and outlet type. Then,
having the expenditure weights for most of the basic headings and the eight types of outlets, the authors
computed, successively, weighted CPD regression of prices relating to the same BH; weighted CPD regressions
for goods, services and rents; and aggregated them further leading to RPIs. A separate hedonic regression ofregional rent levels has been computed, because in Germany price levels are largely driven by the cost of
housing. Finally, interesting experiments have been conducted to verify whether and how much simplifiedcompilation procedures affect the results.
In summary, there have been a number of studies conducted by national statistical institutes/organizations aswell as individual researchers with the objective of estimating credible subnational PPPs. These studies differ
in the type of price data used for PPP compilation as well as in the aggregation methodology used. It suffices
to note that the existing studies are a source of confidence and optimism for the compilation of subnational
PPPs in countries around the world.

11 Conclusion
Subnational PPPs and PPPs from the ICP are conceptually similar in that both are measures of spatial pricedifferences. While PPPs and real income comparisons from ICP have been widely recognized and used byresearchers, policy makers and international organizations for over three decades, it is only recently the roleand significance of subnational PPPs is coming into prominence. Methods for the compilation of PPPs from the
ICP have been well established and are available through the World Bank (2013) publication Measuring theReal Size of the World Economy: The Framework, Methodology, and Results from the International Comparison
41

Program. Despite the experience gained through the implementation of ICP over the last 50 years, compilationof subnational PPPs raises its own challenges in terms of the scope and establishment of the survey frameworknecessary for the compilation of subnational PPPs. The main objective of this guide is to highlight some of thepractical issues that arise in setting up a program for subnational PPPs including establishing the use andapplications of subnational PPPs. Where possible, clear recommendations have been made throughout theguide, but users are advised to use this guide in conjunction with the existing material and guidelines for thecompilation of PPPs in the ICP. Four case studies summarizing their experiences with compilation ofsubnational PPPs are included as appendices to this guide.

42

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Power Parities and Real Expenditures — Results and Methodology”, Asian Development Bank, Manila,
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Bureau of Labor Statistics, Washington, DC, September.
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Analysis, June.
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Economic Analysis, forthcoming.
Biggeri L., Ferrari G. and Zhao, Y. (2017), Estimating cross province and municipal cities price level differencesin China: Some experiments and results, Social Indicators Research, 131 (1), pp. 83-121
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on Italian Data Using Country Product Dummy Models. Social Indicators Research, 131(1), 93-121.
Biggeri L. and Rao D.S.P. (2018), Background Paper on subnational PPPs, paper presented at the meeting of ICP
Task Force on the Country Operational Guidelines and Procedure, held in March 2018.
Biggeri L. and Laureti T. (2018), Publications, experiments and projects on the computation of spatial price level
differences, in Italy, paper presented at the 3rd meeting of ICP Task Force on the Country Operational
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Blades, Derek (2013), “Health and Education”, Chapter 11 in Measuring the Real Size of the World Economy,
World Bank, Washington D.C.
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Proceedings of the 60th World Statistics Congress of the International Statistical Institute, 2017.
Daskiran M. (2011), Regional PPPs for Consumer Goods and Services: Turkey Experience, paper presented at
PPP Workshop in Paris, 23-24 May 2011
Chen, Shaohua and Martin Ravallion (2010), “The Developing World is Poorer than We Thought, But No Less
Successful in the Fight Against Poverty”, Quarterly Journal of Economics, Volume 125, Issue 4, November 2010,
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Deaton, Angus and Olivier Dupriez (2011), “Purchasing Power Parity Exchange Rates for the Global Poor”,
American Economic Journal (Applied Economics), Vol. 3, No.2, pp. 137-66.
Diewert, W.E. (2010), “New Methodological Developments for the International Comparison Program”,
International Association for Research in Income and Wealth.
Diewert, W.E. (2013), “Methods of Aggregation above the Basic Heading Level within Regions”, Chapter 5 inMeasuring the Real Size of the World Economy, World Bank, Washington D.C.
Dijkstra, Lewis, Ellen Hamilton, Somik Lall and Sameh Wahba (2020), “How do we define cities, towns, and rural
areas?” Blob on World Bank website, 10th March, 2020.
Dikhanov Y., Palanyandy C., and Capilit E. (2011) subnational purchasing power parities toward integration ofinternational comparison program and the consumer price index: The case of Philippines. ADB Economics
Working Paper Series, No. 290, Asian Development Bank.
Eurostat (2017), Spatial adjustment factors in PPP surveys, Paper presented at the Meeting of the PPP working
Group held in Luxembourg on 14-15 November 2017, (https://circabc.europa.eu/w/browse/eaf0fc12-3e9a43d7-b1ba-b5821fa7a72b)
Eurostat (2012), Eurostat-OECD Methodological Manual on Purchasing Power Parities, European Union/OECD,
Luxembourg and Paris.
Eurostat (2017), Spatial adjustment factors in PPP surveys, Paper presented at the Meeting of the PPP working
Group held in Luxembourg on 14-15 November 2017, (https://circabc.europa.eu/w/browse/eaf0fc12-3e9a43d7-b1ba-b5821fa7a72b).
Francisco H. G. Ferreira & Shaohua Chen & Andrew Dabalen & Yuri Dikhanov & Nada Hamadeh & Dean Jolliffe& Ambar Narayan & Espen Beer Prydz & Ana Revenga & Prem Sangraula & Umar Serajuddin & Nobuo Yosh,(2016). "A global count of the extreme poor in 2012: data issues, methodology and initial results," The Journalof Economic Inequality, Springer; Society for the Study of Economic Inequality, vol. 14(2), pages 141-172, June.
Government of Western Australia (2017), Western Australia Regional Price Index, Department of Primary
Industries and Regional Development, data.wa.gov.au
GSO, General Statistical Office of VietNam (2019), Subnational PPPs in Vietnam, paper presented at the 4th
meeting of ICP Task Force on the Country Operational Guidelines and Procedure, May 2019
Insee (2016), Price difference in France, https://www.insee.fr/en/staistiques/Fihier
Insee (2019), Enquete de comparaison spatiale des niveaux de prix à la consommation entre territoires
francais, www.insee
Jansky, P. and Kolcunova, D. (2017), Regional differences in price levels across the European Union and theirimplications for its regional policy: The Annals of Regional Science, Springer; Western Regional Science
Association, vol. 58(3), pages 641-660, May.

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International Comparison Program (2021), A Guide to the Integration of Consumer Price Index (CPI) andInternational Comparison Program (ICP) Production Activities, World Bank, Washington D.C.
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Kocourek A., Simanova J. and Smida J. (2016), Estimation of Regional Price Levels in the Districts of the Czech
Republic, Statistika: Statistics and Economy Journal, 96(4), pp.56-70
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Parities in Italy, Proceedings of 61st ISI World Statistics Congress, Marrakesh.
Laureti T. and Rao D.S.P. (2018), Measuring Spatial Price Level Differences within a Country: Current Status andFuture Developments, Estudos de Economia Aplicada, vol. 36-1, 119-14.
McCarthy P. (2010), Asia and Pacific Region: subnational Purchasing Power Parities. Case study for thePhilippines, 2011 ICP Round, 2nd Technical Advisory Group, Document 10.02.
Menggen C., W. Yan and D.S.P Rao (2018), Measuring the Spatial Price Differences in China with RPPs Method,
ppt of the paper presented at the Conference on International Comparisons – Fifty Years of International
Comparison Program: Achievements and Moving Forward, Beijing , Normal University, 29-30 October.
ONS (2011), UK 2010 Spatial Adjustment Factors for use in the PPP Calculation, Final Report to Eurostat,authors Johnston S., Bailey S. and Merrington L., Office for National Statistics, London.
ONS (2018), Relative regional consumer price levels of goods and services, UK:2016, 1 March 2018.
Rokicki, B. and Hewings, G. J. D. (2019), Regional Price Deflators in Poland: Evidence from NUTS-2 and NUTS-3
Regions, Spatial Economic Analysis, 14 (1), 88-105.
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the Real Size of the World Economy, World Bank, Washington D.C.
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Skaini M. (2016), Synergies between CPIs and PPPs and Integration of Survey Activities, Paper presented at the
ILO/UNECE meeting of CPI experts, Geneva, 2-4 May 2016.
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TurkStat (2009), Regional Purchasing Power Parity, Regional Price Level Indices, 2008, Press Release, April 2,
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International Comparison Program”, World Bank, Washington, DC

46

13 Appendices: Country Case Studies

A.

United States ............................................................................................................................................. 48

A.1

Introduction and Scope ....................................................................................................................... 48

A.2

Methodology and Source Data ........................................................................................................... 48

A.3

Empirical Results ................................................................................................................................. 51

A.4

Summary ............................................................................................................................................. 51

A.5

References........................................................................................................................................... 52

B.

United Kingdom ......................................................................................................................................... 59

B.1

Data collection .................................................................................................................................... 59

B.2

Validation and Data cleaning .............................................................................................................. 77

B.3

Synergies between CPI and PPPs ........................................................................................................ 81

B.4

Calculation of spatial adjustment factors and relative regional comparative price levels ................. 84

B.5

Importance and uses of SAFs and regional price levels ...................................................................... 88

C.

Italy ............................................................................................................................................................ 93

C.1

Introduction and Scope ....................................................................................................................... 93

C.2

Methodological approaches and results ............................................................................................. 95

D.

Vietnam ................................................................................................................................................... 114

D.1

Introduction ...................................................................................................................................... 114

D.2

Scope and uses of subnational PPPs ................................................................................................. 114

D.3

Methodology used ............................................................................................................................ 115

D.4

Price and weights data ...................................................................................................................... 118

D.5

Empirical Results ............................................................................................................................... 120

D.6

Conclusions ....................................................................................................................................... 123

47

A. United States
Introduction and Scope
The subnational PPPs in the United States are called Regional Price Parities (RPPs) and are estimated by
the Regional Prices Branch of the Bureau of Economic Analysis (BEA). Annual results are published in theSurvey of Current Business. 14 Experimental estimates began in 2003 (Aten 2005, 2006). The current
methodology
isavailableathttps://www.bea.gov/system/files/methodologies/RPP2020methodology_1.pdf .
12F

The RPPs are published annually at the state level (50 states plus the District of Columbia) and at theMetropolitan Statistical Area level (384 MSAs), as well as for the metropolitan and non-metropolitan
portions of states. They reflect household consumption only. The RPPs are estimated independently of
the national price averages provided by the Bureau of Labor Statistics for Eurostat-OECD PPP
computations and are thus not currently part of the ICP.

Methodology and Source Data
The RPP methodology is based on those developed in Kravis, Heston & Summers (1982) for the Penn
World Table (PWT) and from the ICP. It includes the CPD approach below the basic heading, and Geary
multilateral aggregations above the basic heading.2

Major data sources
The source data are the Consumer Price Index (CPI) microdata collected by the Bureau of Labor Statistics(BLS) which are not publicly available, and housing data from the Census that are publicly available tousers. 15 In addition, bi-annual expenditure data from the Consumer Expenditure survey are converted toannual cost weights by the CPI branch and are the relative weights used in aggregating prices at the itemstrata level. These item-strata correspond roughly to basic headings in the ICP.
13F

The CPI microdata consist of approximately one million product quotes per year, for about 200 basic
headings. Tenant rents, imputed owner-occupied rents and total expenditures on rents are estimatedfrom the Census Bureau’s housing survey, called the American Community Survey (ACS). The ACS
microdata has over 2 million housing observations annually, weighted to represent about 44 million
tenant-occupied and 76 million owner-occupied homes in the U.S. Lastly, the current dollar expenditure
14

Last year’s results, for reference year 2019, can be found at: https://www.bea.gov/data/pricesinflation/regional-price-parities-state-and-metro-area. Work has been done comparing Geary results toother multilateral spatial indexes (Aten & Reinsdorf, 2010).

15

The CPI microdata are available to us through an interagency agreement, between the Bureau of Labor
Statistics (BLS) and BEA. The housing are annual surveys called PUMS (Public Use Microdata Sample)
downloadable from the Census Bureau website

48

weights above the basic heading level are the Personal Consumption Expenditure (PCE) series publishedby BEA.

Survey Framework
The CPI uses a probability sampling framework to price products that covers all urban areas in the U.S.,
representing about 93% of the population. 16 The subnational geographies are 23 metropolitan areas andnine Census divisions. Indexes are available for major groups of consumer expenditures (food andbeverages, housing, apparel, transportation, medical care, recreation, education and communications,and other goods and services), for items within each group, and for special categories, such as services(https://www.bls.gov/cpi/overview.htm).
14F

The publicly available ACS housing data is a 1% sample weighted to represent all housing units in the U.S.
with a state level and a sub-state level identifier called a PUMA (Public Use Microdata Areas), comprisedof about 2870 areas with a population greater than 100,000.

Estimating relative price levels using CPI data
The products priced below the basic heading level in the CPI are not identical, although they have sample
weights associated with each observation. This is one of the main issues in using the U.S. CPI: the survey
is not designed for place-to-place surveys and requires additional work to obtain matching productspecifications. To make the products as comparable as possible, we estimate a hedonic regression, below
the basic heading, that is an extension of a CPD regression: the log of the price is regressed against thegeographic areas and with class variables that include the product’s characteristics, such as type, size,
packaging and brand. 17
15F

The estimation of detailed hedonic regressions is feasible because the price quotes are collected in a waythat allows the product’s specification to be easily transformed into class variables. For example,
observations on Milk may have two checklist variables: A for type and B for size, where A1 is Whole Milk,A2 is Skim Milk, B1 is a quart container, B2 is a gallon container. Thus, even if the same exact product is
not priced in all areas, it is possible to estimate the relative price of milk holding constant the type andcontainer size. 18 The disadvantage is that hedonic regressions can be time-consuming and labor intensive,
requiring analyst judgment to determine which variables should be part of the regression. One way tomaintain consistency is to create decision protocols that favor simplicity, and to document each regression
16F

16

See also https://www.bls.gov/cpi/questions-and-answers.htm,

17

We estimate detailed hedonic regressions for about 80 individual item-strata (basic headings), thosecorresponding to roughly 85% of expenditure weights. In practice, the regressions are at a more detailed
level, called the Entry Level Item Cluster or ELIC level. For the remaining strata, we run simple weighted
CPDs at either the Item Strata or the ELIC level.

18

Of course, there must be some overlap of characteristics across the areas, or the regressions will not be
estimable.

49specification so that prior years can be referenced. We also run a comprehensive outlier checking
procedure based on the EU’s Quaranta tables. Details of these stages of estimation, including the
Quaranta analysis, are described in Aten, Figueroa and Martin (2011). 19
17F

The areas in the CPI are not directly comparable to those in the Census ACS, nor to administrative areasused by the BEA. This means that the CPI price levels must first be allocated to a smaller geographic unit,
such as a county, then re-aggregated to states and metropolitan areas. We assume that all counties withina sampled area-size CPI combination have the same price level. a process described in detail in Figueroa,
Aten and Martin (2014). Expenditure weights based on BEA’s Personal Consumption Expenditure series
are also allocated to counties from published state level estimates.

Estimating Housing price levels and expenditures using the ACS
Current-dollar rental values for tenant units and imputed owner units will follow the new housing
methodology described in Rassier, Aten & Figueroa (2021) beginning with publication of the 2020 RPPs in
December 2021. Tenant rents are sums of actual observations while imputed rental values for ownerunits include two components: a rental equivalence and an owner premium component 20.
18F

The background paper for the owner premium is described in Aten & Heston (2020). The premium is theratio of the owners’ reported values of their homes to the median of homes with similar characteristics,
as long as the ratio is one or higher. In other words, it is the maxima of the rental equivalence and thepremium. Because the ACS consists of actual housing unit observations, both relative price levels andexpenditures can be estimated simultaneously.

19

The process is modeled after the Quaranta method used by the Organisation for Economic Co-operations andDevelopment and Eurostat (OECD 2012), and the International Comparison Program of the World Bank
(World Bank 2015).

20

Rental equivalence is calculated from stratified averages of reported tenant rents applied to owner-occupied

units following Aten (2017). For each year and PUMA (Public Use Microdata Area), stratified rental equivalence
for owner units is imputed by regressing tenant rents, from which utilities have been excluded, on characteristics
of tenant units reported in the ACS. A PUMA is a Census Bureau statistical geographic area defined for the
dissemination of Public Use Microdata Sample (PUMS) data, including the ACS. PUMAs are built on census tracts
and counties, contain at least 100,000 people, cover the entirety of the United States, and do not span morethan one state. The characteristics include structure type, number of rooms, number of bedrooms, and age ofstructure. ACS data on the same characteristics of owner units are then applied to the parameter estimates fromthe tenant regressions to calculate the imputed owner rental value. These imputations are done at the unit level.

50

Final aggregation
Once the CPI-derived price levels for consumption goods and services at the basic heading level have been
allocated to counties, a five-year rolling average is obtained using a weighted Country-Product-Dummy(CPD-W) procedure, yielding relative prices for states and metropolitan areas for fifteen major
expenditure groups of the CPI (Food and Beverages, Apparel, Transportation, for example). These groups
are then aggregated with the annual housing price levels and expenditures from the PCE to a single overall
RPP using the Geary method.

Empirical Results
Table 1 shows subnational RPPs for 3 major private consumption expenditure groups: rents, goods andservices, and the overall RPP for each state in 2019. One main result is the much wider range for rents(from 0.600 in Mississippi to 1.536 in California) than for goods, and to a slightly lesser extent, to services.
These are also published for 384 Metropolitan Areas and for the metropolitan and nonmetropolitan
portions of states. 21 All tables are indexed so that the U.S. overall RPP is equal to one.
19F

Table 2 shows the overall RPPs for state metro and nonmetropolitan portions for 2019. Subnational dataon RPPs are available from 2008 to 2019. 22 The range is larger for the metropolitan portions in 2019 (from0.864 in Arkansas to 1.221 in Hawaii) than for nonmetropolitan areas (0.774 Arizona to 1.091 in
Massachusetts). As expected, the RPPs for the metropolitan portions of states is much higher than for thenonmetropolitan portions (1.01 versus 0.86 respectively). This is driven primarily by rent price level
differences.
20F

Summary
The U.S. CPI microdata lends itself to subnational comparisons because of the way prices are collected
and recorded, through a set of checklists that describe the product characteristics. This facilitates the useof hedonic regressions to control for differences in the outlet, type, packaging, size and other productcharacteristics. It is not an ideal method as some products and characteristics are thinly dispersed acrossareas. Ideally one would have dedicated surveys with products specified more narrowly, together withcorresponding surveys on the relative expenditures on these products. However, attempts at securing
funding for such surveys have not succeeded in the past. We hope that in the future, other data sources
or external surveys will augment and improve our current RPPs. 23
21F

21

The full tables are available on the web at www.bea.gov.

22

The 2020 RPPs will be released on December 14th, 2021.
Research is underway using J.D. Power marketing data on new automobile and truck prices, for example, and
BEA is also searching for a new comprehensive medical prices survey. A revision of the medical services
group in the CPI unfortunately resulted in price quotes that no longer lend themselves to robust hedonic
specifications.

23

51

The second major data source in the RPPs is the annual housing survey of the Census Bureau (ACS). Thisis a nationwide survey that has a wealth of information on rents and housing characteristics, and its
sample size and coverage is comprehensive and well-suited to subnational comparisons. This has enabled
BEA to update its current dollar tenant- and owner-occupied housing output estimates at the national andregional levels, as well as to improve its subnational RPPs for rents.

References
Aten, Bettina (2005), ‘Report on Interarea Price Levels, 2003’, working paper 2005–11, Bureau of
Economic Analysis, May.
Aten, Bettina (2006), ‘Interarea Price Levels: an experimental methodology’, Monthly Labor Review, Vol.
129, No.9, Bureau of Labor Statistics, Washington, DC, September.
Aten, Bettina (2008), ‘Estimates of State and Metropolitan Price Parities for Consumption Goods andservices in the United States, 2005’, Bureau of Economic Analysis, October.
Aten, Bettina and Marshall Reinsdorf (2010), ‘Comparing the Consistency of Price Parities for Regions of
the U.S. in an Economic Approach Framework.’ 31st General Conference of the International Association
for Research in Income and Wealth, August.
Aten, Bettina, Eric Figueroa and Troy Martin (2011), ‘Notes on Estimating the Multi-Year Regional Price
Parities by 16 Expenditure Categories: 2005-2009’, Bureau of Economic Analysis, April.
Figueroa, Eric, Bettina Aten and Troy Martin (2014), ‘Expenditure Weights in the Regional Price Parities’,
Bureau of Economic Analysis, August.
Aten, Bettina H. (2017), ‘Regional Price Parities and Real Regional Income for the United States’, Social
Indicators Research, March 2017, Volume 131, Issue 1, pp 123-143.
Aten, Bettina H. (2018), “Valuing Owner-Occupied Housing: an empirical exercise using the American
Community Survey (ACS) Housing files”, Bureau of Economic Analysis, May.
Aten, Bettina H. and Alan Heston (2020), “The Owner-Premium Adjustment in Housing Imputations”,
Bureau of Economic Analysis, June.
Aten, Bettina H., Dylan Rassier and Eric Figueroa (2021). “Improved Measures of Housing Services for the
U.S. Accounts”, Bureau of Economic Analysis, forthcoming.
BLS Handbook of Methods (2011), Bureau of Labor Statistics.
http://www.bls.gov/opub/hom/homtoc.htm
Kravis, Irving B., Alan Heston and Robert Summers 1982), “World Product and Income, International
comparisons of real gross product”, John Hopkins University Press.

52

Table 1. State RPPs for 2019 for Rents, Goods, Services and Overall
State

Rent

Goods

Services

Overall

United States

1.014

0.992

1.000

1.000

1

Alabama

0.619

0.959

0.903

0.858

2

Alaska

1.223

1.023

0.991

1.051

3

Arizona

0.941

0.948

0.997

0.963

4

Arkansas

0.609

0.947

0.910

0.847

5

California

1.536

1.049

1.083

1.164

6

Colorado

1.247

0.971

0.960

1.019

7

Connecticut

1.076

1.024

1.062

1.050

8

Delaware

0.950

0.983

1.036

0.994

9

District of Columbia

1.464

1.052

1.065

1.152

10

Florida

1.094

0.978

0.990

1.010

11

Georgia

0.826

0.968

0.967

0.932

12

Hawaii

1.526

1.118

1.050

1.193

13

Idaho

0.806

0.962

0.958

0.922

14

Illinois

0.947

0.990

0.974

0.974

15

Indiana

0.728

0.961

0.918

0.887

16

Iowa

0.730

0.949

0.916

0.890

17

Kansas

0.727

0.951

0.931

0.892

18

Kentucky

0.677

0.948

0.906

0.874

19

Louisiana

0.721

0.958

0.910

0.879

20

Maine

0.917

0.994

1.036

0.993

21

Maryland

1.193

1.024

1.048

1.077

22

Massachusetts

1.243

1.029

1.092

1.104

23

Michigan

0.802

0.969

0.947

0.923

24

Minnesota

0.961

1.017

0.953

0.980

53

25

Mississippi

0.600

0.944

0.903

0.844

26

Missouri

0.724

0.955

0.917

0.887

State

Rent

Goods

Services

Overall

United States

1.014

0.992

1.000

1.000

27

Montana

0.853

0.970

0.944

0.935

28

Nebraska

0.756

0.949

0.916

0.895

29

Nevada

1.018

0.939

0.985

0.974

30

New Hampshire

1.139

1.014

1.071

1.065

31

New Jersey

1.307

1.036

1.176

1.160

32

New Mexico

0.781

0.948

0.974

0.911

33

New York

1.300

1.082

1.152

1.163

34

North Carolina

0.788

0.958

0.949

0.917

35

North Dakota

0.752

0.949

0.916

0.893

36

Ohio

0.717

0.958

0.915

0.884

37

Oklahoma

0.686

0.952

0.911

0.872

38

Oregon

1.097

1.018

0.982

1.022

39

Pennsylvania

0.868

0.997

1.003

0.970

40

Rhode Island

1.012

0.994

1.035

1.013

41

South Carolina

0.782

0.960

0.949

0.915

42

South Dakota

0.690

0.948

0.915

0.878

43

Tennessee

0.768

0.959

0.903

0.897

44

Texas

0.949

0.967

0.974

0.965

45

Utah

0.982

0.945

0.978

0.965

46

Vermont

1.098

0.993

1.034

1.031

47

Virginia

1.074

0.989

1.001

1.013

48

Washington

1.229

1.058

1.030

1.084

49

West Virginia

0.609

0.946

0.960

0.871

54

50

Wisconsin

0.837

0.962

0.923

0.919

51

Wyoming

0.824

0.967

0.948

0.928

maximum

1.536

1.118

1.176

1.193

minimum

0.600

0.939

0.903

0.844

Source: Regional Prices Branch, Bureau of Economic Analysis

55

Table 2. Metro and Nonmetropolitan portions of States 2019
Metropolitan

2019

Nonmetropolitan

2019

United States

1.019

United States

0.869

1

Alabama

0.869

1Alabama

0.803

2

Alaska1.056

2

Alaska1.030

3Arizona

0.972

3

Arizona

0.774

4Arkansas

0.864

4

Arkansas

0.801

5California

1.169

5

California

1.004

6

Colorado

1.027

6

Colorado

0.960

7Connecticut

1.053

7

Connecticut

1.040

8

Delaware

0.995

-

-

-

9

District of Columbia

1.157

-

-

-

10 Florida

1.015

8

Florida0.913

11 Georgia

0.952

9

Georgia

0.835

12 Hawaii

1.221

10

Hawaii

1.056

13 Idaho

0.931

11

Idaho

0.886

14 Illinois

0.991

12

Illinois

0.832

15 Indiana

0.897

13

Indiana

0.837

16 Iowa

0.904

14

Iowa

0.864

17 Kansas

0.909

15

Kansas0.851

18 Kentucky

0.895

16

Kentucky

0.827

19 Louisiana

0.893

17

Louisiana

0.797

20 Maine

1.010

18

Maine

0.955

21 Maryland

1.087

19

Maryland

0.885

22 Massachusetts

1.107

20

Massachusetts

1.091

23 Michigan

0.932

21

Michigan

0.862

56

24 Minnesota

1.004

22

Minnesota

0.884

25 Mississippi

0.877

23

Mississippi

0.800

Metropolitan

2019

Nonmetropolitan

2019

1.019

United States

0.869

United States
26 Missouri

0.899

24

Missouri

0.831

27 Montana

0.948

25

Montana

0.919

28 Nebraska

0.912

26

Nebraska

0.860

29 Nevada

0.979

27

Nevada0.934

30 New Hampshire

1.087

28

New Hampshire 1.022

31 New Jersey

1.164

-

-

-

32 New Mexico

0.924

29

New Mexico

0.876

33 New York

1.184

30

New York

0.926

34 North Carolina

0.927

31

North Carolina

0.849

35 North Dakota

0.897

32

North Dakota

0.883

36 Ohio

0.891

33

Ohio

0.844

37 Oklahoma

0.888

34

Oklahoma

0.828

38 Oregon

1.032

35

Oregon0.954

39 Pennsylvania

0.979

36

Pennsylvania

0.900

40 Rhode Island

1.015

-

-

-

41 South Carolina

0.925

37

South Carolina

0.828

42 South Dakota

0.899

38

South Dakota

0.852

43 Tennessee

0.908

39

Tennessee

0.833

44 Texas

0.978

40

Texas

0.857

45 Utah

0.972

41

Utah

0.901

46 Vermont

1.060

42

Vermont

1.004

47 Virginia

1.036

43

Virginia

0.856

57

48 Washington

1.096

44

Washington

0.970

49 West Virginia

0.877

45

West Virginia

0.847

50 Wisconsin

0.930

46

Wisconsin

0.870

51 Wyoming

0.916

47

Wyoming

0.928

maximum

1.221

maximum

1.091

minimum

0.864

minimum

0.774

Source: Regional Prices Branch, Bureau of Economic Analysis

58

B. United Kingdom
The main aim of the note is to provide practical guidance for countries wishing to undertake datacollection for the calculation of regional purchasing power parities (PPPs) and spatial adjustment factors(SAFs). It aims to cover all levels of experience and/or knowledge from very limited experience (or indeednone) to countries that have some level of understanding on how to undertake an effective and efficient
successful data collection process. The main aim is to concentrate primarily on data collection for regional
PPPs and SAFS although it will touch lightly on data collection for the Consumer Price Index (CPIs) as there
are close synergies between PPPs and CPIs.
Although the note will primarily concentrate on the data collection process it will also present practicalguidance on how to clean the data file and discuss the stages of the validation process and how toundertake them. The paper will also briefly present synergies between the CPI and PPPs and integrationof the two although this will be covered in more detail in the integration of CPI-ICP component of the Task
force.
The final section will look at future developments, specifically in the UK, that will be undertaken as partof a wider initiative within the Office of National Statistics (ONS) to introduce new ways of capturing datavia, for example, web scraping and scanner data, and explore how these new data sources could be used
to strengthen price data used in SAFs and PPPs in general.

Data collection
This section will aim to discuss the theory, sources and methods and practicalities of the actual datacollection for calculating average prices for use in SAFS and Regional indices. The aim of this section is toprovide practical guidance for regions/countries who want to conduct successful data collection for use
in PPPs generally, specifically SAFs and regional price levels. Areas covered will include:
–

–

–
–

Sampling of items (covering how many items to include in the sub-sample, which items to include in
sub-sample, treatment of representative and non-representative items, and use of proxy items in theabsence of not being able to collect certain items).
Sampling of locations/regions (covering method used to sample locations and how many locations tosample to ensure adequate coverage, to ensure each country that makes up the UK was represented
accordingly).
Pre-enumeration prior to the survey (practical guidance of how to conduct pre-enumeration and its
importance in the whole process, including item descriptions).
Method of data collection (to cover areas such as paper collection, handheld devises (as used by UK),use of IT to collect data such as web scraping and scanner data).

59

One serious consideration for any SAF project is to ask the question ‘do we need to conduct a full SAF
project data collection for all elements of consumer spend, with the nationalization and globalizationof the market with many key retailers and service providers dominating the market. Additionally, theway in which consumers purchase goods and services is changing and therefore the approach to theprice collection should reflect this changing market.

Sampling of items
There is very little guidance provided by Eurostat on the calculation of spatial adjustment factors including
the processes from sample design of items and retailers and service providers through to validation andthe actual calculation of SAFs. Therefore, to some extent, at least as far as the data collection is concerned,a similar format was followed as for the regular PPP consumer surveys.

Sampling of items – how many to sample
The UK took responsibility for sampling of the items for the SAF basket of goods and services. The starting
point was the regular PPP survey item lists with consideration being given to representative items, nonrepresentative items, and the use of Proxy items (these are covered in more detail below). It was not
feasible or practical from a financial and/or resource perspective to collect the complete PPP item list.
Therefore, the process used was to create a sub-sample of the total items that were both representativeand non-representative (and indeed proxy items).
Design of the sub-sample of the total PPP item list required two questions to be asked. Firstly, how
MANY items to be sampled and secondly WHICH items to sample.

The UK made the decision to create a sub-sample of items; two important factors were WHICH items andhow MANY items to include in the basket. From a practical level, there was a debate on this as somemembers of the Project Board (which was the main decision-making body of the SAF project) had mixed
opinions. A decision was made to design and replicate the sampling methodology used by Eurostat-OECD
for the European Comparison Programme (ECP). The number of items or products priced for each basic
heading was very much dependent on the heterogeneity of items covered by the item and the importanceof the basic heading. For each basic heading, the number of representative items selected wascommensurate (proportionate) with the price variation within the basic heading and its expenditure at
the basic heading level. This information was taken from the latest UK National Accounts data; where theexpenditure is greater this is where resources should be invested. Additionally, if historically we knew that
there were basic headings with a lot of variation in the dataset, resources were also directed here. In
order to establish this, we looked at the variation coefficient of the CPI data and also knowledge and60metadata from the PPP regular surveys and price collectors to identify those items with high levels ofvariance.
Example – Table 1 Shares of household expenditure

GDP expenditure weights [millions of national currencies]

Code

Description

YEAR
2014

2015

2016

GDP

Gross Domestic Product

1837062

1888737

1961125

A

Individual consumption expenditure by households

11439191181868

1233323

A.03

Clothing and footwear

61815

65064

67697

A.03.1

Clothing

52653

54842

56334

A.03.1.1

Clothing materials

728

721

676

A.03.1.1.0

Clothing materials

728

721

676

A.03.1.2

Garments

47305

49739

51078

A.03.1.2.1

Garments for men

13245

13927

14302

A.03.1.2.2

Garments for women

24599

25864

26560

A.03.1.2.3

Garments for infants (0 to 2 years) and children (3 to13 years)
9461

9948

10216

A.03.1.3

Other articles of clothing and clothing accessories

3610

3510

3687

A.03.1.3.0

Other articles of clothing and clothing accessories

3610

3510

3687

A.03.1.4

Cleaning, repair and hire of clothing

1010

872

893

A.03.1.4.0

Cleaning, repair and hire of clothing

1010

872

893

A.03.2

Footwear

9162

10222

11363

A.03.2.1

Shoes and other footwear

9107

10168

11311

A.03.2.1.1

Footwear for men

3252

3631

4039

A.03.2.1.2

Footwear for women

4283

4781

5319

A.03.2.1.3

Footwear for infants and children

1572

1756

1953

61

A.03.2.2

Repair and hire of footwear

55

54

52

A.03.2.2.0

Repair and hire of footwear

55

54

52

As you can see the share of individual household consumption of GDP is approximately 63%. Of thehousehold consumption, the division of Clothing and Footwear of £67,697m accounts for approximately
5.5% - the biggest share of which is attributed by the aggregate of Garments for Women which accounts
for nearly 50% of the total Clothing aggregate. With this is mind, more resource was dedicated to Clothing
and specifically Clothing for Women. This is a difficult aggregate as not only is the division an important
one in terms of expenditure but also notoriously difficult due to high variation and therefore it required
more prices to be collected.
Individual household expenditure and high levels of variation in prices are key factors in determining
how many prices to collect for each item. ONE in FOUR (25%) of items from the total PPP list was
sampled.

Returning to the subject of the actual number of items to collect, the UK decided, based on resources, toselect a fixed proportion of items from the PPP item list, selecting, in the main, the same proportion of
items from each basic heading. However, there were some exceptions. The sub-sample contained aminimum of ONE in FOUR or 25% of the items under each basic heading in the PPP items list. However,there were some exceptions and CPI data were used extensively, particularly for food items where thespecification for both PPPs and CPI were very comparable and therefore more than 25% of PPPs items
were sampled. This ensured a more robust dataset particularly for the COICOP division of Food, Beverages
and Tobacco.
Breakdown of items
The sub-sample for the SAF calculations consisted of 565 PPP items across 167 basic headings.
A total of 565 PPP items were included in the sub-sample covering the following COICOP aggregates:•i.
House and Gardenii.
Personal Appearanceiii.
Services
iv.
Transport, Recreation, Hotels and Restaurantsv.
Food, Beverages and Tobaccovi.
Health and Furniture
Overall, the UK sampled 333 items out of a total sub-sample of 565 for the third-party subcontractor, TNS UK Ltd, to collect in the shops or outlets covering all aspects of consumer spend.

62

•
•

Of the 565 PPP items, data from the UK national Consumer Price Index (CPI) were used for 120
items.
The ONS (primarily Prices division where the UK PPP team and UK National CPI team was situated)collected prices for the remaining 112 items. These were either collected using the internet andtelephone, or collectors went out to locations locally to collect for those items that the third-party
subcontractors had difficulty pricing.

Breakdown of Items
ONS Staff, 112,20%UK CPI , 120,
21%

Third Party,
333, 59%

Third Party
UK CPIONS Staff

Once we decided on how MANY items to include the next key question was WHAT items to include.

Sampling of items – which items to include in sub-sample
Once the number of items has been established the next challenge was to establish which items to include
in the sub-sample of 565.
Items were selected purposively from the PPP item list. Firstly, items were selected if they had a direct
specification match with the CPI item with the intention that CPI items could be used instead of having tocollect prices for that particular item. Initially the intention was to produce some coding that would allow
for matching of the specifications, but this proved to be a challenge. Unfortunately, the final matchingundertaken was done manually as this was the only method where it could be certain that it was an
absolute match. For future SAF projects it is hoped that coding that the UK PPP team are developing in
Python will be used for matching purposes, therefore saving resources at this stage of the process.
If more items were needed to make up the required sample size, items were initially randomly selected
so that there was sufficient coverage of items across all the basic headings.

63

Treatment of representative and non-representative items
When considering the sample, it was important to consider both representative and non-representative
items.
What are representative items?
Representative items are those which are important in terms of relative total expenditure within a basic
heading or, in other terms, among the most important items purchased in the national market. However,for many countries, including the UK, it is sometimes difficult to ascertain which ones are important (ornot) as there is no national accounts expenditure below the basic heading. Therefore, the UK relied on
several criteria to determine if an item was representative or not. They include the following nonexhaustive criteria:
a. The amount of shelf space that an item occupied. The larger the amount of shelf space relative tosimilar products, it could be assumed the more important the item was.
b. The experience of the price collectors who had practical experience of working with the CPI andwere very knowledgeable of both the regional and the national market.
c. The use of the UK national CPI. Items that were in the CPI sample are deemed as being
representative of the UK market and therefore could be marked as such for the SAFs.
d. Reports from research companies such as Mintel, research papers, and publications such as The
Grocer (a monthly publication detailing trends and items in the UK retail and service market).
What are non-representative items?
In contrast, non-representative items are those items that are not considered important in terms of total
expenditure within a basic heading and are not among the more important items purchased in the market.
For the 2016 SAF project, the UK took a different approach to the treatment of non-representative
items in the sample design. For previous rounds (2004 and 2010) the UK priced a higher proportionof non-representative items. For 2016 the proportion was reduced.
For SAF projects or rounds previous to 2016, namely 2004 and 2010, both representative and nonrepresentative items were sampled. The reasoning behind this approach at the time was that the UK was
adhering to Eurostat guidelines as per the regular PPP surveys, where it was instructed that both
representative and non-representative items were sampled and priced to ensure that there wereadequate prices to allow for comparison between countries. However, this approach caused major
challenges in 2010 for the UK price collectors out in the shops in the regions as time was spent looking foritems that were not freely available the UK across all the regions. This was a key lesson learnt for future
SAF projects. Consequently, a decision was made ahead of the 2016 SAF project to ensure that the nonrepresentative items were freely available on the UK market to ensure efficient use of resources. There
was concern by the UK that the latest edition of the Eurostat/OECD Methodological Manual on Purchasing
Power Parities states that “representative items usually have a lower price level than unrepresentative
items.” That said, however, since the SAFS will only be used to adjust the PPP prices collected in the capital
city of London, this would not be a major issue. Howeverit was also felt that it was important to include64some non-representative items in the sub-sample to avoid biasing the SAFs and to remain compliant with
Eurostat-OECD PPP methodology.
For the 2010 SAF project the number of non-representative items in the sub-sample was approximately
equivalent to the proportion of non-representative items under each basic heading of the PPP item list.
However, for the 2016 SAF project, this approach was changed to include a lower proportion of nonrepresentative items to ensure adequate resource was available.
Use of proxy items in the item sample
The use of PROXY ITEMS was a significant development in the calculation of Spatial Adjustment
Factors for the UK during the 2016 round. Previous SAF calculations for the UK did not include the useof PROXY ITEMS. This allowed for optimum use of resources both financially and in terms of time.

During the SAF process, the UK experienced issues with the fact that for there were some basic headings,
where the UK either had difficulty pricing, particularly in the case of items which had a service element or
we did not have the necessary resources. Alternatively, the proxy items could be one that wasrepresentative and widely available in the UK but was not collected as part of the regular PPP surveys, i.e.
not on the PPP item list. This was discussed with Eurostat at the time of the UK SAF project and wasconfirmed as a viable approach in ensuring comparability across regions/locations with the aim ofcalculating SAFs across the regions of the UK. In these instances, a decision was made to use other similar
items in the same basic heading where we could make use of CPI data. This was important as it limited
the resources required and ensured that maximum use was made of the hundreds of thousands of pricesthat had been collected for the UK national CPI. Items within the same basic heading have an element ofhomogeneity and if there is no reason to assume or to perceive that prices for a particular item would not
behave in the same way as others within the same basic heading, then it is acceptable to use other items
as a proxy.
For the UK one such example was the inclusion of a new item relating to the basic heading Recreationand Sporting Services A.09.4.1.0, namely Latin Classes. This is not a PPP item but closely correlates to theestablished PPP item of Salsa classes for beginners, 1 lesson. In this instance, we used the data from CPI.
A variation on the PROXY ITEMS is to change the specification of an existing item to allow for greater
coverage of items during the collection period. Such examples included:
Regular PPP survey item specification for TABLE SALT
Brand
Well Known
Reference Qty
1000g
Minimum Qty
500g
Maximum Qty
1200g
Type
Fine table Salt
Package Type
Simple box, bag, without pourer
Excludespecial salts, with pourer
65Spatial Adjustment Factor PPP specification
As above but with one significant adjustment to the ‘Package Type’, and ‘Exclusion’ criteria namely:
Package Type
ExcludeSimple box, bag, with pourer
special salts, with pourer

Being able to collect the salt with a pourer meant that coverage was excellent; the pourer was mostpopular within the UK and was not a price determining factor. What is important in determining true pricedifference is comparability.
The exact item must be collected within the same time frame to allow for true spatialcomparisons. This was one of the major challenges of the price collection.

In delivering the SAFs the concept of COMPARABILITY is key, and all items collected across the regions
within the UK must be comparable to allow for that spatial comparison. The pricing of comparable items(identical or if not identical, then equivalent) ensures that the difference in prices across the regions or
locations for a good or service reflect actual price differences between those regions or locations and is
not influenced by differences in quality of the product. This was a key concept which was impressed upon
the price collectors, specifically, the company that conducted the UK price collection out in the locations.
The exact item had to be collected within the same time frame to allow for true spatial comparisons. Thiswas one of the major challenges of the price collection and on many occasions the UK had to discuss withthe company undertaking the price collection that this concept was imperative to a successful collection
and must not be confused with the collection of items for the UK national CPI. The same company
collected data for both the CPI and PPP, and it took a change in mindset for some of the price collectors
to adopt this concept. However, overall, the company was successful in their price collection for the SAFs.

Sampling of locations/regions
A different approach was used for the more recent spatial adjustment factor and regional
price levels surveys from that of previous surveys. A new technique called ‘Hotspot analysis’
was introduced.
The sampling or selection of locations was the responsibility of ONS and not the third party contacted toundertake the external price collection. A list of locations for price collection was determined using the
Hotspot analysis technique to identify clusters of shopping centres in the UK, and Retail Sales data. There
was however, one exception to this, as one location that had been sampled as part of this process, namely
Swindon, was replaced by Bristol. The reason was that feedback from the collector involved in the 201066

survey expressed concern of the lack of outlets for Furniture and Clothing items. Each of the locations
required the same amount of data to be collected.
The price collection was split into seven broad regions within the UK, with three locations within each of
the seven regions, totalling 21 locations in all. The locations and regions are listed in Table 2 – Regionsand Locations.
A decision was made to use the same methodology that was used for the 2010 project and retain threelocations per region. This would ensure an adequate number of price quotes per item; most of the actual
locations are the same as 2010 with the exception of Bristol which has already been covered above andmore notably the locations for London. In 2010 the three sampled locations were West End,
Knightsbridge, and Baker Street. For 2016 the three London locations sampled were West End, Croydon,
and Bromley, all of which are sampled for the UK CPI. Two of three locations, namely Croydon and WestEnd, are sampled locations for the regular PPP survey for Consumer Goods and Services. Bromley is withinclose proximity of a number of the PPP locations.
Table 2 – Regions and locations used in SAF 2016
NorthernMidlands

Southern

LondonWales

ScotlandNorthern
Ireland

Leeds

Birmingham

NorwichWest End

Cardiff

Aberdeen

BelfastManchester

Leicester

Bristol

CroydonSwansea

Edinburgh

Londonderry

Newcastle

Nottingham

ReadingBromley

TorfaenGlasgow

Coleraine

The locations selected for the SAF project were a sub-sample of those locations currently used in the 2016
UK CPI collection. TNS visits 141 locations each month, collecting prices for use in the CPI. From this list of141, locations were selected using purposive sampling, where locations with the largest retail turnover ineach region were selected (Hotspot analysis). To ensure sufficient coverage, the 12 Government Office
Regions (GORs) across the UK contained at least one location each. The eight GORs within England(excluding London) were combined to produce three larger regions. Each of these regions contained threelocations. Prices were collected in three locations in Wales, Scotland, and Northern Ireland.
Three locations were selected for London, the reference location. Two of the selected locations featurein the ongoing PPP survey programme, namely West End and Croydon. The third location sampled for
London was Bromley.

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Pre-enumeration prior for survey
A significant change to the 2016 SAF survey was the use of pre-enumeration. It is highly
recommended that any country or region undertaking SAFs incorporate the process
In previous SAF projects, the UK did not undertake any pre-enumeration work. This was included in a‘Lessons Learnt’ document from the previous survey in 2010, noting that pre-enumeration was a valuablepart of the process and as such should be included. Valuable resources had been spent looking for items
that were not readily available on the UK market.
The third party that was subcontracted by the UK to conduct the price collection, on the instruction of
ONS, undertook the pre-enumeration work using the item list. They were experienced in this field andhad successfully undertaken this process many times. A meeting was held between both parties following
the pre-enumeration with the outcome that this process allowed for the change of some of the originalitems with some changes to the actual item specification. It is recommended that any country or region
undertaking a SAF or regional surveys include this use of pre-enumeration as it is highly effective in
identifying ahead of the main price collection any items that are either not available or not important or
indeed both.

Method of data collection
This section will look at data collection using the handheld device. This will cover the following topics:–––

The collection device used to record price data from the different shops/outlets. This section will
provide details of the device the ONS use to perform their data collection.
This section will cover the data security of the prices collected. This will include the entry of
Personal Identification Numbers (PIN) to ensure data are secure and only visible for one specificcollector.
Information on data entry and the different types of data entry used – for example, drop downmenus, free text.
The collection device and programme

The ONS uses the Samsung Galaxy Note 3 as their collection device. After consultation with their thirdparty providers, an application (APK file) was created for collection on android devices.

The programme is developed so that it can be used on other similar devices; it is therefore compatible
with Android 4.4 and with a UI layout optimised for a 5.7” touchscreen.
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Data security
This section will detail the security and access codes to ensure that the data collected is only visible tothose who have collected the prices, i.e. the price collectors.

Here you have the main screen on entry to the tool. This is the first stage of data security. Each pricecollector will enter their unique collectors ID in the ‘Collector Number’ field, for example “0123”. This willallow the collector access to the files stored on the collection application only for the unique code.

The second stage of security is to import the Item List for collection using a csv file. From the Home Screen,
selecting the “Settings” option, you are transferred the Import and Export screen. It is important to ensurethat the import file path is coded to match that of the Price Collector. As seen above, the Price Collector
code of ‘0123’ is the same on the CSV file to be imported. Once confirmed, this is saved, and you returnto the home screen to complete the Data Transfer / Import. This is completed by clicking on the Data
Transfer tab and selecting ‘Load Input File’.

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Data collection, data entry

When the Input file is imported, the price collectors can begin their collection. Entering the item list fromthe home screen using the ‘Start Collection’ tab. This will bring them to the Item List screen, which lists all
item numbers and the start of the item information. To retrieve a more detailed description of the item,the collector can click on an item code which shows the item in more detail.

By clicking on the green icon for each item, the price collector is directed to all observations that havealready been collected. This screen shows top level information for each observation collected – the outlet
type, the outlet name, and the price collected.
To insert a new observation, the price collector will press. This takes them to the ‘Observation
Details’ screen where they are required to collect the following information:–––

Shop Type Code – here the collector inputs a numeric value for the type of shop they are collecting
from, e.g. 2 for a Supermarket.
Requested Quantity and Measuring Unit – these are pre-set fields and are automatically updated
when the collector inputs their Item List file.
Brand – here the collector inputs the name of the product brand.
70

–
–
–
–
–
–

–
–
–

Model – here the collector inputs the product Model.
Comment – this is an open text field, here the collector can type extra information they feel isimportant to the collection.
Observed Quantity – here the collector is required to put the size of the item they have collected.
Observed Unit – here the collector inputs the unit that is supplied in the Measuring Unit field.
Observed Price – here the collector inputs the Price for the item at point of sale.
Discount Code – this is a drop-down menu allowing the price collector to choose the code that
represents the Price they have collected, e.g. N – Normal Price, Q – Quantity Discount, T –Temporary Discount and R – Regular Retail Price.
Shop Name – the name of the outlet the price is being collected from.
Location – the price collector inputs the Town, City or Region they are collecting pricing in.
Once all required fields are completed the price collector can save their information and thisobservation will be added to the ‘Observations’ for that specific item.

Once all prices have been collected and inputted into the hand-held device, the Price Collector cannavigate back to the home page using
Office.

, ready for exporting the data back to the National Statistics

From the home page, the collector can again select the data transfer tab, but now select ‘Export Data’ tobegin the email process. This will give the Collector the option to send an email. When clicking Yes, thedata stored within the application will be sent to a predetermined email address coded into the software.
This allows the collectors to provide a daily update to the office.

Paper form
For those who do not have access to handheld or similar devices, there is the option of completing datacollection manually, using booklets and manually handwriting each individual observation. (The UK used
this method of collection until 2010 when it introduced its firsthand device (SOMO). The current device isa Samsung Galaxy Notebook 3).
71

Each item will have its own collection sheet, allowing for 10 observations.

All the details required are entered into a row with any further comments about a product are added into
the final column.
A price collector will continue to complete inputs until the close of the survey period / month. They will
send the collection booklet back to the National Statistics Office PPP Team to commence validation of thedata.
This works differently to the handheld device, where daily validation can be conducted as the data aresent, via email, on a more regular basis.
TO NOTE: Information relating to the IT used for scanner and web scrapped data will be included in final
draft as they are still being developed.

Data collection – price data sources
Three main data sources for price observations were used in the calculation of the UK SAFs and relative
regional consumer price levels (RRCPLs):•••

price observations from the existing monthly Consumer Prices Index collection2016 regional price survey, andcentrally (ONS) collected price observations.

It should be noted that the RRCPL dataset is fundamentally the same dataset used for the calculation of
the SAFs delivered to Eurostat.
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To calculate the SAFs, several approaches to acquire data were used. Refer to Graph 1, Survey bycollection method. Where possible, secondary data were obtained from data collected for use in thecomputation of the UK Consumer Price Index (CPI). One year’s worth of CPI data was used, to remove any
effect of seasonality. This was checked to ensure that the item prices were comparable across regions.
Primary data collection focused on those items which had no spatial information, had a relatively highweight, or made a relatively large impact on the final indices.
The data for the SAF project were sourced from three areas: a central collection conducted by ONS staff;
a local collection performed by an external contractor TNS; and price data from the UK Consumer Price
Index (CPI).
The proportion of items allocated to each collection method is shown in Graph 2. This graph demonstrates
the relatively even split between the three collections methods in terms of item coverage.
Graph 2: Number and Proportion of items allocated to each collection method

Breakdown of Items
ONS Staff, 112,20%UK CPI , 120,
21%

Third Party,
333, 59%

Third Party

UK CPIONS Staff

The decision on the mode of collection was made at the individual item level. The first consideration being
if it was suitable to use CPI data. If CPI data was deemed unsuitable but it was relatively simple to collectprices in the field, and regional variation was expected, then local collection in the field for the item was
employed. The remaining items were collected centrally from within ONS as they were either viewed asproblematic, had no physical outlet, or had national pricing. These mainly related to items in the Services
survey as in many cases there was no physical outlet to visit out in the field and prices were taken fromthe internet or by contacting the service provider directly by telephone. This was time consuming (butnecessary) and resource intensive as these were some of the most difficult items to price. The result of
these decisions ensured that the various surveys typically had a combination of sources as can be shownin Graph 2 below.
Graph 1 demonstrates the extensive use of CPI for the Food, Beverages and Tobacco survey, while CPI
data had limited use in all remaining surveys. It is not unexpected as food, beverages and tobacco items
73

have very generic and narrow specifications. The opposite can be said for Furniture and Health whereapproximately 60% of the items were priced centrally, out in the field by TNS with approximately 40%
priced centrally by ONS (Prices Division). This was primarily the case for the Health based items and somefurniture items where national pricing exists (for example IKEA). The majority of furniture items werecollected in the field.

Consumer Price Index data
The use of CPI price data formed an important component of the 2016 SAF project. CPI data collection
involves collecting the same product month after month. If the product matches the CPI item description,
the actual quality of the product being priced can deviate from location to location. Spatial collection, on
the other hand, involves collecting prices for a product ensuring comparable quality across regions. It is
for this reason that PPP item specifications are much tighter than CPI item descriptions. For the SAF
project, CPI data was only used where comparable quality across regions could be ensured for a particular
CPI item. CPI/PPP synergy was a must if the CPI was to be used.
Price data are collected for around 700 items for use in the UK CPI. The initial stage to determine whether
the use of CPI data was appropriate was to map CPI items to PPP items. On a case by case basis, the CPI
item description was compared to the PPP item specification. Decisions were made by the UK project
team about whether the CPI item description was comparable to the PPP item specifications. One hundred
and twenty CPI items were identified as having a sufficiently comparable item description to beconsidered for use in the SAF project.
In many cases, the decision to use CPI data was relatively straightforward, particularly for food items. For
those CPI items where their suitability was not obvious, the Coefficient of Variation (CV) of the arithmetic
mean of CPI prices for each item was used to aid in the decision of whether the use of CPI data wasappropriate or not. Prior to analysing the CVs, it was important to understand what these values meant
for the purpose of the SAF project. The smaller the CV, the lower the variability, or dispersion of CPI prices.
It was not possible to determine whether this dispersion was due to differences in price levels acrossregions, or differences in the quality of the item being priced. For example, a high CV indicates a large
variability in prices for a particular item, which may be due to differences in price levels, differences inquality, or both. It was for this reason that the value of the CV was not the only criteria used in determining
the suitability of the CPI, but it was a useful tool.
Care was taken to only use CPI data where appropriate for the SAF project. On a case by case basis,consideration was given to both the tightness of the CPI item description and the value of the item's CV.
The use of CPI data (approximately 277,500 prices were used) was suitable for 120 PPP items. Utilising the
CPI data meant a significant reduction in the number of items that needed to be collected locally, while
ensuring the data was fit for purpose in the calculation of SAFs. This in turn allowed the resources to beeffectively employed elsewhere.
To ensure the integrity of the produced statistics, the use of CPI data was limited outside of the Food,
Beverages, and Tobacco survey. For the other five consumer surveys, in the main, it was felt that the CPI
74

descriptions could not ensure comparisons between items of equal quality. This was especially the casewith furniture, where the item specifications needed to be followed very precisely to allow for truecomparison.

Local collection
The external or local collection was conducted by a company called TNS Ltd. This company is currently
contracted to collect price data for the UK Consumer Price index. TNS was also engaged in the pricecollection for both the 2004 and 2010 surveys, collecting the majority of items, 333 (of which 332 wereused in the calculations) in total or 59% of the total 564 items. A member of the PPP team collected
additional prices in Swansea (Wales) to improve coverage and in Torfaen where staff could be easily
deployed from the Newport office (based in Wales) to support the TNS effort.
The external data collection was conducted between the period 26th September to 4th November 2016.
•

The external collection was less challenging for 2016 due to ‘Lessons Learnt’ during the 2010
survey, which has been improved upon including an improvement to the item list (updated toinclude pictures and important guidance notes by basic heading). The overall process wasmuch better organised, and communication was quicker making the overall process more
efficient. The main difference and improvement was field staff working in geographical areas
as opposed to specified outlets made the collection much quicker.

Food, Beverages, and Tobacco was the most successful category. This is not unexpected as the items are
typically easily sourced and also have some similarity to the items collected for CPI which assisted thecollectors. The Services, Personal Appearance, Furniture and particularly Transport, Hotels andRestaurants surveys all proved more challenging in reaching targets. These are the areas that the majorityof the price collectors had difficulties.

Pre-Survey
From the 2010 SAFs local collection, communication was identified as a key factor in ensuring the successof the 2016 project. The SAF project team initially engaged TNS at their price collector conference at their
headquarters in London, giving a high-level overview of the project, highlighting key themes,
requirements and lessons learned from 2010. Key of which was the explanation of the differences
between CPI and PPP price collection and the application of the Basic Model Rule. This conference wasalso key in receiving feedback from those collectors who were part of the 2010 exercise and gave guidanceon the initial stages of preparation for the project.
Significant time was spent composing and finalising the price collection item list and guidance document
for local collection by TNS. This went through several stages in consultation with TNS and the project
support team to ensure that the documents were fit for purpose. Confirming all price collectors
understood the importance of collecting prices for comparable items against the item descriptions toachieve credible data for use in the calculations.
75A pre-survey meeting was held with TNS with supervisor price collectors dialling in via teleconference
having received with copies of the draft list in advance. Each item was reviewed in turn and any
uncertainty on item descriptions or requirements for the item was queried and refined for ease of pricecollection in the field. The supervisor price collectors then conducted pre-enumeration in the field andwere able to flag unavailable items before price collection commenced to ensure a high level of availability
across the regions in the UK in advance of price collection.
This was a very effective way of preparing the survey materials for the local price collection and wasinvaluable in ensuring price collectors from TNS understood what was required in terms of quality in thedata they provided.
A similar process was applied to the items collected centrally by ONS. Basic headings were checked foritems that were most representative for the UK. For those headings where the UK PPP team havehistorically been challenged in gathering the target number of price quotes per item, i.e. Services for the
maintenance and repair of the dwelling, enabled the team to take the opportunity to create a UK item
description to ensure success in price collection across all regions.

Regional price survey
Graph 3 details the average number of price observations that were collected in the local and central
locations by PPP survey. Items where the number of observations was one (due to invariability throughnational pricing) or zero (due to unavailability of items) have not been included.
Graph 3: Average number of observations per item by survey

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Central collection based in office, ONS
The final 112 items were collected by ONS staff in an internal central collection. This collection wasreserved for items where it was possible to collect reliable quotes by phone or internet such as in transport
or service areas, where it was felt that inexperienced PPP collectors may find it difficult to match detailed
specifications in stores such as televisions, laptops, and for items that had national pricing.
The central collection was conducted by ONS staff experienced in PPP collection and also utilised otherstaff across the Prices division.
The collection began on the 26th September and ceased on the 4th November 2016. Before the collection
began, certain items were deemed to have national pricing policies. These included 20 items for cars andmotorcycles. This brought the number of items to be centrally collected to 112. Further research wasundertaken, firstly on items where national pricing policies were in effect and secondly for those items
where prices could not be collected; this ensured a targeted central collection.
Where items had a national price, a single price observation was collected and replicated across all regions
where the outlet had been sampled. Intelligence on national pricing was collected direct from retailers
and through the analysis of prices as they were received from the local collection. In these cases, once
national pricing was identified, collection ceased. It was also identified that for the heading A.04.5.5 "Heat Energy" it would not be possible to provide UK prices as the PPP descriptions of the productprovided did not match anything provided in the UK.
It became apparent quite early in the survey process that for many items it would not be possible to collectfive price quotes from each location selected. An example of this being items relating to flights. Someregions in the UK do not have a large number of airports, for example, Wales has only one national airport,
Cardiff, and the number of flights matching the PPP specifications was limited. As many of these items
was collected as possible, but it was impossible to completely fill the initial requirement for collecting 15
price quotes per region.

Validation and Data cleaning
As with section one, this section will aim to discuss the methods used in the actual validation and cleaningof the dataset and to provide practical guidance for regions/countries undertaking validation and data
cleaning once they have received the actual price data. Working examples will be provided. Areas covered
will include:••••

Methods used in validating dataset at item level, basic heading level and aggregate level. Use of
specific criteria in the validation.
Validation conducted – covering both validation conducted during the price collection period andfollowing the end of the collection
Future development for validation purposes.
Checking of data (this covers ONS’ internal auditor’s actual revisiting the outlets to check theaccuracy of the prices and specification of the item). A sub-sample of items and locations are77

•

sampled. This is key for quality assurance purposes and to have trust in the actual data. Working
example will be provided.
Discuss how the procedures used in the UK (fundamentally ECP processes) compare with thoseused in the ICP (Quaranta Tables and Dikhanov tables, both of which are used to provide similar
measures of price variation for both products and countries).

Validation methods
The UK have developed a Management Information System (MIS) in Microsoft Excel, developed
specifically for the survey management and validation stages. The MIS is used to store data at specificstages of collection and validation allowing an audit throughout. Data stored will include regional average
prices, minimum and maximum prices, number of observations, items with no observations, items withlow observations ,and those items with increased variation in data.
The MIS is also used to compare data survey on survey, looking at movements in data. The UK team will
research changes and detail these in the spreadsheet. This increases the audit trail and allows the team
to comment on changes.
Below is a screen shot of the MIS used by the UK in SAFs and the regular PPP surveys. This illustates at the
top level previous prices compared to new prices (average, minimum and maximum), price variation foreach survey, representativity of the items ,and also the percentage change in price from previous to new
average price.

Following on from the top-level data validation, the data are scrutinized further at observation level. The
following information is collated, and each price is compared to all others for that specific item. Below isa snapshot of the table used to compare. It illustrates the variance coefficient of a selected item, the ratioof that items minimum and maximum price, the location/region of the price collected, and the ratio of
the price collected against the average price of the. As there is a price flag of three question marks (???),
this data will be checked and those observations with a high or low-price ratio will be investigated.
The high variation is always the starting point for the validation as high variation can indicate errors indata as well as genuine price divergence.

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Validation stages
This section will cover the validation undertaken both during the price collection period and following theend of price collection period, covering both the validation conducted by the third party subcontracted tocollect out in the field and the validation conducted in house by the UK PPP team. Limited validation was
conducted on the CPI data as it had already been used in the construction of the UK national CPI and assuch had been validated.

Third Party Contractor validation
Throughout collection, the third party subcontracted to collect the data complete their own validation
before delivery of data to the ONS. This is conducted throughout the collection period and prior to deliveryof the final dataset. Based on the top-level data, items are sorted by Item Code, Locations, Shop Names
and Collected Date Time and duplicate records were removed.
For each item, the following actions are taken:
•
•
•
•
•

A review that shop and brand levels are correct.
A review that the model number and comments matched the specification or highlighted wheresome of the specification are not met.
A check that observed quantity and unit are generating the correct reference price.
A review that the highest and lowest prices are not indicating an incorrect item.
A comparison of prices by shop.

For all data:
•
•
•
•
•

Check on £0 prices
Check no £0 reference prices
Check on Price ratio
Review shop types for consistency
Review shop names for consistency

All checks are carried out piecemeal as data are received at the office and on the dataset before final datadelivery. Attention was made on the more difficult commodity groups, such as clothing, furniture, andelectrical items where historically these have a lot of ‘noise’ or high variation. Interpretation of brands
was also an issue, i.e. definition of Well-Known Brand low, medium, and high.

79Incorrect items are deleted from the data file and submitted to the ONS separately so that they could be
included in the final dataset if they were deemed to be sufficiently close to the item definition as to be
acceptable.

In-house validation (ONS)The UK Project team received data files from the third-party contractor every few days. The validationconsisted of the following:
–

–

–
–

–

Identification of national pricing. This is where the price for an item is the same in each of theoutlets owned by the same company. The number of key retailers and service providers in termsof contributions to the industry adopting national pricing is increasing, particularly with theadvent of internet shopping. There are also a number of items, that regardless of which outlet
they are purchased from, are identified as having national pricing such as Apple products.
Unavailability of items. Validation during the early stage of the data collection allows the UK
Project team to provide the third-party with alternative items which can be collected thereby
ensuring that representative items are included in the final dataset.
Multiple and Independent retailers. Checks are made to ensure that each item has the correct
ratio of multiple and independent retailers.
Problematic items. The validation conducted during the price collection period proves invaluable
in quickly identifying items which price collectors are finding difficult to price. The project teamare able to provide the third-party with advice on the way forward.
Branding. During validation, checks are made to ensure that the items collected adhered to thecorrect branding.

Once the final data file is received from the third-party, further comprehensive validation is conducted.
This involves the following:
–
–
–
–
–

meeting certain validation criteria;
identification of £’000s errors;
cross checking of brands;
validation of reference quantities and the resultant prices; andensuring that the price observation adhere to the item specification.

Future development of validation process
The UK PPP team are currently developing the use of the computer programming tool, Python, to conduct
the validation of their collected data. The use of Python enables the team to carry out detailed and quick
analysis of the large dataset.
The use of Python allows the UK team to analyse each item and collate all information on thelocations/regions that have been priced and those locations that haven’t during the collection period. The

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team can then allocate resource to those areas where further collection is required, saving time andresource in areas already covered.

Back checking of data
Further analysis of the data can be conducted once all data has been collected. The team will use Python
to analyse price movement from location to location which highlights those areas where prices arehighest, price changes survey on survey and the team will conduct analysis on the effects of discounts.
The analysis on discounts will allow the team to look at whether the inclusion of discounts within the data
increase or decrease the average prices.
Back checking or validation of the actual price collected out in the shops is part of the process of validationand is undertaken by the sub-contractor’s field supervisors in collaboration with ONS auditors. Backchecking is a common term applied to checking the data inputs from a sample of observations, in this caseas collected by the sub-contractor. Back checks are carried out before final data are submitted.
For the SAF project, ONS instructed its auditors to pay special attention to the actual specification firstly
and then secondly, where possible to confirm the price. Three out of the 21 locations were randomly
sampled to be audited. The number of observations to be audited equated to approximately 2% of total
prices collected. This level of audit is in line with current data collection practise for the UK CPI. In the caseof the 2016 SAF project the majority of observations passed with specification and price check, well within
the limits set.

Synergies between CPI and PPPs
This section will briefly address the issues around the historic lack of synergy between CPI and PPPs anddiscuss ways in which synergies could be improved as this subject matter is being addressed in detail in a
separate agenda item in the third meeting of the Country Operational Guidelines and Procedures Task
force. It will also look at the current synergies from a UK perspective between CPI and PPPs.

Historical issues of UK using CPI for PPP purposes and the challenges the UK faced
For the UK, there has been continued historical difficulties in using CPI data for PPP purposes. This is
primarily caused by the detailed specifications required in the PPP collection in comparison to broader
CPI items specifications. For example, in the UK CPI a “t-shirt” item allows a collector to price for any tshirt regardless of brand or material composition if it is the same or comparable to the previous month’s
CPI item. For PPP purposes, the t-shirt will have a more detailed specification, including details such asbrand level or the material composition. Due to these differences in the specifications, the PPP team areunable to use the average prices taken from the CPI collection, as it is unknown if observations would
meet the PPP specification requirements.

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The example below shows the difference between the PPP and the CPI specification and the level of detailrequired for the PPP collection.
PPP Specification
Men’s polo T-shirt, short sleeves, WKB-H
Material – approx. 100% cotton
Collar-style – shirt; placket with 2-3 buttons
Sleeves – short
Colour – one.
CPI Specification
Men’s Casual, sleeve Top with Collar, e.g. Polo.
As can be seen from the graph below, Food is the aggregate where most use is made of CPI data for PPP
purposes, particularly for the SAF project. This is due to the specification being very comparable if not
identical.
The UK has previously been involved in a Eurostat project called Detailed Average Prices (DAP) to consider
using average prices across several different calculations for the food aggregate. The 2015 data collection
was the eighth and final exercise. The DAP project has been discontinued given some limitations of thecomparability of the results, the existence of other data alternatives for DG JUST (Director General for
Justice and Consumers) and the slow decrease of the number of countries taking part in the project.

Current practice on the use of CPI price data in the PPP exercise
As noted, currently the UK PPP team do not use CPI price data in the regular PPP surveys. Due to thedetailed nature of the PPP item list, it has been found difficult to find synergies between the data. Thatsaid, at basic heading level, movements in the CPI are used for quality assurances purposes during thevalidation rounds of the PPP regular surveys; movement in CPI is mapped against the movement in PPPs
between the three year intervals and any significant divergence is investigated.
However, when conducting the Spatial adjustment project, the UK used CPI data, mainly for food where
there was very tight correlation between the specifications. One year’s worth of CPI data was used toremove any effect of seasonality. This was checked to ensure that the price of items was comparable
across regions.

82

Graph 1 - Survey by collection method

As seen in the graph above, the CPI was used for most of Food and Beverages, and a large proportion of
the Transport basic heading in the UK Spatial Adjustment Project 2016. Care was taken to only use CPI
data where appropriate for the SAF project. On a case by case basis, consideration was given to both thetightness of the CPI item description and the value of the item's CV.

Future developments
There are several initiatives being undertaken in ONS which could potentially improve synergies between
the CPI and PPPs. These initiatives will be discussed in detail in this section.
The UK are currently in the development stages of acquiring and analysing test data for web-scraped andscanner data. It is hoped that, once data are received directly from websites or companies, then there will
be increased synergies between CPI and PPP data.
The use of web-scraped data has progressed further; this provides a snapshot of prices on companies’
websites. It is hoped that the data will be used in both CPI and PPP data calculations, as the detail can be
specifically collected from the websites. For example, a specific brand of jeans can be collected from a
website – this can be used in the monthly CPI under a jean item, but it can also be used in the PPP
calculations under the specific item.
The PPP item list asks for the specific items – Men’s blue jeans, LEVIS 501. Using web-scraped data, allobservations for this item can be used to create an average price for the survey collection month for PPPs.
But also, as the web-scraped data will be collected throughout all months, the CPI can use the data forthis specific item to help calculate inflation month on month.
The UK CPI and PPPs are in the development stage of including scanner data within their calculations. The
inclusion of scanner data will increase the synergies between the CPI and PPP data. The average prices
collated from CPI collection of scanner data will be used with PPPs as the data can be provided for specific83items. The individual average prices will be for specific items that can be matched to the PPP specifications
and either included or excluded.

Calculation of spatial adjustment factors and relative regional comparative price levels
Methodology used in constructing SAFs and regional price levels, including the calculation of
average prices
Calculation of both the UK SAFS and UK RRCPLs starts with the calculation of the average prices. The
calculation of the average prices is the first step in the actual calculation of both SAF and RRCPLs and thesame method is adopted for both, albeit the SAFs were primarily concerned with arriving at a correction
coefficient for the UK and the UK excluding England, whereby the RRCPLS were concerned with calculating
average price levels relative to other regions/countries. For this paper, the calculation and the methods
used in arriving at SAFs and RRCPLs have been dealt with separately as the reader might only be interested
in one and therefore it makes for easier reading to keep them separate.

Spatial Adjustment Factors
As previously explained, there are two main stages to the calculations of the SAFs; namely the calculationof the average prices themselves and secondly construction of the actual SAFs and the method used. Bothare described in detail below.

Calculating average pricesi.

Consumer Prices Index data

One of the advantages of using UK CPI data is that we are able to calculate an annual average price. Whenusing CPI data over a period of 12 months, an annual average price for each region was calculated. Thiswas done by first calculating a monthly average price for each region using an unweighted, arithmetic
mean. The annual average price was then calculated by using an unweighted, arithmetic mean of themonthly average prices. The reason for calculating a monthly average price first was due to the fact thatthe number of prices collected each month differed slightly. Averaging the monthly average prices meant
allocating an equal weight for each month. This approach also had the advantage of implicitly weighting
by location within a region, as the larger locations generally had more price observations.
ii.

Direct collection (in the field and at the desk)

For data collected centrally (collected by ONS) and data collected in the field (collected by subcontractor),
the average price for each location was calculated by taking the unweighted, arithmetic mean of all prices
collected in each region.

84

Methodology used in constructing spatial adjustment factors
i.

Using the EKS method

The methodology ONS used to construct the spatial adjustment factors is consistent with the approach
used by Eurostat in calculating their PPPs at the basic heading level. The EKS (Èltetö-Köves-Szulc) method
was used to impose transitivity on an existing set of intransitive binary indices.
Transitivity is a desirable property as the same result is obtained when comparing PPPs directly between
two regions and when comparing PPPs indirectly through the introduction of a third region. In addition to
being transitive, the resulting multilateral EKS PPPs differ as little as possible from the original binary PPPs.
The use of EKS can lead to bias when one region has a larger number of representative items than theother region. EKS-S is a modified version of EKS that takes into account the representativity of items in
different regions. ONS has assumed equal representativity across the UK; therefore, it was not necessary
to use the EKS-S method. This assumption was deemed plausible as the items were being compared withina country rather than across countries where more variation would be expected. Therefore, it wasconsidered that the use of EKS should not lead to bias due to representativity differences between
regions.
Due to the unavailability of weighting information for PPP items below the basic heading level, a FisherPPP index could not be calculated. Instead, a Jevons-PPP index, or an equally weighted geometric meanof the price relatives, was calculated. Although a Jevons index is usually transitive, the use of EKS was stillneeded to enforce transitivity because some regions contained missing prices. A weight was then appliedat the basic heading level which was sourced from the Living Cost and Food Survey, the UK’s equivalentof the Household Expenditure (Budget) survey.

Construction of the spatial adjustment factors
The following steps were used by ONS to construct the spatial adjustment factors:
i.
ii.
iii.
iv.
v.
vi.

A bilateral comparison of the PPP item's average price was calculated for each combinationof regions. With seven regions, this meant 49 different price relatives for each item.
The price relatives were then mapped to their respective basic heading for each region.
The unweighted, geometric mean of the PPP item price relatives were calculated for eachbasic heading.
EKS applied to enforce transitivity on the binary PPPs.
The weighted geometric mean of each row in the EKS matrix was taken, giving an
adjustment factor where UK=1 for each region.
The inverse of the adjustment factor London (UK=1) was taken to produce the spatial
adjustment factor delivered to Eurostat.

85

Relative regional consumer price levels of goods and services
As already commented the construction of UK regional price levels were only calculated as a result of thework undertaken by the UK on updating of the UK SAFs for Eurostat as part of a requirement under the
PPP regulation. However, they were a welcomed-by-product and it did not require a significant amount
to work to produce; the same dataset was used for both the SAFs and RRCPLS outputs.

Calculating average prices
Consumer Prices Index data
The use of CPI price data formed an important component of the RRCPLs calculations. CPI data was
obtained for approximately a quarter (124) of the items in the basket used in the calculation of the RRCPLs.
The majority of the items where CPI data was used were for the Food and Non-Alcoholic Beverages andAlcohol and Tobacco divisions; for these two divisions, just over 50% of the data used were CPI items.
These divisions contain items that are well defined in the CPI item description and which ensure a like forlike comparison across different regions. A major consideration in choosing items from the CPI basket was
their closeness in specification to items in the PPP basket of goods and services. Those CPI items that alignvery closely to the PPP specification were selected and their existing price observations were included in
the dataset.
The prices extracted from the CPI database refer to the twelve-month period July 2015 to June 2016 andaccounted for approximately 277,500 observations. An average price for the item in each region was
calculated for each month. An annual average price was then calculated by taking an unweighted averageof the monthly average prices.

Methodology used in constructing relative regional consumer price levels
The approach used to construction relative regional consumer price levels is consistent with that
adopted by Eurostat in the calculation of the PPPs.
It is important to note that the methodology adopted by the UK in the calculations of RRCPLs is consistent
with that used by Eurostat in the calculation of PPPs for the Eurostat-OECD Programme. As commented
previously, it is important to note that while RRCPLs compare regions and countries within the UK to eachother, the ECP produces PPPs which compare participating countries to each other within the Programme.
The basic approach to calculating relative regional consumer price levels is to measure the cost ofpurchasing a common basket of goods and services in each region and express that cost relative to buying
the same basket nationally (where the UK=100). That is, how much more (or less), relatively speaking,does it cost to buy the basket in one particular region, compared with a UK average cost for the samebasket. Similar to the CPI, it is not feasible to collect prices for every type of good and service that
consumers spend their money on. Nor is it possible to collect prices from every single outlet or service
86

provider that consumers make purchases from. Therefore, it is necessary to sample for items, locations,outlets, and service providers. The main difference compared with the CPI, and important to note, is that
regional price level comparisons are designed to compare prices of a common basket of goods andservices at one particular point in time, i.e. a spatial comparison, in different regions in the UK, whereas
the CPI measures the difference in prices of the same basket of goods and services throughout the UK
over a period of time, i.e. temporal comparison. This is a significant difference that is important tounderstand. It is important to note that the RRCPLs cannot be compared over time as they are a spatialcomparison and not a temporal one.
To be able to compare prices at a particular point in time, it is important to ensure that an identical basketof goods and services is priced for all of the regions. This is critical in developing comparable outputs andensures that observed price differences in the regions are due to price alone and not influenced byvariability in the quality of items priced across regions. For example, a comparison of an observed priceof a branded item in one region with the observed price of an unbranded item in another region willreflect in part that the items are not comparable and that unbranded items are typically cheaper.
Having collected observed prices for the goods and services included in the basket an average price iscalculated in each region for each item. Two stages were employed to calculate and aggregate the RRCPLs.
The first stage was below the elementary aggregate level, referred to here as the basic heading. Basicheadings are the building blocks for the RRCPLs and are the lowest level for which expenditure weights
can be obtained. A basic heading comprises a group of similar, well-defined goods or services. In total,
168 basic headings have been defined for this process. A few examples of basic headings are; Ladies Coatsand Jackets, Jewelry, Wine, Wardrobes and Chocolate. Above the basic heading level, RRCPLs were
calculated and aggregated using the Classification of Individual Consumption according to Purpose(COICOP) used in the CPI. Regional expenditure was obtained from ONS’s Living Costs and Food survey
and adapted to create regional weights for the 168 basic headings.
As there is no data available on the expenditure on the individual items below a basic heading, a basic
heading RRCPL has to be calculated from price data only. Below the basic heading, price relatives for eachpair of regions were first calculated; with five regions (London, England (excl London), Wales, Scotland
and Northern Ireland), this resulted in 25 unique price relatives. To combine the price relatives of theitems at the basic heading level, an equally weighted geometric mean of these relatives was calculated
for each pair of regions.
Once the RRCPLs had been calculated at the basic heading level, regional weights were used to aggregate
the basic headings to successive COICOP levels. For each pair of regions, the basic heading RRCPLs areweighted, summed and averaged using first the expenditures on the basic headings of the first region as
weights, and then the expenditures on the basic headings of the second region as weights. This gave twoweighted RRCPLs: a Laspeyres-like RRCPL and a Paasche-like RRCPL. The geometric mean of these two
RRCPLs was then calculated, which produced a single Fisher-like RRCPL between the two regions.
Once each level of aggregation is provided with a matrix of Fisher-like RRCPLs, it was necessary to apply amethod to impose transitivity on the Fisher-like RRCPLs. Transitivity is a desirable property for spatial price87

indices as the same result is obtained when comparing RRCPLs directly between two regions and whencomparing the RRCPLs indirectly through the introduction of a third region.
The method used by Eurostat, and adopted by ONS, to impose transitivity is the EKS (Èltetö-Köves-Szulc)
method. The RRCPL that results from application of the EKS method (the EKS RRCPL) is defined as thegeometric mean of the direct RRCPL and all the indirect RRCPLs between a pair of regions, with the direct
RRCPL having twice the weight of each indirect RRCPL. To produce the data in Table 1, England (excl.
London) and Wales would need to be compared directly and through London, Scotland and Northern
Ireland.
In addition to being transitive, the resulting EKS RRCPLs differ as little as possible from the original Fisherlike RRCPLs. After applying EKS, we are left with a 5x5 matrix of the bilateral EKS RRCPLs. Standardizationof the EKS RRCPLs is required in order to obtain a set of RRCPLs that has the UK as its base. This is doneby dividing each RRCPL by the geometric mean of the RRCPL in its column of the matrix. This results in five
EKS RRCPLs, one for each region (all the entries in each row have the same value after standardization),with the UK as the base, where UK=100.

Importance and uses of SAFs and regional price levels
Use of SAFs
Allows for true comparability across countries/regions
Producing comprehensive SAFs is always going to be a challenge for any country or region but it isimportant that they are calculated if there are known differences in prices across the country. SAFs are an
important element of the calculation of national annual average prices. For many regions or countries,
price collection for consumer goods and services are restricted to the ‘capital city’. For example, in thecase of the UK, prices are collected only in the UK capital city of London. However, to arrive at national
average prices that are representative of the country and which can them be compared across all
countries, SAFs should be produced.
Regular surveys are administered on a three-year rolling basis (this is the case for the European
Comparison Programme) (ECP) and it is being considered for adoption for the International Comparison
Programme (ICP). That said, for both the ECP and ICP, national annual average prices are required. It is forthis reason why SAFs are used and are key to the calculation of a national average price; the fundamental
aim is to take average prices to national average prices. In the case of the UK, to take average London
prices for specific goods and services to a UK national average price.

Reduces burden on National Statistical Institutes
Another important factor is that producing SAFS say, every six years reduces the burden on National
Statistical Institutes (NSIs) both in terms of finances and resources. As previously mentioned, annual
national average prices are required and for most countries it would be an impossible task to conduct anationwide price collection every year for consumer goods and services. In the most cases, the prices
88submitted by the NSIs to Eurostat and OECD are not national as they generally refer only to the capital
city of the country in question. However, there are several member states in the ECP that do not have any
regional price variation and therefore do not supply SAFs to Eurostat. They do however, provide TAFs
(Temporal Adjustment Factors).
Regulated by Eurostat/OECD
For countries participating in the ECP (members of the European Union, EFTA countries and candidate
countries) they are regulated 24 by the Purchasing Power Parities Regulation, in the absence of averagenational prices, to undertake a Spatial Adjustments Factor exercise to supply SAFs that take city prices tonational prices. For information – there is a second stage of this process - temporal factors will takemonthly prices to annual prices. The regulation stipulates that spatial adjustment fact (which are to besupplied at basic heading level) should be no more than six years old at the reference period of the survey.
22F

Uses of regional indices
Regional price levels have been produced several times by the UK and involve comparing price levels
across regions – a spatial index. Refer to section 1.2.2 ‘Demand for regional price data’. This was also thefocus of other regional prices published and shows the relative costs of a fixed basket of goods andservices in different regions. Many prices varied very little by region and some prices that are set nationally
may not vary at all. Indeed, some items have national pricing such as postage and communications andsome brands have national pricing such as APPLE products. With strong competition on the UK market,
particularly among the top four supermarkets there is very little divergence or variation. Indeed, many of
SAFs for the aggregate of Food, Beverages and Tobacco are close to 1, due to this competition and national
pricing. A few commodities may behave very differently in different regions and the most key one ishousing. This would not be needed for CPI but is a key major component of CPIH and therefore would bea major component of regional differences in the UK.
The existing publication of Relative Regional Consumer Price Levels (RRCPLs) as already commented
previously is derived from the information used in the six yearly calculations of the Spatial Adjustment
Factors (SAFs). This information used in the calculations also provides estimates of regional; price levels
for a fixed national basket of goods and services, excluding n housing. Regional weights for the aggregationof each basic heading are derived from the UK household budget survey, namely the Living Costs and Foodsurvey, reflecting at least part of the differences in expenditure patterns by region.
An alternative approach is to produce a temporal price index in each region, in effect regional priceinflation. However, such indices will not produce information that is suitable for comparing price levels
between regions. To have both regional inflation and regional levels, it will necessary to produce two
types of index.

24

Regulation (EC) No 1445/2007 of the European Parliament and of the Council establishing common rules for theprovision of basic information on Purchasing Power Parities and for their calculation and dissemination.

89

For the UK one of the major by-products or benefits of conducting the Spatial Adjustment Factor project
was the opportunity to produce Relative Regional Consumer Price Levels (RRCPLs). An article waspublished by the UK providing analysis of price levels for consumer goods and services for many regionsin the UK (including the composite countries of Wales, Northern Ireland, and Scotland) as well as London
and other key regions within England. The analysis primarily focused on price level indices which provided
a comparison of regions price levels in the UK relative to the UK average.
As commented, the results were based on a sample of a large sample of observations using the underlying
price data from the three main sources that was used in the SAF calculators; namely CPI data, a nationwide
regional survey of prices conducted by a third part, and finally data collected internally by the UK statistical
office, ONS. It therefore took minimal work, (relative to the importance of the outputs) to undertake ananalysis of regional price levels as the dataset had been validated and SAFs already calculated.
Comparability across regions was ensured by having tightly defined specifications for each of the items inthe basket.

Allows for comparison of piece levels across regions
The main use of Relative Regional Consumer Price Levels (RRCPLs) and the reason the UK made thedecision to publish such data was to allow for a comparison of price levels across regions of the UK.
Supplementary data was published to a more detailed regional level within England but only at theaggregate level as it was no felt that the dataset was robust enough for more detailed disaggregated data.
Data were published at total and division level (based on COICOP classification) for five regions (London(England (excluding London), Scotland, Wales, and Northern Ireland.

Demand for regional price data
There has always been a demand and interest for regional price levels from the devolved assemblies ofWales, Scotland and Northern Ireland and England as well as other government departments, policy
makers, academics, researchers, etc. The latest data published by the UK was in March 2018, Relative
regional consumer price levels of goods and services, UK:2016 (refer to link below).
In the 2003 budget, the then UK Chancellor of the UK government announced plans to produce regional
price levels for the UK. We published our plans for addressing this need the same year. More recently
regional price data were published in 2011 and now in 2018. Both datasets were produced because of therequirement by Eurostat for the UK to deliver SAFs.
The UK has also responded to user demands for regional price data in recent years;
i.
ii.
iii.
iv.
v.

2001 - produced indicative figures for 2000 on the variation in prices between regions
2004 - published estimates of 2003 regional price levels, partial update on 2000
2005 - published regional price levels for 2004, similar to 2003 estimates2011 - published regional price levels for 2010, regions price levels compared with UK
2018 – published regional price levels for 2016, regions price levels compared with UK
90

In November 2017, ONS commissioned work by Southampton University to conduct a feasibility study
into producing a regional CPIH (Consumer Price index, including owner occupied housing costs. Refer tosection 6 Future developments.
Development of UK CPIH - calculating at regional level
There is great user demand for regional data, both regional price levels and Consumer Price
Index at regional level

The UK CPIH (Consumer Prices Index, including owner occupiers housing costs) is used to measureconsumer price inflation in the UK. It was introduced in 2013 as a more complete measure of inflation as
it included owner occupiers housing costs (OOH), which make up a major proportion of household budgets
in the OOH calculations.
There is a demand within the UK for regional data, whether this be price levels or inflation.
Users of price statistics have for a long time suggested that regional indices of consumer prices would bevaluable statistics in helping to understand how inflation varies across the UK, and whether there areimportant differences in regional inflation (RPI Advisory Committee 1971, Fenwick and O’Donoghue 2003,
UK Statistics Authority 2013). The assumption has been that the number of price quotes is too small at aregional level to support the calculation of indices, and it has not been a sufficiently high priority to invest
in additional price collection for this purpose. Some limited information on variation in regional prices has
been made available through ONS publications on RRCPLs. These publications have used information fromadditional price collections made every six years for the purposes of adjusting PPP statistics.
PPP prices are collected in the capital city of the country, and a periodic exercise is undertaken to adjust
to the whole country. RRCPLs show the differences in price levels between regions but are not designed
to show price change (inflation), and because of the methodology and differences in the weights, theycannot be used even for a once-every-six-years approximation to regional inflation. Therefore, the ONS
has commissioned some work to investigate the potential for the existing consumer price collections to
support the calculation of regional price indices for CPIH at regional level for the nine regions of Englandand the devolved assemblies of the UK, Wales, Scotland. and Northern Ireland. This is the same regional
breakdown as for the RRCPLs.
A regional CPIH measure would provide valuable insight for policy makers into the nature of how
variable inflation rates are within the UK and the potential causes of inter-regional inflation differences

There are however major hurdles or restriction; that is whether the currently available data sources for
the CPIH lead to sufficiently reliable measures at the regional level. The feasibility study concluded that
regional CPIH are not suitably reliable statistics when using the same methods currently used for thenational CPIH. The main challenges were inadequate sample size at regional level, a modification of91

region-based weights within strata and lack of expenditure weights at item level. However, the regional
CPIH does capture the general trends similar to that of the CPIH which show that there is potential for it
to be developed to be more useful. It is proposed that a study into alternative statistical methods issuggested to overcome the limitations of the smaller regional sample sizes. Such statistical methods which
may provide improved regional CPIH reliability are estimation methods, smoothing methods, and finite
population corrections. Also aggregating the available data into larger categories may provide morerobust, but less sensitive indices.

Next steps for development of regional price inflation and regional price levels
There continues to be a demand for regional price data. A limitation of the approach for example used toconstruct SAFs is that due to the need to have a large field-based collection to address the areas where
CPI data is insufficient, it can only reasonably be completed every six years. This is when, as previously
noted, ONS is required to meet its obligations to Eurostat in updating SAFs.
At the time of writing this article, there are no immediate plans to assess the feasibility and usefulness ofdeveloping and publishing annual results for the areas where CPI data can be used for spatial comparisonor to produce regional price data more regularly. That said, however, as already mentioned, ONS has
recently commissioned work by Southampton University to conduct a feasibility study into producing a
regional CPIH.
It is important to note that these are different from the RRCPLS which show the relative difference in pricelevels between regions. In comparison, the regional CPIH consistent (referred to as rCPIH) inflation rates
are designed to show price change over time (inflation). The aim of the feasibility study was to investigate
the potential for the existing consumer price collections to support the calculation of regional priceindices. More specifically, it assesses the feasibility of calculating the CPIH at a regional level for the nine
regions of England, and Wales, Scotland, and Northern Ireland from existing data. A reasonable rCPIH
measure would provide valuable insight into the nature of how variable inflation rates are within the UK
and the potential causes of inter-regional inflation differences. However, the major restriction is whether
the currently available data sources lead to sufficiently reliable measures at the regional level.
In brief, the study found that it is possible to construct rCPIH series from the available data sources. The
basic patterns in the series are similar to those in the national CPIH. The individual rCPIHs differ in waysthat could be expected, for example, with London prices increasing at a greater rate than other regions,driven primarily by housing. Although these provisional rCPIH are somewhat useful, the reliability of
specific components of the data and procedures are relatively low. Small sample sizes create a great dealof irregularities and uncertainties in the indices as measured by approximate variances, which is the main
issue. Therefore, although it is feasible to construct rCPIHs, considerable further development is required
to ensure that the rCPIH can reliably represent the inflation within each of the regions. ONS will continue
to work with Southampton University to progress some of the recommended next steps at outlined in thefeasibility study, including investigating the assumptions of the provisional rCPIH such as using national
item indices when regional data are not available. Further updates will be published as the work
progresses.
92

C. Italy
Introduction and Scope
Several players of the economic and social debate recognize the need for compiling subnational
household consumption purchasing power parities indexes (SPPPs) for Italy due to the high socioeconomic heterogeneity across its geographical areas. 25
23F

Compared to other OECD countries, the Italian regions vary widely in terms of household economic
conditions. Italy has an almost dualistic economy with all Southern regions attaining a lower level ofhousehold income on average than the Centre and Northern regions.
Some key indicators clearly show this situation.
Looking at the geographical areas (NUTS 1 level), on the side of income, in 2004 the households’ averageannual income without imputed rents (including imputed rents the situation is the same), derived fromthe European harmonized survey on income and living conditions (EU-SILC), was €22,657 in the Islands,€23,308 in the South, €29,729 in the Centre, €30,132 and €30,416 respectively in the North East and theNorth West. Therefore, the households’ average income in the Islands was 25% less than that in the North
West. After 13 years, in 2017, the average household annual income in the Islands was €24,065, in the
North East (that overcame the North West) €35,386 (the gap increased to more than 33% for the two
economic crises, in 2008-2009 and 2012-2013, that enlarged the territorial differences).
In terms of regions (NUTS 2 level), in 2017 the highest households’ average annual income was recordedin the autonomous province of Bolzano/Bozen (€37,892), in the North East and the lowest one in Sicilia(€22,745), in the South, with a gap of 40% between the two.
On the side of expenditures, the difference amongst the geographical areas of Italy are similar. In 2017 in
the North West the households’ average monthly expenditure (net of imputed rents) was €2,249.19 (thehighest one) whereas in the Islands it was €1,558.04 (the lowest one) with a gap of more than 30%, with
the North East where it recorded €2,186.11, the Centre €1,970.56 and the South €1,638.86.
At the level of regions, in 2017 the autonomous province of Bolzano/Bozen recorded an average monthly
households’ expenditure almost double of that of Calabria (in the South).
Moreover, indicators concerning absolute poverty (measured on the expenditure side) confirm thedifferences across the Italian territory. In 2007, before the two economic crises, the absolute poverty rate
in terms of households was 3.1% in the North, and 4.6% in the South and Islands. In 2017, the gap between

25

The second territorial level of the Nomenclature of territorial units for statistics (NUTS 2) divides Italy into 19
regions and the two autonomous provinces of Trento and Bolzano, which make up the Trentino-Alto Adige region.
More specifically, Italy is subdivided into the following 20 regions, each of which has its own regional capital(specified in brackets): Aosta Valley (Aosta), Piedmont (Turin), Liguria (Genoa), Lombardy (Milan), Adige TrentinoAlto Adige (Trento), Veneto (Venice), Friuli-Venezia Giulia (Trieste), Emilia-Romagna (Bologna), Tuscany (Florence),
Umbria (Perugia), Marche (Ancona), Lazio (Rome), Abruzzo (L'Aquila), Molise (Campobasso), Campania (Naples),Apulia (Bari), Basilicata (Potenza), Calabria (Catanzaro), Sicily (Palermo), Sardinia (Cagliari).

93the two geographical areas had increased. In the framework of a general worsening of the absolute
poverty in Italy, its rate arrived at 10.3% in the South and Islands, almost doubling that of the North (5.4%).
In the more recent data (2019) this gap narrowed (8.6% vs 5.8%) but remained wide. This gap becomeshuge if we consider the households’ relative poverty rate (in expenditure terms). In 2017, this rate was
5.9% in the North and 24.7% in the South, 6.8% and 21.1% respectively in 2019. It means that thehouseholds in relative poverty in the South still in 2019 are (in percentage terms) three times those in the
North, whereas in absolute poverty they are about 1.5 times.
This difference between the two indicators (absolute and relative poverty rate) provides further evidence(beside those already emerging from the data regarding the level of income and expenditure) thatstrengthen the reasons for measuring spatial price indices (SPIs) for Italy. The Italian National Institute for
Statistics (Istat) calculates absolute poverty indicators taking into account thresholds that differ byhousehold and municipality typology and by geographical areas. In terms of geographical areas, thedifferences amongst the thresholds are due, above all, to the consumer price component that wasconsidered in their estimation. For the relative poverty, the threshold is unique for the entire Italian
territory and thus it does not consider the differences of prices amongst regions. Thus, the different widthof gap between the North and the South of Italy in terms of absolute (on one side) and relative (on theother side) poverty is an indirect indicator of the need of compiling SPPPs.
Therefore some crucial indicators (income, consumption, poverty rates) together with the evidence
coming from the current survey on consumer prices to estimate inflation, prove that households’
purchasing power is different across Italian regions and that one of the main reason of this difference isascribable to consumer prices.
SPPPs are a crucial tool to measure how much consumer prices differ between Lazio and Toscana, Sicilia
and Bolzan/Bozen, Marche and Puglia and between each region and the national average level of prices.
Indeed, Italy is one of the few European countries that carried out official experimental SPPPs estimations(using CPI data and ad-hoc surveys) referring specifically to household consumption and considering
regional capitals.
In particular in 2005 the Italian National Institute for Statistics (Istat,) in cooperation with Unioncamere
and Istituto Tagliacarne, launched a research project aimed at testing the possibility of using andintegrating the statistical information currently supplied by CPI surveys. Experimental estimates were
produced in 2008, with reference to 2006 data, and the GEKS formula was adopted for computing spatial
price indices (SPIs) concerning three expenditure divisions: Food and Beverages, Clothing and Footwear,
and Furniture, which represented approximately 34% of the total consumer expenditure (Istat, 2008). To
calculate subnational PPPs for consumer prices for 20 Italian cities (the regional chief towns), Istat used
the same procedures as those used in the ICP by following the principle of strict comparability of products.
Moreover, ad-hoc surveys were designed and carried out for the Clothing and Footwear, and Furniturecategories.

94

The second project (whose results were disseminated in 2010 with reference to 2009 data) to compile
subnational PPPs was compiled for all the COICOP 26 expenditure divisions. The source of data andmethodology remained the same with the exception of rents for which spatial comparisons were carriedout using Country Product Dummy (CPD) models and Household Budget Survey data that include somedetailed information on the characteristics of the dwellings (Istat, 2010).
24F

The 2010 results showed significant differences in the level of consumer prices across the regional capitals(Istat, 2010). Consumer price levels in the Northern cities are generally higher than in the Centre andespecially in South of Italy. Bolzano (105.6) and Milano (104.7) showed the highest prices compared with
the Italian average (100) while the least expensive city proved to be Napoli (93.8).

Methodological approaches and results
The SPPPs obtained from the first two experimental estimates encouraged Istat to go ahead with the
project of regularly producing spatial indices of consumer prices at the regional level. To this aim, further
research studies were implemented by Istat in cooperation with the University of Florence and the
University of Tuscia providing interesting results (Biggeri et al, 2016; Laureti and Polidoro, 2017; Laureti
et al, 2017).
These studies have mainly focused on exploring the use of two different sources of data for constructing
spatial price indexes: CPI and scanner data. However, it is worth noting that, in order to produce
subnational PPPs for the entire household consumption expenditure, other sources should be consideredin addition to scanner data and CPI data as we will explain in section 3 below.

The use of CPI data: methods and SPPPs estimation results
In order to use traditional CPI data an in-depth analysis of the basket of goods and services and of the
micro data has to be carried out. One of the main issues in using the Italian CPI data is that the survey is
not designed for place-to-place comparisons and using these data for spatial price index estimations
requires additional work to obtain matching product specifications based on detailed specifications which
define the type of product (size, brand) and the type of outlet from which the product is purchased. By
definition, the prices used for constructing CPIs belong to specific products that are representative of theexpenditures in that geographical area, otherwise these products would not have been included in thebasket of goods and services priced. Therefore, products whose prices are collected for compiling CPIs
could not be comparable across different areas due to differences in local consumer behavior. For spatialcomparison aims, it is essential to match the prices for those items that not only meet the CPI
specifications, but which have the same brand, package, and model (variety) if they are price-determining

26

The Classification of individual consumption by purpose, abbreviated as COICOP, is a classification developed by
the United Nations Statistics Division to classify and analyze individual consumption expenditures incurred byhouseholds, non-profit institutions

95

characteristics. Therefore, when identifying the products to be compared, a ‘‘tight’’ product specification
can be adopted but it will produce a price matrix (regions by products) with several missing values, thus
resulting in a poor coverage of spatial price indexes. On the other hand, using ‘‘loose’’ productspecifications reduces the number of missing values, but may result in price comparisons of dissimilar
items with price differences that may be contaminated by quality differences (Silver and Heravi, 2005;
Silver 2009).
The use of CPI data for comparing prices across geographical areas in Italy has been hampered by thecomplex and time-consuming analyses needed for capturing and processing the information contained in
CPI data in order to make them suitable for comparing product prices. Therefore, after the 2008 and 2010
experimental releases, various studies focused on products, such as fruit and vegetables, fresh fish andmeat, that are comparable by definition and whose prices, indeed, can be used for compiling spatial priceindexes.
Biggeri et al (2016) focused on seven basic heading (BH) groups of products belonging to the Food andnon-alcoholic beverages CPI group for carrying out further experiments with the aim of investigating theperformance of various CPD models estimated using data characterized by various levels of aggregation. 27
More specifically, the authors selected fresh meat, all fresh fish species, all types of fresh fruit andvegetables, which accounts for 5.2 % of the entire CPI basket.
25F

Subnational household consumption PPPs for the above mentioned BHs were estimated by using adataset composed of 218,228 monthly price quotes collected in the 19 regional chief towns considered in
the 2014 CPI survey. Although the varieties available in the different regional markets may change due todistinct local consumer and production behavior, the products considered were characterized by a highdegree of overlap across the Italian regional chief towns. After having obtained annual individual pricequotes for each product in the seven BHs, hedonic CPD models, which include the characteristics of theoutlet where products are sold, were estimated for compiling regional price indexes for Italy.
Table 1 shows the results obtained. These show significant consumer price level differences across thevarious Italian regions and support the notion that price levels are higher in the Northern-Central regions
than in the South, especially for the Fresh or chilled fruit BHs and Fresh or chilled vegetables other thanpotatoes BHs.
Moreover, after data quality controls and preliminary analyses of the basket, Laureti et al (2017)considered CPI data including annual average prices for 151 vegetable products collected in the 20
regional chief towns. They ran hedonic CPD by including retail chains. Table 2 shows SPPPs results for the“Fresh and chilled vegetables” BH based on CPI data referring to the six most important modern
distribution chains. It is possible to observe significant differences between the results obtained from the27

The dataset was provided by the Italian Statistical Institute (Istat) and the elementary price quotes were treatedin order to respect the statistical confidentiality and the authors worked at Istat in order test the various
hypotheses of the CPD approach.

96

CPD and the hedonic CPD. These results appear to be coherent with our expectations and the territorial
characteristics of the Italian macro areas.

The use of scanner data: methods and SPPPs estimation results
Over the last decade, there has been a growing interest in using scanner data for constructing official priceindexes due to the increasing availability of this new data source. In the last few years, several EU
countries have started to use scanner data for compiling CPIs adopting different methods (Italy in 2018,
France in 2020). They acquire scanner data either directly from retail chains or indirectly from market
research companies, such as Nielsen and GfK. Regardless of which provider scanner data come from, NSOs
must reclassify them in order to make them suitable for constructing official CPIs and to achieve this they
followed various procedures. Since the primary use of scanner data is not to measure temporal and spatial
price differences, methodological and empirical issues regarding scanner data quality need to beaddressed. This reclassification and data cleaning phase is essential and consumes a lot of IT resources.
The characteristics of scanner data where elementary items, whose prices are available, are clearly
identified through Global Trade Item Number (GTINs) and the contextual availability of information
concerning quantity sold and turnover make these data specifically fit to the aim of building PPPs as theyevaluate both comparability and representativeness of each product.
Therefore, scanner data may enable countries to compile also SPPPs and improve international spatialcomparisons.
For the time being, few studies have been carried out on using scanner data for compiling spatial prices
indexes (Heravi, Heston, and Silver, 2003; Laureti and Polidoro, 2017; Laureti and Polidoro, 2018, Laureti
and Rao, 2018) and many of them are focused on Italian cases.

The Italian project on scanner data
Istat has recently introduced scanner data to the official CPI computation and has been exploring thepossibility of using them for compiling subnational PPPs.
Since 2014 scanner data have been regularly collected and provided by the market research company
ACNielsen (Istat project on scanner data) that is authorised to do it by the chains of modern distribution
in the framework of an agreement with the Association of Modern Distribution. ACNielsen provide Istat
with scanner data on a weekly basis by uploading the data files on a dedicated Istat web portal.
As aforementioned, the itemized information contained in scanner data through GTINs and the availabilityof information concerning turnover and quantities for each item code may be fruitfully used in order tocompile weighted spatial price indexes at the detailed territorial level.
Istat has initiated a research project, in collaboration with Tuscia and Florence Universities, focused onusing scanner data for producing SPPPs on an annual basis, thus filling a gap in the statistical information97

concerning consumer prices in Italy. Although official estimates have not yet been published, several
analyses have been carried out since 2016.

Preliminary phase
In a first phase of the research project (Laureti and Polidoro, 2016; Laureti and Polidoro, 2017) the
potential advantages and the empirical issues deriving from the use of scanner data for constructing SPPPs
have been addressed by using data for the 20 Italian regional chief towns.
Laureti and Polidoro (2017) focused on computing Italian subnational PPPs at the BH level. After a processof data cleaning and trimming outliers, unit value prices per item code were computed by dividing thetotal turnover for that item by the total quantities sold over the week. Several analyses were carried out
in order to understand how best to aggregate the detailed information contained in the Italian scanner
data for constructing spatial price indexes. More specifically, by referring to each Italian regional chief
town, ANOVA and t tests on a sample of items were carried out in order to verify if the price of the same
item could reflect auxiliary services provided by the seller. Indeed, within each city, the same item is foundin different supermarket chains and in different stores, which belong to the same retail chain. Results
showed significant differences in prices of the same items thus suggesting product differentiation whichis embodied in the range or quality of services offered by different retailers across chains.
Therefore, with respect to item groupings, the most detailed classification of item that is available within
the BH, that is the GTIN, which is identical across the Italian territory, was chosen. As regards the time
dimension, annual regional average prices for each GTIN code and outlet were used (Ivancic et al, 2011).
Results obtained from the estimation of Time-interaction-Country Product Dummy models (TiCPD)
confirm the high variability of SPI based on monthly data.
One issue considered in the first phase was how to use the available information on turnover to computeunit value price per item code. The unit value index should be used for a single homogeneous product.
This is a basic axiom underlying the use of scanner data. Therefore, unit values for each GTIN code andoutlet were calculated by dividing the total turnover for that item code by the total quantities sold over
the week.
In order to compile the SPI for the 20 regional chief towns at BH level, 931 points of sale of the six mostimportant modern distribution chains (Coop Italia, Conad, Selex, Esselunga, Auchan, and Carrefour), thatcover approximately 57% of the turnover of total modern distribution, were considered. The dataset usedconsisted of 3,659,286 annual price quotes from the 20 regional chief towns covering 69 BHs for a totalof 49,489 products (GTIN codes). They refer to 2015 and to grocery products.
From a methodological point of view, the CPD regression-based methodology, which is also used by the
ICP at the World Bank for aggregating price data at BH level, was adopted. Due to the characteristics ofthe scanner data, which allow for including weights when computing subnational PPPs, as well as to theresults of preliminary analyses, the authors used various CPD model specifications. More specifically, inorder to account for the economic importance of each item in its market, which is essential in index
98number literature as demonstrated also by our analyses, they estimated weighted CPD models using both
expenditure share and quantity as weights.
Moreover, in order to explore the effects of including information on the type of outlet and retail chain
they estimated hedonic weighted CPD models.

Second phase
In a second phase of the research project (Laureti and Rao, 2018; Laureti and Polidoro, 2018) household
consumption subnational PPPs for Italy were estimated by using a larger scanner dataset constructed forexperimental CPI computation after having carried out data by cleaning and trimming outliers processes.
The dataset used refers to a random sample of approximately 1,800 hypermarkets (more than 500) andsupermarkets (almost 1,300), concerning the grocery products sold in 2017 in the most important retailchains (95% of modern retail chain distribution that covers 55.4% of total retail trade distribution for thiscategory of products). Since data are available for the 107 Italian provinces, within-regional and betweenregional spatial price indexes for the food and non-food consumption aggregates included in the scanner
dataset were estimated.
To this aim, Laureti and Polidoro (2018) referred to a list of products derived from the dataset thatspecifically covers 54 grocery product aggregates, belonging to five divisions of the ECOICOP (01, 02, 05,
09, 12). By aggregating the weekly prices of each GTIN code sold in the supermarkets and hypermarkets
of 16 modern distribution chains located in the 107 Italian provinces using turnover weights, a total of
487,094 annual provincial prices was obtained, each one referred to a specific GTIN. These data comefrom a stratified random sample of outlets and items. More specifically the universe of about 9,000 outlets
belonging to the 16 most important retail chains (94% of modern retail chain distribution) was stratified
by province, distribution chains and kind of outlets (888 strata). Outlets are selected with probabilities
proportional to the 2016 turnover (thus obtaining 1,781 outlets composed of 510 hypermarkets and 1,271
supermarkets). The identification of the items is based on barcodes (GTINs), which univocally classify the
products across the entire national territory. In each outlet, items were selected with probabilities
proportional to the 2016 turnover for each product aggregate in order to cover up to 60% of the total
turnover of the product aggregate 28.
26F

28

Although this dataset was constructed for CPI compilation, it can also be used for making spatial pricecomparison among Italian regions bearing in mind that only the best selling products, which are typicallyconsumed in each Italian province and region, may have be included according to the CPI selection procedure.
Therefore these products may not be strictly comparable across different provinces and regions. It is useful to notethat not all of the listed products must be priced in all of the regions. However, reliable regional price comparisons
can be made as long as there is reasonable overlap in the items priced in different regions. We checked thisrequirement by verifying if product overlaps exhibit a chain structure.

99

On the basis of the results of the first phase of the research project, annual averages of weekly prices(average of prices paid by consumers) for each item and outlet were computed using turnover as weights.
Then provincial averages using sampling weights for each outlet were calculated.
In order to estimate regional spatial indices for products sold in modern distribution chains by using data
for the 107 Italian provinces, a two-step procedure similar to the one used in the ICP was adopted whereby
provinces are grouped into regions (World Bank, 2013). In the first step, within-region PPPs are computedby comparing price and quantity data referring to products sold in the various provinces within eachregion while in the second step, between-region PPPs are obtained for each region by using deflated pricedata for each province.
Moreover, as in international practice, subnational PPP compilation is undertaken at two levels: at the BH
level and at a more aggregated level (Food and Non-Food products that are personal and home careproducts). The methods selected for making multilateral comparisons is based on several axiomatic
properties, including two basic properties: transitivity and base region invariance. Transitivity simply
means that the PPP between any two regions should be the same whether it is computed directly or
indirectly through a third region. The second requirement is that the PPPs be base region–invariant, whichmeans that the PPPs between any two regions should be the same regardless of the choice of base region.

Aggregation methods at the BH level. First step: within-region PPPs
Let us assume that we are attempting to make a spatial comparison of prices between R regions, r=1,…,R,with Mr provinces in each region r. In the first stage of aggregation of price data at item level, which leads
to price comparisons at BH level, and represent price and quantity of i-th item in j-th province and in r-th
region i=1,2…,N; j=1,2,…,M; r=1,2,…R.
In order to compute within-region PPPs, we explored different methods. 29 However, due to lack of space,
we only illustrate the Region-product-dummy (RPD) method which was also used to compute betweenregional PPPs. All methods are implemented using R. If product overlaps exhibit a chain structure thus the
RPD method exhibits some aspects of spatial chaining.
27F

The RPD is the regional version of the country-product-dummy (CPD) method used in international
comparisons. This method suggests that price levels are estimated by regressing logarithms of prices onprovinces for each province and product dummy variables; the model is given for each BH by:𝑙𝑛𝑝𝑖𝑗𝑟 = 𝑙𝑛𝑃𝑃𝑃𝑗 + 𝑙𝑛𝑃𝑃𝑃𝑖 + 𝑙𝑛𝑢𝑖𝑗𝑟= 𝜋𝑗 + 𝛾𝑘 + 𝑣𝑖𝑗

29

We used various spatial index formulae, including Fisher-based GEKS, Geary-Khamis and CPD (World Bank, 2013;
Laureti and Rao, 2018). We found interesting results which suggest a high variability of prices within various
regions. However, they cannot be reported here due to lack of space.

100

𝑀𝑟

𝑁
𝑗

= ∑ 𝜋𝑗 𝐷 + ∑ 𝛾𝑖 𝐷𝑖 + 𝑣𝑖𝑗𝑟
𝑗=1

𝑖=1

(1)

where 𝐷 𝑗 is a provincial-dummy variable that takes value equal to 1 if the price observation is from j-thprovince in the r-th region; and 𝐷 𝑖 is a i-dummy variable that takes value equal to 1 if the price observation
is for i-th commodity.
The random disturbance is assumed to satisfy the standard assumptions of a multiple regression model.
𝑀𝑟
In order to estimate parameters of this model we impose normalization∑𝑗=1
𝜋𝑗 = 0 thus treating all

regions in a symmetric manner. If𝜋̂𝑗 = (1,2, … , 𝑀𝑟 ) are estimated parameters, the within-regional PPP
for the province j in region r is given by 𝑊𝑅_ 𝑃𝑃𝑃𝑗 = 𝑒 𝜋̂𝑗 . The RPD method-based price comparisons aretransitive and base-invariant. With the aim of taking into account the economic importance(representativeness) of each product expressed by expenditure weights wijr based on turnover we used aweighted RPD model:

𝑀𝑟

𝑛
𝑗

𝑖
√𝑤𝑖𝑗𝑟 𝑙𝑛𝑝𝑖𝑗𝑟 = ∑ 𝜋𝑗 √𝑤𝑖𝑗𝑟 𝐷 + ∑ 𝜂𝑖 √𝑤𝑖𝑗𝑟 𝐷 + √𝑤𝑖𝑗𝑟
𝑗=1

𝑖=1

(2)

Aggregation methods at BH level. Second step: between-region PPPs
In order to use provincial prices adjusted for differences among provinces within the r-th region, item
prices in all the provinces of region r are converted by using:𝑝̂𝑖𝑗𝑟 =

𝑝𝑖𝑗𝑟
𝑊𝑅_ 𝑃𝑃𝑃𝑗𝑟(3)

The deflated prices (in log form) were used for estimating a weighted RPD model with regionaldummies and weights defined by deflated expenditure for each item in the r-th region.
𝑅𝑁𝑘√𝑤𝑖𝑗𝑟 𝑙𝑛𝑝𝑖𝑗𝑟 = ∑ 𝜋𝑘 √𝑤𝑖𝑗𝑟 𝐷 + ∑ 𝜂𝑖 √𝑤𝑖𝑗𝑟 𝐷𝑖 + √𝑤𝑖𝑗𝑟 𝜐𝑖𝑗𝑟
𝑗=1

𝑖=1

(4)

The between-region PPP for the region r is given by 𝑅_ 𝑃𝑃𝑃𝑟 = 𝑒 𝜋̂𝑟 and transitive price comparisons basedon RPD method are given by:
101

𝑅𝑃𝐷
𝑃𝑟𝑘
=

𝑒𝑥𝑝(𝜋̂𝑘 )𝑓𝑜𝑟 𝑎𝑙𝑙 𝑟, 𝑘 = 1,2, … 𝑅
𝑥𝑝(𝜋̂𝑟 )(5)

Aggregation method above basic heading level
The next and final step for compiling regional price comparisons is to aggregate the results from BH levelcomparisons to higher level aggregates. Let us assume that there are L basic headings (l=1,…,L) and 𝑒𝑖𝑟
expenditure for i-th BH in region r. We decided to use the Fisher price index since it has a range of
axiomatic and economic theoretic properties.
The Fisher index is given by:
𝐿𝑎𝑠𝑝𝑒𝑦𝑟𝑒𝑠𝐹𝑖𝑠ℎ𝑒𝑟𝑃𝑟𝑘= √𝑃𝑟𝑘𝑃𝑎𝑎𝑠𝑐ℎ𝑒
∙ 𝑃𝑟𝑘

(6)

Where
𝐿𝑎𝑠𝑝𝑒𝑦𝑟𝑒𝑠

𝑃𝑟𝑘

=

𝑘
∑𝐿𝑙=1 𝑝𝑙𝑘 𝑞𝑙𝑟
𝑟 𝑝𝑙
=
∑
𝑠
𝑖 ( 𝑟)∑𝐿𝑙=1 𝑝𝑙𝑟 𝑞𝑙𝑟
𝑝𝑙
−1 −1

𝑃𝑎𝑎𝑠𝑐ℎ𝑒
𝑃𝑟𝑘
=

𝑘 𝑘
∑𝑁
𝑝𝑙𝑘
𝑙=1 𝑝𝑙 𝑞𝑙
𝑘
=
𝑠
[∑
(
) ]
𝑙
𝑟 𝑘
𝑝𝑙𝑟
∑𝑁
𝑙=1 𝑝𝑙 𝑞𝑙
𝑙𝑒𝑟

𝑝𝑟 𝑞 𝑟𝑙=1 𝑝𝑙 𝑞𝑙

With 𝑠𝑖𝑟 = ∑𝐿 𝑖 𝑟 = ∑𝐿 𝑙 𝑙𝑟 𝑟
𝑙=1 𝑒𝑙As the Fisher binary index in (6) is not transitive, it is possible to use the procedure suggested by Gini
(1931), Elteto and Koves (1964) and Szulc (1964) referred to as the GEKS index to generate transitive
multilateral price comparisons across different regions.
The resulting index is given by:𝑅
1
𝐺𝐸𝐾𝑆−𝐹𝐼𝑆𝐻𝐸𝑅
𝐹𝑖𝑠ℎ𝑒𝑟 ⁄𝑅
𝐹𝑖𝑠ℎ𝑒𝑟𝑃𝑟𝑘= ∏[𝑃𝑟𝑠
∙ 𝑃𝑠𝑘
]
𝑟=1

(7)

The GEKS-Fisher based formula is used in cross-country comparisons made within the ICP at the WorldBank (2015) and the OECD-Eurostat comparisons. In order to obtain a set of R_PPPs that refer to thegroup of regions (Italy) we standardized the GEKS-Fisher based PPPs (S-GEKS).

102

The Italian project on scanner data: main results of second phase
As shown in Figure 2 and Table 3, Southern regions appear to have price levels that are below the national
average both for Food and Non-Food products, with the exception of Abruzzo (101.90 and 101.33,
respectively), Molise (102.90 and 101.24), and Sardegna (101.93 and 101.57). However, it is worth notingthat some Northern regions also show lower price levels than the national average, such as EmiliaRomagna (98.31 and 98.40), Veneto (99.09 and 98.48) and Piemonte for Food products (99.80). On
average, Toscana proved to be the less expensive region for both product aggregates (96.24 and 95.17).
These results seem to suggest that when considering the retail trade modern distribution, the expected
relationship in terms of price levels between the North and South of Italy partially changes and propose
an interesting line for future research on the influence of the various distribution channels when defining
subnational PPPs.
Caution is required when interpreting these results since: a) they may be influenced by the characteristicsof the modern retail trade which is not uniformly distributed across Italian territory in terms of types ofretail chains and market share; b) we excluded two groups of products “Whole Milk” and “Low-Fat Milk”
since there were no reliable overlaps among regions enabling spatial price comparisons; and c) theseresults are based on data selected for CPI compilation and hard discounts are excluded.
In some BHs, the usual divide between North and South is not confirmed. Regional spatial price indexes
for two specific groups of products, that is “Pasta products” (BH1), which belongs to the aggregate Food
products, and “Non-electrical appliances” (BH2, e.g. razors, scissors, hairbrushes, toothbrushes, etc.)
included in the Non-Food aggregate are illustrated in Table 4. Our findings confirm large differences inprice levels among Italian regions even if BH2 shows a higher territorial heterogeneity than BH1 (range isequal to 19.03 and 13.23 respectively). In the case of BH1, five regions located in the South and Islands(out of 8) and two Northern- Central regions (out of 11) show lower prices than Lazio while for BH2 higher
price indexes are observed in three Southern regions and eight regions in Northern and Central Italy. Thisdifferent territorial pattern of consumption spatial price indexes is not confirmed when aggregatedregional PPPs are computed for Food and Non-food products (Italy=100).
Price levels can also vary greatly within a region. Figure 2 shows within-region household consumption
PPPs for Tuscany where Siena proved to be the most expensive province for Food products compared to
Tuscany as a whole while Prato is the cheapest. A higher heterogeneity in price levels can be seen for NonFood products as Livorno is the most expensive province. In contrast to Tuscany, Lombardy, whose results
are illustrated in Figure 3, is more homogeneous for both product aggregates as we can see from theresults.
These findings could be used for obtaining spatial adjustment factors to be used when prices for otherproduct categories are collected using traditional survey (on-field price collection) or ad-hoc surveys
carried out in the regional capital.

103

Future projects and concluding remarks
The encouraging results obtained using scanner data strengthen Istat strategy to achieve the aim of
regularly producing SPPPs for Italy comparing consumer prices amongst different regions.
The key of this project is the multi-source approach to estimate SPPPs, replicating in the field of spatialcomparison what is already done in the field of temporal comparison.

Implementing data base in the framework of multi-source approach to SPPPs estimation
To build up a database that could allow the estimation of SPPPs related to the entire universe of household
consumption, Istat is working on the following sub databases:A)
An appropriate use of CPI data starting from a deep analysis on one hand of the basket and on
the other hand of the microdata.
The analysis of the basket is aimed at selecting all the products which, by definition, are comparable andindeed they do not need further specifications in additions to those ones already present in the basket.
This is the case of fresh fish, all the different varieties of fresh fruit and fresh vegetables that are well
specified in the CPI basket, laundry and repair services for clothing, repair services for footwear, somegoods and services relating to the dwelling and its maintenance (as gas, water supply, sewerage, waste
collection, electricity, heating oils, etc.), some medicines, some medical services, some paramedical andhospital services, fuels, car rental and car parking, some urban transportation services, some catering
services, canteens, some accommodation services (for a total weight on the consumer price basket thatis about 20%).
Thus, the analysis of microdata is aimed at selecting all the elementary products that are present in a
sufficient number of regions and in a remarkable amount (it has to be established in how many regions
and when considering remarkable the amount of elementary quotes available) in order to be both
representative and comparable.
B)
Scanner data for grocery products (about 10% of the basket if we do not take into account foodproducts listed in the previous point). As aforementioned, scanner data should bring the detailed
information about the characteristics of the elementary product and the information about turnover ofthat specific product, allowing the comparison of representative “like to like”.
C)
Administrative data that are already used for CPI purposes (in particular those concerning
automotive and heating fuels) and other administrative data that are on the way to be used (the databaseof rents provided by Italian Tax Office).
D)
Excluding the products for which it does not make sense to compare among different geographicalareas (because the prices are the same or the differences not relevant such as tobacco products or
telecommunications, for a total weight of about 8%).

104

E)
For the remaining CPI products (or for a part of them where the microdata analysis has notprovided or will not provide satisfactory results), specific price data collection will be carried out.
Since 2013 Istat has focused the attention on this fifth point whose implementation is crucial for the
subnational comparison, by building on the results, in terms of use of electronic devices, obtained for themonthly territorial data collection for CPI.
At the end of 2013 Istat started the design and the implementation of a new software that translated in
the field of the spatial comparison the technical advancements achieved in the field of temporalcomparison (for the territorial data collection it was designed and developed a software dedicated to
manage all the statistical issues of the consumer price data collection, that is P1J).
The software to collect price data for spatial comparison aims was tested (and then currently used) in the
recent years to collect prices in the capital, Rome, for international PPPs, taking into account the Eurostat
basket in terms of list of products and their description.
In 2020, it will start the use of this software (P3A) to collect prices across different regions (at the
beginning in the chief towns) to fill the gap of basic information that is not covered by CPI, scanner oradmin data. The architecture of the IT system for the use of P3A is based on a web oriented client server
system where:•
data collectors download the workload of each cycle of the survey from a dedicatedserver using UMTS. In this occasion, they download the basket including all the specific descriptions of the
products. The basket is the same (in a reduced version) of that adopted for the ICP;•

data collectors visit the same outlets where the data collection for CPI is carried out;•
data collectors send to the server the elementary prices collected and their notes
together with the information about new brands and varieties;•
data collectors will receive, via the server, information coming from other data collectors
in order to check their work in the field and return the information checked and possibly revised;•
Istat statisticians monitor the entire production process through a web application thatwill produce the necessary qualitative indicators.
The basic idea is that the data collector who visits a shop for the monthly CPI survey, in certain occasions
will be asked, before leaving the shop, to make another tour of data collection for the basket of SPPPs,
using the same tablet but launching a different application (the realization of this perspective requires
adequate training of the data collectors).

Compiling SPPPs at BH and upper level of classification
As illustrated in the previous paragraph, Istat’s strategy to implement currently the database to compile
spatial indices of consumer prices is actually quadruple: use of CPI data (already representative and insome cases comparable and in other cases made comparable through appropriate ex-post treatments),105

of scanner data, of administrative data and the implementation of ad hoc data collection for certain
groups of products using electronic devices and a dedicated software.
The objective of building up and regularly updating at least four matrices (one for each of the above data
sources) where regions crosses the products and whose sum covers the entire household consumption
universe.
The matrices will have different contents, depending on the data that are referred to and their granularity.
In each cell of the matrix of data coming from territorial data collection and comparable on their own(sub-database A), BH SPPPs index should be available, provided that it is obtained through the use of theunweighted version of RPD model (as described in paragraph 2.2.4), exploiting the prices collected in theprovinces of each region (given that, generally speaking, just a limited amount of prices in each province
are available and that have to grouped at a more aggregated level with respect to the GTINs details). RPD
could be used for the second step to estimate between region PPPs.
In each cell of the matrix of sub-database B (scanner data) SPPPs index should be available, provided that
it is obtained through the weighted version of RPD. Also in this case, RPD could be used for the second
step to estimate between region PPPs.
In the matrix of the sub-database C for each fuel product, average prices should be available, allowing thedirect compilation between region PPPs. For the data of rentals for housing, each cell should be moregranular taking into account some further stratification (by type of contract, type and size of dwelling)allowing the, also in this case, the direct compilation of between region PPPs.
Also in the case of sub-database E, in each cell of the matrix average prices of the data collected should
be available, given that the products in the basket for which data collection will be carried out are well
specified in order to make them comparable across Italian regions. To make them completely
representative of the regions (at the beginning they are collected just in the chief tows), special spatial
coefficients (derived from other sources such as scanner data) could be used to take into account thevariability amongst the provinces within each region.
For the aggregation from BH level to higher level of comparison (BHs aggregation), GEKS indices should
be compiled. In this passage, data coming from different sources could be used (i.e. from BHs calculatedusing scanner data and BHs calculated using territorial data collection). In all these cases, an intermediate
aggregation has to be carried out before going up to further level.
SPPPs that will result from this strategy should be released preliminarily in 2021 through experimental
statistics in order to allow the scientific debate to develop, providing suggestions and improvements
before starting the official production of such a crucial indicator.

References
Biggeri, L., Laureti, T., and Polidoro, F.(2017) Computing subnational PPPs with CPI data: an empiricalanalysis on Italian data using country product dummy models. Soc Indic Res, 131(1), pp. 93-121.
106

Heravi, S., Heston, A., and Silver, M. (2003). Using scanner data to estimate country price parities: A
hedonic regression approach. Review of Income and Wealth, 49(1), 1-21.
Ivancic, L., Diewert, W. E., and Fox, K. J. (2011). Scanner data, time aggregation and the construction ofprice indexes. Journal of Econometrics, 161(1), 24-35.
Laureti T. and Polidoro F. (2017) “Testing the use of scanner data for computing subnational Purchasing
Power Parities in Italy”, Proceeding of 61st ISI World Statistics Congress, Marrakech.
Laureti T, Ferrante C and Dramis B. (2017) “Using scanner and CPI data to estimate Italian subnational
PPPs” Proceeding of 49th Scientific Meeting of the Italian Statistical Society, Florence.
Laureti, T., and Polidoro, F. (2018) Big data and spatial comparisons of consumer prices Testing the use ofscanner data for computing subnational Purchasing Power Parities in Italy, Proceeding of 49th Scientific
Meeting of the Italian Statistical Society, Palermo.
Laureti, T., and Rao, D.S.P.(2018) Measuring Spatial Price Level Differences within a Country: Current
Status and Future Developments. Estudios de economía aplicada, 36(1), pp.119-148.
Silver, M. (2009). The hedonic country product dummy method and quality adjustments for purchasing
power parity calculations. IMF working paper WP/09/271.
Silver, M., and Heravi, S. (2005). Purchasing power parity measurement and bias from loose item
specifications in matched samples: An analytical model and empirical study. Journal of Official Statistics,
21(3), 463.
World Bank (2015) Purchasing Power Parities and the Real Size of the World Economies-A Comprehensive
Report of the 2011 International Comparison Program, Washington, DC

107

TABLES AND FIGURES
Table 1 Estimates of subnational PPPs for regional capitals using hedonic CPD models and CPI data,
ROME=100
Other
meats

Beef
Sig.

Pork
Sig.

Lamb
Sig.

Fruit
Sig.

Vegetables
Sig.

Fish
and
seafood

Sig.

Sig.

North
Aosta

106.1

109.8

111.1

Torino112.9

Genova

110.0

**

105.9

**

98.5

Milano

87.2

105.5

Trento105.4

Venezia
TriesteBologna

95.6

125.2

***

125.5

100.1

104.7

93.6

95.5

102.3

98.8

113.7

***

111.1

***

97.5

96.3

97.0

150.6

***

155.0

***

125.2

***

106.1

87.6

100.3

125.2

***

119.7

92.4

**

104.5

98.9

94.9

108.9

123.2

***

116.3

76.1

***

102.5

123.3

98.1

116.9

121.5

***

121.4

91.1

***

99.9

104.1

103.0

101.3

126.8

***

123.8

82.5

***

108.9

***

98.2

122.6

*

**

**

*

***

93.7

*

100.7

***

CentreFirenze

87.7

**

92.5

87.7

97.6

Ancona

107.3

**

103.2

105.3

110.0

***

112.5

Perugia

101.6

87.3

95.9

110.4

111.1

***

101.7

***

97.6
***

86.3

***

111.1

***

South and Islands
Aquila

100.7

113.9

100.1

98.4

92.1

***

87.3

***

85.2

***

Campobasso

92.5

96.2

95.8

95.5

93.9

***

89.2

***

85.3

***

Napoli

81.2

***

92.6

87.1

***

87.2

**

96.0

*

86.0

***

76.9

***

Potenza

77.9

***

86.3

87.4

**

88.3

***

99.3

74.4

***

Bari

89.1

**

81.6

96.4

Catanzaro

78.8

***

85.2

Palermo87.1**

Cagliari

86.1

***

Obs.

177

Root MSE
AIC

**

101.8

96.6

89.2

***

84.4

***

74.6

***

***

82.3

***

79.7

***

82.4

***

76.5

***

81.4

90.4

85.1

***

80.1

***

101.9

102.5

104.1

87.5

86.9

***

74.5

**

104.2

98.3

91.5

74

89

1673

2018

888
0.194
-340.52

42

*

0.176

0.147

0.148

0.105

0.181

0.246

-89.70-56.26-69.18-57.08-879.98167.93108**

Table 2 Estimates of subnational PPPs for regional capitals using hedonic CPD models and CPI data,
ROME=100

Fresh and chilled vegetable
HEDONIC CPD
PPPs

HEDONIC CPD
Sig.

PPPs

Sig.

North
Aosta

100.44

101.43***

Torino101.5096.37***

Genova108.20**103.96***

Milano

92.45

***

98.01

***

Trento94.14***

103.26***

Venezia108.26

***

95.14

***

Trieste99.42

102.16***

Bologna

103.47

99.63*

Firenze

100.9889.72***

Ancona108.22

Perugia

101.57

Centre***100.04100.51**

South and Islands
L'Aquila

86.55

***

101.37***

Campobasso

84.63

***

100.78**Napoli

75.37

***

96.01

***

Potenza

88.33

***

97.08

***

Bari

97.47

94.62

***

Catanzaro

103.67

98.15***

Palermo103.7098.57***

Cagliari

102.87

98.57

***

Obs.

3,327

66,604

Root MSE

0.17573

0.1170*109

Table 3. SPIs estimation results for Food and Non-Food products (Italy=100) using scanner data
Food products

Non-Food
products

PIEMONTE

99.80

100.35

VALLEDAOSTA

104.95107.86

LIGURIA

102.44102.65LOMBARDIA

100.18100.59TRENTINO

101.56102.41VENETO

99.09

98.48

FRIULI

100.77100.70EMILIAROMAGNA

98.31

98.40

TOSCANA

96.24

95.17

UMBRIA

98.53

98.02

MARCHE

101.08101.44

LAZIO

100.23

99.82

ABRUZZO

101.90101.33MOLISE

102.90101.24

CAMPANIA

98.65

97.20

PUGLIA

97.74

97.78

BASILICATA

97.53

99.54

CALABRIA

98.02

98.22

SICILIA

101.93101.57SARDEGNA

98.61

97.88

North-Center

South and Islands

110Table 4. WRPD estimation results for “Pasta products” and “Non-electrical appliances” Italy=100

RegionCoef

North-Center
PIEMONTE
0.0028VALLEDAOSTA
0.0367
LIGURIA0.0323
LOMBARDIA
0.0104
TRENTINO
0.0557VENETO
0.0188FRIULI
0.0276EMILIA-ROMAGNA 0.0068
TOSCANA-0.0209
UMBRIA-0.0254
MARCHE
0.0398LAZIO-0.0159South and Islands
ABRUZZO
0.0401MOLISE
0.0311CAMPANIA-0.0256PUGLIA-0.0547
BASILICATA-0.0570
CALABRIA-0.0445
SICILIA-0.0758
SARDEGNA
0.0176

Pasta Products (BH1)
std.error p.value
RPP

Coef

Non-electrical appliances (BH2)
std.error p.value
RPP

0.00270.00280.00340.00270.00290.00270.00260.00310.00280.00290.00310.00260.30710.00000.00000.00010.00000.00000.00000.02700.00000.00000.00000.0000100.28103.74103.28101.05105.73101.89102.80100.6897.93
97.50
104.0698.42-0.0550
0.0528-0.0061-0.04020.0268-0.0133-0.0079-0.0386-0.1205
0.00270.02580.00750.00560.00590.00560.00560.00570.00560.00570.00560.00570.00560.00560.00560.00000.00000.28290.00000.00000.01830.16110.00000.00000.63570.00000.182394.65
105.4399.40
96.06
102.71
98.68
99.21
96.22
88.65
100.27102.61100.750.00300.00310.00290.00290.00290.00290.00340.00360.00000.00000.00000.00000.00000.00000.00000.0000104.09103.16
97.47
94.68
94.46
95.65
92.70
101.780.00360.03540.0348-0.0071
0.02360.02700.0679-0.0192

0.00570.00580.00570.00570.00570.00570.00570.00570.52540.00000.00000.21320.00000.00000.00000.0007100.36103.60103.5499.29
102.39102.74107.03
98.10

Figure 1 Annual average price across Italian regional capitals (GTIN/EAN=8001250120113; Item
description=«DE CECCO SEM LUNGA SPAGHETTINI N.11 SEM PASTA 500 GR 1 SACCHETTO»

111

Figure 2 Regional PPPs based on scanner data (Italy=100)

Figure 3 within-Region PPPs based on scanner data for Tuscany (Florence=100)

112

Figure 4 within-Region PPPs based on scanner data for Lombardy (Milan=100)

113

D. Vietnam
Introduction
The General Statistics Office (GSO) of Vietnam calculates subnational purchasing power parities (SN-PPPs)
for its cities, provinces, and regions. These are used in a Spatial Cost of Living Index (SCOLI), which reflects
the difference in price of goods and services consumed by people and households in their daily lives at apoint in time (either for a month, quarter, or year). In 2010, the World Bank supported a pilot study by
GSO with the aim of calculating the SCOLI for 2012 using a list of 64 goods and services with food andfoodstuff making up the majority. Since 2012, GSO has officially conducted surveys and calculated SCOLI
by state budget, and increased the number goods and services included by 37 items to now cover 101 in
total. These new items are mainly non-food and non-foodstuff goods which improve the representationof SCOLI as well as extends its use to other purposes such as calculating the Human Development Index(HDI). However, there have been many challenges in collecting SCOLI prices in provinces and cities; thus
the quality of SCOLI data has not yet reached the desired standard.
In 2011, GSO participated in the ICP 2011 cycle through the Asia and Pacific comparison coordinated by
the Asian Development Bank (ADB). World Bank and ADB experts introduced a method to calculate
subnational PPPs in each country based on the consumer price index (CPI) data available.
Using CPI data to calculate SN-PPPs has some advantages as follows:•••

It avoids an obligation to conduct a subnational PPP survey every two years in 63 provinces andcities. Thus budget, human resources, and time savings can be realized. Furthermore the essential
data can be published more frequently such as quarterly or yearly.
Commodity items in the CPI are adequate, covering food, foodstuff and non-food goods andservices.
Using CPI data to calculate subnational PPPs can better serve analyses and assessments of poverty
alleviation programs, HDI calculation, and other purposes.

Given these above advantages of calculating subnational PPPs using CPI data, GSO initiated a project toimprove the subnational PPPs calculation method using consumer price data sources. From 2014, the
SCOLI price survey will not be conducted in provinces and cities. The budget for the 2014 SCOLI survey
will be moved to the estimation for studying the methodology and calculation on a pilot basis of a
subnational PPP index using consumer price data source in 2014.

Scope and uses of subnational PPPs
Scope
The SCOLI index is compiled and grouped according to the following :•••According to 63 provinces: compare with the average price of Hanoi city.
According to 6 geographical regions: compare with the Red River Delta region.
According to 11 groups of consumer goods by purpose (COICOP).
114

Uses of subnational PPPs
Subnational PPPs are used in analyzing the effects of poverty reduction policies, difficulty subsidies, wage
subsidies, and living standards studies among provinces and regions throughout the country. These
indicators are also the basis for calculating the HDI, calculating PPP-based GDP, assessing the minimum
living standards and adjusting regional wages, calculating investment costs, evaluating thecompetitiveness of prices, diets, accommodation, and working costs according to regional prices.
Currently, the GSO uses the subnational PPPs index to eliminate price differences in household incomesand expenditures across regions. These price-adjusted incomes and expenditures help to determine thenumber of poor households and the poverty rate.
In order to compile the HDI indicator, the subnational PPPs are used to calculate the PPP-based GDP ofthe provinces in terms of the overall price level of the whole country or of a selected province, in the local
Vietnamese currency, and then to calculate the PPP-based GDP in US dollars (USD) to calculate therelevant component of the composite HDI index.
Subnational PPPs are also used to calculate minimum living standards in regions and in other spatial or
geographic comparisons.

Methodology used
The methodology and processes of the ICP are used to calculate subnational PPPs at the provincial level
and to compare price levels among regions or provinces in the country. In fact, comparing prices acrossregions in the country is easier than across countries. The major difficulty in calculating subnational PPPs
is to collect detailed values of product groups in each region. This is also an essential requirement toaggregating the real expenditure (and average expenditure per capita) for expenditure groups andcomputing the weights of the components of subnational PPPs to tabulate total regional expenditure. On
the other hand, comparing related prices across regions in the country is easier than that across countries
because the regional comparisons do not need to take into account market exchange rate differences.
CPI data are important analytical tools for decision-makers, economists, universities and economic
organizations. Subnational PPPs can be used to determine a national poverty line and compare poverty
rates across regions or provinces based on a basket of goods and services.
In order to develop subnational PPPs at the provincial level, we need to consider three main issues:
(i)
(ii)
(iii)

compiling data to calculate subnational PPPs at the provincial level;
identifying product overlap among provinces/cities; andaggregating Subnational PPPs at the provincial level.

(i) Compiling data to calculate subnational PPPs at the provincial level
Review and code the item list from the consumer price survey database in 2010 to calculate SCOLI; checkand convert to the same unit for calculating commodity prices among provinces. Based on specifications,115

grades, units, and prices of each item of the same province to put in the same group then add the code
to create the code of Subnational PPPs list. Placing Subnational PPPs code was created and used uniformly
in compiling Subnational PPPs indexes. Database is first checked to determine the average price of thecommodities which are directly comparable among provinces/cities. This process also determines
commodities that are considered similar and their average price is not impacted too strictly by thedifference in main features, where its specification and package are especially important. For instance,
average price of “soft drink” bottled from 300ml to 1.5 liter will unbalance the average price even when
the price is converted to unit price because price on one volume unit of a big bottle of soft drink is oftenmuch lower than that in a small bottle. In such cases, they should be broken down into two or morecommodity groups (for example: bottle less than 600ml and bottle more than 600ml).
Average price, which is extracted from the CPI database, is the basic input information but when used forinternational comparison it needs further data on expenditure. Accordingly, subnational PPPs data oneach commodity will be aggregated into subnational PPPs for larger commodity groups.

(ii) Identifying product overlap among provinces/cities
One of the criteria for selecting products for subnational PPPs compilation is their representation of
consumer purchases. It is assumed that all products in CPI basket are representative. However, eachprovince or city has its own “commodity basket” and that basket represents the consumption of theprovince or city and reflects its consumption patterns. Therefore, it needs to identify the list ofrepresentative products for the province or city and collect the price in at least two provinces or cities to
set up the overlap. In case datasets are scattered, it cannot calculate subnational PPPs even when theoverlap is set up. Thus, it needs to establish to a certain extent that the double combination and its
transition are clear and can create subnational PPPs that are more practical and reliable.
The CPI basket of goods and services basket is relatively stable in short-term and mid-term because themain purpose of CPI is to compare prices over time. Changes will arise when old products do not exist andthe new ones are not available before. The accurate technical parameters of CPI commodities can bedifferent among provinces and cities depending on local conditions (for example: package size can bedifferent among regions and the diversity of one product can be different). It results in some issues arisingwhen CPI data are used in the subnational comparison. These issues can be addressed simply. Different
package sizes can be adjusted by price unit, regulation as package sizes are relatively similar.
Note that the structure of the CPI is as follows:+ Group level 1, includes:-

Restaurant and catering services
Beverages and tobacco
Garments, hats, and footwear
Housing, electricity, water, fuel, and construction materials
Household equipment and appliances
Medicament and health services
116-Transportation
Post and telecommunication
Education
Culture, sports, entertainment, and tourism
Other goods and services+ 32 groups at level 2; 86 groups at level 3 and 256 groups at level 4(iii) Aggregating Subnational PPPs at the provincial level
After arranging price data and weight and determining overlapped commodities, national subnational
PPPs estimation will be carried out at different levels, including at grass-root and higher-aggregated levels.
Aggregate price index at basic level
Calculating the price index at the lowest level is called basic aggregated level. At this level there is noweight. This is the product level in CPI and the basic group in ICP. In this study, the Country-ProductDummy (CPD) index is used at basic aggregated level.
The basic dataset needed to calculate CPD is a price matrix of all products with prices in each region.
Apparently, there are blank cells in the matrix because it cannot get a price for each product in eachprovince or city due to the unavailability of some products in some provinces and cities and the differencein quantity of products priced in provinces and cities. The CPD’s basic model is multiplying, and assumes
that the difference in prices of products in provinces and cities is the same rate in all provinces and cities
and price differences within a province or city is the same rate for all products.
The CPD index is often used in spatial international comparisons. However, for this study CPD is the basic
general combination of space and time. CPD was first introduced by Summers (1973) and presented in
two equivalent forms, interception and non-interception. The regression equation for CPD is as follows:
Inpcp =Ycp = xcpβ +εcp

(1)

Where: pcp: là Price of product p in the country c
Dcj and Dpi: product and country dummy variables
Np and Nc: number of products and number of countries
xcp = [Dc2…DcNc; Dp1Dp2…DpNp]
β = [α2…αNc; 1, 2,..., Np] T(2)

In matrix denotation, arrangement by separate observations can be
Y = X β +ε(3)Aggregate price index at higher level
Using Laspeyres geometric mean formula, PPP for region k with region j as base is given by
117

p 
PPPjk =   ik 
i =1 
 pij 
N

wij

(4)

Price and weights data
Sources of data
The GSO uses sources of consumer price survey data to calculate the SCOLI index according to the methodof the World Bank (WB) and Asian Development Bank (ADB).
•••

Consumer price survey is being carried out monthly by the General Statistics Office in 63 provinceswith 572 goods and services in the 2014-2019 period.
Data quality ensures a close reflection of the trend and level of the cost of living price fluctuations
among provinces and regions because the list of items has a high level of representation of common
consumption of residents and this list is periodically reviewed and updated by the GSO. Consumer
prices are surveyed three times per month for twelve months of the year
The use of available data from the consumer price survey (instead of organizing a separate survey tocalculate the subnational PPPs) will realize State budget savings, time savings, and reduce theopportunity cost. Field surveys for the statistics industry, thereby helping to improve the quality ofother professional jobs, which improve the quality of periodic surveys on consumer prices.
Survey framework

PSOs review and strengthen the network for price surveys in provinces and cities as follows:
Enumeration area
Based on administrative, geographical and population scope, GSO allocates the number of enumeration
areas for provinces/cities (see Annex 3). With allocated enumeration areas, PSOs select and distributeenumeration areas suitable with real conditions of provinces and cities and the following requirements
are met:•••Selecting enumeration areas representing whole urban and rural areas of the province;
It is possible to collect price of all goods and services by list of representative items in the enumeration
areas
Refer to field materials of Household Living standard survey and weight survey in 2014 to selectenumeration areas for precisely reflecting reality of price fluctuation of province/city.

Enumeration point
The enumeration point should satisfy two requirements:••

Enumeration point as an establishment with stable business operations;
If the item has a different price among enumeration points, more enumeration points need to be
selected; if the item has relatively similar variation in price among enumeration points, fewer
118

enumeration points are selected (Annex 2). For example, Market A (enumeration area) has seven porkoutlets located in different positions. By observation, the price of griskin (selected item for survey) is
different among these outlets. Therefore, in order to reflect the price of griskin correctly in theenumeration area, it is necessary to select three outlets (three enumeration points) to collect prices.
Conversely, the retail price of condensed milk “Ong Tho”, 380 gram is quite similar in outlets in themarket, and thus one outlet is selected for collecting the price.
To meet the two requirements above, GSO regulated necessary enumeration points for each item in eachenumeration area as follows:••

Food and foodstuff: three enumeration points per each item
Other items: one enumeration point is needed

And the norm for the enumerator is:••

Items for collecting price with one period/month, each enumerator is responsible for gathering
price of 80 items (maximum).
Items for collecting price with three periods/months, each enumerator is responsible forgathering price of 50 items (maximum).

With 572 items and services representing the period 2014-2019, it is necessary to have seven to eight
enumerators for one enumeration area.

Weights
The weights in CPI calculations are the expenditure proportions of commodity and services groups in the
total expenditure of the people. The weights in CPI calculations for the whole country are the expenditureproportion of each region in comparison with total expenditure of the country by each commodity group.
The weights in CPI calculations at regional level are the expenditure proportions of each province/city inthe region over total expenditure of the region. The weights in CPI calculations for each province/city are
the expenditure proportions of each commodity group over total expenditure of province/city. The weightis calculated by urban and rural area and generally calculated for two areas.
There are two kinds of weights in the CPI:••

Vertical weight: this is ratio of expenditure on each group of items with total expenditure ofpeople. The vertical weight is calculated for urban and rural areas and for two areas of eachprovince, each region and the whole country.
Horizontal weight: this is ratio of expenditure of each urban and rural area with expenditure ofwhole province, region or country.

Weights in CPI are stable over five years and for the base year (identical with year updating classificationof representative goods and services). In the period 2014-2019, the base year is 2014, thus, price of baseperiod is followed by new classification of representative goods and services, and weight for CPI is datafrom year 2014.

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•

2014 weights are compiled from results of the Household Living Standard Survey and the GSO
survey on CPI weight in 2014.
Groups of weights are classified consistently with the structure of the CPI (including 11 groups ofitems at level 1, 32 groups at level 2, 86 groups at level 3, and 256 groups at level 4).
2014 weights are calculated by the GSO for each province and city, six economic zones and wholecountry (divided by urban and rural area).

••

Issues in using CPI Data for subnational PPPs
Determining product overlap among provinces and cities is the most difficult. In CPI, each province andcity has a representative list of popular consumer goods and services, so the review of provinces and cities
with the same representative item to calculate sub PPP takes a lot of time.

Empirical Results
1.

Spatial cost of living index among regions (Red River Delta = 100)
Unit: %
2011

2012

2013

2014

2015

2016

2017

2018

100.00100.00100.00100.00100.00100.00100.00100.00

Northern midlands and mountain101.73areas

102.32103.02102.99104.77101.34

101.41100.54

North Central and Central coastal
97.37
areas

98.17

99.37

100.09101.90100.33100.43

99.50

Central Highlands

101.16101.32100.60100.81103.85101.12101.01100.41

South East

103.91103.35103.05103.96104.31101.73101.88101.57

Mekong River Delta

97.32

96.39

95.62

95.73

96.29

98.29

98.56

98.15

Red river delta

2. Spatial cost of living index among provinces (Ha Noi city = 100)
Unit: %
2011

2012

2013

2014

2015

2016

2017

2018

Hà Nội100.00100.00100.00100.00100.00100.00100.00100.00Vĩnh Phúc

87.48

87.88

87.85

88.37

88.54

94.74

93.17

92.62

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Bắc Ninh

87.89

91.84

92.23

92.86

93.62

97.11

94.58

94.95

Quảng Ninh

93.70

93.53

93.12

93.02

95.56

97.05

95.78

96.12

Hải Dương

86.89

87.39

88.24

88.08

89.26

94.52

93.11

92.87

Hải Phòng

91.48

91.53

93.89

94.88

95.53

97.03

95.41

96.13

Hưng Yên

84.08

83.68

83.98

84.47

85.01

93.10

90.44

91.09

Thái Bình

81.48

81.04

80.54

79.40

81.61

93.23

92.87

91.60

Hà Nam

84.29

84.84

85.03

84.63

86.94

93.48

90.63

91.52

Nam Định

81.15

84.29

84.63

84.38

83.23

92.35

92.09

91.80

Ninh Bình

88.75

90.09

89.70

88.44

89.68

95.49

93.51

92.69

Hà Giang

91.17

92.30

93.80

94.30

96.50

97.98

96.10

96.12

Cao Bằng

91.20

90.10

87.90

87.50

91.18

96.15

93.68

94.23

Bắc Kạn

83.57

83.67

84.16

83.61

86.70

94.63

92.84

92.85

Tuyên Quang

87.39

87.12

88.60

88.96

88.91

95.09

94.05

94.33

Lào Cai

94.78

96.60

96.54

96.95

99.02

99.97

96.05

96.25

Yên Bái

90.17

91.22

90.98

91.20

92.90

96.68

93.68

94.23

Thái Nguyên

89.36

90.91

90.71

89.99

92.46

96.98

94.13

93.63

Lạng Sơn

90.02

91.82

92.92

92.40

94.38

98.21

95.84

96.20

Bắc Giang

87.13

87.51

87.28

87.08

88.79

94.86

93.21

92.25

Phú Thọ

86.23

86.34

87.98

87.42

87.66

92.76

89.97

91.20

Điện Biên

94.77

95.82

98.41

99.50

98.85

99.45

95.99

96.04

Lai Châu

96.50

97.61

98.34

99.58

100.30

99.45

96.00

95.96

Sơn La

96.33

97.98

99.46

99.01

99.27

97.49

95.58

96.17

Hoà Bình

89.87

89.18

90.85

90.93

94.83

96.95

94.76

94.40

Thanh Hoá

85.22

84.07

86.22

86.01

87.96

94.46

91.74

90.85

Nghệ An

85.31

84.90

86.92

86.49

87.52

93.00

92.15

92.23

Hà Tĩnh

88.98

90.96

95.21

97.09

97.14

97.89

95.21

95.29

Quảng Bình

88.54

89.14

88.66

89.50

92.10

96.44

95.19

95.77

Quảng Trị

90.34

90.03

90.45

92.21

93.90

96.45

94.18

93.11

121

Thừa Thiên - Huế

86.52

88.50

91.47

91.55

94.30

96.90

95.43

96.38

Đà Nẵng

91.93

92.42

94.29

93.53

96.44

97.98

96.68

97.81

Quảng Nam

85.20

87.68

86.93

87.14

90.33

95.99

94.59

94.86

Quảng Ngãi

86.05

85.05

83.79

84.24

88.05

94.01

92.33

91.18

Bình Định

86.43

86.48

86.05

87.42

90.04

95.13

93.43

93.68

Phú Yên

82.98

82.65

86.31

87.79

87.79

93.05

92.31

92.03

Khánh Hoà

87.44

88.13

88.69

88.68

91.56

96.15

94.90

95.80

Ninh Thuận

86.63

87.56

88.10

89.13

91.61

95.32

92.55

92.24

Bình Thuận

86.02

87.24

86.75

87.91

91.39

95.84

93.79

93.64

Kon Tum

87.74

89.05

89.58

89.98

94.06

96.33

94.86

95.17

Gia Lai

91.65

91.46

89.45

90.12

90.90

94.30

92.99

92.34

Đắk Lắk

90.02

90.07

90.12

90.53

95.20

96.19

94.85

93.77

Đắk Nông

89.55

90.25

89.37

89.86

93.84

96.63

93.46

92.82

Lâm Đồng

88.34

90.25

90.19

90.52

93.61

97.28

95.79

95.95

Bình Phước

93.80

93.47

92.95

95.09

96.12

96.80

94.55

95.27

Tây Ninh

83.75

83.96

85.02

85.34

89.36

94.63

93.54

93.11

Bình Dương

88.74

89.05

89.70

88.63

93.64

97.49

95.38

95.95

Đồng Nai

87.64

89.41

89.47

88.76

93.13

95.83

93.50

92.98

Bà Rịa-Vũng Tàu

91.27

91.98

91.20

92.17

95.74

97.50

95.54

96.22

TP. Hồ Chí Minh

100.84

99.70

97.80

96.86

97.39

99.67

101.38101.52

Long An

88.73

88.35

88.20

88.07

92.08

95.29

93.18

93.14

Tiền Giang

86.97

85.47

85.95

86.39

88.40

94.80

92.48

92.27

Bến Tre

90.31

90.67

91.35

91.33

93.02

95.94

92.72

91.72

Trà Vinh

79.12

78.17

79.17

79.33

80.72

92.93

89.45

90.84

Vĩnh Long

84.31

84.55

83.77

83.54

82.93

91.91

91.87

91.32

Đồng Tháp

87.37

87.38

87.27

86.96

87.03

93.26

90.13

90.67

An Giang

90.74

90.46

90.27

89.67

91.82

95.33

93.69

93.08

Kiên Giang

84.27

84.28

84.43

85.09

87.96

93.29

90.89

91.53

122

Cần Thơ

90.88

89.82

89.24

89.33

92.14

96.88

94.24

94.22

Hậu Giang

83.30

81.89

83.11

83.23

83.53

90.70

89.38

90.50

Sóc Trăng

81.70

80.90

81.67

82.04

83.50

92.41

89.92

91.07

Bạc Liêu

85.61

85.48

84.79

85.45

87.29

94.11

92.70

91.84

Cà Mau

83.61

84.03

84.73

85.24

88.42

93.59

91.51

91.43

Conclusions
Vietnam's subnational PPPs are compiled by the General Statistics Office in order to provide inputs into
the policy making process related to regional socioeconomic development serving research, comparing
economic performance results, economic growth rate, general productivity, and price competitiveness.
Enterprises use SCOLI indexes to evaluate competitiveness related to price, output, market share, andproduct cost. Individuals use the SCOLI index to negotiate wage rates and consider inter-provincial
migration.
Looking at Vietnam's SCOLI Index from 2010 to 2018, the Southeast region is the most "expensive" in the
country, with a price level that is higher than the price level in the Red River Delta region, and the price inthis region is increasing. Areas with "expensive" levels are followed by North Central and Central coastal
areas, Central Highlands, and Northeast.
The Mekong River Delta region has the lowest price with the price lower than the average price of the
Red River Delta, and the price trend is declining. The Mekong River Delta has an average price lower than
the average price in the Red River Delta because it is a flat terrain, favorable climate and hydrologicalconditions for intensive cultivation in agricultural production. Therefore, food and eating out by families
are less expensive.
Hanoi and Ho Chi Minh City are the provinces with the highest prices in the country. In addition, thenorthern mountainous provinces have a relatively high price level and tend to be higher because this is a
mountainous area where many goods are not produced locally and must be brought from the lowlands.
Furthermore, roads are quite difficult and freight rates are high. In addition, the region's distribution
system is very fragmented: the cost of maintaining a high distribution system, along with the cost of
stockpiling goods in warehouses, has pushed commodity prices higher than those of other areas.
The publication of a cost of living index for users of statistical information is an effort to improve thequality of statistical information in Vietnam.

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