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Social Indicators Research

04.01.2016

Computing Sub-national PPPs with CPI Data: An Empirical Analysis on Italian Data Using Country Product Dummy Models

verfasst von: Luigi Biggeri, Tiziana Laureti, Federico Polidoro

Erschienen in: Social Indicators Research | Ausgabe 1/2017

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Abstract

The Italian National Statistical Office is implementing a new project for computing sub-national Purchasing Power Parities (PPPs) on a regular basis, which is based on an appropriate use of existing Consumer Price Index (CPI) data and new sources of data. Concerning the use of CPI data, in this paper the role of the Country Product Dummy (CPD) method for compiling sub-national PPPs at Basic–Heading (BH) level is analysed together with the specific issues that arise in this context, such as the need to take spatial autocorrelation among price relatives into account. The results of various experiments based on CPI data concerning 7 BHs and 19 Italian regional chief towns are presented and discussed with the aim of exploring the performance of various CPD models and analysing to what extent the type and characteristics of the data affect the estimates obtained. The statistical uncertainty associated with the ranks derived from the regional PPPs was then calculated by means of a simulation procedure. Our findings prove to be interesting and confirm that methods and CPI data for spatial comparisons are reciprocally influenced.

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Fußnoten
1
In the ICP, the Basic Heading (BH) is defined as the lowest level of aggregation within the National Accounts at which expenditure and expenditure share data are available. In the 2011 ICP Round the PPPs have been computed for 155 BHs.
 
2
In order to estimate consumer sub-national PPPs a different approach have been proposed which is based on the Engel’s Curve and/or a demand system model applied to data collected with sample surveys on household consumption expenditure. To the authors’ knowledge, this approach was used for the first time by Coondoo et al. (2004), who reported results obtained for India. The Authors explained that their procedure follows the CPD methodology in a generic sense as price equations essentially share the hedonic feature. Majumder, Ray and Sinha have continued to work on this approach in order to compute regional PPPs for India, Indonesia and Vietnam, with the aim of proposing a unified framework for estimating intra and inter-country PPPs, and obtained interesting results (Majumder et al. 2013a, b, 2014; Mishra and Ray 2014). This approach proved to be a useful tool and should be extended to other countries in order to check the validity and compare the results with the findings obtained from analyses carried out with other methods of computation.
 
3
In the 2005 ICP round the CPD approach has been the recommended method of aggregation below the basic heading level. Hill and Syed (2010) used detailed price data provided by the Word Bank with the aim of analysing how the CPD could improve PPP estimates in the context of the ICP.
 
4
Usually, monthly observations are averaged by using an arithmetic mean. Recently, Dikhanov et al. (2011) suggested a combined spatial–temporal model, called Country-Time-Product-Dummy method, to use quarterly CPI data.
 
5
Silver (2009) suggested that it is possible to improve on the specification of (1) by also introducing quality and product interaction terms.
 
6
Hill and Syed (2014) underlined that their derivation assumes only that the price quotes are identically and independently distributed.
 
7
As outlined in Anselin (2006) “spatial dependence or spatial autocorrelation is a special case of cross-sectional dependence in which the structure of the covariation between observations at different locations is subject to a spatial ordering. This ordering is related to the relative positioning, distance or spatial arrangement of the observations in geographic space, or, more generally, in (social) network space. Spatial heterogeneity is a special instance of structural instability, which can be observed or unobserved. The spatial aspect of this issue is that spatial structure provides the basis for the specification of the heterogeneity. This may inform models for spatial structural change (referred to as spatial regimes), heteroskedasticity, or spatially varying and random coefficients”.
 
8
The interpretation of the nuisance parameters differs greatly from the spatial autoregressive coefficient in the spatial lag model, in that there is no particular relation to a substantive model of spatial interaction.
 
9
In order to assess the statistical foundation of this choice, an analysis should be carried out to determine the presence of spatial autocorrelation among all the capital towns of the provinces belonging to the same region. It is clear that this kind of analysis requires data which may not be necessarily be available for all the products, even if the new sources of data (first of all scanner data) could enhance data availability for some products.
 
10
The data set was provided by the Italian Statistical Institute (Istat) and the elementary price quotes were treated in order to respect the statistical confidentiality and the authors worked at Istat in order test the various hypotheses of the CPD approach.
 
11
Campobasso was not included in our analysis because it was the only regional chief town which did not participate in the CPI survey, from which it was eliminated at the beginning of 2014 for inconsistencies in the data collected and unavailability of data collectors.
 
12
It is worth noting that we specified the economic distance by considering other measures of the size of the towns’ economy, such as the unemployment rate and the GDP per capita, obtaining similar results.
 
13
For example, “Ground beef meat, first cut” is one of the products representative of “Beef” in the CPI basket. Within this representative position, data collectors may select different varieties that are to be specified without being coded a priori. In contrast in the case of “Fruit”, the variety of the representative position “Apple” presents an “a priori” specification (for example “Red delicious apple”) that drives the data collection, is coded and easily manageable in the CPD models with hedonic adjustments.
 
14
The MI scatter plots are obtained using STATA and the splagvar command.
 
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Metadaten
Titel
Computing Sub-national PPPs with CPI Data: An Empirical Analysis on Italian Data Using Country Product Dummy Models
verfasst von
Luigi Biggeri
Tiziana Laureti
Federico Polidoro
Publikationsdatum
04.01.2016
Verlag
Springer Netherlands
Erschienen in
Social Indicators Research / Ausgabe 1/2017
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-015-1217-x

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