1. 1 Biggeri L*., Brunetti* A. and. Laureti° T *Italian National Statistical Institute (Istat), Rome, Italy; ° University of Tuscia, Viterbo, Italy Geneva, 8-9 May 2008 The interpretation of the divergences between CPIs at territorial level: Evidence from Italy Joint UNECE/ILO Meeting on Consumer Price Indices

2. 2 Structure of the paper Joint UNECE/ILO Meeting on Consumer Price Indices Geneva, 8-9 May 2008 1.Introduction 2. The divergence between CPIs at territorial level: a method for decomposing and interpreting it 3. The organisation of the decomposition analyses on Italian data 4. Analysis of the results 5. Concluding remarks

3. 3 Joint UNECE/ILO Meeting on Consumer Price Indices Geneva, 8-9 May 2008 1.Introduction The aim of the paper is twofold Empirical analyses to show the usefulness of the method Method for the decomposition CPIs divergences For the construction of CPIs most NSIs make use of the Laspeyres formula Considering the same basket of products and services, the divergence between two CPIs in different areas l and j [1] [2]

4. 4 Joint UNECE/ILO Meeting on Consumer Price Indices Geneva, 8-9 May 2008 From a different point of view, by considering area j as reference area: A comparison between two local CPIs depends on Elementary indices Weighting system [5] Examine and decompose these divergences Understand which are the factors that cause them Four equivalent decomposition formulae 2. The divergence between CPIs at territorial level

5. 5 Geneva, 8-9 May 2008 [3] Factors influencing the Elementary Price Index Effect Factors influencing the Weight Effect Joint UNECE/ILO Meeting on Consumer Price Indices 2. A method for decomposing and interpreting the differences (a) [3bis]

6. 6 The factors can give different results since they are defined relating to different distributions of elementary price indices and weights Joint UNECE/ILO Meeting on Consumer Price Indices 2. A method for decomposing and interpreting the differences (b) [6] Geneva, 8-9 May 2008  interesting interpretation from an economic point of view  determining the “price effect”  influencing the overall difference between the two CPIs considered. difference between the two arithmetic means of elementary price index distributions

7. 7 Joint UNECE/ILO Meeting on Consumer Price Indices Geneva, 8-9 May 2008 3. Data set description and organisation of analyses on Italian data DATA DESCRIPTION:  Monthly CPIs for the whole nation for elementary aggregates 40,000 outlets; 85 municipalities; 400,000 elementary prices; 540 representative products  System of weights for 85 municipalities using household expenditure shares  Period: January 2002-December 2007 DATA DESCRIPTION:  Monthly CPIs for the whole nation for elementary aggregates 40,000 outlets; 85 municipalities; 400,000 elementary prices; 540 representative products  System of weights for 85 municipalities using household expenditure shares  Period: January 2002-December 2007 CALCULATIONS: analysis limited to December of the years 2002 and 2007 Similar basket of products and services Selection of 9 chief regional towns CALCULATIONS: analysis limited to December of the years 2002 and 2007 Similar basket of products and services Selection of 9 chief regional towns Considering  the cities where it is reasonable to assume a different behaviour of the sellers and consumers and a different evolution of attitude regarding sale and purchase.  The territorial location of the cities in the north, centre and south Italy

8. 8 Joint UNECE/ILO Meeting on Consumer Price Indices Geneva, 8-9 May 2008 4. Analysis of the results (a) Table 2 Overall Differences between pairs of cities Comparing Naples with the other cities the differences (all positive but with different values) have a “hidden meaning” concerning the degree of importance of the various factors

9. 9 Joint UNECE/ILO Meeting on Consumer Price Indices Geneva, 8-9 May 2008 4. Analysis of the results (b) Naples (j) –Turin (l) Price change distribution Joint UNECE/ILO Meeting on Consumer Price Indices CPI difference 0.885 Price Effect 0.516 Weight Effect 0.369 factors 12.335 0.002 0.034 -0.639 0.0035 13.029 0.0441 Correlation coefficient between elementary indices and weights Turin -0.01 Naples 0.056

10. 10 Joint UNECE/ILO Meeting on Consumer Price Indices Geneva, 8-9 May 2008 4. Analysis of the results (c) Naples (j) –Florence (l) Price change distribution Joint UNECE/ILO Meeting on Consumer Price Indices CPI difference 1.691 Price Effect 1.396 Weight Effect 0.295 factors 12.335 0.0018 0.0240 0.596 0.0037 12.131 0.031 Correlation coefficient between elementary indices and weights Florence 0.026 Naples 0.056

11. 11 Geneva, 8-9 May 2008 4. Analysis of the results (d) The evolutions of local CPIs is quite different across Italy and influence the divergences between the CPIs in different ways and degrees. The divergences in the evolution of the CPIs depend:  mostly on the price effects.  in same cases on the different share of expenditures (two southern cities with all the other cities)  on the characteristics of the corresponding distribution of the elementary price indices and on the value of the correlation between elementary indices and weights Similar results from the comparisons for December 2007 Joint UNECE/ILO Meeting on Consumer Price Indices

12. 12 Geneva, 8-9 May 2008 5. Concluding remarks A simple method for calculating the decomposition of the divergence between two CPIs to obtain a measure of the importance of the factors that affect it. Very interesting results Further research  improve the method of the decomposition of the divergences between the CPIs  Analysis for CPIs by classes and groups of products to understand the importance of the different products in affecting the price and weights effects  analyse the degree of the price and weights effects during periods with different inflation rates. Joint UNECE/ILO Meeting on Consumer Price Indices Understanding why the local CPIs often differ among one another

13. 13 Joint UNECE/ILO Meeting on Consumer Price Indices Thank you for your kind attention!