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Cristina Conflitti

17 June 2022
Using CPI micro data for 11 euro area countries covering about 60% of the euro area consumption basket over the period 2010-2019, we document new findings on consumer price rigidity in the euro area: (i) each month on average 12.3% of prices change, which compares with 19.3% in the United States; when we exclude price changes due to sales, however, the proportion of prices adjusted each month is 8.5% in the euro area versus 10% in the United States; (ii) differences in price rigidity are rather limited across euro area countries but much larger across sectors; (iii) the median price increase (resp. decrease) is 9.6% (13%) when including sales and 6.7% (8.7%) when excluding sales; cross-country heterogeneity is more pronounced for the size than for the frequency of price changes; (iv) the distribution of price changes is highly dispersed: 14% of price changes in absolute values are lower than 2% whereas 10% are above 20%; (v) the overall frequency of price changes does not change much with inflation and does not react much to aggregate shocks; (vi) changes in inflation are mostly driven by movements in the overall size; when decomposing the overall size, changes in the share of price increases among all changes matter more than movements in the size of price increases or the size of price decreases. These findings are consistent with the predictions of a menu cost model in a low inflation environment where idiosyncratic shocks are a more relevant driver of price adjustment than aggregate shocks.
JEL Code
D40 : Microeconomics→Market Structure and Pricing→General
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
Price Micro Setting Analysis Network (PRISMA)
21 September 2021
This paper – which takes into consideration overall experience with the Harmonised Index of Consumer Prices (HICP) as well as the improvements made to this measure of inflation since 2003 – finds that the HICP continues to fulfil the prerequisites for the index underlying the ECB’s definition of price stability. Nonetheless, there is scope for enhancing the HICP, especially by including owner-occupied housing (OOH) using the net acquisitions approach. Filling this long-standing gap is of utmost importance to increase the coverage and cross-country comparability of the HICP. In addition to integrating OOH into the HICP, further improvements would be welcome in harmonisation, especially regarding the treatment of product replacement and quality adjustment. Such measures may also help reduce the measurement bias that still exists in the HICP. Overall, a knowledge gap concerning the exact size of the measurement bias of the HICP remains, which calls for further research. More generally, the paper also finds that auxiliary inflation measures can play an important role in the ECB’s economic and monetary analyses. This applies not only to analytical series including OOH, but also to measures of underlying inflation or a cost of living index.
JEL Code
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy