n the matching of information about consumer and producer prices. This limits the set of countries and
yne et al. (2008) provides the basis for an alternative approach. Using e discussion in section 3.1, their model can be written as
indicator is a situation when material and other input costs move in opposite directions. In that case the inflation persistence of the CPI sub-index might be smaller than the persistence of the cost movement of the material input considered. Low inflation persistence at the retail level would then be the result of compensating cost movements rather than an indication of price flexibility. However, with the assumption that the production cost of the product captures the major part of the total costs, movements in the other cost components would have to be rather large in order to compensate changes in the production cost, which is quite unlikely. Thus, proxying costs with the PPI should not distort the ranking of products according to their price rigidity as measured by this indicator.
Another pr p
employed by retailers (Hi-Lo pricing). In that case the sector may be characterized by low persistence of consumer prices even when producer prices are persistent. Therefore, the values of the indicator should not be interpreted in absolute terms but only relative to the other sectors.
The previous two indicators are subject to data constraints as they rely o
sectors we consider for the first indicator and leads to some statistical difficulties for the second indicator. The analysis in Dh th 1 if 1 i t i t it otherwise it it it p p p s p p (9)
This model illustrates the two sources of price stickiness discussed above. The current rice, pit, may remain unchanged if the difference between the current optimal price and
f sources of rigidity into extrinsic and intrinsic is, in general, very complex. Dhyne et al. (2008) use the mathematical expression of the range of inaction p
previous periods price (|pi,t-1 – p*it|) is small, or if the range of inaction (sit) is large. In the
first case the lack of price adjustment is due to extrinsic rigidity, in the second, it is due to intrinsic rigidity.
proposed by Dixit (1991) to properly distinguish the two sources of price stickiness. Their methodology provides policy-relevant results by disentangling extrinsic and intrinsic rigidity. Unfortunately, their approach cannot be easily extended to analyze the situation observed in the other euro area countries as it requires the use of very detailed micro data which are not publicly available56. We therefore developed an alternative way of disentangling the two dimensions of price rigidity. Our third indicator is:
PI
RigidC P (10)
This decomposition is based on two main arguments. First, as can be s under the assumption of constant range of price in
action, s, will be provided by the average absolute size of price adjustments, |P|57.
ermine the variability of the optimal rice using individual price data allowing to identify the relative contribution of common
I, firms tend to change their price by large amounts. This would be a signal
f strong price rigidities, as large price changes would proxy large range of inactions or
een in Figure 3.1, action, a simple estimate of the range of in
Dhyne et al. (2008)show that there is a strong correlation between |P| and the estimated range of inaction that incorporates intrinsic rigidity.
Second, extrinsic rigidity corresponds to the variance of shocks: common (sectoral) and idiosyncratic (store level). Dhyne et al. (2008) det
p
and idiosyncratic shocks to price volatility. As we do not have access to such detailed dataset, we have rely on sectoral price indices only to try to approximate at least the magnitude of the common shocks that affect prices. The sectoral prices are publicly available at Eurostat. To proxy the size of the common shocks, we use the standard deviation of the log of the monthly price index over a given period of time58, PI. This
indicator is considered as a proxy of the degree of extrinsic rigidity. This argument is supported by the results presented in Dhyne et al. (2008) showing that the price index of a given product category is highly correlated with the unobserved common factor of the optimal price.
A large value of the third indicator means that, relative to the size of the common shocks captured by P
o
price adjustment costs. Based on the statistical information presented in appendices A and B, this indicator can be computed for 10 euro area countries for consumer prices at the
56 The main results of this article, based on a subset of the French and Belgian CPI basket, are presented in
Section 5.1.
57 Statistical information on the average size of price changes by COICOP category can be found for nine
euro area countries in Glatzer, Rumler (2007), Dhyne, Konieczny (2007), Hoffmann, Kurz-Kim (2006), Baudry et al. (2006), Veronese et al. (2005), Lünneman, Mathä (2005), Jonker et al. (2005), Dias, Dias, Neves (2004), Alvarez, Hernando (2004)
58 We use the same observation period for the computation of the volatility of the price index for a given
product as the one used for the computation of the average size of price changes for that product. See the different national papers for the definition of the sample period used for each type of product in the different countries.
COICOP 2 digit level and for 6 euro area countries for producer prices at the NACE 2 digit level.
This third indicator provides an interesting alternative to the measure of the frequency of rice changes but it has some drawbacks, especially for consumer prices.
rice indices and o it only captures the common shocks that are affecting one particular product category.
nce of end-of-season sales, specially for COICOP categories 3 and 9, which involve large price changes and increase
ces etween the frequency and magnitude of price changes at the consumer and producer
y comparing the characteristics of price changes at the consumer and producer levels using price indices p
First, the proposed measure of extrinsic rigidity is based only on sectoral p s
This means that it misses the idiosyncratic dimension of price adjustment. As shown in Dhyne et al. (2008) or Golosov and Lucas (2007), this idiosyncratic dimension is important, especially for consumer prices for which temporary promotions are common (especially in supermarkets that follow Hi-Lo pricing strategy). This means that our approach probably underestimates the volatility of the shocks that are affecting optimal prices and therefore our third indicator may be overestimated.
Second, our third rigidity indicator is affected by the occurre e
its value. Contrary to the occurrences of temporary promotions which are firm-specific price changes, the occurrences of end-of-season sales are common across stores. Therefore, they can be considered as a common shock. However, during most of the observation period, end-of season sales were not included in the price indices and in the average size of price changes, except in three countries (Austria, Germany and France) where the average size of price changes includes end-of-season sales. As end-of-season sales involve larger price changes than usual, this increases the average size of the price changes and therefore the value of our indicator in these countries, for COICOP 3 and 9. To summarize, each of these three indicators have its own advantages and drawbacks: The first indicator is probably the most intuitive as it directly reflects the differen b
levels. Such differences may indeed be considered to characterize the degree and nature of price rigidity at the consumer level. This indicator is then informative about the contribution of the retail and wholesale trade sector to consumer price rigidity. Unfortunately, this indicator is quite demanding in terms of information. Moreover, this information cannot be updated as it is based on computations made using the raw data on price changes at the micro-level which are not publicly available. This induces a limitation in the coverage of countries and sectors for which it could be computed.
The second indicator aims, in a way, at mimicking the first one b
rather than “raw characteristics” (frequency, magnitude) of price changes. This is done by comparing the statistical properties of consumer and producer price inflation at the disaggregate level, which can be done for a larger set of sectors and countries than feasible for the first indicator. This is a clear advantage of this second indicator over the first one.
However, both the first and second indicators require comparing consumer and producer prices and thus could only be computed for assessing price rigidity at the consumer level. The third indicator does not have this drawback. Its definition entails the comparison of a
easure of intrinsic rigidity with a measure of extrinsic rigidity either at the consumer, or m
producer, level. Under the assumption of lumpy price adjustment costs, intrinsic rigidity can reasonably be approximated by the average size of price changes while extrinsic rigidity is approached through the variability of the sectoral price index. This indicator can thus be computed for assessing both consumer and producer price rigidity and this can be done for a large set of sectors and countries.