4.1 Análisis e Interpretación de Resultados
4.1.2 Resultados obtenidos en la aplicación de encuestas
The inflation rates in Euro area countries have been changing very significantly in 2008. The increase in inflation in the first three quarters was caused by rapid increase in the prices of food and energy. This was followed by a collapse of food and energy prices and a slowdown in the inflation rate that would last into 2009. While it is difficult to make predictions in the current economic environment, there are concerns about strong disinflation in the Euro area some time in 2009.
Given these changes in inflation, a relevant question is how they affect price adjustment at the level of individual firms. In this section we provide some evidence from Belgium and Austria.
6.3.1. Inflation and Food Prices in Belgium
In January 2008, the Belgian government asked the National Bank of Belgium to analyze the price developments observed in the second semester of 2007. The results of this analysis were published in a special issue of the NBB Economic Review in April 2008. Part of the analysis focused on the question of price adjustment and provided useful information about the firm reactions to cost variations.
Using micro data, characterizing price developments of the 84 processed food products included in the Belgian CPI basket over the January 2003 – January 2008 period, the report focuses on the COICOP categories that experienced the highest inflation rate during the second semester of 2007 (‘‘Bread and cereals’’, ‘‘Milk, cheese, eggs’’ and ‘‘Oils and fats’’).
The inflation rate is given by
, 1
1 ln ln N t it it i t i w p p
, where pit is the price of aproduct sold in period t in outlet i and wit is the weight attached to that particular product
in the total basket. This simple equation illustrates the two possible sources of inflation. Inflation may be the result of either an increase in the average magnitude of non zero price adjustments or from an increase in the frequency of price changes.
The analysis of the NBB clearly indicates that the increase of the inflation rate observed in those three COICOP categories was mainly driven by increases in the frequency of price changes, and more precisely by an increase in the occurrence of price increases. During the same period, the frequency of price cuts has slightly decreased, so that overall the frequency of price changes rose significantly. The average size of price adjustment did not change much. This is illustrated in Figure 6.9. The fact that higher inflation makes price
Figure 6.9: Frequency and Average size of Price increases and Decreases for Three COICOP Categories.
increases more, and price decreases less common is well known; see, for example, section 2.3.7 and Figure 2.7). In response to the increase in food commodity prices, retailers seem therefore to have sped up their price adjustments but they continue to adjust their prices by regular amounts. These results are in line with state dependent pricing models such as the traditional (S,s) model which imply that the size of the adjustment is constant but the frequency of adjustment varies with the volatility of the shocks.
The NBB also reports that the price adjustments that occurred in the second half of 2007 were mostly carried out in a single move rather than gradually. In terms of competition, it seems also that the lowest prices were adjusted more speedily and by larger amounts than the highest prices; as a result price dispersion has fallen.
6.3.2. Inflation and Price Stickiness in Austria.
Since most of the analyses on price stickiness and price rigidity in this report are based on IPN data, they do not cover the most recent period of rapid price increases since about fall 2007. As the results are all based on datasets that span over a time period of low and stable inflation, there is no automatic extension of our findings to the environment of higher and more variable inflation rates. Therefore, a natural question is if and how the findings in this report extend to a high inflation environment. Given that there are no micro price data for high inflation periods available, an indirect way of examining this question is by looking at the cross-sectional differences in inflation rates. Specifically, we ask how price stickiness is affected by average inflation in the cross section of products by regressing average frequency of price changes on average inflation of these products and a number of control variables. The coefficient obtained provides a measure of the elasticity of price stickiness with respect to inflation.
This analysis is based on the same dataset of micro prices for Austria as used in section 6.1: monthly price observations from January 1996 to June 2006 for 641 products and services. The variables we use in the regression are averages for each product over time, i.e. we exploit the cross-sectional dimension of the data.
The particular regression we run includes the frequency of price changes76 of each product as the dependent variable, which is explained by the product-specific average inflation over the observation period, and a number of other characteristics of these products: the average size of price changes, the share of attractive prices (psychological prices ending in
76 Since the dependent variable in this regression, the frequency of price changes is bounded between 0 and
1, estimating a linear model is not appropriate. One solution to this problem is transforming the dependent variable to the log-odds ratio, ln
freq
1 freq
, which is unbounded. The coefficients of this regression are not directly interpretable, but the marginal effect can be obtained by a simple transformation. For more details on the method, see Konieczny and Rumler (2006).9 and round prices ending in 10 or 100) for each product, the share of prices changed in January, the share of sales prices for each product and dummies for the product groups (unprocessed food, processed food, energy, non-energy industrial goods and services) to control for group-specific effects. The regression results are shown in Table 6.4.77
The results indicate that products with higher average inflation are also characterized by a significantly higher frequency of prices changes. The marginal effect implies that when average monthly inflation increases by 1 percentage point, the frequency of price changes would increase on average by 5.2 percentage points. Although this result is statistically significant at the 5% level, its effect is quite small given that a 1 percentage point increase in monthly inflation is substantial. In annualized terms, this means that when inflation increases by 1 percentage point, the frequency increases only by 0.44 percentage points.
Table 6.4: Explaining the Frequency of Price Changes
Variable Marginal effect Sample means
Constant -0.18***
Average monthly inflation 0.052** 0.12%
Size of price changes 0.10 14.5%
Share of attractive prices -0.04 60.1%
Share of price changes in January -0.01** 195%
Share of sales prices 0.70*** 4.7%
Processed food dummy -0.06***
Energy dummy 0.12**
Industrial goods dummy -0.11***
Services dummy -0.09***
Adjusted R-squared 0.47
Notes: Dependent variable is the log-odds ratio of the frequency of price changes across products. Marginal effects are evaluated at the sample means given in the last column. Estimation method is OLS; standard errors are White heteroskedasticity consistent. The number of observations is 641; observation period is 1996M1-2006M6. * indicates significance at the 10%, ** at the 5%, and *** at the 1%
confidence level.
Apart from product-specific inflation, also the average size of price changes is positively related to the frequency of price changes. This has probably to do with sales pricing. Sales induced-price changes, which do occur frequently for a number of products like food items, tend to be bigger in size than regular price changes. However, the effect is not significant, once we control for sales (by including the share of sales in the regression).
77 The total of 641 products contains a large number of products whose prices are subject to some form of
regulation. Since the prices of those products are not determined by market forces, it could be argued that they should be excluded from our analysis (see Konieczny and Rumler, 2006). Therefore, we also perform the estimation with the sample constrained to those 517 products which are not subject to any form of price regulation. The results from this estimation are qualitatively the same as in Table 1 which confirms the robustness of our findings.
Furthermore, products for which attractive prices are common show a smaller number of price changes than others (p-value is 0.12). This is in line with the finding in the literature, see e.g. Baumgartner et al. (2005) and Konieczny and Rumler (2006), that adjustments of attractive prices are sometimes delayed when the optimal price changes just a little bit until a new attractive price is near optimal.
Seasonal price setting affects the frequency of price changes significantly negatively, which is to say that for those products and services for which we observe a larger proportion of price changes in January, the overall frequency is lower because a number of them are likely to be set in a time-dependent fashion every January which represents a longer-than-average duration.
Finally, the share of sales prices is positively related to the frequency of price changes because there seem to be many products in our database – mainly food items – for which we observe a large number of price changes that are induced by sales and promotions. Our results provide only indirect evidence of how higher aggregate inflation affects price stickiness because it draws its information from the cross section of products only. But assuming that the relation between inflation and the frequency of price changes found from 1996 to 2006 holds up also in the recent period of rapid price increases, we can draw the general conclusion that price stickiness should be somewhat smaller in a higher inflation environment, in particular for those products where rapid price increases have been observed, like food and energy items. But the effect of inflation on price stickiness appears to be quantitatively small. Thus, we may conclude that our results for aggregate price stickiness and the comparison across countries hold also in the recent period of rapid price increases. The sectoral composition of price stickiness, though, might change, likely showing more frequent price changes for food and energy items, but a largely unaffected frequency for services and industrial goods (which account for the bulk of items in the CPI).