Review of the empirical literature on data aggregation and VPT

We focus on spatial or cross-sectional data aggregation whereby price data from individual actors such as producers or retailers is used to produce regional or national averages.² Hence, we do not consider possible impacts of temporal aggregation on the estimation of vertical price transmission processes.

Several studies have considered the effects of spatial data aggregation on the results of vertical price transmission analysis. Schwartz & Schertz Willet (1994) state that the characteristics of the data collection process, such as the timing of price data collection at different levels of the marketing chain, and how these data are aggregated, might affect estimates of vertical price transmission. They also speculate whether the presence of promotions in price data might affect the estimation of vertical price transmission. Powers (1995) finds differences in the speed of price adjustment for lettuce depending on whether national (USA) or state-level data are

2In addition to calculating the arithmetic mean, statistical authorities sometimes apply other transformations to raw data such as first eliminating individual observations that are deemed to be outliers or non-representative, for example because they deviate from the mean by more than 'X' standard deviations, or because they belong to the largest or smallest 'Y' percent of all observations.

9 | P a g e

analyzed, and conjectures that these differences might be due to spatial aggregation. Schroeder (1988) considers vertical price transmission for individual cuts of pork as opposed to an aggregate of these cuts, and concludes that studies that use aggregated retail prices over-simplify the true pricing behavior of individual retailers. In his extensive study of asymmetry in vertical price transmission, Peltzman (2000) includes disaggregated price data from one supermarket chain in the Chicago area. He finds that estimated price transmission is stronger at the individual supermarket level than at the aggregated level. He also finds little evidence of asymmetric vertical price transmission in the disaggregated supermarket level, which contrasts with his finding that asymmetry is prevalent when aggregated prices are used. Peltzman (2000) points out, however, that his findings are based on only one retail chain and cannot be generalized.

von Cramon-Taubadel et al.(2006)address the effects of spatial aggregation on the measurement of vertical price transmission theoretically and empirically. They demonstrate that key parameters in an error correction model (ECM) that is specified with average prices are not the arithmetic averages but rather non-linear functions of the corresponding parameters in the ECMs that are specified with the underlying individual prices. They also present theoretical considerations that suggest that vertical price transmission processes estimated with disaggregated prices will on average be faster than the corresponding process estimated with the average of these prices. Specifically, they demonstrate that the aggregation of stationary autoregressive processes creates fractionally integrated or 'long memory' processes. They confirm these theoretical considerations using retail price data for chicken and lettuce in Germany. von Cramon-Taubadel et al.(2006) also find that while vertical price transmission appears to be symmetric at the aggregate level, vertical price transmission from the wholesale to the individual store level appears to be asymmetric for roughly one-quarter of stores in their dataset.

In summary, a few studies note that spatial aggregation affects the estimated speed and symmetry of vertical price transmission. However, most of these studies are based on comparatively small data sets (e.g. Peltzman's analysis of a single supermarket chain).

Furthermore, no study to date has considered the possible influence of TSP on estimates of vertical price transmission. As described below, we estimate vertical price transmission using a

10 | P a g e

large scanner dataset of butter prices in Germany (over 1000 series of roughly 300 weekly observations each), and we explicitly analyze the effects of TSP on this estimation.

There are theoretical reasons to expect that TSP will affect estimates of vertical price transmission. Consider the wholesale price for a food product, and the corresponding retail price in a specific store. In equilibrium the retail price will exceed the wholesale price by the amount of the retail mark-up. If the retail store drops its price for a temporary sale, the margin between the retail and the wholesale prices will be squeezed, and the two prices will no longer be in equilibrium. When the retail price is returned to its regular level in the next period, it will appear as if this squeezed margin has been rapidly and completely corrected. The more frequent the use of TSP, the more such episodes of rapid and complete correction of squeezed margins will be contained in a given sample of price data.

This could have two effects on estimates of vertical price transmission. First, we hypothesize that episodes of TSP will make vertical price transmission appear more rapid. To test this hypothesis, we compare the speed of vertical price transmission for raw retail prices that include TSP with the speed of vertical price transmission for the same retail prices that have been filtered to remove TSP. Second, since TSP by definition always involve first reducing and subsequently increasing prices, they will add to retail price data only sharp downward spikes or ‘valleys’ in which squeezed margins are rapidly corrected upward, and never corresponding sharp upward spikes or ‘peaks’ in which stretched margins are rapidly corrected downward. Hence, we hypothesize that TSP will bias the results of vertical price transmission analysis using retail prices in favor of essentially spurious findings of asymmetric vertical price transmission. This might help explain the higher prevalence of asymmetric vertical price transmission in disaggregated retail price data reported by von Cramon-Taubadel et al.(2006). We test this hypothesis by estimating asymmetric ECMs first with raw retail price data and second with corresponding retail price data that has been filtered to remove TSP. The following section explains our empirical strategy and data in greater detail.

11 | P a g e