Negociación de intenciones: profesor-estudiantes

Dataset

The dataset consists of a large panel of business surveys for the retail sector con- ducted by the Ifo Institute for Economic Research. The surveys are mainly used for the construction and analysis of business tendency indicators. In 2003 the average number of retail firms surveyed each month was 900, while the average response rate was about 70%. The participating firms’ share of total revenues generated in the retail sector was about 10%. For more details on the survey data see Becker and Wohlrabe (2008) and Appendix 2.A.1.

The econometric sample constructed from this dataset covers about 930 retail firms. Because some of the firms responded to several questionnaires for differ- ent product groups, the observation unit is firm-products leading to a total of 2,017 observation units. The sample runs from January 1991 to January 2006.4 As firms take part in the survey on a voluntary basis, not every firm responded every month resulting in an unbalanced dataset. To obtain a workable sample, we follow Gopinath and Rigobon (2008) and drop observation units with fewer

4_{Even though the micro survey data is available from 1990:01 the econometric sample}

constructed from the data starts in 1991:01 since the data for the sector-specific rate of inflation is not available prior to this date. Moreover, the sample only includes observations up to 2006:01 to avoid a break due to a significant change of the questionnaires afterwards.

than six data points. Because at the observation unit level the time elapsed since the last price adjustment is unknown before the first price change the data has been left-censored by dropping all observation units prior to the first price change for the respective firms. This is a standard approach in the literature; see for instance Lein (2010). Moreover, in order to be able to correctly calculate the cumulative changes in the macroeconomic variables since the last price adjust- ment observations following (preceding) the missing observations are dropped until the next (previous) price change.5 After these manipulations the sample contains a total of about 78,000 observations. Each retail firm can be allocated to one of the following sectors: motor vehicles, food and beverages, communica- tion and information technology, household products, recreational products and other industrial products.

Amongst other questions, firms are asked whether they changed the price of their products in the last month (denoted priceit for firm i in period t). The answers are coded as 1 (“increased”), 0 (“not changed”) and -1 (“decreased”). Moreover, firms are asked whether they expect to change their prices in the com- ing three months; possible answers are again “increase”, “decrease” and “no changes” (epriceit). Further questions considered in the analysis include vari- ables concerning the state of the firm. For instance, firms are asked how they appraise the current state of business (stateit). Moreover, there are questions related to their business volume (volumeit) versus the previous month as well as their expectations concerning their orders for the next three months relative to the same month in the previous year (ordersit). Finally, firms are asked about their expectations concerning the overall business development in the coming six months (develpit).

As has been mentioned before, relative to other datasets, the survey dataset pro- vides several advantages with respect to analyzing price setting at the firm level. More conceptually, a further advantage of the data is that firms are not asked directly on their pricing strategies as in one-time interview studies conducted by, for instance, Blinder (1991) for the US and Fabiani et al. (2006), for the Euro area. Such an interview method may lead to biased responses as firms might be unwilling to respond truthfully to questions regarding their pricing strategies.

5_{Results are robust to only dropping the observations following the missing data point until}

the next price change. Moreover, the main results still hold if missing observations are replaced by 0. This implies the assumption that firms did in fact not change their price in the months they did not report. While this assumption seems reasonable for a dataset that is dominated by observation 0 (“no price change”) for the price variable, it is of course rather strong.

Moreover, in contrast to the one-time interviews firms are asked every month, which better reveals their pricing behavior over time.

Despite these advantages it should be kept in mind that due to the qualitative nature of the questionnaires the data do not contain information concerning the size of price changes. Thus, all price changes are implicitly assumed to be equally sized in this analysis. A further limitation is that the survey data contains both single- and multi-product firms without providing any information on how the latter firms answer the question concerning their prices. This problem is miti- gated by the fact that firms are asked to fill in different questionnaires for their respective product groups. Nevertheless, firms still have to cluster the price de- velopment of several sub-products within the same category resulting in a certain degree of aggregation of individual prices. However, a recent meta-study on the survey provides details on how multi-product firms tend to fill in the question- naires; approximately half of the firms indicate the average price development of all of their products, while the other half report prices of their most important commodities. See Appendix 2.A.1 for more details.

A final issue concerns price changes related to temporary sales, which can not easily be identified in our survey dataset since firms are not asked for the rea- sons of changing their price. To the extent that sales prices may be independent of macroeconomic conditions (Taylor, 1999) the presence of such price changes may potentially conceal the true adjustment mechanism underlying regular price setting. Therefore, following Nakamura and Steinsson (2008), we identified ”V- shaped” price changes in the data using a sale filter for the period 1990-2009. Apparently, however, the occurrence of sales in the data is relatively limited. Thus, our results are robust to the exclusion of these temporary price changes, which is not surprising given the relatively lower importance of sales for the Euro area compared to the US as documented in Dhyne et al. (2006).