Analyses of Industry Averages in Retailing

Abstract

In this chapter, two databases of industry averages in the retail trade are analysed. The information in these databases consists of averaged financial statements for narrowly defined shoptypes and is publicly available: the information has been published by the collectors for purposes such as interfirm comparisons¹. Analysing data on this level of aggregation offers several advantages over the obvious alternative of analysing data of individual firms within an industry. First of all, analysing industry averages allows the inclusion of variables that differ strongly between, but hardly within shoptypes, such as the characteristics of the principal items in the assortment (e. g. Titman’s durable effect). Conversely, using aggregated data also allows abstraction from variables that vary widely within, but probably much less between shoptypes, such as differences in valuation caused by differences in recency of investments (i. e. the effects of cumulative depreciation). Another example is the influence of the personal wealth of the entrepreneur on the financial behaviour of the firm. This type of variables typically cannot be modelled for lack of available data. Finally, industry averages are published more often than information of individual firms, so using the former increases the number of available data sources.