Effect of Length of Time on Measured Demand Elasticities: The Problem Revisited

Abstract

INTRODUCTIONTo quote Manderscheid (1964, 131), "the length of time period being considered is quite important when specifying elasticities." Elasticity estimates based on shorter time periods usually differ from those based on longer time periods (Manderscheid 1964;Pasour and Schrimper 1965; Sexauer 1977). Shepherd (1963), in considering the effect of time, suggests that two opposing forces exist influencing the elasticity of demand: storage activities and product substitution. Shorter-term elasticities (week, month) are likely to be greater than longer-term elasticities because of storage activities. Product substitution is likely to be positively related to the length of time period under consideration. Within short periods of time, Shepherd contends that the influence of product substitution on elasticity measures is likely to be more than offset by the opposite effect of storage activities.The role of inventory demand and habits on consumer expenditure patterns also depends on the time dimension. The inventory adjustment/habit formation process also varies from commodity to commodity (Pasour and Schrimper). Generally though, inventory demand tends to dominate habits in the short term. Sexauer shows that short-run consumer behavior, as opposed to longer-run consumer behavior, is influenced more by consumer inventories than habits, particularly for food. Wohlgenant and Hahn (1982) reinforce this view by indicating that for pork, using monthly data, inventory behavior predominates and that demand is more elastic within a given month than over a longer period. Although not statistically significant, their structural parameter estimates for beef also indicate the predominance of inventory adjustments. For chicken, however, consumption habits are found to dominate inventory adjustment in the Wohlgenant and Hahn study.This paper focuses on the effect of the length of time on demand for fresh beef products. But unlike the other studies mentioned above, this paper deals with more disaggregate fresh beef products grouped by carcass section (brisket, chuck, ground, loin, rib and round). As well, this paper focuses attention on relatively

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“…The availability of commercial scanner data allows significant advances in understanding food marketing (Cheng and Capps 1988;Nayga 1992;Cotterill 1994;Roheim et al 2007). Some of the important studies using scanner data for food demand analysis include Capps (1989), Capps and Nayga (1990), Bergtold et al (2004), Chidmi et al (2005), Torrisi et al (2006), Anders and Moser (2010), and Ahmad and Anders (2012). Capps (1989) used a scanner data set from a retail food firm located in Houston, Texas looking into the demand relationships for steak, ground beef, roast beef, chicken, pork chops, ham, and pork loin products.…”

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