Consumer Shopping Behavior: How Much Do Consumers Save?

Abstract

This paper documents the potential and actual savings that consumers realize from four particular types of purchasing behavior: purchasing on sale; buying in bulk (at a lower per unit price); buying generic brands; and choosing outlets. How much can and do households save through each of these behaviors? How do these patterns vary with consumer demographics? We use data collected by a marketing firm on all food purchases brought into the home for a large, nationally representative sample of U.K. households in 2006. We are interested in how consumer choice affects the measurement of price changes. In particular, a standard price index based on a fixed basket of goods will overstate the rise in the true cost of living because it does not properly consider sales and bulk purchasing. According to our measures, the extent of this bias might be of the same or even greater magnitude than the better-known substitution and outlet biases.

The Elasticity of Substitution Between Time and Market Goods: Evidence from the Great Recession

“…A recent study by Stroebel and Vavra (2015) also show a correlation between house prices and shopping activities. 9 An alternative approach, used by Griffith et al (2009), is to regress transaction-level prices, rather than a monthly price index, on the shopping activity. The theory we present below is that of an aggregated…”

“…For instance, Shapiro and Wilcox (1996) and the Boskin Commission report (1996) show that store-substitution can cause biases in the measurement of consumer inflation, while Griffith et al (2009) documents the reduction in effective prices paid from a range of shopping activities which may not be fully reflected in a fixed-weight inflation index. More recently, studies such as Chevalier andKashyap (2011) andCoibion, Gorodnichenko andHong (2012) show that the gap between effective prices paid and posted prices can vary over time as households change their shopping effort.…”

“…Large Size Items To define large sized items, we follow Griffith et al (2009) and rank by size all UPCs in our data within a narrowly defined category. An item is defined as 'large' if the size of the item is in the upper two quantiles of this distribution, i.e., in the top 40 percent of UPCs in the category, ranked by product size.…”

Recent Developments in Empirical IO: Dynamic Demand and Dynamic Games

“…41 The distribution of this random effect has the following properties: (A.1) it has a discrete and finite support Each market type  has its own equilibrium mapping (with a different level of profits given   ) and its own equilibrium. Let P  be a vector of strategies (CCPs) in market-type : P  ≡ {  (x  ) : 42 It is straightforward to extend the description of an equilibrium mapping in CCPs to this model. A vector of CCPs P  is a MPE for market type  if and only if for every firm  and every state x  we have that: Let  be the vector of parameters in the probability mass function of , i.e., ≡ {  :  = 1 2  }, and let P be the set of CCPs for every market type, {P  :  = 1 2  }.…”

“…This is the so called initial conditions problem (Heckman, 1981). In short panels (for  relatively small), not 42 The introduction of unobserved market heterogeneity also implies that we can relax the assumption of only 'a single equilibrium in the data' to allow for different market types to have different equilibria. taking into account this dependence between   and x 1 can generate significant biases, similar to the biases associated to ignoring the existence of unobserved market heterogeneity.…”