Identification and management of nonsystematic cross-commodity data: Toward best practice

Data systematicity has been an important area of consideration for behavioral economic demand. Stein et al. (2015) introduced criteria and an accompanying algorithm to aid researchers in identifying data series that may be considered "nonsystematic"-that is, data that may not follow empirically based assumptions such as an overall decrease in consumption as the cost of a commodity increases and consistency in decreases in consumption. However, those criteria and algorithm are only directly applicable to own-price demand, or demand for a commodity that is increasing in price. Cross-price demand, or demand for a second commodity that changes as a function of some other commodity, does not have a similar set of criteria or algorithm for assessing cross-commodity demand systematicity. Cross-price or cross-commodity demand is useful in understanding how changes in one substance or commodity may change the consumption of another substance or commodity. Thus, we extend Stein et al.'s criteria and algorithm to classify if a cross-commodity can be considered a substitute, complement, or independent, and then assess its systematicity based on its classification. We demonstrate this algorithm on three different cross-commodity demand data sets and describe important considerations regarding data exclusions to prevent biasing results from own-price and cross-price demand. (PsycInfo Database Record (c) 2026 APA, all rights reserved).