"Harbinger households": neighborhoods that consistently buy products that get discontinued, buy real-estate that underperforms, and donate to losing political candidates

In The Surprising Breadth of Harbingers of Failure (Sci-Hub mirror), a trio of economists and business-school profs build on a 2015 Journal of Marketing Research paper that claimed that some households' purchasing preferences are a reliable indicator of which products will fail -- that is, if households in a certain ZIP code like a product, it will probably not succeed. The original paper calls these "harbinger households."

In the new paper, the researchers consider very large data-sets on consumer goods and fashion purchasing, house-buying, and political donations, to examine whether being a "harbinger household" correlates to other predictors of failure, and find that these households are also likely to buy real-estate that makes lower profits (or generates larger losses) than nearby properties; they are likely to buy fashion and consumer goods that get discontinued due to lackluster sales; and they are more likely to donate to losing politicians' campaigns than winners.

The researchers also claim that harbinger households voluntarily cluster: that when a harbinger household moves, it is likely that it will move to another habringer ZIP code (and nonhabringers move to nonhabringer households). Moreover, harbingers don't appear to learn their preferences from one another -- a nonhabringer household that locates in a harbinger ZIP code doesn't alter its purchasing and political contributions to "loser" products and candidates.

Harbinger households tend to be white, suburban and headed by older, less-educated single parents. They tend to make above-average use of coupons, and the coupons they use have above-average values.

The researchers don't claim a causal relationship between these different factors -- donating to losing political candidates doesn't make you prefer Crystal Pepsi, for example -- but rather speculate that there is an "unobserved intervening variable" that explains both factors. Read the rest