Information provision and network externalities (Job Market Paper)

Abstract

We use a differences-in-differences with a matched control group method to estimate the long-term impacts of genomic selection in the American market for dairy cattle genetics. Genomic selection is an application of big data that uses the entire genome of an animal to test for the presence of a set of traits. Unlike pre-existing technologies that require several years of data from a bull’s daughters, an animal can be tested as soon as it is born, allowing breeders to identify the “best” animals much faster. Using a data set of all bulls marketed in the US from 2000 to 2020, we find that genomic selection significantly increased genetic gains for all measured traits, particularly milk production, protein, and fat yields, but also increased levels of inbreeding depression, a reduction in the performance of animals whose parents have a high degree of relatedness, as a consequence of genetics companies breeding more animals from established lines to respond to an increased “brand” loyalty towards such lines. Our estimation shows that the increased inbreeding rate of American bulls caused a loss of between 2.5 to 6 billion dollars to the entire industry from 2011 to 2019. Solving this externality will require either a mechanism to internalize the harmful effects, such as paying a much higher price for more inbred sires, or a collective action mechanism to select which lines will be bred in the next generation.