Technological Shocks and Algorithmic Decision Aids in Credence Goods Markets

In credence goods markets such as health care or repair services, consumers rely on experts with superior information to adequately diagnose and treat them. Experts, however, are constrained in their diagnostic abilities, which hurts market efficiency and consumer welfare. Technological breakthroughs that substitute or complement expert judgments have the potential to alleviate consumer mistreatment. This article studies how competitive experts adopt novel diagnostic technologies when skills are heterogeneously distributed and obfuscated to consumers. We differentiate between novel technologies that increase expert abilities, and algorithmic decision aids that complement expert judgments, but do not affect an expert's personal diagnostic precision. We show that high-ability experts may be incentivized to forego the decision aid in order to escape a pooling equilibrium by differentiating themselves from low-ability experts. Results from an online experiment support our hypothesis, showing that high-ability experts are significantly less likely than low-ability experts to invest into an algorithmic decision aid. Furthermore, we document pervasive under-investments, and no effect on expert honesty.