Natarajan Balasubramanian

Research Summary We investigate how worker mobility influences the adoption of a new technology using state‐level changes to the enforceability of noncompete agreements as an exogenous shock to worker mobility. Using data on over 153,000 establishments from 2010 and 2018, we find that changes that facilitate worker movements are associated with a significant decline in the likelihood of adoption of machine learning. Moreover, we find that the magnitude of decline depends upon the size of the establishment, the extent of predictive analytics adoption in its industry, and the number of large establishments in the same industry‐location. These results are consistent with the view that increases in outward worker mobility increase costs for adoption of a new technology that involves significant downstream investments in the early years of its diffusion. Managerial Summary Successful business adoption of new technologies such as machine learning requires skilled workers with experience in implementing those technologies. In the early years of technology diffusion workers in early adopting businesses typically acquire these skills through on‐the‐job learning that is paid for by the adopter. So, if such early adopters face an increased risk of those skilled workers quitting, then their incentives to adopt the technology decrease. We examine this possibility using changes in noncompete enforceability as a proxy for changes in worker mobility and find that the likelihood of adopting machine learning decreases as the risk of worker mobility increases, particularly for larger establishments, establishments in industries where adoption may be more beneficial and in locations with many large competing establishments.

Research Summary Cost advantage helps a firm at the expense of its rivals, but may hurt some rivals worse than others. Conventional wisdom suggests that an advantaged firm will do more harm to closer competitors, but the opposite may occur if competitors can reposition themselves. Closer competitors have stronger incentives to reposition away from the advantaged firm, thereby potentially encroaching on rivals more distant from the advantaged firm and propagating the harm to them like the cue ball in billiards transfers energy from cue stick to target ball. Our formal model compares an advantaged firm's closer and farther competitors, when repositioning is allowed or prohibited, and demonstrates when its advantage hurts farther competitors worse than closer ones. We provide an illustrative case study from grocery retailing. Managerial Summary When Walmart brought its advantage in distribution efficiency to the low end of the grocery retailing industry, it displaced the inefficient downscale incumbent Winn‐Dixie in many geographic areas. One might have expected such increased efficiency at the low end of the market to hurt midscale supermarkets like Kroger more than premium grocers like Whole Foods, yet the opposite occurred. Why? In a word, repositioning. Midscale competitors retreated away from Walmart by repositioning upscale via renovations, which thereby transferred the impact to premium rivals who could not escape any further upscale. Our economic model of this “cue‐ball effect” predicts that the impact propagated onto upper‐end competitors is greater in markets with less income inequality, and our empirical results are consistent with this prediction.