Technological change can increase the productivity of the various factors of production in equal terms, or it can be biased toward a specific factor. We directly assess the bias of technological change by measuring, at the level of the individual firm, how much of it is labor augmenting and how much is factor neutral. To do so, we develop a framework for estimating production functions when productivity is multidimensional. Using panel data from Spain, we find that technological change is biased, with both its labor-augmenting and its factor-neutral components causing output to grow by about 1.5 percent per year.

We study industries where the price that a firm sets serves as an investment into lower cost or higher demand. We assess the welfare implications of the ensuing competition for the market using analytical and numerical approaches to compare the equilibria of a learning-by-doing model to the first-best planner solution. We show that dynamic competition leads to low deadweight loss. This cannot be attributed to similarity between the equilibria and the planner solution. Instead, we show how learning-by-doing causes the various contributions to deadweight loss to either be small or partly offset each other.

We document the evolution of the new market for frequency response within the UK electricity system over a six-year period. Firms competed in price while facing considerable initial uncertainty about demand and rival behavior. We show that prices stabilized over time, converging to a rest point that is consistent with equilibrium play. We draw on models of fictitious play and adaptive learning to analyze how this convergence occurs and show that these models predict behavior better than an equilibrium model prior to convergence.

We formally characterize predatory pricing in a modern industry-dynamics framework that endogenizes competitive advantage and industry structure. As an illustrative example we focus on learning-by-doing. To disentangle predatory pricing from mere competition for efficiency on a learning curve we decompose the equilibrium pricing condition. We show that forcing firms to ignore the predatory incentives in setting their prices can have a large impact and that this impact stems from eliminating equilibria with predation-like behavior. Along with the predation-like behavior, however, a fair amount of competition for the market is eliminated.

We explore the implications of ownership concentration for the recently concluded incentive auction that repurposed spectrum from broadcast TV to mobile broadband usage in the United States. We document significant multilicense ownership of TV stations. We show that in the reverse auction, in which TV stations bid to relinquish their licenses, multilicense owners have an incentive to withhold some TV stations to drive up prices for their remaining TV stations. Using a large-scale valuation and simulation exercise, we find that this strategic supply reduction increases payouts to TV stations by between 13.5 percent and 42.4 percent. (D44, D47, H82, L13, L82, L88)