Patents, Spillovers and Competition in Biotechnology

I perform an event study on 600+ patents awarded primarily to 20 leading biotechnology firms and find significant changes in market values at the time of the awards. Adjusting for partial anticipation of events, I estimate that core technology patents in highly contested research areas are expected to generate between $13 and$21 million of economic value. They also generate spillover benefits for the patentee’s rivals—presumably including knowledge transfers—valued at $3 to$6 million per firm. Awardees may appropriate only half of private benefits, although I observe negative spillovers for some high-profile awards. Most patents have no significant market impact.

Accidents Waiting to Happen: Liability Policy and Toxic Pollution Releases

Proponents of environmental policies based on liability assert that strict liability imposed on the polluter will induce firms to handle hazardous wastes properly and to avoid disposing them into the environment. Economic theory and a few well-publicized cases, however, suggest that a number of factors may dilute the incentives posed by strict liability. In this paper, the authors run regressions relating unintended releases of pollution into the environment (aggregated at the state level, and followed over nine years from 1987 to 1995) to the imposition of strict liability on the polluter, exploiting variation across states in the liability provisions of their mini-Superfund laws, and in the years these were adopted. The authors experiment with instrumental variable estimation, fixed effects, and endogenous switching, and find that only after they explicitly model the endogeneity of states' liability laws is strict liability seen as reducing the seriousness of spills and releases. They also find evidence consistent with the notion that under strict liability, firms may spin off into, or delegate riskier production processes to, smaller firms, which are partially sheltered from liability. This tendency appears to be widespread.strict liability, negligence, hazardous waste, state environmental policy, endogenous policy adoption

Accidents Waiting to Happen: Liability Policy and Toxic Pollution Releases

Proponents of environmental policies based on liability assert that strict liability imposed on polluters induces firms to handle hazardous wastes properly. We run regressions relating unintended pollution releases to strict liability imposed on polluters, exploiting variation across states and over time in the liability provisions of state mini-Superfund laws. Strict liability reduces the frequency and severity of pollution releases, provided it is modeled endogenously with the latter. Its effects vary with firm size. Partially sheltered from liability, small firms may have specialized in riskier production processes, but their number has not necessarily grown in response to the states’ liability policy.strict liability, negligence, hazardous waste, state environmental policy, endogenous policy adoption

Estimating Future Consumer Welfare Gains from Innovation: The Case of Digital Data Storage

We develop a quality-adjusted cost index to estimate expected returns to investments in new technologies. The index addresses the problem of measuring social benefits from innovations in service sector inputs, where real output is not directly observable. We forecast welfare gains from two U.S. Advanced Technology Program innovations equaling 25%-50% of expected price, and aggregate consumer benefits of $1-$2 billion, relative to trends in existing technologies. Our model’s probabilistic parameters reflect uncertainty about prospective outcomes and in our hedonic estimates of shadow values for selected product attributes. The index can be readily adopted by research and development (R&D) managers in industry and government.

On and Off the Liability Bandwagon: Explaining State Adoptions of Strict Liability in Hazardous Waste Programs

We analyze factors in states' decisions to switch their approaches to hazardous waste liability policy from negligence standards to policies based on strict liability. Many, but not all, states have switched in recent years. We explain differences in the timing of states' adoption of strict liability into their "mini-superfund" programs using data on states' industrial activities, environmental programs, wealth and education, and political orientation. We test implications of a theoretical model in which states adopt the liability regime (strict versus negligence-based liability) that they see as having greater net benefits. We test this model by estimating a probit equation of the presence or absence of strict liability in a state hazardous waste cleanup program. We find that the likelihood of a state adopting strict liability is positively associated with the number of large manufacturing plants located in that state, but negatively associated with the number of large mining establishments. We also find that educational attainment of residents, state government resources, effectiveness of other state environmental programs, and political variables are significant determinants of the likelihood of strict liability adoption. Our findings suggest states may view strict liability as better suited to industrial waste sites than to mining pollution, that they may be partly motivated by a "deep pocket" mentality, or that they may anticipate engaging in "precaution targeting" (T. H. Tietenberg, 1989, Land Economics 65:4, 305-319). Non-adopters may have fewer resources available to confront environmental problems, may not wish to discourage business activity, or may have other programs in place which effectively substitute (at least for a time) for strict liability imposed on parties responsible for hazardous waste releases.

Network Density of States

Spectral analysis connects graph structure to the eigenvalues and eigenvectors of associated matrices. Much of spectral graph theory descends directly from spectral geometry, the study of differentiable manifolds through the spectra of associated differential operators. But the translation from spectral geometry to spectral graph theory has largely focused on results involving only a few extreme eigenvalues and their associated eigenvalues. Unlike in geometry, the study of graphs through the overall distribution of eigenvalues - the spectral density - is largely limited to simple random graph models. The interior of the spectrum of real-world graphs remains largely unexplored, difficult to compute and to interpret. In this paper, we delve into the heart of spectral densities of real-world graphs. We borrow tools developed in condensed matter physics, and add novel adaptations to handle the spectral signatures of common graph motifs. The resulting methods are highly efficient, as we illustrate by computing spectral densities for graphs with over a billion edges on a single compute node. Beyond providing visually compelling fingerprints of graphs, we show how the estimation of spectral densities facilitates the computation of many common centrality measures, and use spectral densities to estimate meaningful information about graph structure that cannot be inferred from the extremal eigenpairs alone.Comment: 10 pages, 7 figure

Efficiency and Political Economy of Pollution Control with Ancillary Benefits: An Application to NOx Control in the Chesapeake Bay Airshed

This paper examines implications for cost-effective allocation of pollution controls when preferences of coalitions organized along regional lines, or according to preferences for air vs. water quality improvements, are accounted for. Results are compared to a base case in which NOx emissions reductions must satisfy only a water quality standard, and total costs are minimized over emissions sources. Relative to base-case result that marginal control costs must be equal across sources, stronger relative preferences for air imply shifting of control toward sources that produce greater ancillary benefits to air quality. Regional differences may require side payments to induce cooperation where benefits are low, but this will not affect how controls themselves should be allocated.