On competition and school efficiency

Education

Allocative inefficiency and school competition

A substantial literature indicates that the public school system in the United States is inefficient. Some have posited that this inefficiency arises from a lack of competition in the education market. On the other hand, the Tiebout hypothesis suggests that public schools may already face significant competition. In this paper, the authors examine the extent to which competition for students influences public school inefficiency in Texas. They use a Shephard input distance function to model education production and use bootstrapping techniques to examine allocative inefficiencies. Switching regressions estimation suggests that school districts in noncompetitive metropolitan areas are more than twice as allocatively inefficient as school districts in competitive metropolitan areas.Competition ; Education

An experiment on first-price common-value auctions with asymmetric information structures: The blessed winner

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.In common-value auctions bidders have access to public information, and may also hold private information prior to choosing their bids. The literature has predominately focused on the case in which bidders are ex-ante symmetric and privately informed, and finds that aggressive bidding such that payoffs are negative is common (the winner's curse). In practice, bidders often only have access to public information, and use this information to form (possibly differing) beliefs. In addition, a bidder who is not privately informed may face bidders who are. We examine bidding behavior of both informed and uninformed bidders, and vary the information structure they face. We find that uninformed bidders underbid dramatically and persistently, while informed bidders tend to overbid in the two-bidder case. Our results highlight the importance of correctly modeling the information available to bidders.Financial support from the Department of Economics at Texas A&M University and the BID cluster at the University of Exeter is gratefully acknowledged

Reconstructing nonparametric productivity networks

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra-and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown

Data Envelopment Analysis (DEA): A Framework for Assessing Capacity in Fisheries When Data are Limited

The Sustainable Fisheries Act (SFA) and Code of Conduct for Responsible Fisheries Management require restoration of fishery resources and a matching of capacity to desired resource levels. There is, thus, a need to reduce harvesting capacity throughout many of the fisheries of the world. Yet, even the term capacity is not well defined, and it is even more complicated to measure. In this paper, we introduce several definitions and measures of capacity that are consistent with economic theory and empirical analyses. Since economic data on production activities are usually unavailable, we introduce the concept of data envelopment analysis (DEA) which may be used to calculate a physical or primal-based concept of capacity in fisheries. We initially introduce DEA and dispel many of the myths believed to be problems of DEA. We discuss how DEA may be used to calculate capacity in single and multiple-species fisheries. We also introduce how the DEA-derived measure of capacity may be formulated to include undesirable outputs (e.g., bycatch).Keywords: Capacity, capacity utilization, data envelopment analysi

Substitutability among undesirable outputs

In recent years, economists have started to move beyond calculating regulatory effects on a pollutant-by-pollutant basis since their interaction is important. In this study, we take up this issue. To allow for joint production of multiple pollutants and marketable output, we specify our technology using a directional distance function. This allows us to treat pollutants as joint outputs, yet accounts for their ‘undesirability’. We estimate the distance function for a sample of coal-fired electric power plants from 1985 to 1998, which includes the first 4 years of Phase I of the Clean Air Act Amendments of 1990. We focus on the interaction between SO₂ and NOₓ , as they became more highly regulated and estimate shadow prices of the pollutants and the Morishima elasticity of transformation between two pollutants, NOₓ and SO₂, as well as with respect to the desirable output, kilowatt-hours of electricity. As expected, we find that power plants increase NOₓ emissions as they decrease SO₂, i.e. they are substitutes

Observation of collapsing radiative shocks in laboratory experiments

This article reports the observation of the dense, collapsed layer produced by a radiative shock in a laboratory experiment. The experiment uses laser irradiation to accelerate a thin layer of solid-density material to above 100 km/s100km∕s, the first to probe such high velocities in a radiative shock. The layer in turn drives a shock wave through a cylindrical volume of Xe gas (at ∼ 6 mg/cm3∼6mg∕cm3). Radiation from the shocked Xe removes enough energy that the shocked layer increases in density and collapses spatially. This type of system is relevant to a number of astrophysical contexts, providing the potential to observe phenomena of interest to astrophysics and to test astrophysical computer codes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87760/2/082901_1.pd